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The intra-annual dynamics of wood formation, which involves the passage of newly produced cells through three successive differentiation phases (division, enlargement, and wall thickening) to reach the final functional mature state, has traditionally been described in conifers as three delayed bell-shaped curves followed by an S-shaped curve. Here the classical view represented by the Gompertz function (GF) approach was challenged using two novel approaches based on parametric generalized linear models (GLMs) and data-driven generalized additive models (GAMs). These three approaches (GFs, GLMs, and GAMs) were used to describe seasonal changes in cell numbers in each of the xylem differentiation phases and to calculate the timing of cell development in three conifer species [Picea abies (L.), Pinus sylvestris L., and Abies alba Mill.]. GAMs outperformed GFs and GLMs in describing intra-annual wood formation dynamics, showing two left-skewed bell-shaped curves for division and enlargement, and a right-skewed bimodal curve for thickening. Cell residence times progressively decreased through the season for enlargement, whilst increasing late but rapidly for thickening. These patterns match changes in cell anatomical features within a tree ring, which allows the separation of earlywood and latewood into two distinct cell populations. A novel statistical approach is presented which renews our understanding of xylogenesis, a dynamic biological process in which the rate of cell production interplays with cell residence times in each developmental phase to create complex seasonal patterns.

In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which have been traditionally used for downscaling purposes (linear and generalized linear models), concluding that CNNs are well suited for continental-wide applications. That analysis is extended here by assessing the suitability of CNNs for downscaling future climate change projections using Global Climate Model (GCM) outputs as predictors. This is particularly relevant for this type of “black-box” models, whose results cannot be easily explained based on physical reasons and could potentially lead to implausible downscaled projections due to uncontrolled extrapolation artifacts. Based on this premise, we analyze in this work the two key assumptions that are made in perfect prognosis downscaling: (1) the predictors chosen to build the statistical model should be well reproduced by GCMs and (2) the statistical model should be able to reliably extrapolate out of sample (climate change) conditions. As a first step to test the suitability of these models, the latter assumption is assessed here by analyzing how the CNNs affect the raw GCM climate change signal (defined as the difference, or delta, between future and historical climate). Our results show that, as compared to well-established generalized linear models (GLMs), CNNs yield smaller departures from the raw GCM outputs for the end of century, resulting in more plausible downscaling results for climate change applications. Moreover, as a consequence of the automatic treatment of spatial features, CNNs are also found to provide more spatially homogeneous downscaled patterns than GLMs.

To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.

Drought substantially affects ecosystem structure and function, shaping vegetation dynamics and influencing long-term environmental sustainability. This study examines drought effects on forest and rangeland ecosystems across three bioclimatic zones in the Hyrcanian region of Iran. MODIS-derived Leaf Area Index (LAI) data (2001–2022) and Standardized Precipitation Index (SPI) were used with a generalized linear model (GLM) to assess vegetation responses. The findings indicate that rangeland ecosystems, especially in Zones II and III, are susceptible to drought, with SPI accounting for over 80% of the observed LAI variability in these regions. Forests better withstand dry conditions, with SPI explaining about half of the changes in LAI Zone III, with high elevation and snow-dominated precipitation, is drought-sensitive. Zone I near the Caspian Sea has higher humidity and more stable conditions. Zone II, with a semi-humid cold climate, exhibits the largest LAI fluctuations due to its strong dependence on moisture. Elevation, vegetation type, and climate critically influence drought responses. Targeted land management, including water optimization and conservation, is essential. Future research should integrate additional factors such as soil moisture, land cover change, and anthropogenic pressures such as deforestation, overgrazing, and environmental degradation alongside predictive modeling to enhance ecological sustainability.

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM‐based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks. Transcranial functional Doppler Ultrasound has been used to assess brain lateralization, but the methods used to extract a laterality index (LI) have involved simple averaging over a period of interest. Here, we compare results from that method with results from a generalized linear models (GLM) approach similar to that used in functional magnetic resonance imaging (fMRI), as well as generalized additive models (GAM) to estimate the cerebral blood flow response. All methods give similar estimates for the mean LI, but error of measurement is much reduced with GAM.

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway—Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson regression generalizes the well-known Poisson and logistic regression models, and is suitable for fitting count data with a wide range of dispersion levels. With a GLM approach that takes advantage of exponential family properties, we discuss model estimation, inference, diagnostics, and interpretation, and present a test for determining the need for a COM-Poisson regression over a standard Poisson regression. We compare the COM-Poisson to several alternatives and illustrate its advantages and usefulness using three data sets with varying dispersion.

This study investigates the relationship between energy consumption and oil production in petroleum extraction using a Generalized Linear Model (GLM). By analyzing actual production data from a Daqing Oilfield production plant (2021–2024), key energy consumption features such as electricity usage, mechanical extraction, water injection, and transportation were selected to construct a GLM predictive model. Comparisons with Generalized Least Squares (GLS) and linear regression models demonstrated the superior performance of GLM, achieving a coefficient of determination (R2) of 0.884, with mean absolute error (MAE) and mean squared error (MSE) of 0.095 and 0.012, respectively. The study highlights the nonlinear impacts of energy consumption on oil production, offering theoretical insights for optimizing energy use and supporting low-carbon transitions in oilfield operations. Future research will explore interaction effects to enhance model generalizability.

•Combining CBPT with GLMEs allows statistical analysis to match experimental design.•CBPT with GLMEs accounts for subject variability and hierarchical random effects.•The proposed method maintains control of type I error, type II error, and FWER.•CBPT with GLMEs can be applied to individual channels and pseudo-population data. Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. We first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. First, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of “suboptimal” data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. We propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35–48) introduced generalised model‐based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child‐friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4‐ to 7‐year‐old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period‐of‐interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates. We tested recent statistical methods for quantifying the lateralisation of cognitive functions as measured by fTCD. We found that using a generalized additive model approach decreased individual standard error estimates in the laterality estimates for adult data collected using child‐friendly tasks and for data collected from young children (4–7 years).

Studies taking into account numerous aspects of climate change, disaster, and risk are necessary in order to emphasize the diverse issues such as threats to human lives, their asset base, and their livelihood vulnerability etc. that people confront in different regions. This study explores how institutions may help char dwellers, who reside in Assam, India's flood-prone and erosion-affected areas, become less vulnerable to climate change. The study measures the char dwellers' vulnerability to climate change using the adjusted livelihood vulnerability index (ALVI). The study also evaluates the quality and efficiency of the char institutions in raising the adaptability of the char inhabitants using the adaptive capacity wheel (ACW) and the generalized linear model (GLM). The study finds that the physical circumstances such as geographical location and structure of the char and social circumstances such as different socio-cultural and ethnic belongings of char residents place them at high risk and making the char institutions ineffective and performing unevenly among locations. The GLM result shows that institutions play a substantial role in reducing vulnerability. Land ownership, hazard prevention, and adaptation measures are all important variables in lowering their risk. The study suggests that boosting the char dwellers' resilience requires cooperation and diversity across different types of institutions.