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The COVID-19 pandemic led to a surge in interest among scholars and public health professionals in identifying the predictors of health shocks and their transmission in the population. With temperature increases becoming a persistent climate stress, our aim is to evaluate how temperature specifically impacts the incidences of contagious disease. Using annual data from 1 AD to 2021 AD on the incidence of contagious disease and temperature anomalies, we apply both parametric and nonparametric modelling techniques and provide estimates of the contemporaneous, as well as lagged, effects of temperature anomalies on the spread of contagious diseases. A nonhomogeneous hidden Markov model is then applied to estimate the time-varying transition probabilities between hidden states where the transition probabilities are governed by covariates. For all empirical specifications, we find consistent evidence that temperature anomalies have a statistically significant effect on the incidence of a contagious disease in any given year covered in the sample period. The best fit model further indicates that the contemporaneous effect of a temperature anomaly on the response variable is the strongest. As temperature predictions continue to become more accurate, our results indicate that such information can be used to implement effective public health responses to limit the spread of contagious diseases. These findings further have implications for designing cost effective infectious disease control policies for different regions of the world. [ABSTRACT FROM AUTHOR]

All ecosystems contain both sources and sinks for atmospheric carbon (C). A change in their balance of net and gross ecosystem carbon uptake, ecosystem‐scale carbon use efficiency (CUEECO), is a change in their ability to buffer climate change. However, anthropogenic nitrogen (N) deposition is increasing N availability, potentially shifting terrestrial ecosystem stoichiometry towards phosphorus (P) limitation. Depending on how gross primary production (GPP, plants alone) and ecosystem respiration (RECO, plants and heterotrophs) are limited by N, P or associated changes in other biogeochemical cycles, CUEECO may change. Seasonally, CUEECO also varies as the multiple processes that control GPP and respiration and their limitations shift in time. We worked in a Mediterranean tree‐grass ecosystem (locally called 'dehesa') characterized by mild, wet winters and summer droughts. We examined CUEECO from eddy covariance fluxes over 6 years under control, +N and + NP fertilized treatments on three timescales: annual, seasonal (determined by vegetation phenological phases) and 14‐day aggregations. Finer aggregation allowed consideration of responses to specific patterns in vegetation activity and meteorological conditions. We predicted that CUEECO should be increased by wetter conditions, and successively by N and NP fertilization. Milder and wetter years with proportionally longer growing seasons increased CUEECO, as did N fertilization, regardless of whether P was added. Using a generalized additive model, whole ecosystem phenological status and water deficit indicators, which both varied with treatment, were the main determinants of 14‐day differences in CUEECO. The direction of water effects depended on the timescale considered and occurred alongside treatment‐dependent water depletion. Overall, future regional trends of longer dry summers may push these systems towards lower CUEECO. [ABSTRACT FROM AUTHOR]

Abstract Camera‐trap surveys are increasingly used to assess species interactions, with a growing focus on proximate co‐occurrence, the conditional probability of one species occurring after another within a given time window. However, existing time‐to‐first‐event models, commonly employed for this purpose, suffer from multiple limitations. For instance, they cannot fully quantify the interaction strength, that is how strongly one species affects the occurrence probability of another, nor the duration of this effect, nor identify the directionality of the interaction. We propose reframing the question in terms of recurrent event analysis, which enables the use of a rich set of techniques to provide more information about the data‐generating process. In particular, we use the piece‐wise exponential additive mixed model (PAMM) to take into account all available information (including recurrent occurrences of species) and to estimate the non‐linear temporal dynamics of the visitation rate of a species after the occurrence of another. As the estimation is performed within the framework of generalized additive models, it offers a rich, robust and established inferential toolbox. We evaluated PAMM's performance in estimating species interactions through a simulation study involving two species under various scenarios of attraction or repulsion, with and without imperfect detection. Additionally, we compared the PAMM to six time‐to‐first‐event methods commonly used in camera‐trap studies and illustrated its application with real data. PAMMs outperformed the other methods in terms of statistical power, output interpretability and ability to disentangle interactions. The robust performance of PAMMs underscores their potential as a valuable tool for studying species interactions based on camera‐trap data. To facilitate its adoption, we present the new package ctrecurrent and provide a reproducible workflow from data preparation to model fitting.

To comprehensively examine the hook depth distribution and influencing factors of tuna longline fishing in Western and Central Pacific Ocean, data collected aboard the vessel "Songhang" from August to October 2021 were analyzed. A combination of Support Vector Regression (SVR) and Generalized Additive Model (GAM) was employed to investigat the distribution of hook depths across different operational parameters and environmental variables. The objective was to elucidate the impact of each factor on hook depth. The results showed that: (1) Among the calculated values of hook depth, Hook No. 1 had the smallest value at (130.03±3.32) m, while Hook No. 8 had the largest value at (363.25±36.52) m. Regarding the measured values, Hook No. 1 had the smallest depth at (130.81±17.94) m, whereas Hook No. 7 exhibited the largest depth at (329.91±54.37) m. (2) The SVR model exhibited varying goodness of fit, with Hook No. 1 displaying the lowest (R²=0.38) and Hook No. 6 demonstrating the highest (R²=0.77) overall fit. The mean squared error (MSE) for training data was smallest for Hook No. 6 (0.138) and lar gest for Hook No. 8 (0.309), while for testing data, it was smallest for Hook No. 4 (0.086) and largest for Hook No. 5 (0.282). (3) GAM analysis reveals that water layer average velocities at 0-64 m and 128-192 m, as well as the casting speed, exhibit the highest correlations with hook depth across all hooks. Following these, water layer average velocities at 64-128 m, 192-256 m, and 256-320 m, along with vessel speed, show significant correlations. However, water layer average velocity at 320-384 m and wind speed demonstrate no significant correlations with hook depth. (4) The average differences between predicted hook depths by SVR and GAM models and the actual measured values are 1.75% and 6.38%, respectively. The predictions from the SVR model align well with the measured values, showing consistent trends with the variation in hook positions, thus demonstrating good agreement. The results of this study will contribute to understanding the distribution pattern of tuna longline fishing hooks in operation, and provide a basic reference for optimizing the performance of longline. [ABSTRACT FROM AUTHOR]

GRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities. Plain Language Summary: We used GRACE data to understand how water storage has changed over time across the Indus Basin at a resolution of 1 square kilometer. We generated the new high‐resolution data using machine learning techniques that implemented statistical methods. The new data for analyzing water storage matched well with the original data on a larger scale. Additionally, comparing this detailed data with measurements from 2,200 wells showed that our new method works well. The new high‐resolution data help us detect hotspots of water storage decline where water availability may face challenges in the future if status quo continues. Human activities like more farming, using more water, and building more areas for people to live are a major driver of the water storage decline. In upstream areas less influenced by human impacts, the decline is driven more by climatic factors. By improving understanding of local vulnerabilities, our study supports planning interventions for specific regions based on the need to reduce the impact of human activities or adapt to climate change. Key Points: Terrestrial water storage (TWS)/groundwater storage (GWS) derived from downscaled GRACE data show a declining trend across most sub‐regions of the Indus Basin between 2002 and 2023Anthropogenic stressors explain 47%–83% of TWS decline in the majority of sub‐regionsTWS/GWS changes in upstream sub‐regions, where shortwave radiations mainly control the TWS changes, are well explained by climatic factors [ABSTRACT FROM AUTHOR]

Camera‐trap surveys are increasingly used to assess species interactions, with a growing focus on proximate co‐occurrence, the conditional probability of one species occurring after another within a given time window. However, existing time‐to‐first‐event models, commonly employed for this purpose, suffer from multiple limitations. For instance, they cannot fully quantify the interaction strength, that is how strongly one species affects the occurrence probability of another, nor the duration of this effect, nor identify the directionality of the interaction.We propose reframing the question in terms of recurrent event analysis, which enables the use of a rich set of techniques to provide more information about the data‐generating process. In particular, we use the piece‐wise exponential additive mixed model (PAMM) to take into account all available information (including recurrent occurrences of species) and to estimate the non‐linear temporal dynamics of the visitation rate of a species after the occurrence of another. As the estimation is performed within the framework of generalized additive models, it offers a rich, robust and established inferential toolbox.We evaluated PAMM's performance in estimating species interactions through a simulation study involving two species under various scenarios of attraction or repulsion, with and without imperfect detection. Additionally, we compared the PAMM to six time‐to‐first‐event methods commonly used in camera‐trap studies and illustrated its application with real data.PAMMs outperformed the other methods in terms of statistical power, output interpretability and ability to disentangle interactions. The robust performance of PAMMs underscores their potential as a valuable tool for studying species interactions based on camera‐trap data. To facilitate its adoption, we present the new package ctrecurrent and provide a reproducible workflow from data preparation to model fitting. [ABSTRACT FROM AUTHOR]

Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM‐based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature–suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high‐resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species–temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species–environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change. [ABSTRACT FROM AUTHOR]

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Background. There has been little quantification of the extent and duration of micrometeorological changes within a forest after airtanker drops of water-based suppressant. It has been speculated that a period of prolonged relative humidity - referred to as a 'relative humidity (RH) bubble' - temporarily exists in the canopy understorey post-drop. Aims. We quantify the RH bubble from the drops of five airtankers commonly used by wildland fire management organisations in Canada. Methods. We measured airtankers dropping water, foam concentrates, and gel enhancers in a mature jack pine stand. We examined the duration of change in RH and temperature using Generalised Additive Models, and the consequence of these changes on fine fuel moisture. Key results. The average maximum RH increased and temperature decreased, indicating that the effects of the 'RH bubble' in-stand lasted from 25 to 76 min, depending upon the airtanker type and load configuration. Conclusion. Airtanker drops cause an in-stand increase in RH and decrease in temperature, but this ambient change has a limited effect on potential fire behaviour. Implications. The direct effect of water wetting the fuel is the most impactful effect of an airtanker drop. The 'RH bubble' created, though observable, does not change fine fuel moisture enough to impact fire behaviour. [ABSTRACT FROM AUTHOR]

Hypothesis-based reasoning with conditionals is a skill that is required for engaging in integral activities of modern elementary school science-curricula. The teaching of this skill at this early stage of education, however, is demanding, particularly in whole school classes in which it is difficult to adapt teaching to children's individual needs. We examine whether a scaffold that is static yet tailored to the context, in which the teacher explicitly models the reasoning process, manages to meet students' individual cognitive preconditions for learning this skill. Within an inquiry-based learning setting, N = 143 third-graders underwent either an experimental condition in which they received the explicit scaffold, or a control condition in which they did not receive this specific scaffold. Employing a latent transition analysis and a general additive model, it is examined how the additional scaffold interacted with students' prior knowledge, inhibition ability, and logical reasoning judged by their own teachers. It is found that the additional scaffolds managed to meet the needs of students with little prior knowledge; under the control condition, students with little prior knowledge showed decreased learning achievement, whereas under the experimental condition, students with differing prior knowledge learned to comparable extent and on a higher level. The scaffolds also almost fully diminished a disadvantage for students with lower teacher-judged logical reasoning, and supported students with high inhibition ability in mastering the most difficult aspect of reasoning based on irrelevant evidence. Implications for science education are discussed. [ABSTRACT FROM AUTHOR]

The effects of short-term ambient ozone (O3) exposure on health outcomes have received growing concerns, but its effects on psoriasis is still unclear. The purpose of our study was to investigate the effects of short-term exposure to O3 on psoriasis, and to find out potential modifiers. A hospital-based time-series study with outpatient visit data of psoriasis was performed in Chongqing, the largest metropolitan in Southeast China. General additive models combined quasi-Poisson regression were implied to reveal the association between short-term exposure to O3 and psoriasis. Moreover, the potential modifiers were found out through subgroup analyses. Totally, 23,921 psoriasis outpatient visits were included. For the whole patient population, no significant association between exposure to O3 and outpatient visits for psoriasis was observed. Interestingly, the stratified analyses based on concentrations showed that low concentration of O3 (below the recommended limitation of 100 μg/m3 by WHO and China), rather than high concentration of O3, was negatively associated with psoriasis outpatient visits at various lag days, and the greatest decrease was observed at lag03 (-2.05%; 95%CI: -3.55%, -0.53%). Moreover, stronger associations were observed in cool seasons, patients aged 40 or above, and males. Additionally, the negative associations of low-concentration O3 on psoriasis outpatient visits were still robust in sensitivity analyses. Our study discovered that low-concentrations of O3, rather than high concentrations of O3, can be associated with decreased outpatient visits for psoriasis, especially for males, population aged 40 and above, and in cool seasons. [ABSTRACT FROM AUTHOR]

PM 2.5 is still one of the major atmospheric pollutants worldwide. Extracting contributions of anthropogenic emission control from the observed PM 2.5 variations (PM 2.5_anth), which are also strongly affected by meteorological changes, is critical for effective pollution control. Statistical and machine learning methods are usually used for such purpose, but the effectiveness of these methods is hard to evaluate due to the lack of observed anthropogenic contributions. In this study, we use the chemical transport model GEOS-Chem standard simulation to mimic PM 2.5 variability in the real atmosphere, and use the model simulation with fixed meteorological fields as the "true value" for PM 2.5_anth. We evaluate the effectiveness of three methods in meteorological normalization of PM 2.5 on decadal (2006–2017) and synoptic (one month) scale: multiple linear regression (MLR), general additive model (GAM), and random forest (RF) algorithm. For meteorological normalization of PM 2.5 on decadal scale, 67–72% of the MLR simulations show positive biases and 56–75% of the RF simulations show negative biases. The "true value" of PM 2.5_anth falls within the range of meteorological normalization results of the three methods in most cases, but consistent positive/negative biases are observed in ∼30% of the cases, when meteorological changes dominate PM 2.5 variability. In addition, the biases are correlated to the contribution of meteorological changes. As such, multiple statistical or machine learning methods are recommended to quantify the uncertainties associated with method choice in cases anthropogenic emission changes dominate PM 2.5 variability. On synoptic scale, RF performs better in reproducing the daily variations of the PM 2.5_anth differences than MLR (GAM) in all (83% of) the cases, and is recommended for meteorological normalization of PM 2.5 in short-term in eastern China. • We use GEOS-Chem to simulate the real atmosphere and "true value" of anthropogenic contribution. • In most cases, "true value" of PM 2.5_anth falls within the range of extractions by the three methods. • Consistent positive/negative biases are observed when meteorology controls PM 2.5 variations. • Random forest performs the best in extracting daily PM 2.5_anth differences on synoptic scale. [ABSTRACT FROM AUTHOR]

Extreme precipitation events that occur in close succession can have important societal and economic repercussions. Here we use 42 years of reanalysis data (ERA-5) to investigate the link between Euro-Atlantic large-scale pattern of weather and climate variability and the temporal clustering of extreme rainfall events over Europe. We implicitly model the seasonal rate of extreme occurrences as part of a Poisson General Additive Model (GAM) using cyclic regression cubic splines. The smoothed seasonal rate of extreme rainfall occurrences is used to (i) infer the frequency of significant temporal clustering and (ii) implicitly serves as the baseline rate when modeling the effects of atmospheric drivers on extreme rainfall clustering. We use GAMs to model the association between the temporal clustering of extreme rainfall events and seven predominant year-round weather regimes in the Euro-Atlantic sector as well as a measure of synoptic-scale transient recurrent Rossby wave packets. Sub-seasonal clustering of precipitation events is significant at all grid-points over Europe; the proportion of extreme rainfall events that cluster in time ranges between 2% to 27%. The most relevant weather regime is the Atlantic Trough (corresponding to NAO+ with a southward shift of the jet) explaining most of the significant increase in clustering probability over Europe. The Greenland Blocking regime explains most of the clustering over the Iberian Peninsula. The Scandinavian Blocking regime is associated with a significant increase in clustering probability over the western Mediterranean, with a northwards shift in the signal to central Europe in summer.

Moose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model‐based analytical approach and finite‐population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well‐documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon‐Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to explore the impact of atmospheric pollutants on the incidence of tuberculosis (TB), and provide new ideas for the prevention and control of TB in the future. Methods: It explored the relationship between air pollutants and meteorological factors, as well as between air pollutants and heating through Spearman correlation analysis and rank sum test. Additionally, it analyzed the relationship between air pollutants and TB incidence using the general additive model. Statistical analysis results at the p

A new and general approach is proposed for interpolating 6-h precipitation series over large spatial areas. The outputs are useful for distributed hydrological modelling and studies of flooding. We apply our approach to large-scale data, measured between 2014 and 2016 at 159 weather stations network of Meteo Romania, using weather radar information and local topography as ancillary data. Novelty of our approach is in systematic development of a statistical model underlying the interpolation. Seven methods have been tested for the interpolation of the 6-h precipitation measurements: four regression methods (linear regression via ordinary least squares (OLS), with and without logarithmic transformation, and two models of generalized additive model (GAM) class, with logarithmic and identity links), and three regression-kriging models (one uses semivariogram fitted separately every 6-h, based on the residuals of the GAM with identity links models, and other two with pooled semivariograms, based on the OLS and GAM with identity links models). The prediction accuracy of the spatial interpolation methods was evaluated on a part of the dataset not used in the model-fitting stage. Due to the good results in interpolating sub-daily precipitation, normal general additive model with identity link followed with kriging of residuals with kriging parameters estimated from pooled semivariograms was applied to construct the final 6-h precipitation maps (PRK-NGAM). The final results of this work are the 6-h precipitation gridded datasets available in high spatial resolution (1000 m × 1000 m), together with their estimated accuracy. [ABSTRACT FROM AUTHOR]

• A delay of 3 months was found for the effect of air pollutants (PM 2.5 , SO 2 , NO 2 , and CO) to tuberculosis incidence. However, the effect of PM 10 to tuberculosis incidence was delayed by 9 months. • A single pollutant model showed that all air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3) in this study would have an increased risk of tuberculosis. It found that if concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 increased by 1 μg/m3 (or 1 mg/m3), the number of tuberculosis cases would increase by 0.09%, 0.08%, 0.58%, 0.42%, 6.9%, and 0.57%, respectively. • The results of this optimal multi-pollutant model showed that the optimal model was a two-factor model (PM 10 + NO 2) and factors in the model have an increased risk of tuberculosis. This study aimed to explore the impact of atmospheric pollutants on the incidence of tuberculosis (TB), and provide new ideas for the prevention and control of TB in the future. It explored the relationship between air pollutants and meteorological factors, as well as between air pollutants and heating through Spearman correlation analysis and rank sum test. Additionally, it analyzed the relationship between air pollutants and TB incidence using the general additive model. Statistical analysis results at the p < 0.05 level were considered significant. Three months after exposure to air pollutants (PM 2.5 , SO 2 , NO 2 , and CO) TB incidence increased. However, TB incidence increased 9 months after exposure to PM 10. The single pollutant model showed when concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 increased by 1 μg/m3 (or 1 mg/m3), the number of TB cases increased by 0.09%, 0.08%, 0.58%, 0.42%, 6.9%, and 0.57%, respectively. The optimal multi-pollutant model was a two-factor model (PM 10 + NO 2). Air pollutants including PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 increased the risk of TB. Few studies have been conducted in this area of research, especially regarding the mechanism. The results of this study should contribute to the understanding of TB incidence and prompt additional research. [ABSTRACT FROM AUTHOR]

Objectives: Study examines the curvilinear associations of working time dimensions (working hours, time pressure, work schedules, and control of work time and pace) on psychological and somatic symptoms. Methods: Representative Finnish Quality-of-Work-Life Surveys conducted in 2003, 2008, and 2013 were restricted to those (N= 11,165) regularly working over lOh/wk with more than 1-year tenure in their job. Generalized additive models were utilized in analysis. Results: Working hours had U-shaped relationships with psychosomatic symptoms, while time pressure had a threshold effect. Work pace control had linear effect. The effects of work time control and work schedules were insignificant. There were interaction effects between working time dimensions. Conclusions: Organizations should acknowledge the dynamics of working time dimensions. Notably, time pressure has a hazardous relation to psychosomatic symptoms, but working pace control can buffer the negative effect. [ABSTRACT FROM AUTHOR]

Oceanic squids have distinct population dynamics and life histories compared to most fishes, necessitating tailored fishing and management strategies. To conserve and sustain oceanic squid stocks, the Chinese government has implemented seasonal high seas closures from September 1st to November 30th each year since 2020. Our study aims to evaluate closure efficacy for oceanic squid conservation using the central-eastern Pacific closure as a case study and propose targeted improvements. Based on biological data from equatorial waters in two selected periods (before closure: December 2017 to May 2018; and after closure: December 2021 to May 2022), comparative analyses of mantle length distribution and length at maturity (L m) were used to detect the differences before and after closure for the jumbo squid Dosidicus gigas. Based on biological data from 2009 to 2022, a three-dimensional environment-based general additive model (GAM) was applied to predict the intermonthly distribution of spawning grounds. Comparison analyses indicated increased mantle length and L m following closure. The best-fitted GAM explained 38 % of the deviance. Environmental factors significantly influenced gonad development. Model predictions recognized seasonal core spawning grounds at the Costa Rica Dome and Cocos ridge plus year-round secondary spawning grounds off the Pervian-Chilian waters south of 8 ° S. The current closure protects overlapping feeding grounds and migratory routes for the Costa Rica Dome and Peruvian coastal spawning groups, while Peruvian-Chilian waters south of 8 ° S also represent similar grounds for the Peruvian coastal and Chilean groups. Overall, seasonal closures could have a positive conservation effect on oceanic squids. Maintaining the current closure delayed or advanced 1 month alongside another closure off the southern Peruvian EEZ from October to December or November to January could further enhance jumbo squid conservation [ABSTRACT FROM AUTHOR]

To investigate their diel vertical migration (DVM), 599 sablefish (Anoplopoma fimbria) were implanted with electronic archival tags in the Gulf of Alaska, Aleutian Islands, and the eastern Bering Sea. Of these tags, 98 were recovered with usable depth data (7,852,773 recordings representing 81,233 days) that we used to identify DVM and to classify DVM into one of two types: normal DVM (rise from the bottom during nighttime) and reverse DVM (rise from the bottom during daytime). The results of our study highlight three important attributes of DVM for sablefish. First, all tagged sablefish carried out DVM, although the occurrence was intermittent (26% of the days with usable data) and most commonly for short durations (10 days or less). Second, bottom depth for normal DVM was about 78 m shallower than for reverse DVM. Third, normal DVM occurred most often in fall and least often in spring, whereas this high/low pattern was shifted about 3 months later for reverse DVM; reverse DVM occurred most often in winter and least often in summer. Normal DVM likely occurred to increase foraging opportunity (e.g., nightly shift to match depth of prey). Reverse DVM more commonly occurred during winter and may represent an increase in foraging by sablefish during the daytime to compensate for decreased pelagic resources. The default foraging strategy for sablefish may be benthic because of the uncertainty of vertically migrating to a location where the occurrence of prey is not guaranteed; sablefish may invoke DVM when the non‐DVM foraging benefit is reduced. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, the building sector has highlighted the importance of operational energy and efficient resource management in order to reduce the environmental impacts of buildings. However, differences in building-specific properties (building location, size, construction material, etc.) pose a major challenge in development of generic policy on buildings. The aim of this study was to investigate the relationship between energy and resource management policies, and building-specific characteristics on environmental impacts of refurbished office buildings in New Zealand. Methods: Life Cycle Assessment (LCA) was performed for 17 office buildings operating under seven representative climatic conditions found in New Zealand. Each building was assessed under four policy scenarios: (i) business-as-usual, (ii) use of on-site photovoltaic (PV) panels, (iii) electricity supply from a renewable energy grid, and (iv) best practice construction activities adopted at site. The influence of 15 building-specific characteristics in combination with each scenario was evaluated. The study adopted regression analysis, more specifically Kruskal-Wallis and General Additive Modeling (GAM), to support interpretation of the LCA results. Results and discussion: All the chosen policies can significantly contribute to climate change mitigation as compared to business-as-usual. However, the Kruskal-Wallis results highlight policies on increasing renewable energy sources supplying national grid electricity can substantially reduce the impacts across most environmental impact categories. Better construction practices should be prioritized over PV installation as use of on-site PV significantly increases the environmental impacts related to use of resources. The GAM results show on-site PV could be installed in low-rise buildings in regions with long sunshine hours. The results also show the strong influence of façade elements and technical equipment in determining the environmental performance of small and large buildings, respectively. In large multi-storied buildings, efficient HVAC and smaller window area are beneficial features, while in small buildings the choice of façade materials with low embodied impacts should be prioritized. Conclusions: In general, the study highlighted the importance of policies on increasing renewable energy supply from national grid electricity to substantially reduce most of the impacts related to buildings. In addition, the study also highlighted the importance of better construction practices and building-specific characteristics to reduce the impacts related to resource use. These findings can support policy makers to prioritize strategies to improve the environmental performance of existing buildings in New Zealand and in regions with similar building construction and climate. [ABSTRACT FROM AUTHOR]

The Stormwater Treatment Areas (STAs) are constructed wetlands used to reduce the total phosphorus (TP) level in water discharged to the environmentally sensitive areas of the Everglades Protection Area. Analysis of high temporal frequency TP data reveals that TP concentrations discharged from selected STA flow-ways exhibit diel and seasonal cyclic patterns and are most affected by discharge rate and water depth. The objective of this paper is to describe the diel TP pattern and evaluate the possibility of using it to reduce TP in STA discharge. The amplitude of diel TP pattern ranged from 0.7 to 1.5 μg L−1 (3–6% deviation from daily mean) at three flow-ways, peaking in the early afternoon and reaching lowest concentrations between midnight and 4 am. To assess the possibility of further reducing TP concentration discharged from the STAs by increasing the proportion of daily flow discharged at night when diel TP is at a minimum, a General Additive Model (GAM) was created with discharge rate, water depth, year, location, day-of-year, and time-of-day as covariates. While time-of-day was a statistically significant factor in the model (p < 0.001), its average marginal effect was smaller than all other significant model covariates: discharge rate, season, and water depth. Modeling suggests that within specific ranges of discharge rate and water depth, the opportunity exists to provide additional TP reduction in STA outflow by discharging a greater proportion of the daily flow at night when TP concentrations are lower. Further research is needed to quantify the effect of increasing nighttime flow on annual flow weighted mean TP. Additionally, an evaluation of the practicality and logistical cost required to redistribute flow is needed before attempting to take advantage of this natural cyclic pattern to improve STA TP retention. • A diel pattern in totla phosphorus (TP) was found in south Florida wetlands. • Peak diel TP occured between noon and 3 pm, a minimum concentration between midnight and 4 am. • Amplitude of diel pattern ranged between 0.7 and 1.5 μg L-1 TP or 3–6% from daily mean TP. • Under certain hydrologic conditions increasing the proportion of overnight flow could reduce TP in wetland discharge. [ABSTRACT FROM AUTHOR]

In this study, the horizontal and vertical distribution of primary production (PP) and its monthly variations were described based on field data collected from the Daya Bay in January-December of 2016. The relationships between PP and environmental factors were analyzed using a general additive model (GAM). Significant seasonal differences were observed in the horizontal distribution of PP, while vertical distribution showed a relatively consistent unimodal pattern. The monthly average PP (calculated by carbon) ranged from 48.03 to 390.56 mg/(m2·h), with an annual average of 182.77 mg/(m2·h). The highest PP was observed in May and the lowest in November. Additionally, the overall trend in PP was spring>summer>winter>autumn, and spring PP was approximately three times that of autumn PP. GAM analysis revealed that temperature, bottom salinity, phytoplankton, and photosynthetically active radiation (PAR) had no significant relationships with PP, while longitude, depth, surface salinity, chlorophyll a (Chl a) and transparency were significantly correlated with PP. Overall, the results presented herein indicate that monsoonal changes and terrestrial and offshore water systems have crucial effects on environmental factors that are associated with PP changes. [ABSTRACT FROM AUTHOR]

Abstract: We have extracted information on the habitats of bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis) and yellowfin (Thunnus albacares) in the Eastern Tropical Pacific Ocean by matching the spatial‐temporal distribution of catch and effort of purse seine and longline fleets collected by the Inter‐American Tropical Tuna Commission with oceanographic conditions and subjecting the matched data to Quotient Analysis and General Additive Models (GAMs). These analyses yielded the following results. The habitats defined by the GAM analysis of young fish differ significantly between two periods, one before and one after the introduction of fish aggregation devices (FADs). This was not true for the older fish caught by longline. We speculate that these changes were caused by the extensive use of FADs. Younger bigeye and yellowfin caught by the purse seine fleet have a different preference of environmental variables compared to older fish caught by longline. This is to be expected since tuna of different age groups have different sizes, metabolic capabilities and swimming skills. Moreover, as revealed by GAMs, the habitats of young fish differ between species to a much larger degree than those of older fish. Our results indicate the fundamental differences between fishing methods, targeted species, and operating region of the two fisheries. Specifically, young bigeye occupy equatorial waters farther from the coast and where the hypoxic layer is deeper, young skipjack occupy more productive waters associated with equatorial and coastal upwelling, and young yellowfin occupy broad areas where waters are underlain by a shallow hypoxic layer. [ABSTRACT FROM AUTHOR]

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Droughts, with their increasing frequency of occurrence, especially in the Greater Horn of Africa (GHA), continue to negatively affect lives and livelihoods. For example, the 2011 drought in East Africa caused massive losses, documented to have cost the Kenyan economy over 12 billion US dollars. Consequently, the demand is ever-increasing for ex-ante drought early warning systems with the ability to offer drought forecasts with sufficient lead times The study uses 10 precipitation and vegetation condition indices that are lagged over 1, 2 and 3-month time-steps to predict future values of vegetation condition index aggregated over a 3-month time period (VCI3M) that is a proxy variable for drought monitoring. The study used data covering the period 2001−2015 at a monthly frequency for four arid northern Kenya counties for model training, with data for 2016−2017 used as out-of-sample data for model testing. The study adopted a model space search approach to obtain the most predictive artificial neural network (ANN) model as opposed to the traditional greedy search approach that is based on optimal variable selection at each model building step. The initial large model-space was reduced using the general additive model (GAM) technique together with a set of assumptions. Even though we built a total of 102 GAM models, only 20 had R2 ≥ 0.7, and together with the model with lag of the predicted variable, were subjected to the ANN modelling process. The ANN process itself uses the brute-force approach that automatically partitions the training data into 10 sub-samples, builds the ANN models in these samples and evaluates their performance using multiple metrics. The results show the superiority of 1-month lag of the variables as compared to longer time lags of 2 and 3 months. The best ANN model recorded an R2 of 0.78 between actual and predicted vegetation conditions 1-month ahead using the out-of-sample data. Investigated as a classifier distinguishing five vegetation deficit classes, the best ANN model had a modest accuracy of 67% and a multi-class area under the receiver operating characteristic curve (AUROC) of 89.99%.

Detailed understanding of forest disturbance interactions is needed for effective forecasting, modelling, and management. Insect outbreaks are a significant forest disturbance that alters forest structure as well as the distribution and connectivity of combustible fuels at broad spatial scales. The effect of insect outbreaks on fire activity is an important but contentious issue with significant policy consequences. The eastern spruce budworm ( Choristoneura fumiferana) is a native defoliating insect in eastern North America whose periodic outbreaks create large patches of dead fir and spruce trees. Of particular concern to fire and forest managers is whether these patches represent an increased fire risk, if so, for how long, and how the relationship between defoliation and fire risk varies through space and time. Previous work suggests a temporary increase in flammability in budworm-killed forests, but regional and seasonal variability in these relationships has not been examined. Using an extensive database on historical lightning-caused fire ignitions and spruce budworm defoliation between 1963 and 2000, we assess the relative importance of cumulative defoliation and fire weather on the probability of ignition in Ontario, Canada. We modeled fire ignition using a generalized additive logistic regression model that accounts for temporal autocorrelation in fire weather. We compared two ecoregions in eastern Ontario (Abitibi Plains) and western Ontario (Lake of the Woods) that differ in terms of climate, geomorphology, and forest composition. We found that defoliation has the potential to both increase and decrease the probability of ignition depending on the time scale, ecoregion, and season examined. Most importantly, we found that lagged spruce budworm defoliation (8-10 yr) increases the risk of fire ignition whereas recent defoliation (1 yr) can decrease this risk. We also found that historical defoliation has a greater influence on ignition risk during the spring than during the summer fire season. Given predicted increases in forest insect activity due to global change, these results represent important information for fire management agencies that can be used to refine existing models of fire risk. [ABSTRACT FROM AUTHOR]

Growing evidence indicates that short-term ozone (O 3) exposure has substantial health consequences, but the relationship between short-term ambient O 3 and insomnia, a common sleep disorder, is not clear. This study aimed to investigate the short-term effects of ambient O 3 exposure on outpatient visits for adult insomnia and to explore the potential modifiers. A large-scale multihospital-based study was carried out in Chongqing, the largest city in Southwest China. Daily data on outpatient visits for adult insomnia, average concentrations of ambient air pollutants and meteorological factors were collected. We conducted quasi-Poisson regression with generalized additive model to assess the association between ambient O 3 and outpatient visits for adult insomnia in varied windows of exposure. Subgroup analyses were applied to identify its modifiers. Totally, 140,159 adult insomnia outpatient visits were identified. The daily maximum 8-h average concentration of O 3 was 69 μg/m3 during the study period, which greatly below the updated Chinese and WHO recommended limits (daily maximum 8-h average, O 3 : 100 μg/m3). Short-term O 3 exposure was significantly negatively associated with outpatient visits for adult insomnia in different lag periods and the greatest decrease of outpatient visits for adult insomnia was found at lag 02 [0.93% (95% CI: 0.48%, 1.38%)]. Additionally, stronger links between O 3 and adult insomnia outpatient visits were presented in cool seasons, and we did not observe any significant modified effects of gender and age. Moreover, the negative O 3 -insomnia association remained robust after controlling for other common air pollutants and comorbidities. In summary, short-term exposure to lower level of ambient O 3 , was associated with reduced daily outpatient visits for adult insomnia and such association showed to be more obvious in cool seasons. [Display omitted] • A multihospital-based study with 140,159 adult insomnia outpatient visits. • Short-term ambient O 3 was negatively associated with adult insomnia visits. • The exposure-association curve was more slope at low level of O 3. • The negative association remained robust in sensitivity analyses. [ABSTRACT FROM AUTHOR]

Aquafeed formulation is susceptible to affect amino acid ( AA) availability for metabolic functions. Statistical models were applied to quantify the effect of dietary methionine level (from 6.01 to 16.17 g kg−1 dry matter) and form (free, coated or bound) on postprandial concentrations of plasma essential amino acid ( EAA) in rainbow trout. Twelve diets were formulated with pea and soya protein concentrate or fish meal as the main protein ingredients and were supplemented or not with increasing amount of either crystalline or agar-coated methionine. Fish were acclimatized to one of the 12 diets for 6 weeks before postprandial plasma sampling (six sampling points up to 36 h, seven fish each time), further analysed for EAA content. Using generalized additive models, we show that (i) dietary methionine level and form explained 74% postprandial methionine plasma variations and that (ii) the methionine dietary form and plasma concentrations significantly affected the plasma concentrations of the other EAAs. Finally, linear model revealed a positive relationship ( R2 > 0.9) between plasma concentrations of the three branched-chain AAs under the present experimental conditions. The results obtained add new information on the dietary effects on EAAs in the plasma availability and the interactions between them. [ABSTRACT FROM AUTHOR]

This study was conducted to analyze the habitat preference of six major mammals for various environmental factors based on 100 random points within a mountain area in South Korea. In-situ presence and absence data for the mammals were surveyed and collected, and twelve explanatory variables related to topography, water, greenness, and anthropogenic influence were applied to create a habitat distribution model. The best combination of variables was determined using Moran's I coefficients and Akaike criteria information, and applied to estimate the habitat preference for each species using GRASP v.3.0. The predictive map showed that wildlife animals in this study were mainly categorized into two groups: Group I (Korean squirrel, Sciurus vulgaris, mole, Talpa micrura and water deer, Hydropotes inermis), showed equal preference for all mountainous areas; Group II (weasel, Mustela sibirica, leopard cat, Felis bengalensis and raccoon dog, Nyctereutes procyonoides) showed different preferences in a mountain. [ABSTRACT FROM AUTHOR]

In Bimini, Bahamas, the consistent employment of longlines, beginning in 1982, provided a rare opportunity to explore population trends for large resident sharks. This study assessed three shallow water longline survey periods at this location; 1982-1989, 1992-2002 and 2003-2014, with the aim of determining trends in annual catch per unit effort ( CPUE) for an IUCN listed near-threatened species, the lemon shark Negaprion brevirostris. A general additive model ( GAM) was used to analyse the non-linear annual CPUE values over the entire 32-year research period. The GAM displayed high variability of annual CPUE, with a peak value of 0·026 N. brevirostris per hook day (hooks day−1) in 2000. The temporal pattern of CPUE indicated an abundance trend with a complete cycle, from trough to trough, occurring over a period of approximately 18 years. The 1982-1989 survey period saw the highest proportion of mature individuals (19·8%) and the smallest average pre-caudal length ( LPC; 124·8 cm). The 1992-2002 survey period had the highest average annual CPUE (0·018 hooks day−1), while the 2003-2014 research period saw largest average LPC size (134·8 cm) and the lowest average CPUE values (0·009 hooks day−1) of the entire research period. The long-term trend identified in this study provides a baseline for future assessment. [ABSTRACT FROM AUTHOR]

The Atchafalaya River Basin Floodway (ARBF), a regulated river/floodplain distributary of the Mississippi River, experiences an annual flood pulse that strongly influences floodplain physicochemistry. We developed several metrics to investigate the relationship between the timing and magnitude of the flood pulse and floodplain hypoxia, which in most years is a spatially extensive and temporally prolonged problem in the lower ARBF. Principal components analysis of flood metrics from 2001 to 2009 revealed contrasting flood types (early cool and late warm), but component-based general linear models were unable to predict the magnitude of hypoxia in ARBF water management areas (WMAs). Further analyses based on temperature and geographic information system-determined WMA inundation with generalized additive models (GAMs) revealed WMA-specific patterns of hypoxia, but the likelihood of hypoxia consistently increased when temperatures approached 20°C and inundation rose above 20-30%. Validation with held-out data based on logistic regression indicated that the models constructed with the 2001-2009 temperature and inundation data were able to accurately predict the probabilities of hypoxia in two WMAs based on data collected from 2010 to 2013. The GAMs were an effective tool for visualizing and predicting the probability of hypoxia based on two easily generated parameters. Our analyses indicate that modification of the Atchafalaya River flood pulse could reduce the magnitude of hypoxia within the lower ARBF, subject to engineering (control structure operation) and economic (commercial fisheries production) constraints, by minimizing floodplain inundation after water temperatures reach 20°C. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In the Eastern Tropical Pacific ( ETP), a region of high fishing activity, olive ridley ( Lepidochelis olivacea) and other sea turtles are accidentally caught in fishing nets with tuna and other animals. To date, the interaction between fishing activity, ocean conditions and sea turtle incidental catch in the ETP has been described and quantified, but the factors leading to the interaction of olive ridleys and fishing activity are not well understood. This information is essential for the development of future management strategies that avoid bycatch and incidental captures of sea turtles. We used Generalized additive models ( GAM) to analyze the relationship between olive ridley incidental catch per unit effort ( iCPUE) in the ETP purse-seine fisheries and environmental conditions, geographic extent and fishing set type (associated with dolphins, floating objects or in free-swimming tuna schools). Our results suggest that water temperature, set type and geographic location (latitude, longitude and distance to nesting beaches) are the most important predictor variables to describe the probability of a capture event, with the highest iCPUE observed in sets made over floating objects. With the environmental predictors used, sea surface temperatures ( SST) of 26-30°C and chlorophyll- a (chl- a) concentrations <0.36 mg m−3 were associated with the highest probability of an incidental catch. Temporally, the highest probability of an incidental catch was observed in the second half of the year (June to December). Four regions were observed as high incidental catch hotspots: North and south of the equator between 0-10°N; 0-10°S and from 120 to 140°W; and along the Colombian coast and surrounding regions. [ABSTRACT FROM AUTHOR]

In order to preserve forest ecosystem services under climate change, site suitability for tree species has to be re-assessed and management strategies have to be developed to adapt species composition. Thereby, it is reasonable to consider information on both site suitability estimations based on current species distribution and also stand productivity determined by tree growth. Currently, models for species distribution (SDMs) and tree growth are used to investigate the response of tree species to climate. However, both approaches were only applied separately so far. In this study, SDMs and growth models for Picea abies, Fagus sylvatica, Abies alba and Pinus sylvestris were calculated based on the German national forest inventories. We asked whether there is a positive relation between a tree species' occurrence and its growth and what can be learned by their joint interpretation. The two approaches resulted in different patterns with respect to the considered environmental variables. Tree growth and occurrence probabilities were not positively correlated. This may be explained by the influence of forest pathogens and competition on species distribution by means of an increase in mortality. We concluded that the consideration of demographic processes as drivers of species distribution improves the reliability of estimates for site suitability and additionally provides information on productivity. [ABSTRACT FROM AUTHOR]

Euphausiids constitute a major biomass component in shelf ecosystems and play a fundamental role in the rapid vertical transport of carbon from the ocean surface to the deeper layers during their daily vertical migration (DVM). DVM depth and migration patterns depend on oceanographic conditions with respect to temperature, light and oxygen availability at depth, factors that are highly dependent on season in most marine regions. Here we introduce a global krill respiration ANN (artificial neural network) model including the effect of latitude ( LAT ), the day of the year ( DoY ), and the number of daylight hours ( DLh ), in addition to the basal variables that determine ectothermal oxygen consumption (temperature, body mass and depth). The newly implemented parameters link space and time in terms of season and photoperiod to krill respiration. The ANN model showed a better fit ( r 2 = 0.780) when DLh and LAT were included, indicating a decrease in respiration with increasing LAT and decreasing DLh . We therefore propose DLh as a potential variable to consider when building physiological models for both hemispheres. For single Euphausiid species investigated in a large range of DLh and DoY , we also tested the standard respiration rate for seasonality with Multiple Linear Regression (MLR) and General Additive model (GAM). GAM successfully integrated DLh ( r 2 = 0.563) and DoY ( r 2 = 0.572) effects on respiration rates of the Antarctic krill, Euphausia superba , yielding the minimum metabolic activity in mid-June and the maximum at the end of December. We could not detect DLh or DoY effects in the North Pacific krill Euphausia pacifica , and our findings for the North Atlantic krill Meganyctiphanes norvegica remained inconclusive because of insufficient seasonal data coverage. We strongly encourage comparative respiration measurements of worldwide Euphausiid key species at different seasons to improve accuracy in ecosystem modeling. [ABSTRACT FROM AUTHOR]

We used historical documents, stand mapping, and new methods of dendrochronological analysis to reconstruct 250 y of land-use history of the Simes Tract in Petersham, MA. These data were then used to interpret the origin of the current forest's stand structure within the experimental plots of the Harvard Forest Hemlock Removal Experiment within the Simes Tract, an experiment that examines the effects of the ongoing decline of Tsuga canadensis on forest ecology. Data from tree cores revealed that the trees in the experimental plots were < 150 y old. These trees have established continually since the 1870s, with recruitment pulses following successive episodes of land division and re-aggregation, logging, irruptions of nonnative insects and pathogens, two moderate droughts, and the 1938 'Great Hurricane'. Our new method of dendrochronological analysis used generalized additive models and focused on year-to-year dynamics to highlight associations between fine-scale changes in tree growth, and both episodic (pulse) and longer-term environmental and biotic drivers. Tsuga canadensis, hypothesized to be a foundation species in this system, achieved its current dominance at the Simes Tract by responding rapidly, both positively and uniformly across age classes, in the 1920s to three concomitant environmental changes that had occurred in the preceding two decades: loss of Castanea dentata to the chestnut blight; selective logging; and a ∼ 7 y drought. In contrast, Betula lenta, Quercus rubra, Acer rubrum, and Pinus strobus have declined in importance since the early 1900s in stands otherwise dominated by T. canadensis. Acer rubrum and P. strobus were selectively harvested or severely damaged by the 1938 hurricane, the peak of B. lenta establishment followed the 1938 hurricane, and Q. rubra growth was impacted by the severe 1981 gypsy moth outbreak. Together, the data illustrate the contingent nature of the establishment dynamics of a foundation species in a New England forest and suggest a more nuanced approach to the role of T. canadensis as a foundation species. [ABSTRACT FROM AUTHOR]

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT ), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP ), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2 (3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2 (3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2 (3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects., Competing Interests: Conflicts of Interest Dr. Abdallah has served as a consultant, speaker and/or on advisory boards for FSV7, Lundbeck, Psilocybin Labs, Genentech and Janssen, and editor of Chronic Stress for Sage Publications, Inc.; he has filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Davidson is the founder and president of, and serves on the board of directors for, the non-profit organization Healthy Minds Innovations, Inc. Dr. Jahanshad, Dr. Thompson and Dr. Ching received partial research support from Biogen, Inc. (Boston, USA) for research unrelated to the content of this manuscript. Dr. Krystal is a consultant for AbbVie, Inc., Amgen, Astellas Pharma Global Development, Inc., AstraZeneca Pharmaceuticals, Biomedisyn Corporation, Bristol-Myers Squibb, Eli Lilly and Company, Euthymics Bioscience, Inc., Neurovance, Inc., FORUM Pharmaceuticals, Janssen Research & Development, Lundbeck Research USA, Novartis Pharma AG, Otsuka America Pharmaceutical, Inc., Sage Therapeutics, Inc., Sunovion Pharmaceuticals, Inc., and Takeda Industries; is on the Scientific Advisory Board for Lohocla Research Corporation, Mnemosyne Pharmaceuticals, Inc., Naurex, Inc., and Pfizer; is a stockholder in Biohaven Pharmaceuticals; holds stock options in Mnemosyne Pharmaceuticals, Inc.; holds patents for Dopamine and Noradrenergic Reuptake Inhibitors in Treatment of Schizophrenia, US Patent No. 5,447,948 (issued September 5, 1995), and Glutamate Modulating Agents in the Treatment of Mental Disorders, U.S. Patent No. 8,778,979 (issued July 15, 2014); and filed a patent for Intranasal Administration of Ketamine to Treat Depression. U.S. Application No. 14/197,767 (filed on March 5, 2014); US application or Patent Cooperation Treaty international application No. 14/306,382 (filed on June 17, 2014); Filed a patent for using mTOR inhibitors to augment the effects of antidepressants (filed on August 20, 2018). Dr. Schmahl is a consultant for Boehringer Ingelheim International GmbH. Dr. Stein has received research grants and/or consultancy honoraria from Lundbeck and Sun. Dr. Lebois reports unpaid membership on the Scientific Committee for the International Society for the Study of Trauma and Dissociation (ISSTD) and spousal license payment for Vanderbilt IP from Acadia Pharmaceuticals unrelated to the topic of this manuscript. All other authors have no conflicts of interest to declare., (Copyright © 2022. Published by Elsevier Inc.)

According to the widely accepted Lexical Category Prominence Rule (LCPR), prominence assignment to triconstituent compounds depends on the branching direction. Left-branching compounds, that is, compounds with a left-hand complex constituent, are held to have highest prominence on the left-most constituent, whereas right-branching compounds have highest prominence on the second of the three constituents. The LCPR is, however, only poorly empirically supported. The present paper tests a new hypothesis concerning the prominence of triconstituent compounds and suggests a new methodology for the empirical investigation of compound prominence. According to this hypothesis, the prominence pattern of the embedded compound has a decisive influence on the prominence of the whole compound. Using a mixed-effects generalized additive model for the analysis of the pitch movements, it is shown that all triconstituent compounds have an accent on the first constituent irrespective of branching, and that the placement of a second, or even a third, accent is dependent on the prominence pattern of the embedded compound. The LCPR is wrong. [ABSTRACT FROM PUBLISHER]

The purpose of this study is to investigate whether the often-debated association between physical disorder and crime exists longitudinally and operates through a specific spatial process. We combine four administrative and official databases to analyze patterns of crime occurring each month in New Orleans, LA, between 2012 and 2018 at the block group level. We adopt a generalized additive model (GAM) framework to efficiently account for potential spatial, temporal, and spatiotemporal autocorrelation to investigate this association. The resident-driven self-report measure of physical disorder (i.e., 311 calls) consistently predicts neighborhood criminal activities in New Orleans. The spatial effects of 311 calls in adjacent areas on drug violations, property crime, and violence in a focal neighborhood are also observed. In contrast, official reports of physical disorder, captured with code violation, is either weakly related to crime or lacking an empirical relationship. Overall, our findings suggest that the association between physical disorder and neighborhood crimes is robust over time. In addition, this association is conditioned by how physical disorder is operationalized, with dimensions such as residential report and official report of physical disorder playing a unique, and occasionally important role in understanding overall patterns of neighborhood criminal activity. [ABSTRACT FROM AUTHOR]

We investigated here the relation of environmental variables with the distribution of mitochondrial lineages using a bark beetle species of Mediterranean distribution as a model. We analysed a total number of 460 DNA sequences of Tomicus destruens provided by intensive and extensive collection and GenBank entries. We combined phylogeography and regression models to study the role of five environmental predictors at fine scale in the distribution of a local genealogy. The analysis revealed a high genetic diversity, with 52 haplotypes present in Sierra Espuña forest (SE Spain) and 21 haplotypes in the other 14 Spanish populations, all included in the western clade of the Mediterranean phylogeography of the species. We found a micro-distribution of the species related to altitude and putative niche segregation between lineages associated with the micro-environmental conditions of their host pine trees. We compared the phylogeographic hypothesis obtained here with the phylogeography obtained integrating our data with all data published elsewhere. Here, we demonstrate a relation between the environmental heterogeneity and the haplotypic diversity at Mediterranean Basin scale. This analysis allows us to support the evolutionary scenario where the phylogeography and current molecular diversity of T. destruens is a consequence of the recolonization from two principal refugia at both extremes of the Mediterranean Basin and, according to our data, we propose that the heterogeneity of habitats allows fixation of the mitochondrial lineages. [ABSTRACT FROM AUTHOR]

Dolphin distributions have been related to a range of oceanographic determinants. The complex topography and hydrography of the Faroe-Shetland Channel have a significant influence on the distribution of many species. However, there is no published detail on how dolphin distributions there are influenced by either topography or hydrography. The study therefore aims to relate dolphin distributions in the Faroe-Shetland Channel to environmental variables, using a general additive modelling framework applied to passive acoustic survey data. Models were created using data from 2001, and were cross-validated to test their predictive power. Predictions were calculated at each stage in the model-building process, and were tested against data from 2002. The results suggest that water noise level, time of day, month, water depth, and surface temperature were significant influences on the probability of detecting dolphins acoustically during 2001. Furthermore, the model was a significant predictor of dolphin distribution in 2002. The model with the greatest predictive power included the terms water noise level, time of day, month, and water depth. The results provide information of potential use in understanding the determinants of dolphin distributions, and hopefully will help managers address concerns about the potential impacts on dolphins of anthropogenic activity. [Copyright &y& Elsevier]

Aim, location Tomicus (Coleoptera, Scolytidae) species are some of the principal pests of Eurasian forest and are represented by three coexisting species in Spain, Tomicus piniperda (Linnaeus, 1758), Tomicus destruens (Wollaston, 1865) and Tomicus minor (Harting, 1834). The distribution of two taxa are unknown as they have until recently been considered separate species. Therefore, we model the potential distribution centres and establish the potential distribution limits of Tomicus species in Iberia. We also assess the effectiveness of different models by comparing predicted results with observed data. These results will have application in forest pest management. Methods Molecular and morphological techniques were used to identify species from 254 specimens of 81 plots. For each plot, a Geographical Information System was used to extract a set of 14 environmental (one topographic, six climatic) and biotic variables (seven host tree distributions). General Additive Models and Ecological Niche Factor Analysis models are applied for modelling and predicting the potential distribution of the three especies of Tomicus. Results The results of both modelling methodologies are in agreement. Tomicus destruens is the predominant species in Spain, living in low and hot areas. Tomicus pin iperda occurs in lower frequency and prefers wet and cold areas of north-central Spain. We detected sympatric populations of T. destruens and T. piniperda in Northern coast of Spain, infesting mainly P. pinaster. Tomicus minor is the rarest species, and it occupies a fragmented distribution located in high and wet areas. The remarkable biotic variable is the distribution of P. sylvestris, incorporated into the models of T. destruens and T. piniperda. Main conclusions These results indicate that in wet areas of north-central Spain where T. piniperda occurs (and possibly the high altitudes of the southern mountains), T. destruens has a climatic distribution limit. In the northern border of this area, both species overlap their distributions and some co-occurrences were detected. Tomicus minor potentially occurs in high and wet fragmented areas. [ABSTRACT FROM AUTHOR]

To better understand and predict Oregon coho ( Oncorhynchus kisutch) marine survival, we developed a conceptual model of processes occurring during four sequential periods: (1) winter climate prior to smolt migration from freshwater to ocean, (2) spring transition from winter downwelling to spring/summer upwelling, (3) the spring upwelling season and (4) winter ocean conditions near the end of the maturing coho's first year at sea. We then parameterized a General Additive Model (GAM) with Oregon Production Index (OPI) coho smolt-to-adult survival estimates from 1970 to 2001 and environmental data representing processes occurring during each period (presmolt winter SST, spring transition date, spring sea level, and post-smolt winter SST). The model explained a high and significant proportion of the variation in coho survival ( R2 = 0.75). The model forecast of 2002 adult survival rate ranged from 4 to 8%. Our forecast was higher than predictions based on the return of precocious males (‘jacks’), and it won't be known until fall 2002 which forecast is most accurate. An advantage to our environmentally based predictive model is the potential for linkages with predictive climate models, which might allow for forecasts more than 1 year in advance. Relationships between the environmental variables in the GAM and others (such as the North Pacific Index and water column stratification) provided insight into the processes driving production in the Pacific Northwest coastal ocean. Thus, coho may be a bellwether for the coastal environment and models such as ours may apply to populations of other species in this habitat. [ABSTRACT FROM AUTHOR]