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Although the importance of edaphic factors and habitat structure for plant growth and survival is known, both are often neglected in favor of climatic drivers when investigating the spatial patterns of plant species and diversity. Yet, especially in mountain ecosystems with complex topography, missing edaphic and habitat components may be detrimental for a sound understanding of biodiversity distribution. Here, we compare the relative importance of climate, soil and land cover variables when predicting the distributions of 2,616 vascular plant species in the European Alps, representing approximately two-thirds of all European flora. Using presence-only data, we built point-process models (PPMs) to relate species observations to different combinations of covariates. We evaluated the PPMs through block cross-validations and assessed the independent contributions of climate, soil, and land cover covariates to predict plant species distributions using an innovative predictive partitioning approach. We found climate to be the most influential driver of spatial patterns in plant species with a relative influence of ~58.5% across all species, with decreasing importance from low to high elevations. Soil (~20.1%) and land cover (~21.4%), overall, were less influential than climate, but increased in importance along the elevation gradient. Furthermore, land cover showed strong local effects in lowlands, while the contribution of soil stabilized at mid-elevations. The decreasing influence of climate with elevation is explained by increasing endemism, and the fact that climate becomes more homogeneous as habitat diversity declines at higher altitudes. In contrast, soil predictors were found to follow the opposite trend. Additionally, at low elevations, human-mediated land cover effects appear to reduce the importance of climate predictors. We conclude that soil and land cover are, like climate, principal drivers of plant species distribution in the European Alps. While disentangling their effects remains a challenge, future studies can benefit markedly by including soil and land cover effects when predicting species distributions.

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions.

Secondary inorganic aerosols (sulfate, nitrate, and ammonium, SNA) are major contributors to fine particulate matter. Predicting concentrations of these species is complicated by the cascade of processes that control their abundance, including emissions, chemistry, thermodynamic partitioning, and removal. In this study, we use 11 flight campaigns to evaluate the GEOS-Chem model performance for SNA. Across all the campaigns, the model performance is best for sulfate (R2 = 0.51; normalized mean bias (NMB) = 0.11) and worst for nitrate (R2=0.22; NMB = 1.76), indicating substantive model deficiencies in the nitrate simulation. Thermodynamic partitioning reproduces the total particulate nitrate well (R2=0.79; NMB = 0.09), but actual partitioning (i.e., ε(NO3-)= NO3- / TNO3) is challenging to assess given the limited sets of full gas- and particle-phase observations needed for ISORROPIA II. In particular, ammonia observations are not often included in aircraft campaigns, and more routine measurements would help constrain sources of SNA model bias. Model performance is sensitive to changes in emissions and dry and wet deposition, with modest improvements associated with the inclusion of different chemical loss and production pathways (i.e., acid uptake on dust, N2O5 uptake, and NO3- photolysis). However, these sensitivity tests show only modest reduction in the nitrate bias, with no improvement to the model skill (i.e., R2), implying that more work is needed to improve the description of loss and production of nitrate and SNA as a whole.

ContextAs the frequency of large, severe fires increases, detecting the drivers of spatial fire severity patterns is key to predicting controls provided by weather, fuels, topography, and management.ObjectivesIdentify the biophysical and management drivers of severity patterns and their spatial variability across the 2013 Rim Fire, Sierra Nevada, California, USA.MethodsRandom forest models were developed separately for reburned and fire-excluded (> 80 year) areas within Yosemite National Park (NP) and Stanislaus National Forest (NF). Models included biophysical, past disturbance, and spatial autocorrelation (SA) predictors. Variable importance was assessed globally and locally. Variance partitioning was used to assess pure and shared variance among predictors.ResultsHigh spatial variability in the relative dominance of predictors existed across burn days and between land ownerships. Fire weather was a dominant top-down control during plume-dominated fire spread days. However, bottom-up controls from fuels and topography created local, fine-scale heterogeneity throughout. Reburn severity correlated with previous severity suggesting strong landscape memory, particularly in Yosemite NP. SA analysis showed broad-scale spatial dependencies and high shared variance among predictors.ConclusionsWildfires are inherently a multi-scaled process. Spatial structure in environmental variables create broad-scale patterns and dependencies among drivers leading to regions of similar fire behavior, while local bottom-up drivers generate fine-scaled heterogeneity. Identifying the conditions under which top-down factors overwhelm bottom-up controls can help managers monitor and manage wildfires to achieve both suppression and restoration goals. Restoration targeting both surface and ladder fuels can mediate future fire severity even under extreme weather conditions.

A modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, including the heating rates, was proposed in this study to improve the accuracy of isothermal austenite formation kinetics prediction. Since the ultrafast heating process affects the behavior of ferrite recrystallization and austenite formation before the isothermal process, which in turn influences the subsequent isothermal austenite formation kinetics, the effects of varying austenitization temperatures and heating rates on isothermal austenite formation in cold-rolled quenching and partitioning (Q&P) steel, which remain insufficiently understood, were systematically investigated. Under a constant heating rate, the austenite formation rate initially increases and subsequently decreases as the austenitization temperature rises from formation start temperature Ac1 to finish temperature Ac3, and complete austenitization is achieved more quickly at elevated temperatures. At a given austenitization temperature, an increased heating rate was found to accelerate the isothermal transformation kinetics and significantly reduce the duration required to achieve complete austenitization. The experimental results revealed that both the transformation activation energy (Q) and material constant (k0) decreased with increasing heating rates, while the Avrami exponent (n) showed a progressive increase, leading to the development of the heating-rate-dependent modified JMAK model. The model accurately characterizes the effect of varying heating rates on isothermal austenite formation kinetics, enabling kinetic curves prediction under multiple heating rates and austenitization temperatures and overcoming the limitation of single heating rate prediction in existing models, with significantly broadened applicability.

High invasiveness and well-documented negative impact on biodiversity and ecosystem functioning make Solidago canadensis L. a species of global concern. Despite a good understanding of the driving factors of its invasions, it remains unclear how the surrounding landscape may shape invasion success of this species in human-transformed ecosystems. In our study, we investigated the impacts of different landscape features in the proximity of early successional wastelands on S. canadensis biomass allocation patterns. Further, we examined the relationships between the surrounding landscape, S. canadensis cover, and plant functional diversity, used as a supportive approach for the explanation of mechanisms underlying successful S. canadensis invasion. We found that increasing river net length had positive impacts on S. canadensis rhizome, stem, and total above ground biomass, but negative effects on leaf biomass, indicating that vegetative spread may perform the dominant role in shaping the competitiveness of this invader in riverine landscapes. A higher proportion of arable lands positively influenced S. canadensis above ground and flower biomass; thus promoting S. canadensis invasion in agricultural landscapes with the prominent role of habitat filtering in shaping vegetation structure. Concerning an increasing proportion of settlements, flower biomass was higher and leaf biomass was lower, thereby influencing S. canadensis reproductive potential, maximizing the odds for survival, and indicating high adaptability to exist in an urban landscape. We demonstrated high context-dependency of relationships between functional diversity components and surrounding landscape, strongly influenced by S. canadensis cover, while the effects of surrounding landscape composition per se were of lower importance. Investigating the relationships between the surrounding landscape, invasive species performance, and plant functional diversity, may constitute a powerful tool for the monitoring, controlling, and predicting of invasion progress, as well as the assessment of ecosystem invasibility.

Phycas is open source, freely available Bayesian phylogenetics software written primarily in C++ but with Python interface. Phycas specializes in Bayesian model selection for nucleotide sequence data, particularly the estimation of marginal likelihoods, central to computing Bayes Factors. Marginal likelihoods can be estimated using newer methods (Thermodynamic Integration and Generalized Steppingstone) that are more accurate than the widely used Harmonic Mean estimator. In addition, Phycas supports two posterior predictive approaches to model selection: Gelfand—Ghosh and Conditional Predictive Ordinates. The General Time Reversible family of substitution models, as well as a codon model, are available, and data can be partitioned with all parameters unlinked except tree topology and edge lengths. Phycas provides for analyses in which the prior on tree topologies allows polytomous trees as well as fully resolved trees, and provides for several choices for edge length priors, including a hierarchical model as well as the recently described compound Dirichlet prior, which helps avoid overly informative induced priors on tree length.

Predicting antimicrobial resistance in gram-negative bacteria (GNB) could balance the need for administering appropriate empiric antibiotics while also minimizing the use of clinically unwarranted broad-spectrum agents. Our objective was to develop a practical prediction rule able to identify patients with GNB infection at low risk for resistance to piperacillin-tazobactam (PT), cefepime (CE), and meropenem (ME). The study included adult patients with sepsis or septic shock due to bloodstream infections caused by GNB admitted between 2008 and 2015 from Barnes-Jewish Hospital. We used multivariable logistic regression analyses to describe risk factors associated with resistance to the antibiotics of interest (PT, CE, and ME). Clinical decision trees were developed using the recursive partitioning algorithm CHAID (χ2 Automatic Interaction Detection). The study included 1618 consecutive patients. Prevalence rates for resistance to PT, CE, and ME were 28.6%, 21.8%, and 8.5%, respectively. Prior antibiotic use, nursing home residence, and transfer from an outside hospital were associated with resistance to all 3 antibiotics. Resistance to ME was specifically linked with infection attributed to Pseudomonas or Acinetobacter spp. Discrimination was similar for the multivariable logistic regression and CHAID tree models, with both being better for ME than for PT and CE. Recursive partitioning algorithms separated out 2 clusters with a low probability of ME resistance and 4 with a high probability of PT, CE, and ME resistance. With simple variables, clinical decision trees can be used to distinguish patients at low, intermediate, or high risk of resistance to PT, CE, and ME.

In flooded paddy fields, peak greenhouse gas nitrous oxide (N2O) emission after rewetting the dry soils is widely recognised. However, the relative contribution of biotic and abiotic factors to this emission remains uncertain. In this study, we used the isotope technique (δ18O and δ15NSP) and molecular-based microbial analysis in an anoxic incubation experiment to evaluate the contributions of bacterial, fungal, and chemical denitrification to N2O emissions. We collected eight representative paddy soils across southern China for an incubation experiment. Results show that during the 10-day incubation period, the net N2O emissions were mainly produced by fungal denitrification, which accounted for 58–77% in six of the eight investigated flooded paddy soils. In contrast, bacterial denitrification contributed 6–15% of the net N2O emissions. Moreover, around 11–35% of the total N2O emissions were derived from chemical denitrification in all soil types. Variation partitioning analysis (VPA) and principal component analysis (PCA) demonstrated that initial soil organic carbon (OC) concentrations were the primary regulator of N2O source patterns. Soils with relatively lower OC concentration (7–15 mg g−1) tend to be dominated by fungal denitrification, which accounted for the net N2O production at the end of the incubation period. Overall, these findings highlight the dominance of the fungal denitrification pathway for N2O production in flooded paddy soils, which predominates in soils with relatively lower OC content. This suggests that fungal contribution should be considered when optimizing agricultural management system timing to control N2O emissions in flooded paddy soil ecosystems, and for the relevant establishment of predictive numerical models in the future.

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land-atmosphere-water/energy fluxes to investigate land-atmosphere coupling induced by deforestation. However, interpreting land-atmosphere-water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause-effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.