Generalized Additive Mixed Models Research Articles

Modeling and comparison of count data containing zero values: a case study of Setipinna taty in the south inshore of Zhejiang, China.

To effectively use the fishery count data containing zero values, Setipinna taty in the coastal waters of south inshore of Zhejiang in China from 2017 to 2019 was used in this study. Environmental factors, such as water temperature, water depth, and salinity, were selected to establish models and compare based on the generalized additive model (GAM) of the Tweedie distribution (Tweedie-GAM) and two-stage GAM, Ad hoc method, and generalized additive mixed model (GAMM). The results showed that each station accounted for a higher proportion of zero values and the two-stage GAM model had a higher deviation interpretation rate, and GAM I and GAM II had 19.6% and 60.4% deviation interpretation rates. The cross-validation results showed that the performance evaluation of the two-stage GAM model was the best and showed the highest R2 value, the lowest average absolute error, and the relatively small root mean square error. This study found that the abundance of S. taty in the south inshore of Zhejiang was highest at around 21°C and 18°C in spring and autumn, and the abundance reached the highest at a water depth of about 20 m. In spatial distribution, the high value of the abundance of S. taty was mostly distributed in the coastal waters in the south of 28°N. In future research, models should be fitted and compared for different sampling zero-value ratios, and more environmental factors should be included to accurately find an optimal model and provide references for the conservation of fishery resources.

Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

AbstractThis paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

Non-linear link between temperature difference and COVID-19: Excluding the effect of population density.

The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.

Effects of Outdoor Temperature on Blood Pressure in a Prospective Cohort of Northwest China

ObjectiveThe relationship between outdoor temperature and blood pressure (BP) has been inconclusive. We analyzed data from a prospective cohort study in northwestern China to investigate the effect of outdoor temperature on BP and effect modification by season. MethodsA total of 32,710 individuals who participated in both the baseline survey and the first follow-up in 2011–2015 were included in the study. A linear mixed-effect model and generalized additive mixed model (GAMM) were applied to estimate the association between outdoor temperature and BP after adjusting for confounding variables. ResultsThe mean differences in systolic blood pressure (SBP) and diastolic blood pressure (DBP) between summer and winter were 3.5 mmHg and 2.75 mmHg, respectively. After adjusting for individual characteristics, meteorological factors and air pollutants, a significant increase in SBP and DBP was observed for lag 06 day and lag 04 day, a 0.28 mmHg (95% CI: 0.27–0.30) per 1 °C decrease in average temperature for SBP and a 0.16 mmHg (95% CI: 0.15–0.17) per 1 °C decrease in average temperature for DBP, respectively. The effects of the average temperature on both SBP and DBP were stronger in summer than in other seasons. The effects of the average temperature on BP were also greater if individuals were older, male, overweight or obese, a smoker or drinker, or had cardiovascular diseases (CVDs), hypertension, and diabetes. ConclusionsThis study demonstrated a significant negative association between outdoor temperature and BP in a high-altitude environment of northwest China. Moreover, BP showed a significant seasonal variation. The association between BP and temperature differed by season and individuals’ demographic characteristics (age, gender, BMI), unhealthy behaviors (smoking and alcohol consumption), and chronic disease status (CVDs, hypertension, and diabetes).

Emergent bilinguals in a digital world: a dynamic analysis of long-term L2 development in (pre)primary school children

Abstract In this study, I present dense, longitudinal data exploring the insights that a Complex Dynamic Systems Theory (CDST) perspective can bring to bear on patterns of relationships found between learner individual differences – notably age of onset (AO) and extracurricular L2 English use – in children in (pre)primary programs in Switzerland. We studied 71 children who had received 50/50 bilingual instruction in German and English (so-called “partial CLIL” programs) as well as 105 children in “minimal CLIL” programs with almost uniquely monolingual German instruction (90% German, 10% English). In the data analysis, (1) generalized additive mixed modeling (GAMM) was combined with (2) mixed-effects regression modeling. The findings show that AO may exert an effect on L2 performance in bilingual but not traditional instructional settings. Furthermore, contact with English outside school is a strong predictor for learner outcome, regardless of the intensity of instruction and an early or late start respectively. We conclude that the traditional view of the age factor in instructional settings needs to give way to a new understanding of L2 development in intensive exposure conditions, in which age of acquisition is seen as a major determinant.

Cognitive and Socioaffective Predictors of L2 Microdevelopment in Late Adulthood: A Longitudinal Intervention Study

AbstractThe question of cognition in second language (L2) acquisition later in life is of importance inasmuch as L2 learning is largely mediated by domain‐general cognitive capacities. While a number of these capacities have been shown to decline with age, individual differences in cognition increase over the lifespan. This microdevelopment study investigates the L2 trajectories of 28 older German‐speaking adults (age 64+) who participated in a combined computer‐assisted and classroom‐instructed 7‐month Spanish training for beginners. We made use of generalized additive mixed models (GAMMs) to quantify linear and nonlinear learner trajectories as well as any predictors thereof. Participants were assessed on a range of behavioral, L2, socioaffective, and background variables. We found a significant (linear and nonlinear) increase across all measures of L2 proficiency. Between‐subject cognitive, socioaffective, and background variables significantly predicted the overall level of L2 proficiency as well as developmental patterns over time. Daily variances in cognitive performance and socioaffect had little impact on fluctuations in L2 performance. Findings are discussed against the backdrop of complex dynamic systems theory and highlight the necessity for dense longitudinal research designs to capture nonlinearity in third‐age L2 learning.

Visceral and subcutaneous adipose tissue, which one induced thyroid dysfunction in patients with morbid obesity.

Thyroid dysfunction in patients with morbid obesity usually resolves after bariatric surgery. However, the role of diverse types of adipose tissue in the process remains unknown. We aim to investigate the effects of visceral and subcutaneous fat on thyroid function in a Chinese population with morbid obesity who underwent sleeve gastrectomy (SG). University hospital, Shanghai, China METHODS: Repeated measurement data of thyroid hormone and body fat were collected at 0, 3, 6, 12, 24, and 36 months after sleeve gastrectomy. Dual-energy X-ray absorptiometer and quantitative computerized tomography (CT) were used to compute visceral fat and subcutaneous fat. Repeated measures correlation (rmmcorr) package was employed for correlation analysis with generalized additive mixed model (GAMM) determining the independent factors. Thyroid stimulating-hormone (TSH) showed notable decrease at 36 months after surgery, coupled with reduction of BMI (38.08 kg/cm2 versus 24.28 kg/cm2), C-reactive protein (CRP), visceral adipose tissue (786.74 cm2 versus 367.44 cm2), body fat rate, and waistline (118.13 cm versus 100.87 cm). Only visceral fat, diabetes, and CRP proved to be independent variables for TSH decline, without correlation with subcutaneous fat. The present study is first to report the effects of different types of body fat on thyroidfunction in a Chinese population with morbid obesity, revealing that loss of visceral fat is the key to improving endocrine and metabolic activity after bariatric surgery.

The longitudinal associations between bone mineral density and appendicular skeletal muscle mass in Chinese community-dwelling middle aged and elderly men

BackgroundThe present study aimed to investigate longitudinal associations between bone mineral densities (BMDs) and appendicular skeletal muscle (ASM) mass in different regions of the body using three different indicators, in Chinese community-dwelling middle-aged and elderly men.MethodsA total of 1,343 men aged ≥ 40 years from a Chinese community were assessed at baseline (2014–2016), one-year follow-up (2016–2017; n = 648), two-year follow-up (2017–2018; n = 407), and three-year follow up (2018–2019; n = 208). At all the four time-points, measurements included ASM mass and BMDs for all regions of the body using dual-energy X-ray absorptiometry. A questionnaire was completed by patients and biochemical markers were assessed. We applied three different indicators to define ASM mass or lean mass respectively, including the appendicular skeletal muscle index (ASM adjusted by height, ASMI, according to the Asian Working Group for Sarcopenia), skeletal muscle index (ASM adjusted by weight, SMI, according to the International Working Group on Sarcopenia), and the appendicular skeletal muscle/body mass index (ratio of ASM and Body mass index (BMI), ASM/BMI, according to the Foundation for the National Institutes of Health). After adjusting for potential confounders, the generalized additive mixed model (GAMM) was used to analyze the trend in ASM mass over time, and to test the association between ASM mass and regional and whole-body BMDs.ResultsThe incidence of low lean mass was 8.2% defined by ASMI, 16.3% defined by SMI, and 8.3% defined by ASM/BMI. There was a linear relationship between BMDs and ASM mass, and ASMI, ASM/BMI, and SMI gradually decreased with time. After adjusting for covariances, GAMM analysis determined longitudinal associations between BMDs and ASM mass by three indicators respectively: the skull BMD was negatively associated with ASM mass. For each unit increase in skull BMD, ASMI decreased by 0.28 kg/m2 (95% confidence interval (CI) [−0.39 to −0.16]), ASM/BMI decreased by 0.02 m2 (95% CI [−0.03 to −0.00]), and SMI decreased by 0.01% (95% CI[−0.01 to −0.00]). The remaining parameters (including whole-body mean BMD, thoracic spinal BMD, lumbar spinal BMD, hip BMD, femoral neck BMD, pelvic BMD, left arm BMD, right arm BMD, left leg BMD, right leg BMD) were positively correlated with ASM mass. The ASMI increased by 3.07 kg/m2for each unit increase in the femoral neck BMD (95% CI [2.31–3.84]). The ASM/BMI increased by 0.22 m2for each unit increase in the left arm BMD (95% CI [0.12–0.33]), and the SMI increased by 0.05% per unit increase in the left arm BMD (95% CI [0.02–0.08]).ConclusionsCompared to ASMI and ASM/BMI, SMI was more sensitive to screen for the low lean mass. Skull BMD was negatively associated with ASM mass, while BMDs throughout the rest of the body were positively correlated with ASM mass among the middle-aged and elderly Chinese men.

Predicting the Effects of Climate Change on the Occurrence of the Toxic Dinoflagellate Alexandrium catenella Along Canada’s East Coast

Alexandrium catenella produces paralytic shellfish toxins that affect marine fisheries and aquaculture as well as ecosystem and human health worldwide. This harmful algal species is extremely sensitive to environmental conditions and potentially to future climate change. Using a generalized additive mixed model (GAMM) we studied the potential effects of changing salinity and temperatures on A. catenella bloom (≥1000 cells L–1) occurrence along Canada’s East Coast throughout the 21st century. Our GAMM was applied to two high greenhouse gas emissions scenarios (RCP 8.5) and one mitigation scenario (RCP 4.5). Under present-day conditions, our model successfully predicted A. catenella’s spatio-temporal distribution in Eastern Canada. Under future conditions, all scenarios predict increases in bloom frequency and spatial extent as well as changes in bloom seasonality. Under one RCP 8.5 scenario, A. catenella bloom occurrences increased at up to 3.5 days per decade throughout the 21st century, with amplified year-to-year variability. Blooms expanded into the Gulf of St. Lawrence and onto the Scotian Shelf. These conditions could trigger unprecedented bloom events in the future throughout our study region. In all climate scenarios, the bloom season intensified earlier (May–June) and ended later (October). In some areas of the Gulf of St. Lawrence, the thermal habitat of A. catenella was exceeded, thereby locally reducing bloom risk during the summer months. We conclude that an increase in A. catenella’s environmental bloom window could further threaten marine fauna including endangered species as well as fisheries and aquaculture industries on Canada’s East Coast. Similar impacts could be felt in other coastal regions of the globe where warming and freshening of waters are intensifying.

Evaluating generalised additive mixed modelling strategies for dynamic speech analysis

Generalised additive mixed models (GAMMs) are increasingly popular in dynamic speech analysis, where the focus is on measurements with temporal or spatial structure such as formant, pitch or tongue contours. GAMMs provide a range of tools for dealing with the non-linear contour shapes and complex hierarchical organisation characteristic of such data sets. This, however, means that analysts are faced with non-trivial choices, many of which have a serious impact on the statistical validity of their analyses. This paper presents type I and type II error simulations to help researchers make informed decisions about modelling strategies when using GAMMs to analyse phonetic data. The simulations are based on two real data sets containing F2 and pitch contours, and a simulated data set modelled after the F2 data. They reflect typical scenarios in dynamic speech analysis. The main emphasis is on (i) dealing with dependencies within contours and higher-level units using random structures and other tools, and (ii) strategies for significance testing using GAMMs. The paper concludes with a small set of recommendations for fitting GAMMs, and provides advice on diagnosing issues and tailoring GAMMs to specific data sets. It is also accompanied by a GitHub repository including a tutorial on running type I error simulations for existing data sets: https://github.com/soskuthy/gamm_strategies.

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model.

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents, including malaria, cholera, dengue fever, hand, foot, and mouth disease (HFMD), and the recent Corona-virus pandemic. HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s. The outbreaks may result from a combination of rapid population growth, climate change, socioeconomic changes, and other lifestyle changes. However, the modeling of climate variability and HFMD remains unclear, particularly in statistical theory development. The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling. When dealing with time-series data with clustered variables such as HFMD with clustered states, the independence principle of both modeling approaches may be violated. Thus, a Generalized Additive Mixed Model (GAMM) is used to investigate the relationship between HFMD and climate factors in Malaysia. The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect. This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation. The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia. The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm. Besides, a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances. The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon. The study's outcomes can be used by public health officials and the general public to raise awareness, and thus, implement effective preventive measures.

Trends in sightings of the stingrays of southern Mozambique

Understanding the drivers that influence abundance and distribution of marine species is essential to predict future trends in abundance and inform conservation efforts. This is vital in the largely unregulated coastline of Mozambique, where stingrays are afforded no protection by law and are caught by small-scale fishers. During SCUBA dives from 2012 to 2018, trained citizen scientists recorded 11 environmental, spatial and temporal variables along with the count of four stingray species (Megatrygon microps, Taeniurops meyeni, Neotrygon indica and Pateobatis jenkinsii) in the Inhambane region of Mozambique. By constructing bubble plots and generalised additive mixed models (GAMMs), we analysed the relationship between the probability of sightings of each species with the 11 variables. It is evident that the sightings for each of these four stingrays of the Inhambane region differ spatially and seasonally. The key findings include that T. meyeni and M. microps were found to increase in sighting frequency in different seasons (winter and summer respectively) at similar dive sites typically further from shore. Neotrygon indica commonly occupied the areas closer to shore. Identifying key habitats, and temporal and environmental conditions, is conducive to implementing effective conservation strategies in the region, such as, in this instance, all stingrays could be provided with a refuge in the same area.

Growth of marine ectotherms is regionally constrained and asymmetric with latitude

AbstractAimGrowth rates of organisms are routinely used to summarize physiological performance, but the consequences of local evolutionary history and ecology are largely missed by analyses on wide biogeographical scales. This broad approach has been commonly applied to other physiological parameters across terrestrial and aquatic environments. Here, we examine growth rates of marine bivalves across all biogeographical realms, latitude, and temperature, with analyses to determine regional effects on growth on global scales.LocationGlobal: marine ecosystems.Time period1930–2018.Major taxaBivalves.MethodsWe use a comprehensive data set of bivalve growth parameters (n = 966, 243 species) representing all biogeographical realms to calculate overall growth performances. We use these data with environmental temperature to analyse global patterns in growth, accounting for regional primary productivity and phylogeny using general additive mixed and linear models. The Arrhenius relationship and corresponding activation energies are used to quantify the sensitivity to temperature in each biogeographical realm and province.ResultsOur analyses show that bivalve growth demonstrates latitudinal asymmetry and exhibits nonlinear relationships with latitude. We find that overall growth performance is affected by temperature and particulate organic carbon, but the form of these relationships differs with phylogeny. Growth is slower and more sensitive to increasing temperature in the Antarctic than it is in the Arctic, and decreases with increasing temperature in some tropical realms, a previously unidentified and fundamental difference in growth and physiological sensitivity.Main conclusionsOur findings provide compelling evidence that the widely used curvilinear relationship between temperature and growth rates in marine ectotherms is an inappropriate descriptor of thermal sensitivity, because it normalizes regional variations in physiological performance. Without a more detailed assessment of global physiological patterns, the responses of species to local variations associated with climate change will be under‐appreciated in global assessments of climate risk, minimizing the effectiveness of management and conservation.

Disentangling climatic and anthropogenic contributions to nonlinear dynamics of alpine grassland productivity on the Qinghai-Tibetan Plateau

Alpine grasslands on the Qinghai-Tibetan Plateau are sensitive and vulnerable to climate change and human activities. Climate warming and overgrazing have already caused degradation in a large fraction of alpine grasslands on this plateau. However, it remains unclear how human activities (mainly livestock grazing) regulates vegetation dynamics under climate change. Here, alpine grassland productivity (substituted with the normalized difference vegetation index, NDVI) is hypothesized to vary in a nonlinear trajectory to follow climate fluctuations and human disturbances. With generalized additive mixed modelling (GAMM) and residual-trend (RESTREND) analysis together, both magnitude and direction of climatic (in terms of temperature, precipitation, and radiation) and anthropogenic impacts on NDVI variation were examined across alpine meadows, steppes, and desert-steppes on the Qinghai-Tibetan Plateau. The results revealed that accelerating warming and greening, respectively, took place in 76.2% and 78.8% of alpine grasslands on the Qinghai-Tibetan Plateau. The relative importance of temperature, precipitation, and radiation impacts was comparable, between 20.4% and 24.8%, and combined to explain 66.2% of NDVI variance at the pixel scale. The human influence was strengthening and weakening, respectively, in 15.5% and 14.3% of grassland pixels, being slightly larger than any sole climatic variable across the entire plateau. Anthropogenic and climatic factors can be in opposite ways to affect alpine grasslands, even within the same grassland type, likely regulated by plant community assembly and species functional traits. Therefore, the underlying mechanisms of how plant functional diversity regulates nonlinear ecosystem response to climatic and anthropogenic stresses should be carefully explored in the future.

Newborn anthropometry, maternal capital, and altitude in the highland population from the province of Jujuy, Argentina.

To analyze variability in newborn (NB) anthropometry among Jujenean NBs as a function of geographic altitude (500 m to ≈4000 masl), maternal anthropometry and other maternal characteristics within the maternal capital framework. Data obtained from 41,371 mother/child pairs recorded in the Jujuy Perinatal Information System (SIP) between 2009 and 2014, including: NB and maternal weight, length/height and BMI; gestational age (corrected); maternal age, educational level, nutritional status, and marital status; birth interval; and planned pregnancy. Based on the declared place of residence, the prevalence of unsatisfied basic needs (% UBN) was determined and the data was split into two altitudinal groups: highlands (HL, >2500 masl) and lowlands (LL, <2500 masl). ANOVA, Chi-squared and Pearson tests were applied as needed. Statistical associations between the response variables-NB weight, length and BMI-and maternal and environmental variables were tested using a Generalized Additive Mixed Model (GAMM). All NB and maternal anthropometric variables were lower in HL compared to LL; they also presented negative correlations with altitude, except NB length. Apart from gestational age and birth interval, HL and LL presented statistically significant differences in all study variables. GAMM results showed that maternal anthropometry was the main influence on NB weight and length. Of all the maternal capital features examined, only maternal anthropometric variables were found to protect offspring against the negative impact of HL environments.

Environmental drivers of commercial blue swimmer crab (Portunus armatus) catch rates in Western Australian fisheries

Sustainable fisheries management requires accurate stock assessment and an understanding of how targeted populations vary over space and time and respond to external stressors. Commercial fishery catch rate data (i.e. catch per unit of effort; CPUE) are widely used to assess fishery performance and can provide a measure of stock abundance. However, changes in environmental conditions can influence the two determinants of CPUE, i.e. catchability (q) and abundance (N). We explore the influence of environmental variables (water temperature, wind speed, rainfall, freshwater flow, wave height, lunar illumination, tide height and depth) on commercial blue swimmer crab Portunus armatus CPUE in five Western Australian fisheries. Generalised additive mixed models (GAMMs) were used to identify which variables had the greatest influence on CPUE, and enabled predictions of how these can vary with environmental change. Water temperature had a significant positive effect on CPUE in all fisheries, although some fisheries exhibited a CPUE decline above an optimal temperature range. Wind speed and tide height had significant and generally positive (but lower magnitude) effects in three of the five fisheries investigated, while lunar illumination and freshwater flow caused a decline in CPUE in two or less fisheries. When annual CPUE were standardised for these significant environmental effects, they displayed clear variation from nominal (raw) CPUE. These results highlight the importance of measuring and accounting for environmental effects (e.g. through catch rate standardisation) when using CPUE for fishery assessments.

Drivers of Spruce Bark Beetle (Ips typographus) Infestations on Downed Trees after Severe Windthrow

Research Highlights: Bark beetles are important agents of disturbance regimes in temperate forests, and specifically in a connected wind-bark beetle disturbance system. Large-scale windthrows trigger population growth of the European spruce bark beetle (Ips typographus L.) from endemic to epidemic levels, thereby allowing the killing of Norway spruce trees over several consecutive years. Background and Objectives: There is a lack of evidence to differentiate how outbreaks are promoted by the effects of environmental variables versus beetle preferences of trees from endemic to outbreak. However, little is known about how individual downed-tree characteristics and local conditions such as tree orientation and solar radiation affect beetle colonization of downed trees. Materials and Methods: To answer this question, we investigated the infestation rates and determined tree death categories (uprooted, broken, and stump) in wind-damaged areas in Western Tatra Mts. in Carpathians (Slovakia) from 2014–2016, following a windthrow in May 2014. In total, we investigated 225 trees over eight transects. For every tree, we measured its morphological (tree height, crown characteristics), environmental (solar radiation, terrain conditions, trunk zenith), temporal (time since wind damage), and beetle infestation (presence, location of attack, bark desiccation) parameters. We applied Generalized Additive Mixed Models (GAMM) to unravel the main drivers of I. typographus infestations. Results: Over the first year, beetles preferred to attack broken trees and sun-exposed trunk sides over uprooted trees; the infestation on shaded sides started in the second year along with the infestation of uprooted trees with lower desiccation rates. We found that time since wind damage, stem length, and incident solar radiation increased the probability of beetle infestation, although both solar radiation and trunk zenith exhibited nonlinear variability. Our novel variable trunk zenith appeared to be an important predictor of bark beetle infestation probability. We conclude that trunk zenith as a simple measure defining the position of downed trees over the terrain can anticipate beetle infestation. Conclusions: Our findings contribute to understanding of the bark beetle’s preferences to colonize windthrown trees in the initial years after the primary wind damage. Further, our findings can help to identify trees that are most susceptible to beetle infestation and to prioritize management actions to control beetle population while maintaining biodiversity.

A recipe for accurate estimation of lifespan brain trajectories, distinguishing longitudinal and cohort effects

We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10% of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. While LMMs require a priori specification of a polynomial functional form, SEMs do not easily handle data with unstructured time intervals between measurements. Generalized additive mixed models (GAMMs) offer an attractive alternative, and in this paper we propose various ways of formulating GAMMs for estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to more accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require repeated measures data and questions which can be answered with a single measurement per participant, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.

Is seafloor litter contributing to sea anemone blooms?

Increasing marine litter have become a global environmental disaster. The accumulation of seafloor litter (generally includes anthropogenic litter and natural debris) could change the habitat of benthic organisms and thereby affecting their population dynamics including spatial distribution. Metridium senile fimbriatum (i.e., M. senile), a fast-growing sea anemone, has become a dominant species of benthic community in the north Yellow Sea in recent years. In this study, we tested the hypothesis that the distribution of M. senile is positively correlated with that of seafloor litter, using data collected on seafloor litter and M. senile from three fisheries-independent bottom trawl surveys in the Yellow Sea in May, August and November 2019. Gradient Forest Model (GFM) was used to select appropriate response variables for characterizing the distribution of M. senile, and evaluate the influences of potential environmental factors on M. senile distribution. Surface area of anthropogenic litter (represented as ‘Anthropogenic litter’), surface area of natural debris (represented as ‘Natural debris’) and latitude (Lat) were identified as the most significant variables influencing the distribution of M. senile. Furthermore, Generalized Additive Mixed Model (GAMM) was applied to model the abundance distribution of M. senile in terms of significant environmental variables, and evaluate its correlations with ‘Anthropogenic litter’ and ‘Natural debris’. The best fitting GAMM showed that the abundance of M. senile has a significantly positive association with ‘Anthropogenic litter’ and ‘Natural debris’. We therefore speculated that accumulation of seafloor litter might contribute to the bloom of M. senile, given that seafloor litter could serve as “vectors” for M. senile dispersal and provide with a preferable “natural habitat” for their settlement.

Massive strandings of pleustonic Portuguese Man-of-War (Physalia physalis) related to ENSO events along the southeastern Pacific Ocean

Blooms of stinging pelagic cnidarians have been causing increasing health problems for humans worldwide. Physalia physalis is among the worst stinging species in the Atlantic and Pacific oceans along the North and South American coasts. This siphonophore species has a large gas float and lives at the ocean surface, where its distribution is affected mainly by winds. P. physalis's strandings were observed in the southeastern Pacific Ocean for three consecutive years (2014 to 2016). Data of stranded P. physalis were gathered daily through a collaborative effort between the Chilean Navy and the Chilean Ministry of Health. The association between oceanographic variables and the stranded P. physalis was assessed using a generalized additive mixed model (GAMM) and cross-correlation analysis. The largest stranding occurred along more than 2600 km of coastline and totaled 44,683 colonies. Most of the strandings were along the central and south-central coast of Chile, where almost 200 people were stung, and more than 120 beaches were closed in summer 2016. The GAMM model showed that the oceanographic conditions of warmer temperatures during the winter periods and the weakening of the westerly winds associated with the Niño Southern Oscillation (ENSO) were the most likely causes for the arrival of transport from the colonies to the coast. Also, our analysis illustrates the utility of collaborations among scientists and public organizations to promote large-scale and long-term understanding and provide a model for predicting this dangerous species' arrival and warning for beaches and swimmers, which is of global importance for human health.