Causes Of Spatial Variability Research Articles

Food web attributes to assess spatial–temporal dynamics in estuarine benthic ecosystem

Threatened benthic ecosystems need urgent tools for effective bioassessment and relevant management. EU Marine Strategy Framework Directive (MSFD) obligates member states to achieve GES (Good Environmental Status) for 11 descriptors of environmental state (MSFD; 2008/56/EC). From all of the descriptors, D4 that focuses on Food Webs is the most functional-oriented indicator, but also the most challenging to implement due to our limited knowledge on benthic interactions. Particularly, it is still unclear how spatially and temporally regulated abiotic variables determine the entire benthic food webs, and which benthic food web attributes best respond to these spatially and temporally derived environmental variations. To fill this gap, we measured the natural isotopic ratios (δ13C and δ15N) of macrobenthic organisms and their food sources and build twelve food web topologies across three distinct sites (Navigator, Gambia, Tróia) in summer and winter during two consecutive years. To assess these food web topologies, we applied isotopic metrics, further integrated with univariate and multivariate analysis to find food web-based indicators that best respond to these spatial and temporal variability.We found clear spatial patterns associated to an increase in primary production and quantity and quality of organic matter (OM). Sites with higher organic load and less quality OM (Navigator and Gambia) had simpler food webs, likely associated to high abundance of opportunistic meiobenthic species. Site located inside protected area (Tróia) with high quality OM had the most complex food web characterized by high diversity of specialist consumers that used more efficiently available resources. Similarity metrics were valuable complementary tool that helped to further disentangle the causes of spatial variability, in this case distinguishing between two food webs (Navigator and Gambia) that had similar structures but different resource utilization.The temporal patterns were not so evident than the spatial patterns, although significant differences were reported between sampling occasions for the same metrics (maximum trophic position and the percentage of carnivores and omnivores, p < 0.05). The most complex Tróiás food web demonstrated greater responsiveness in capturing temporal differences in resource use, suggesting that more complex food webs are better equipped to reflect temporal variability. The integration of isotopic metrics complemented with multivariate and univariate analyses proved to be an important tool for the analysis of different aspects of the benthic food web complexity in a spatial–temporal context providing a promising approach to assess the functional integrity of the estuarine ecosystems, especially in the context of the descriptor 4 within MSFD.

Potential of Precise Fertilization through Adoption of Management Zones Strategy to Enhance Wheat Production

The variability in soil properties and crop yield can be overcome by adoption of smart farming practices through interpolation and mapping of spatial variability patterns. Geospatial technologies can be utilized to determine the cause of spatial variability in fields for site-specific application of fertilizer. This study was designed to quantify and identify the spatial variation in soil properties and wheat (Triticum aestivum L.) yield and to delineate prescription maps for precise application of fertilizer in a semi-arid subtropical region of Pakistan. To examine the variability in soil properties on the production of the considered crop, this study comprised two different fields and each field was divided into (20 × 20 m) grids. The samples of soil were collected at 15 cm and 30 cm soil depths before the fertilization to analyze the different soil characteristics i.e., nitrogen (N), electrical conductivity (EC), potassium (K), soil organic matter (SOM), phosphorus (P), and pH. The boundaries of selected fields and grid points were established with a real-time kinematics-global positioning system (RTK-GPS). The soil data were acquired with a soil proximal sensor at a depth of 7 cm after fertilization. The statistical analysis coefficient of variation (CV), geostatistical-analysis-nugget-to-sill ratio (N:S), and the interpolated maps (ArcGIS pro 2.3) were used to characterize the least to moderate variability of soil parameters and yield, demanding site-specific management of fertilizer application. Cluster analysis was conducted using Minitab 21, which classified soil and yield characteristics into five categories: “very good”, “very low”, “good”, “poor”, and “medium”, with an external heterogeneity and internal homogeneity both more than 60%. Significant relationships (p &lt; 0.05) between soil and crop properties were used to develop the management zones (MZs) for the precise application of fertilizer in wheat fields. Significant differences (p &lt; 0.05) in soil nutrients were found in the very high and very low productivity zones at both sampling times, which suggest delineating the MZs for precise application of fertilizer according to the need of crop and soil properties. The results revealed that the optimum number of MZs for the wheat fields was five and there was heterogeneity in the soil nutrients in five MZs. The findings of this study also highlight the necessity of predicting the crop and soil factors by using precision technologies to develop the prescription maps, because sampling and analysis of soil are expensive and time-consuming. Based on the demand of the soil and crops, site-specific fertilization can increase economic and environmental efficiency.

Vegetation interactions with geotechnical properties and erodibility of salt marsh sediments

Salt marshes provide diverse ecosystem services including coastal protection, habitat provision and carbon sequestration. The loss of salt marshes is a global scale phenomenon, of great socio-economic concern due to the substantial benefits that they provide. However, the causes of spatial variability in marsh loss rates are inadequately understood for the purposes of predicting future ecosystem distributions and functions under global environmental change. This study investigated the relationship between the presence of different saltmarsh plants and the mechanical properties of the underlying substrate that relate to its vulnerability to erosion. Relationships between three halophytes (Puccinellia spp., Spartina spp. and Salicornia spp.) and sediment stability were assessed and compared to unvegetated substrates using in-situ and laboratory tests of substrate geotechnical properties and sediment characteristics. Sampling was conducted at two UK sites with contrasting sedimentology, one sand-dominated and one clay-rich. Sediment samples, collected simultaneously with measurements of shear strength, were analysed for moisture content, particle size and organic, carbonate and mineral compositions. These data were then used to explore the contribution of plant type, alongside the sedimentological parameters, to measured shear strength.Shear strength of the sediment varied between and, to a lesser extent, within sites, with the four cover types having a similar effect on shear strength within sites relative to each other. Sediments covered by Puccinellia spp exhibit the highest shear strength, while bare sediments exhibit the lowest. The effect of vegetation type on shear strength was greater in the coarser sediments of Warton Sands. Surface cover type made a significant contribution to exploratory statistical models developed for the prediction of sediment shear strength. The findings support existing recognition that vegetation can enhance sediment shear strengths but extend the insight to reveal differences in this effect that show generality between sedimentological settings. Further, the combination of methods provides insight into the fundamental mechanics by which various measures of sediment stability may be affected by different surface cover types. Cohesion appears to be a more appropriate descriptor of sediment erodibility than shear strength or friction angle and is most greatly enhanced by the presence of a fine, fibrous root system such as that of Puccinellia. A more detailed understanding of the multi-scale mechanisms by which plants confer strength to substrates is needed to better anticipate their impact on sediment erodibility, and therefore salt marsh vulnerability.

Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China.

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0–20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively.

Apparent electrical conductivity and multivariate analysis of soil properties to assess soil constraints in orchards affected by previous parcelling

Fruit production is relevant to the European agricultural sector. However, orchards in semi-arid areas of southern Europe may contain soils with constraints for tree development. This is the case of soils with high CaCO3 content or limiting layers at variable depth. To assess spatial and in-depth variation of these soil constraints, an apparent electrical conductivity (ECa) survey was conducted in an orchard by using a galvanic contact soil sensor (Veris 3100). Different soil properties were randomly sampled at two depths (topsoil and subsoil) in 20 different sampling points within the plot. ECa raster maps were obtained for shallow (0–30 cm) and deep (0–90 cm) soil profile depths. In addition, an inversion modelling software was used to obtain horizontal ECa slices corresponding to 10 cm thick soil layers from 0–10 cm to 80–90 cm in depth. Concordance analysis of ECa slices allowed the soil profile to be segmented into four homogeneous horizons with different spatial conductivity pattern. Then, a multivariate analysis of variance (MANOVA) was key, i) to better interpret the specific soil properties that mainly contributed to the spatial variation of ECa (CaCO3 and organic matter (OM) contents), and ii) to delimit the soil layer and the specific spatial pattern of ECa that allows potential management areas to be delineated by presenting the same trend in CaCO3 and OM for topsoil and subsoil simultaneously. Moreover, assessing 3D variation of ECa made it possible to identify different soil areas that, linked to previous earthworks to optimize the parcelling of the farm, are the main cause of spatial variability within the orchard.

A meta‐analysis of nestedness and turnover components of beta diversity across organisms and ecosystems

AbstractAimThe number of studies investigating the nestedness and turnover components of beta diversity has increased substantially, but our general understanding of the drivers of turnover and nestedness remains elusive. Here, we examined the effects of species traits, spatial extent, latitude and ecosystem type on the nestedness and turnover components of beta diversity.LocationGlobal.Time period1968–2017.Major taxa studiedFrom bacteria to mammals.MethodsFrom the 99 studies that partition total beta diversity into its turnover and nestedness components, we assembled 269 and 259 data points for the pairwise and multiple site beta‐diversity metrics, respectively. Our data covered a broad variation in species dispersal type, body size and trophic position. The data were from freshwater, marine and terrestrial realms, and encompassed geographical areas from the tropics to near polar regions. We used linear modelling as a meta‐regression tool to analyse the data.ResultsPairwise turnover, multiple site turnover and total beta diversity all decreased significantly with latitude. In contrast, multiple site nestedness showed a positive relationship with latitude. Beta‐diversity components did not generally differ among the realms. The turnover component and total beta diversity increased with spatial extent, whereas nestedness was scale invariant for pairwise metrics. Multiple site beta‐diversity components did not vary with spatial extent. Surprisingly, passively dispersed organisms had lower turnover and total beta diversity than flying organisms. Body size showed a relatively weak relationship with beta diversity but had important interactions with trophic position, thus also affecting beta diversity via interactive effects. Producers had significantly higher average pairwise turnover and total beta diversity than carnivores.Main conclusionsThe present results provide evidence that species turnover, being consistently the larger component of total beta diversity, and nestedness are related to the latitude of the study area and intrinsic organismal features. We showed that two beta‐diversity components had generally opposing patterns with regard to latitude. We highlight that beta‐diversity partition may give additional insights into the underlying causes of spatial variability in biotic communities compared with total beta diversity alone.

Assessment of regional variability in crop yields with spatial autocorrelation: Banana farms and policy implications in Martinique

Agricultural research can support farmers and policy makers’ decisions by identifying the causes of spatial variability in crop yield at a regional level. In this paper, we propose a method that combines spatial autocorrelation measures and a farm network survey. This method is intended to describe the causes of spatial variability in crop yields, along with key crop management practices for reaching the best yields and the physical and socio-economic constraints of adopting these practices. This causal and hierarchical analysis of cropping system performance has the advantage of (1) preventing bias in the correlation between variables from the yield gap analysis and (2) formulating spatially targeted policies that are aimed at relaxing adoption constraints at the territorial level. After introducing the method and its different steps, we present the results of the assessment of the spatial variability in banana yields in Martinique (Caribbean). Our study has clearly shown that the planting stage is one of the most important aspects of banana production: allowing a long fallow period, plowing for soil preparation and using seedlings that are produced by tissue culture were associated with the best yields. However, several constraints limit their adoption by farmers at the regional level. The limiting factors were steep slopes, small farm size and low cash flow. We observed no relationship between pesticide use and yields. These study results finally permit the elaboration of spatially targeted policy recommendations to improve crop yields in a sustainable manner. It mainly consists in promoting and facilitating the adoption of good plantation practices for smallholders.

A field study of factors influencing the concentrations of a traffic-related pollutant in the vicinity of a complex urban junction

The paper describes a field study focused on the dispersion of a traffic-related pollutant within an area close to a busy intersection between two street canyons in Central London. Simultaneous measurements of airflow, traffic flow and carbon monoxide concentrations ([CO]) are used to explore the causes of spatial variability in [CO] over a full range of background wind directions. Depending on the roof-top wind direction, evidence of both flow channelling and recirculation regimes were identified from data collected within the main canyon and the intersection. However, at the intersection, the merging of channelled flows from the canyons increased the flow complexity and turbulence intensity. These features, coupled with the close proximity of nearby queuing traffic in several directions, led to the highest overall time-average measured [CO] occurring at the intersection. Within the main street canyon, the data supported the presence of a helical flow regime for oblique roof-top flows, leading to increased [CO] on the canyon leeward side. Predominant wind directions led to some locations having significantly higher diurnal average [CO] due to being mostly on the canyon leeward side during the study period. For all locations, small changes in the background wind direction could cause large changes in the in-street mean wind angle and local turbulence intensity, implying that dispersion mechanisms would be highly sensitive to small changes in above roof flows. During peak traffic flow periods, concentrations within parallel side streets were approximately four times lower than within the main canyon and intersection which has implications for controlling personal exposure. Overall, the results illustrate that pollutant concentrations can be highly spatially variable over even short distances within complex urban geometries, and that synoptic wind patterns, traffic queue location and building topologies all play a role in determining where pollutant hot spots occur.

Quantifying the influence of land-use and surface characteristics on spatial variability in the urban heat island

The urban thermal environment varies not only from its rural surroundings but also within the urban area due to intra-urban differences in land-use and surface characteristics. Understanding the causes of this intra-urban variability is a first step in improving urban planning and development. Toward this end, a method for quantifying causes of spatial variability in the urban heat island has been developed. This paper presents the method as applied to a specific test case of Portland, Oregon. Vehicle temperature traverses were used to determine spatial differences in summertime ~2 m air temperature across the metropolitan area in the afternoon. A tree-structured regression model was used to quantify the land-use and surface characteristics that have the greatest influence on daytime UHI intensity. The most important urban characteristic separating warmer from cooler regions of the Portland metropolitan area was canopy cover. Roadway area density was also an important determinant of local UHI magnitudes. Specifically, the air above major arterial roads was found to be warmer on weekdays than weekends, possibly due to increased anthropogenic activity from the vehicle sector on weekdays. In general, warmer regions of the city were associated with industrial and commercial land-use. The downtown core, whilst warmer than the rural surroundings, was not the warmest part of the Portland metropolitan area. This is thought to be due in large part to local shading effects in the urban canyons.

Intrinsic and extrinsic causes of spatial variability across scales in a metacommunity

The relative importance of extrinsic and intrinsic causes of variability is among the oldest unresolved problems in ecology. However, the interaction between large-scale intrinsic variability in species abundance and environmental heterogeneity is still unknown. We use a metacommunity model with disturbance–recovery dynamics to resolve the interaction between scales of environmental heterogeneity, biotic processes and of intrinsic variability. We explain how population density increases with environmental variability only when its scale matches that of intrinsic patterns of abundance, through their ability to develop in heterogeneous environments. Succession dynamics reveals how the strength of local species interactions, through its control of intrinsic variability, can in turn control the scale of metapopulation response to environmental scales. Our results show that the environment and species density might fail to show any correlation despite their strong causal association. They more generally suggest that the spatial scale of ecological processes might not be sufficient to build a predictive framework for spatially heterogeneous habitats, including marine reserve networks.

Review of spatial variability of snowpack properties and its importance for avalanche formation

Abstract The seasonal snow cover is spatially variable. Spatial variability of layer properties is due to various external and internal process drivers interacting with terrain and ground cover during and after the deposition process. Many processes that act as process drivers such as radiation and wind cause spatial variations of the snowpack at several scales. The most challenging process is probably wind that might hinder prediction of variability at the slope scale. The complexities and uncertainties involved in snow slope stability evaluation and avalanche prediction are largely due to the variable nature of the snow cover. Many studies have tried to quantify spatial variability. Different methods have been used and the studies covered a variety of scales. Accordingly, some results appear contradictory, suggesting that the degree of spatial variation varies widely. This is not surprising, and is partly due to the methods used and of course, due to varying natural conditions. For example, the variation will strongly depend on the measurement scale — the so-called support — of the method which varies from 10− 4 m2 for the SnowMicroPen to 3 m2 for the rutschblock test. The layering was found to be less variable than, for example, the stability of small column tests. Whereas it is often perceived that the results of the studies were not conclusive, they completely changed our view of spatial variability. The importance of scale issues, in particular for avalanche formation became evident. Geostatistical analysis has been introduced and used to determine the length of spatial autocorrelation and to derive appropriate input data for numerical models. Model results suggest that spatial variation of strength properties has a substantial “knock-down” effect on slope stability and that the effect increases with increasing spatial correlation. The focus on scale has also revealed that spatial variations can promote instability or inhibit it. With the awareness of scale the causes of spatial variability can now be addressed. We will review the present state of knowledge, discuss consequences for avalanche forecasting and snow stability evaluation, and recommend future research directions.

Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape

Improved understanding of the factors that limit crop yields in farmers’ fields will play an important role in increasing regional food production while minimizing environmental impacts. However, causes of spatial variability in crop yields are poorly known in many regions because of limited data availability and analysis methods. In this study, we assessed sources of between‐field wheat (Triticum aestivum L.) yield variability for two growing seasons in the Yaqui Valley, Mexico. Field surveys conducted in 2001 and 2003 provided data on management practices for 68 and 80 wheat fields throughout the Valley, respectively, while yields on these fields were estimated using concurrent Landsat satellite imagery. Management–yield relationships were analyzed with t tests, linear regression, and regression trees, all of which revealed significant but year‐dependent impacts of management on yields. In 2001, an unusually cool year that favored high yields, N fertilizer was the most important source of between‐field variability. In 2003, a warmer year with reduced irrigation water allocations, the timing of the first postplanting irrigation was found to be the most important control. Management explained at least 50% of spatial yield variability in both years. Regression tree models, which were able to capture important nonlinearities and interactions, were more appropriate for analyzing yield controls than traditional linear models. The results of this study indicate that adjustments in management can significantly improve wheat production in the Yaqui Valley but that the relevant controls change from year to year.

Possible causes of spatial variability inDiatraea saccharalisFabr. (Lep., Crambidae) and its parasitoid populations in sugarcane fields in Brazil

Abstract:Based on the large data set stored in some sugarcane mills regarding the biological control programme ofDiatraea saccharalisusing the parasitoidsCotesia flavipesand tachinid flies, the aim of the present study was to determine whether sugarcane varieties, number of cuts and plant ages are major determinants in promoting spatial variability inD. saccharalis,C. flavipesand tachinid fly populations in sugarcane fields in the state of São Paulo (Brazil). The data set used to support this study was provided by the São João and Barra sugarcane mills. Coefficient of variation was adopted as a measure of the spatial variability of population density. Spatial variability was estimated for the total density ofD. saccharalis(parasitized + unparasitized larvae), and also forD. saccharalislarvae parasitized by tachinids and byC. flavipes. Statistical analysis revealed that the spatial variation inD. saccharalispopulations was influenced by the number of cuts and by plant ages in the Barra and São João Mills, respectively. Similar results were obtained forD. saccharalislarvae parasitized by tachinids; however, in the São João Mill, the spatial variability of these populations was also influenced by the different number of varieties. Finally, consideringD. saccharalislarvae parasitized byC. flavipes, no significant regressions with any of the three categories were observed for both sugarcane mills. The multitrophic implications for this agricultural system are also discussed.

Variance in ecological consumer-resource interactions.

Food-web models use the effect size of trophic interactions to predict consumer-resource dynamics. These models anticipate that strong effects of consumers increase spatial and temporal variability in abundance of species, whereas weak effects dampen fluctuations. Empirical evidence indicates that opposite patterns may occur in natural assemblages. Here I show that spatial variance in the distribution of resource populations is sensitive to changes in the variance of the trophic interaction, in addition to the mean effect of consumers, relative to other causes of spatial variability. Simulations indicate that both strong and weak direct effects of consumers can promote spatial variability in abundance of resources, but only trophic interactions with a large mean effect size can reduce variation. Predictions of the model agree with the results of repeated field experiments and are consistent with data from published consumer-resource interactions, proving to be robust across widely varying environmental conditions and species' life histories. Thus, food-web models that embody variance in trophic interactions may have increased capacity to explain the wide range of effects of consumers documented in empirical studies.

Root distribution and the cause of its spatial variability inPseudotsuga taxifolia (Poir.) Britt.

The roots of 36-year-old Douglas firs were sampled in a stratified random fashion with a Veihmeyer-type auger. The lengths of root were very variable as was the proportion of fine roots which were dead. From consideration of the possible causes of spatial variability, it was concluded that it was not due primarily to environmental variation; nor to position relative to the tree trunks and crowns except for concentrations adjacent to the trunks where stem flow is considerable. Cyclical initiation, extension and death of fine roots in a spatial pattern with cells perhaps as small as 30 cm diameter could explain the observations. A drought period might have caused more extensive death of fine roots on one of the three plots examined.