Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation?

Is Erica tetralix abundance on wet heathlands controlled by nitrogen deposition or soil acidification?

Methods and criteria that were used to identify plant macrofossils from four ombrotrophic mires in northern Spain are presented. Twelve monocotyledon and ten dicotyledon species were recorded. Some were identified from vegetative or reproductive macroremains (Eriophorum angustifolium, Molinia caerulea, Calluna vulgaris, Erica mackaiana, Erica tetralix, Potentilla erecta), while others were recognised only by their fruits (Rhynchospora alba, Carex durieui, Carex echinata, Carex binervis, Carex demissa, Betula alba), seeds (Juncus squarrosus, Juncus bulbosus, Luzula multiflora, Narthecium ossifragum, Drosera rotundifolia, Drosera intermedia, Caltha palustris, Daboecia cantabrica), rhizome fragments with remains of leaves (Agrostis curtisii), or twigs with buds and leaves (Vaccinium myrtillus). Descriptions of the specific distinctive characters for the plant macrofossils that were recorded are accompanied by illustrations that facilitate their interpretation. Dichotomous identification keys are also provided.

Abstract. Over the past decades outdoor recreation has become progressively more important and as a result human‐induced potential damage has increased. In this study, short‐term effects – a 2‐yr period – of human trampling on some common forest and heath communities in Central Belgium were studied experimentally. Vulnerability to disturbance was compared among plant communities in terms of resistance, resilience and tolerance, which are based on cover measurements. The herb layer of the examined mesophilous forest communities appeared to be more sensitive than the heath and dry forest community, which were dominated by more resistant graminoid (Molinia caerulea, Deschampsiaflexuosa) and dwarf‐shrub species (Vaccinium myrtillus, Calluna vulgaris, Erica tetralix). The analysis showed that site structure and vegetation were already affected by low intensities of trampling, while vegetation recovery during the first year after trampling was limited in most plant communities. Recovery during the second year in vegetation cover as well as height was most pronounced in mesophilous forest communities. Occasional trampling clearly can lead to increased visual evidence of previous use and continued recreational disturbance. Therefore management plans should discourage hiking activity off paths and restrict recreation activities to the least vulnerable communities.

Annual patterns of Cs-133 concentration in british upland vegetation

Transport affordability defined as the share of household income devoted to transport expenditure is a key dimension of urban equity and social inclusion, particularly in contexts characterised by spatial inequality and income disparities. This study examines provincial variation in public transport affordability across South Africa using a hierarchical Bayesian regression framework applied to province–year data from 2015 to 2022 (n = 72). Affordability is operationalised as a transport cost burden, with higher values indicating a greater proportion of household income spent on transport, and is modelled as a function of household income, trip frequency, household population, and total provincial employment, with province-level random intercepts capturing unobserved regional heterogeneity. The results indicate that household income is negatively associated with transport cost burden, suggesting that provinces with higher average income devote a smaller share of income to transport and therefore experience better affordability. In contrast, household population and aggregate provincial employment are positively associated with transport cost burden, reflecting higher overall mobility and commuting demands in larger and more economically active provinces rather than improved affordability. Trip frequency shows no statistically meaningful association with affordability once household composition and income capacity are accounted for. After accounting for observed characteristics, between-province variation is limited, indicating that affordability dynamics are broadly similar across provinces over the study period. Methodologically, the hierarchical Bayesian framework enables partial pooling across provinces and supports probabilistic inference through credible intervals, thereby improving the stability of estimates in a small-sample multilevel context. While the analysis is associational rather than causal, the findings provide policy-relevant evidence for monitoring transport affordability, including benchmarking the prevalence of affordability burdens relative to the commonly used 10% threshold.

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Mitochondrial dysfunction is involved in many complex diseases. Efficient and accurate evaluation of mitochondrial functionality is crucial for understanding pathology as well as facilitating novel therapeutic developments. As a popular platform, Seahorse extracellular flux (XF) analyzer is widely used for measuring mitochondrial oxygen consumption rate (OCR) in living cells. A hidden feature of Seahorse XF OCR data is that it has a complex data structure, caused by nesting and crossing between measurement cycles, wells and plates. Surprisingly, statistical analysis of Seahorse XF data has not received sufficient attention, and current methods completely ignore the complex data structure, impairing the robustness of statistical inference. To rigorously incorporate the complex structure into data analysis, here we developed a Bayesian hierarchical modeling framework, OCRbayes, and demonstrated its applicability based on analysis of published data sets. We showed that OCRbayes can analyze Seahorse XF OCR experimental data derived from either single or multiple plates. Moreover, OCRbayes has potential to be used for diagnosing patients with mitochondrial diseases.

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]A space–time Bayesian hierarchical modeling framework for projection of seasonal high flow riskAuthorsÁlvaroOssandóniDBalajiRajagopalanSee all authors Álvaro OssandóniDCorresponding Author• Submitting AuthorUniversity of Colorado at BoulderiDhttps://orcid.org/0000-0003-2165-8736view email addressThe email was not providedcopy email addressBalaji RajagopalanUniversity of Colorado at Boulderview email addressThe email was not providedcopy email address

Introduction. The vegetation which is the subject of this paper is found on extensive areas of wet heath-land in south-eastern England. What will be called the central associes is a community dominated by Molinia caerulea and Calluna vulgaris, and in which Erica tetralix is usually abundant. The bases of dead Molinia shoots decay rather slowly in the wet habitat and thus there are built up tussocks of varying height, which are an important rooting medium both for the Molinia itself and for the Ericaceae. Under varying conditions any one of these plants may increase in importance at the expense of the other two. As would be expected, increase in Calluna is the usual accompaniment of transition to drier sites, and the change from wet-heath to Callunetum is commonly marked by the entry of Erica cinerea, Ulex minor and Deschampsia flexuosa. Relatively greater abundance of Erica tetralix is found in transitional stages between the central associes and valley-bog communities dominated by Sphagnum spp. In other wet situations Molinia assumes complete dominance. It has then a markedly tussocky habit, the tussocks reaching 30 cm. and sometimes much more in height; and Myrica gale is often associated with it. The wet Molinietum is frequently seen in the bottoms of sloping valleys cut into the sides of plateaux, and in other situations where the ground-water may be supposed to have appreciable flow. The association of Molinia with moving ground-water is well known; it was described on the Yorkshire moors by Jefferies (1915) and on Scottish moors by Fraser (1933). Transitions of the central associes to a rather drier type of Molinietum are also found. In this, tussock building is less pronounced, but there is no ciearly marked distinction between the drier and wetter types of Molinietum; they grade imperceptibly into one another. But whereas at the wet extreme the growth of pine and birch invaders is extremely stunted, and the development of woodland seems impossible, the drier Molinietum is readily colonized by vigorously growing Betula spp., especially B. pubescens. Molinia may also dominate considerable areas of dry heath. But this quite distinct community, in which there is no accumulation of dead shoot bases, and which includes

Personalized federated learning (PFL) based on Bayesian approach tackle the challenges from statistical heterogeneity of client data by computing a personalized posterior distribution over the parameters of each client's local model and constructing a global distribution by aggregating the parameters of these personalized posteriors. However, the heuristic aggregation methods introduce strong biases and result in global models with poor generalization. We thus propose a novel hierarchical Bayesian inference framework for PFL by specifying a conjugate hyper-prior over the parameters of the personalized posteriors. This allows us to jointly compute a global posterior distribution for aggregation and the personalized ones at local level. This hierarchical Bayesian inference framework achieves elegant balance between local personalization and global model robustness. Extensive empirical study shows that by effectively sharing the heterogeneous statistical strength across the local models while retaining their distinctive characteristics, our framework yields state-of-the-art performance. We also show that existing Bayesian PFLs are special cases of our framework.

In this brief, a hierarchical Bayesian network modeling framework is formulated for large-scale process monitoring and decision making, which includes a basic layer and a functional layer. First, the whole process is decomposed into different units, where local Bayesian networks are constructed, providing monitoring information and decision-making capability for the upper layer. The network structure is determined automatically based on the process data in each local unit of the basic layer. Then, through incorporating the topological structure of the process, a functional Bayesian network is further constructed to infer the information from the basic layer, which can be customized according to user demands, such as fault detection, fault diagnosis, and classification of operating status. The performance of the proposed method is evaluated through a benchmark process.

Revegetation of eroded bare peat is commonly facilitated by the import of artificial resources and genetic material (lime, seed and fertiliser), but such techniques are less suitable for remote upland locations with sensitive local flora. Using a BACI (Before-After-Control-Impact) approach, this study explores the effectiveness of alternative treatments (heather (Calluna vulgaris) brash cut onsite and two types of geotextiles) in the following four years at grazed and ungrazed sites at Ben Lawers National Nature Reserve. After an initial colonisation, the mean vegetation cover in grazed plots degraded to 9.4 %, demonstrating that restoration using these methods is impeded by trampling impacts of large herbivores. The vegetation cover and number of indicator species increased annually at the ungrazed site. A significantly greater cover (>85 %) occurred at plots where thick brash (>85 % ground cover) had been topped with GeoJute netting, but plots with only heather brash still reached 56.6 % cover. It provided a suitable seed source and colonising substrate for appropriate local peatbog species, while additional planting of C. vulgaris cuttings did not significantly increase vegetation cover in comparison to unplanted plots. These results show that short-term restoration of bare peat can be promoted using minimal interventions and onsite resources in the absence of grazing.

We propose and investigate dynamic spatial-temporal point process models for independent and interacting events. The models for independent events are dynamic spatial-temporal Poisson point process (DSTPPP) model that account for temporal and spatial clustering. The models proposed for events with interaction are Markov (Gibbs) space-time point process models. We model the intensity function of a DSTPPP via conditioning arguments that allow for additional interpretations and inclusion of well-known point process models as special cases. Depending on the nature of the questions to be answered, the in- tensity function of a DSTPPP can be modeled in several ways. First, we develop models via conditioning on the time component of the events and then consider conditioning on the location component of the events. Modeling, simulation and computation are accomplished in a fully hierarchical Bayesian framework. Finally, we focus on dynamic marked Markov space-time point processes, where the events are allowed to interact with each other across time and space. Once again the hierarchical Bayesian framework is invaluable in this case, since it allows us to introduce dynamic process models via conditioning. The methodolo- gies are illustrated using simulated data, and several applications, including modeling and inference for earthquake events in California, and tornado events in Missouri.

Local-scale landslide susceptibility mapping (LSM) provides detailed information for decision making and planning. Most published landslide susceptibility maps lack reliable information at the local scale due to the spatial heterogeneity being ignored. To enrich the local-scale information of LSM, multiple information fusion methods for the local spatial heterogeneity and regional trends of control factors are needed. However, no information fusion method has been proposed for LSM yet. In this paper, we developed a new integrated statistical method, named B-GeoSVC, under the hierarchical Bayesian framework for LSM. Specifically, this model applied the GeoDetector method to fit the regional trends of control factors and employed spatially varying coefficients (SVC) model to fit the local spatial heterogeneity of each control factor. Then, the regional trends and local spatial heterogeneity information were fused within the hierarchical Bayesian framework. The B-GeoSVC model was verified using data from the Duwen basin of China, which was in the central region affected by the MS 8.0 Wenchuan earthquake that occurred on May 12, 2008. Under a cross-validation experiment, the prediction accuracy rate of the B-GeoSVC model was 86.09%, and the area under the curve was 0.93, which suggested that the B-GeoSVC model was able to achieve relatively accurate local-scale LSM and provide richer local information than traditional regional scale LSM. More importantly, not only the B-GeoSVC model could be employed as a general solution to fuse both regional and local-scale information for landslide mapping, but also offer new insights into the broader earth science and spatial statistics.

The bog of Robenhausen near Seegraben-Wetzikon (Zurich Oberland) belongs to a peatland complex in the nature conservation area of the Lake of Pfaffikon which is surrounded by glacial deposits such as moraines and drumlins. For more than 200 years peat has been used as source of fuel. Since 1996 the bog has become an important nature-conservation and regional recreation area. Primary and secondary peat build-up processes have been studied at 12 core drilling sites within this peat bog. Different kinds of peat have been analysed for plant macro- and microfossils, the degree of peat decomposition and the ratio of organic and inorganic material. In most cases, core samples provided only small amounts (or none) of primary peat, i.e. most peat was secondary and originated mainly from transition moors and raised bogs containing the Spagnum species. Once peat cutting had stopped, peat bog growth at most sites started with an eutrophic fen-peat and built up 5–70 cm of secondary peat. Typical plants are from the Carex species, Phragmites communis, Cladium mariscus, Molinia caerulea, Calluna vulgaris, Frangula alnus, Drosera spec. and the Sphagnum species. Other mosses found locally are Betula pubescens and Pinus sylvestris. Profiles taken in the peat bog show that the degree of organic decomposition increases from top to bottom. Some profiles from sites close to the lake shore show an undisturbed build-up of primary peat. This has been caused through a lowering of the sea level during the 1860’s. Small secondary stands of hairy birch and Scots Pine are approx. 50 years old (peat cutting stopped ca. 1950). This study provides detailed information to peat bog regeneration dynamics after a long period of peat cutting. Furthermore, these results can be used to help maintain this important peatland complex.