Associations between a highly invasive species and native macrophytes differ across spatial scales

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.

AimsDarwin’s naturalization hypothesis proposes that successfully established alien species are less closely related to native species due to differences in their ecological niches. Studies have provided support both for and against this hypothesis. One reason for this is the tendency for phylogenetic clustering between aliens and natives at broad spatial scales with overdispersion at fine scales. However, little is known about how the phylogenetic relatedness of alien species alters the phylogenetic structure of the communities they invade, and at which spatial scales effects may manifest. Here, we examine if invaded understorey plant communities, i.e. containing both native and alien taxa, are phylogenetically clustered or overdispersed, how relatedness changes with spatial scale and how aliens affect phylogenetic patterns in understorey communities.MethodsField surveys were conducted in dry forest understorey communities in south-east Australia at five spatial scales (1, 20, 500, 1500 and 4500 m2). Standardized effect sizes of two metrics were used to quantify phylogenetic relatedness between communities and their alien and native subcommunities, and to examine how phylogenetic patterns change with spatial scale: (i) mean pairwise distance and (ii) mean nearest taxon distance (MNTD).Important FindingsAliens were closely related to each other, and this relatedness tended to increase with scale. Native species and the full community exhibited either no clear pattern of relatedness with increasing spatial scale or were no different from random. At intermediate spatial scales (20–500 m2), the whole community tended towards random whereas the natives were strongly overdispersed and the alien subcommunity strongly clustered. This suggests that invasion by closely related aliens shifts community phylogenetic structure from overdispersed towards random. Aliens and natives were distantly related across spatial scales, supporting Darwin’s naturalization hypothesis, but only when phylogenetic distance was quantified as MNTD. Phylogenetic dissimilarity between aliens and natives increased with spatial scale, counter to expected patterns. Our findings suggest that the strong phylogenetic clustering of aliens is driven by human-mediated introductions involving closely related taxa that can establish and spread successfully. Unexpected scale-dependent patterns of phylogenetic relatedness may result from stochastic processes such as fire and dispersal events and suggest that competition and habitat filtering do not exclusively dominate phylogenetic relationships at fine and coarse spatial scales, respectively. Distinguishing between metrics that focus on different evolutionary depths is important, as different metrics can exhibit different scale-dependent patterns.

In The Origin of Species, Darwin (1859) drew attention to observations by Alphonse de Candolle (1855) that floras gain by naturalization far more species belonging to new genera than species belonging to native genera. Darwin (1859, p. 86) goes on to give a specific example: “In the last edition of Dr. Asa Gray’s ‘Manual of the Flora of the United States’ ... out of the 162 naturalised genera, no less than 100 genera are not there indigenous.” Darwin used these data to support his theory of intense competition between congeners, described only a few pages earlier: “As the species of the same genus usually have, though by no means invariably, much similarity in habits and constitution, and always in structure, the struggle will generally be more severe between them” (1859, p. 60). Darwin’s intriguing observations have recently attracted renewed interest, as comprehensive lists of naturalized plants have become available for various regions of the world. Two studies (Mack 1996; Rejmanek 1996, 1998) have concluded that naturalized floras provide some support for Darwin’s hypothesis, but only one of these studies used statistical tests. Analyses of additional floras are needed to test the generality of Darwin’s naturalization hypothesis. Mack (1996) tabulated data from six regional floras within the United States and noted that naturalized species more often belong to alien genera than native genera, with the curious exception of one region (New York). In addition to the possibility of strong competition between native and introduced congeners, Mack (1996) proposed that specialist native herbivores, or pathogens, may be

AimThe functional composition of local assemblages is hypothesized to be controlled by hierarchical environmental filters, whereby the importance of different abiotic and biotic factors varies across both spatial scales and the different dimensions of functional diversity. We examine scale dependence in functional diversity–environment relationships with the ultimate aim of advancing models that predict the response of functional diversity to global change.LocationCoral reefs surrounding 23 minimally disturbed central‐western Pacific islands.Time period2010–2015.Major taxa studiedCoral reef fishes.MethodsWe surveyed 1,423 reef sites using a standardized monitoring protocol and classified the 547 taxa encountered based on traits related to resource use, body size and behaviour. For each fish community, we calculated species richness and three metrics of functional diversity: functional richness, functional redundancy and functional evenness. We then built nested models at three spatial scales to evaluate the predictive power of environmental conditions over each component of functional diversity.ResultsClimatic variables (e.g., primary productivity) and geomorphic context (e.g., bathymetric slope) were more important in predicting functional diversity at coarse spatial scales. In contrast, local measures of habitat quality, including benthic complexity, depth and hard coral cover, were generally most important at finer scales. All diversity metrics were better predicted at coarser scales, but which predictors were important varied among metrics.Main conclusionsThe observed scale dependence in environmental predictors of functional diversity generally matches models of hierarchical filters on functional community assembly. Contrary to expectation, however, functional evenness and functional redundancy, which incorporate information on biomass distributions, were not better predicted at finer spatial scales. Instead, broad‐scale variation in environmental variables was most important in predicting all components of functional diversity. Furthermore, the distinct responses of each functional diversity metric to environmental variation indicate that each measures a unique dimension of reef‐fish diversity, and environmental change may affect each differently.

Species diversity of insect herbivores associated to canopy may vary local and geographically responding to distinct factors at different spatial scales. The aim of this study was to investigate how forest canopy structure affects insect herbivore species richness and abundance depending on feeding guilds' specificities. We tested the hypothesis that habitat structure affects insect herbivore species richness and abundance differently to sap-sucking and chewing herbivore guilds. Two spatial scales were evaluated: inside tree crowns (fine spatial cale) and canopy regions (coarse spatial scale). In three sampling sites we measured 120 tree crowns, grouped n five points with four contiguous tree crowns. Insects were sampled by beating method from each crown and data were summed up for analyzing each canopy region. In crowns (fine spatial scale) we measured habitat tructure: trunk circumference, tree height, canopy depth, number of ramifications and maximum ramification level. In each point, defined as a canopy region (coarse spatial scale), we measured habitat structure using a vertical cylindrical transect: tree species richness, leaf area, sum of strata heights and maximum canopy height. A principal component analysis based on the measured variables for each spatial scale was run to estimate habitat structure parameters. To test the effects of habitat structure upon herbivores, different general linear models were adjusted using the first two principal components as explanatory variables. Sap-sucking insect species richness and all herbivore abundances increased with size of crown at fine spatial scale. On the other hand, chewer species richness and abundance increased with resource quantity at coarse scale. Feeding specialization, resources availability, and agility are discussed as ecological causes of the found pattern.

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

Accurate identification of spatial patterns and risk factors of disease occurrence is crucial for public health interventions. However, the Modifiable Areal Unit Problem (MAUP) poses challenges in disease modelling by impacting the reliability of statistical inferences drawn from spatially aggregated data. This study examines the effect of MAUP on ecological model inference using locally and overseas-acquired COVID-19 case data from 2020 to 2023 in Queensland, Australia. Bayesian spatial Besag-York-Mollié (BYM) models were applied across four Statistical Area (SA) levels, as defined by the Australian Statistical Geography Standard, with and without covariates: Socio-Economic Indexes for Areas (SEIFA) and overseas-acquired (OA) COVID-19 cases. OA COVID-19 cases were also considered a response variable in our study. Results indicated that finer spatial scales (SA1 and SA2) captured localized patterns and significant spatial autocorrelation, while coarser levels (SA3 and SA4) smoothed spatial variability, masking potential outbreak clusters. Incorporating SEIFA as a covariate in locally-acquired (LA) cases reduced spatial autocorrelation in residuals, effectively capturing socioeconomic disparities. Conversely, OA cases showed limited effectiveness in reducing autocorrelation at finer scales. For LA cases, higher socioeconomic disadvantage was associated with increased COVID-19 incidence at finer scales, but this association became non-significant at coarser scales. OA cases showed significant positive association with higher SEIFA scores at finer scales. Model parameters displayed narrower credible intervals at finer scales, indicating greater precision, while coarser levels had increased uncertainty. SA2 emerged as an arguably optimal scale, striking a balance between spatial resolution, model stability, and interpretability. To improve inference on COVID-19 incidence, it is recommended to use data from both SA1 and SA2 levels to leverage their respective strengths. The findings emphasize the importance of selecting appropriate spatial scales and covariates or evaluating the inferential impacts of multiple scales, to address MAUP to facilitate more reliable spatial analysis. The study advocates exploring intermediate aggregation levels and multi-scale approaches to better capture nuanced disease dynamics and extend these analyses across Australia and replicating in other countries with low population densities to enhance generalizability.

Where land surface air temperature data are not available, satellite land surface temperature are used. However, the coarse spatial resolution of satellite‐derived products may yield errors at the local scale. This work shows the differences between the MODIS Land Surface Temperature and Emissivity (MOD11A1) product and ground measurements at two different scales. We used data from 21 SNOTEL stations across the northern Front Range of Colorado to represent the coarse scale and 17 iButton temperature sensors across the Colorado State University Mountain Campus to represent the fine scale. We found significant differences in the temperature and its changes with elevation for the two spatial scales. At the fine scale, cold air drainage can induce an inversion of the temperature gradient with elevation. A higher correlation was found during the nighttime at the fine scale, while, at the coarse scale, higher correlations were observed during the daytime. On windy nights, temperatures do not cool as much as on calmer nights, and the coarse scale near‐surface temperature gradient with elevation persists, though the fine scale inversions do not develop. The near‐surface temperature gradients with elevation based on the MODIS pixels are similar to the ground‐based data at the coarse scale but not at the fine scale. Thus, one must be cautious in selecting the near‐surface temperature gradients with elevation for mountainous terrain when different scales are considered, and a proper validation of satellite products is necessary prior to their use to avoid the propagation of uncertainties.

<p><b>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.</b></p> <p>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.</p> <p>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.</p>

There is a growing recognition that spatial scale is important for understanding ecological processes shaping community membership, but empirical evidence on this topic is still scarce. Ecological processes such as environmental filtering can decrease functional differences among species and promote functional clustering of species assemblages, whereas interspecific competition can do the opposite. These different ecological processes are expected to take place at different spatial scales, with competition being more likely at finer scales and environmental filtering most likely at coarser scales. We used a comprehensive dataset on species assemblages of a dominant ant genus, Pheidole, in the Cerrado (savanna) biodiversity hotspot to ask how functional richness relates to species richness gradients and whether such relationships vary across spatial scales. Functional richness of Pheidole assemblages decreased with increasing species richness, but such relationship did not vary across different spatial scales. Species were more functionally dissimilar at finer spatial scales, and functional richness increased less than expected with increasing species richness. Our results indicate a tighter packing of the functional volume as richness increases and point out to a primary role for environmental filtering in shaping membership of Pheidole assemblages in Neotropical savannas.OPEN RESEARCH BADGES This article has been awarded Open Materials, Open Data, Preregistered Research Designs Badges. All materials and data are publicly accessible via the Open Science Framework at https://doi.org/10.5061/dryad.31201jg

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

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.

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 diversity of native species is assumed to limit invasion by alien species, but there is a negative relationship between native and alien species diversity at fine spatial scales and a positive one at broad scales. This contradiction has been termed the invasion paradox, and it ensues from various processes operating at different spatial scales, such as species interactions and environmental heterogeneity. We investigated the relationship between native and alien plant species diversity components (α-, β- and γ-diversity) and their response to environmental factors at three spatial scales (1, 50 m2, 0.25 km2) in semi-natural agricultural habitats in Finland. Native and alien species diversity components were positively correlated across spatial scales, and the beta diversity contributed most to the total observed alien and native species richness (γ-diversity). The diversities of native and alien species were positively associated with productivity at the 1 m2 scale. At broader scales, alien and native species diversity responded similarly to geographical location, but differently to the productivity, disturbance and landscape diversity. Alien species diversity was positively correlated with disturbance regime, whereas native species were more strongly related to habitat type, and decreasing land-use intensity. Native and alien diversities were affected by both average and variability in local habitat conditions. Thus, both favourable conditions and heterogeneity in environmental conditions may contribute to the diversity–invasibility relationships. Disturbance regime typical of agricultural habitats may create open niches for both native and alien species, limit species competition even at fine spatial scales and lead to a positive diversity–invasibility relationship.

Understanding the constraints on community composition at multiple spatial scales is an immense challenge to community and ecosystem ecologists. As community composition is basically the composite result of species’ spatial patterning, studying this spatial patterning across scales may yield clues as to which scales of environmental heterogeneity influence communities. The now widely documented positive interspecific relationship between ‘regional’ range and mean ‘local’ abundance has become a generalisation describing the spatial patterning of species at coarse scales. We address some of the shortcomings of this generalisation, as well as examine the cross‐scale spatial patterning (aggregation and density levels) of littoral‐benthic invertebrates in very large lakes. Specifically, we (a) determine whether the positive range‐abundance relationship can be reinterpreted in terms of the actual spatial structure of species distributions, (b) examine the relationship between aggregation and density across different spatial scales, and (c) determine whether the spatial patterning of species (e.g. low density/aggregated distribution) is constant across scales, that is, whether our interpretation of a species spatial pattern is dependent on the scale at which we choose to observe the system.Spatial aggregation of littoral invertebrates was generally a negative function of mean density across all spatial scales and seasons (autumn and spring). This relationship may underlie positive range‐abundance relationships. Species that were uncommon and highly aggregated at coarse spatial scales can be abundant and approach random distributions at finer spatial scales. Also, the change in spatial aggregation of closely related taxa across spatial scales was idiosyncratic. The idiosyncratic cross‐scale spatial patterning of species implies that multiple scales of environmental heterogeneity may influence the assembly of littoral communities. Due to the multi‐scale, species‐specific spatial patterning of invertebrates, littoral zone communities form a complex spatial mosaic, and a ‘spatially explicit’ approach will be required by limnologists in order to link littoral‐benthic community patterns with ecosystem processes in large oligotrophic lakes.