Evaluating the impact of the Modifiable Areal Unit Problem on ecological model inference: A case study of COVID-19 data in Queensland, Australia

The association between invasive and native species varies across spatial scales and is affected by phylogenetic relatedness, but these issues have rarely been addressed in aquatic ecosystems. In this study, we used a non-native, highly invasive species of Poaceae (tropical signalgrass) to test the hypotheses that (i) tropical signalgrass success correlates negatively with success of most native species of macrophytes at fine spatial scales, but its success correlates positively or at random with natives at coarse spatial scales, and that (ii) tropical signalgrass is less associated with native species belonging to the family Poaceae than with species belonging to other families (Darwin’s naturalization hypothesis). We used a dataset obtained at fine (0.25 m2) and coarse (ca. 1,000 m2) scales. The presence/absence of all species was recorded at both scales, and their biomass was also measured at the fine scale. We tested the association between tropical signalgrass biomass and individual native species with logistic regressions at the fine scale, and using the T-score index between tropical signalgrass and each native species at both scales. The likelihood of the occurrence of six species (submersed and free-floating) was negatively affected by tropical signalgrass biomass at the fine scale. T-scores showed that three species were less associated with tropical signalgrass than expected by chance, but 22 species co-occurred more than expected by chance at the coarse scale. Associations between species of Poaceae and tropical signalgrass were null at the fine scale, but were positive or null at the coarse scale. In addition to showing that spatial scale affects the patterns of association among the non-native and individual native species, our results indicate that phylogeny did not explain associations between the invasive and native macrophytes, at both scales.

Incorporating spatial regression model into cellular automata for simulating land use change

Integration of dynamic data typically requires the solution of an inverse problem that can be computationally intensive and practically infeasible for fine scale reservoir models. In this paper we present a new methodology to directly update fine scale geostatistically-based reservoir models by combining gradual deformation parameterization for the fine scale geostatistical model and an upscaling technique for the coarse scale flow simulation model. The proposed methodology includes: Perturbation of the fine scale geostatistical model using the gradual deformation parameterization. Gradual deformation ensures the preservation of the overall geostatistical properties of the fine model. Generation of the coarse scale flow simulation model by upscaling the fine scale geostatistical model. Sensitivity computation of the flow simulation results with respect to the fine scale parameterization. This sensitivity computation is analytical and takes into account the upscaling process. Direct updating of the fine scale geostatistical model using classical optimization process. Direct updating ensures consistency between the fine and coarse scale models. The accuracy of the proposed methodology was improved by calibrating the flow simulation model. The objective of this calibration is to reduce the error introduced by the upscaling step during the flow simulation. We applied successfully our methodology for fine scale reservoir description by integrating permanent down-hole gauge measurements directly into a three-dimensional geostatistical model containing about two million grid blocks. This test is designed to highlight several key issues of the proposed methodology: Efficiency of the upscaling step coupled with gradient-based optimization to speed up the history matching process. Usefulness of the calibration step for a correct integration of upscaling techniques in history matching. Capability of the methodology for maintaining consistency and coherency between fine scale and coarse scale models. Improvement of the reservoir characterization by integrating dynamic data at the fine geostatistical scale. We conclude that the proposed methodology can be used effectively and efficiently for reservoir characterization purposes.

An efficient space-angle subgrid scale discretisation of the neutron transport equation

Fruit house microbial communities that are unique from the rest of the plant. While symbiotic microbial communities complete important functions for their hosts, the fruit microbiome is often understudied compared to other plant organs. Fruits are reproductive tissues that house, protect, and facilitate the dispersal of seeds, and thus they are directly tied to plant fitness. Fruit microbial communities may, therefore, also impact plant fitness. In this study, we assessed how bacterial communities associated with fruit of Solanum carolinense, a native herbaceous perennial weed, vary at fine spatial scales (<0.5 km). A majority of the studies conducted on plant microbial communities have been done at large spatial scales and have observed microbial community variation across these large spatial scales. However, both the environment and pollinators play a role in shaping plant microbial communities and likely have impacts on the plant microbiome at fine scales. We collected fruit samples from eight sampling locations, ranging from 2 to 450 m apart, and assessed the fruit bacterial communities using 16S rRNA gene amplicon sequencing. Overall, we found no differences in observed richness or microbial community composition among sampling locations. Bacterial community structure of fruits collected near one another were not more different than those that were farther apart at the scales we examined. These fine spatial scales are important to obligate out-crossing plant species such as S. carolinense because they are ecologically relevant to pollinators. Thus, our results could imply that pollinators serve to homogenize fruit bacterial communities across these smaller scales.

Abstract. Life tables and rates of parasitism were tabulated from mud nests built by Trypoxylon politum (Hymenoptera: Sphecidae) at nine different nesting sites from Missouri and Mississippi. Most developmental mortality occurred either during the first two instars of development, or during the inactive prepupal phase. The majority (76%) of deaths were caused by insect parasitoids and cleptoparasites. Levels of parasitism and survivorship varied among nesting sites, and among locations within the two sites surveyed at a fine spatial scale. Total developmental mortality, K, was positively associated with the number of hosts (immature T.politum) per site. Within one of two sites sampled at a fine spatial scale, K was negatively associated with the local density of hosts. Levels of total parasitism were positively associated with host population size, and negatively associated with local host density within one of the two sites sampled at a fine spatial scale. Levels of parasitism by Melittobia (Hymenoptera: Eulophidae) were positively associated with the number of hosts per site, but negatively associated with the local density of hosts within sites. Melittobia parasitism was also negatively associated with the local density of old nesting material within sites. Parasitism by Melittobia was a function of both the numbers of nests per quadrat and the mean nest size per quadrat at one of the two sites surveyed at a fine scale. At the other site, parasitism by Melittobia was a function of mean nest size per quadrat. The life cycle and nesting behaviour of T.politum, in relation to the regulation of its numbers, is discussed.

We evaluated the roles of the abiotic environment and dispersal in the assembly of fern communities at contrasting spatial scales within an old-growth, temperate deciduous forest. Specifically, we examined correlations among the geographic location of sampling plots separated by either 135–3515 m (mesoscale) or 4–134 m (fine scale), the abiotic environmental characteristics of the plots, and their constituent fern species. Ferns had predictable distributions along a soil moisture gradient at both spatial scales: six of eight common fern species showed repeatable environmental optima along the soil moisture gradient. By sampling in such a way as to decouple the correlation between distance and environmental variation, we showed the dominant role of environmental variables such as soil moisture in determining fern distributions at the mesoscale. At the fine scale, however, strong spatial autocorrelation in the abiotic environment precluded assigning any definitive role for either dispersal or environmental determinism alone in affecting fern distributions. The expectations of neutral theory that are rooted in dispersal limitation and those of niche theory that are rooted in environmental adaptation converge at fine spatial scales where natural environments have strong spatial structure. The structure of the environment at fine spatial scales may foster the persistence of dispersal-limited plants in the community; neighboring environments are likely to be similar, and thus suitable for propagules dispersing short distances. While patterns of fern distribution in this locality are not consistent with purely neutral or random models of species coexistence, alternative models that rely on strict niche requirements without accounting for dispersal effects and the inherent spatial structure of the environment are inadequate because they neglect the important interaction of these factors. This outcome supports the relevance of developing theory that considers the joint effects of environmental determinism and dispersal on the distribution and abundance of plant species.

In order to run reservoir simulation efficiently, a coarse scale (CS) dynamic model is created by upscaling of a fine scale (FS) static model. All history match (HM) changes usually done in the CS dynamic model need to be downscaled to FS for geological justifications and consistency maintenance between the FS static and CS dynamic models. This paper proposes a robust downscaling method for integration of FS static and CS dynamic models. The proposed method downscales a HMDM (dynamic model) to HMSM (static) in multiple steps. Scale-up the ISM (initial) to CS to create an IDM. Identify the cell changes between HMDM and IDM, and transfer the changes to FS to create a MSM (modified). Scale-up the MSM to CS to create to a MDM and calculate the ratios between HMDM and MDM for all cell properties. Transfer the ratios to FS to create a HMSM. Scale-up the HMSM to CS to confirm its identity to the HMDM. Selection of sampling and zone mapping methods is critical in all steps. The proposed method has been successfully applied in a giant carbonate oil field in the Caspian Sea that consists of a matrix dominated platform and a fracture/karst dominated rim. Due to the field's complex geology and high H2S content (15%), a dual porosity, dual permeability compositional model has been created to model compositional sour crude flow within/between matrix and fracture/karst. The FS static model contains a 236m × 236m horizontal grid with 593 layers while the CS dynamic model has the horizontal cell sizes in a range of 236m to 944m with 73 layers. Rock regions, permeability, and reservoir connectivity in the CS dynamic model were calibrated using the field historical production data (e.g., static pressure, PLT, interference test, and GOR/water-cut data) to create a HMDM. Since the HM process was performed only in the CS dynamic model, the FS static model and HMDM became inconsistent. Appling the proposed downscaling method has helped the HM team to resolve this issue and resulted in a seamless link between the FS static and CS dynamic models for current and future HM and model updates.

The concept of community assembly through trait‐based environmental filtering has played a key role in our understanding of how communities change over space and time, however, the importance of spatial scale in the filtering process remains unclear. We propose that different environmental filters may operate at different spatial scales, and that filters at finer scales would be nested within those acting at coarser scales. We tested for the existence of spatially nested sets of trait‐based filters in a temperate native grassland by applying the recently proposed maximum entropy (MaxEnt) approach to trait‐based community assembly, which we extend through a trait selection procedure. We found that different traits were important in influencing the abundances of species at the three different spatial scales examined (micro‐habitat, habitat, landscape), supporting the idea that trait based filtering processes operating at coarse spatial scales can be quite distinct from those operating at fine scales. Despite this result, we identified several traits which were frequently related to abundance at all spatial scales. Taken together, our results support the proposition that trait‐based environmental filters at finer spatial scales are nested within those operating at coarser scales. We compared our results to those obtained using a simpler trait‐by‐trait analytical approach (correlation analysis and MaxEnt on individual traits). The capacity for MaxEnt to incorporate multiple traits simultaneously provided unique insights into the important traits at each spatial scale and presents significant advantages over existing univariate and multivariate approaches.

&lt;p&gt;&lt;b&gt;Recent ecological studies have started integrate to spatial variation of ecological patterns into the study design rather than treating it as a statistical nuisance. In particular, the influence of the spatial scale at which ecological patterns are measured has gained much attention over the last two decades. Since, for example, sensory abilities as well as the ability to disperse vary among species, different species-specific responses to heterogeneous environments may be expected.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;Plant-insect interactions in heterogeneous landscapes, in particular, have gained much attention as experiments can be conducted on a more accessible scale and may yield new applications for crop and horticulture. Two hypotheses that describe insect herbivore aggregations in the landscape are: a) the resource concentration hypothesis which predicts higher numbers of specialist insect herbivores per unit biomass in dense and pure stands of their host plant, and b) the resource dilution hypothesis which predicts that insect herbivore numbers will decline with increasing plant density. I investigated resource dilution and resource concentration patterns in egg distributions of Pieris rapae and Tyria jacobaeae in relation to host plant density, which I defined differently by applying varying spatial scales of measurement. I also tested for effects of host plant density and the scale of measurement on flight patterns of P. rapae females.&lt;/p&gt; &lt;p&gt;In a natural population of Lepidium oleraceum I investigated effects of scale of measurement of plant density, as well as white rust and hymenopteran parasitoids on P. rapae egg and larvae distributions. In a separate experiment I tested for any potential effects of arthropod predators on P. rapae egg distributions at different spatial scales. The number of P. rapae eggs per plant conformed to predictions made by the resource dilution hypothesis. However, such a pattern was only found for fine scale plant density but not for medium or coarse scale plant density. In contrast, the number of T. jacobaeae egg clutches per plant showed a resource concentration pattern for medium scale plant density but not for fine or coarse scale plant density. However, this result occurred only in one of two experiments with T. jacobaeae. A resource dilution pattern was also found for the number of visits per plant by P. rapae females at both coarse and fine scale measurement. Female flight paths were less directional when plants were present in the study area during fine scale observations and butterflies were attracted to areas containing host plants. Flight observations at coarse scale did not show any change in turning behaviour and butterflies moved at random across the study area. No effect of parasitism, or predation through arthropods was found on the distribution of P. rapae eggs. However, infection by white rust lead to a decreased number of eggs per plant in the natural L. oleraceum population. The results of my thesis underline the importance of spatial scale in ecological studies. Careful thought should be given to the scale of measurement and method of abstraction when describing real world patterns.&lt;/p&gt;

In the coarse scale simulation of heterogeneous reservoirs, effective or upscaled flow functions, e.g., oil and water relative permeability and capillary pressure, can be used to represent heterogeneities at subgrid scales. The effective relative permeability is typically upscaled along with absolute permeability from a geostatistical model. However, the potentially important effects of smaller scale heterogeneities (on the centimeter to meter scale) in both capillarity and absolute permeability will not be captured by this approach. In this paper, we present a new two-stage upscaling procedure for two-phase flow. In the first stage, we upscale from the core (fine) scale to the geostatistical (intermediate) scale, while in the second stage we upscale from the geostatistical scale to the simulation (coarse) scale. The computational procedure includes numerical solution of the finite difference equations describing steady state flow over the local region to be upscaled, using either constant pressure or periodic boundary conditions. The two-stage method is applied to synthetic two-dimensional reservoir models with strong variation in capillarity on the fine scale. Results are presented in terms of both oil production rates and saturation fields. Accurate reproduction of the fine grid solutions (simulated on 500 × 500 grids) is achieved on coarse grids of 10 × 10 for different flow scenarios. It is shown that, although capillary forces are important on the fine scale, the assumption of capillary dominance in the first stage of upscaling is not always appropriate, and that the computation of rate dependent effective properties in the upscaling can significantly improve the accuracy of the coarse scale model. The assumption of viscous dominance in the second upscaling stage is found to be appropriate in all of the cases considered.

Statistical disease mapping is a valuable public health tool, as it identifies spatial patterns of disease occurrence. However, the Modifiable Areal Unit Problem (MAUP) poses challenges to disease mapping, as the aggregation of geographic units can impact statistical inferences. The effect of the MAUP depends on contextual factors, for example the geographic structure, aggregation level, choice of model, and the underlying data-generating process. We conducted a comprehensive simulation study to understand the role of these factors on the MAUP in the context of Australian disease mapping. We aggregated and rezoned disease count data at a fine geographic scale before fitting spatial and non-spatial regression models to assess the impact of the MAUP on coefficients. To aid the exploration of simulation results, we developed an interactive Shiny application that enables detailed and interactive exploration of the simulation results. This study highlights the need for disease mapping researchers to analyse sensitivity with rezoning and aggregation tools.

One of the major objectives in the development of upscaling approaches is to reduce the computational costs associated with solving fine scale flow and transport problems in heterogeneous porous media. This is due to the availability of reservoir rock property data at fine spatial scales, such as facies distributions, obtained from geological models and field data from well logs. The data sets from each of these sources are themselves at different spatial scales which further adds to the computational challenge. The upscaling approach must not only accommodate these disparate data sets but also capture the flow physics accurately while maintaining computational efficiency. We present a novel upscaling approach which draws upon previous developments of two-scale homogenization (Amaziane et al., 2006) to obtain coarse scale properties in addition to dynamic mesh refinement using an enhanced velocity mixed finite element method (EV MFEM) (Wheeler et al., 2002). A transient region is defined where changes in saturation/concentration are above a chosen threshold compared to a non-transient region where these are relatively small. Since most of the recovery technologies employed in oil and gas field operations involve flooding the subsurface porous medium; as in the case of water-flooding, chemical or gas enhanced oil recovery (EOR), these aforementioned transient regions are usually restricted to much smaller subdomains of the entire reservoir domain. The computational efficiency is achieved by using coarse scale parameters, from numerical homogenization, in the non-transient region. Furthermore, the solution accuracy is preserved by using fine scale information only in the transient region. The numerical results section shows that our adaptive homogenization approach closely captures fine scale flow and transport features while maintaining a computational speedup of approximately 4 times for a variety of permeability distributions extracted from the SPE 10 comparative upscaling project (Christie and Blunt, 2001).

More than climate? Predictors of tree canopy height vary with scale in complex terrain, Sierra Nevada, CA (USA)

Subsurface flows are affected by geological variability over a range of length scales. The modeling of well singularity in heterogeneous formations is important for simulating flow in aquifers and petroleum reservoirs. In this paper, two approaches in calculating the upscaled well index to capture the effects of fine scale heterogeneity in near-well regions are presented and applied. We first develop a flow-based near-well upscaling procedure for geometrically flexible grids. This approach entails solving local well-driven flows and requires the treatment of geometric effects due to the nonalignment between fine and coarse scale grids. An approximate coarse scale well model based on a well singularity analysis is also proposed. This model, referred to as near-well arithmetic averaging, uses only the fine scale permeabilities at well locations to compute the coarse scale well index; it does not require solving any flow problems. These two methods are systematically tested on three-dimensional models with a variety of permeability distributions. It is shown that both approaches provide considerable improvement over a simple (arithmetic) averaging approach to compute the coarse scale well index. The flow-based approach shows close agreement to the fine scale reference model, and the near-well arithmetic averaging also offers accuracy for an appropriate range of parameters. The interaction between global flow and near-well upscaling is also investigated through the use of global fine scale solutions in near-well scale-up calculations.