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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]

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]

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]

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]

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]

Machine learning techniques have gained attention in earthquake engineering for their accurate predictions, but their opaque black-box models create ambiguity in the decision-making process due to inherent complexity. To address this issue, numerous methods have been developed in the literature that attempt to elucidate and interpret black-box machine learning methods. However, many of these methods evaluate the decision-making processes of the relevant machine learning techniques based on their own criteria, leading to varying results across different approaches. Therefore, the critical significance of developing transparent and interpretable models, rather than describing black-box models, becomes particularly evident in fields such as earthquake engineering, where the interpretation of the physical implications of the problem holds paramount importance. Motivated by these considerations, this study aims to advance the field by developing a novel methodological approach that prioritizes transparency and interpretability in estimating the deformation capacity of non-ductile reinforced concrete shear walls based on an additive meta-model representation. Specifically, this model will leverage engineering knowledge to accurately predict the deformation capacity, utilizing a comprehensive dataset collected from various locations globally. Furthermore, the integration of uncertainty analysis within the proposed methodology facilitates a comprehensive investigation into the influence of individual shear wall variables and their interactions on deformation capacity, thereby enabling a detailed understanding of the relationship dynamics. The proposed model stands out by aligning with scientific knowledge, practicality, and interpretability without compromising its high level of 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]

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]

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]

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]

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]

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. [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]

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]

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%. [ABSTRACT FROM AUTHOR]