Analysis Of Ecological Data Research Articles

School Mask Mandates and COVID-19: The Challenge of Using Difference-in-Differences Analysis of Observational Data to Estimate the Effectiveness of a Public Health Intervention.

There are considerable challenges when using difference-in-differences (DiD) analysis of ecological data to estimate the effectiveness of public health interventions in rapidly changing situations. To discuss the shortcomings of DiD methodology for the estimation of the effects of public health interventions using ecological data. As an example, the authors consider an analysis that used DiD methodology and reported a causal reduction in COVID-19 cases due to the maintenance of school mask mandates. They did alternate analyses using various control groups to assess the robustness of the prior analysis. School districts in the greater Boston area and Massachusetts during the 2021-to-2022 academic year. Students and school staff. Changes in COVID-19 case rates in districts that did and did not lift mask mandates. Important potential confounders rendered DiD methodology inappropriate for causal inference, including prior immunity, temporal variation in rates of infection, and changes in testing practices. The racial composition and income of intervention and control groups also differed substantially. Compared with maintaining the mask requirement, dropping the requirement was associated with anywhere from an increase of 5.64 cases (95% CI, 3.00 to 8.29 cases) per 1000 persons to a decrease of 2.74 cases (CI, 0.63 to 4.85 cases) per 1000 persons, depending on choice of control group and whether students or staff were examined. Ecological data were used; detailed data on all potential confounders were unavailable. Alternate analyses yielded estimates consistent with a wide range of both negative and positive associations in COVID-19 case rates after removal of mask mandates. The findings highlight the challenges of using DiD analysis of ecological data to estimate the effectiveness of interventions in divergent intervention and control groups during rapidly changing circumstances. None.

Bespoke cultivation of seablite with digital agriculture and machine learning

Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.

Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data.

Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets. Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part. Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks. Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09). Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

Slugs Count: Assessing citizen scientist engagement and development, and the accuracy of their identifications

Abstract How accurately can citizen science participants identify species, and can the information they provide be used to calculate accurate estimates of species richness, abundance and diversity measures? In this paper, we address these questions using data from a citizen science project assessing slug species diversity in gardens. Sixty participants were selected from over 2700 applicants across Britain to sample their gardens regularly for slugs following a standardised method. All slugs collected during the 30‐min search were posted to the lead investigator for verification throughout the 12‐month project. The resulting data were analysed to explore how accurate participants were in identifying slugs and whether this improved over the study period. Prior experience in slug identification was evaluated as a predictor of accuracy. Participants overestimated slug abundance and species richness, which led to overestimates in species diversity indices, illustrating the importance of verification in citizen science projects involving identification. Accuracy of slug identifications increased significantly over time in quantitative analysis of ecological data. However, self‐defined prior experience of identifying slugs before participation was not a good predictor of participant accuracy. Participants reported perceived improvement in slug identification skills to an evaluation survey after the project. However, confidence in identifying and explaining identification of slugs was lower than confidence in understanding and explaining other new science topics. This citizen science approach, including expert verification of physical specimens, illustrates how this method can be used successfully to provide accurate data on species' abundance and richness, alongside improving identification skills among the public for an understudied taxon. Continued engagement and feedback for participants is key in retaining citizen science participation in a project of this type, particularly if the taxon is challenging to identify correctly to species. Read the free Plain Language Summary for this article on the Journal blog.

Leveraging camera traps and artificial intelligence to explore thermoregulation behaviour.

Behavioural thermoregulation has critical ecological and physiological consequences that profoundly influence individual fitness and species distributions, particularly in the context of climate change. However, field monitoring of this behaviour remains labour-intensive and time-consuming. With the rise of camera-based surveys and artificial intelligence (AI) approaches in computer vision, we should try to build better tools for characterizing animals' behavioural thermoregulation. In this study, we developed a deep learning framework to automate the detection and classification of thermoregulation behaviour. We used lizards, the Rough-tail rock agama (Laudakia vulgaris), as a model animal for thermoregulation. We colour-marked the lizards and curated a diverse dataset of images captured by trail cameras under semi-natural conditions. Subsequently, we trained an object-detection model to identify lizards and image classification models to determine their microclimate usage (activity in sun or shade), which may indicate thermoregulation preferences. We then evaluated the performance of each model and analysed how the classification of thermoregulating lizards performed under different solar conditions (sun or shade), times of day and marking colours. Our framework's models achieved high scores in several performance metrics. The behavioural thermoregulation classification model performed significantly better on sun-basking lizards, achieving the highest classification accuracy with white-marked lizards. Moreover, the hours of activity and the microclimate choices (sun vs shade-seeking behaviour) of lizards, generated by our framework, are closely aligned with manually annotated data. Our study underscores the potential of AI in effectively tracking behavioural thermoregulation, offering a promising new direction for camera trap studies. This approach can potentially reduce the labour and time associated with ecological data collection and analysis and help gain a deeper understanding of species' thermal preferences and risks of climate change on species behaviour.

Deep‐learning‐powered data analysis in plankton ecology

ABSTRACTThe implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep‐learning‐based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

The bumpy road to conservation: Challenges and opportunities in updating the Galapagos zoning system

In 2014, the Galapagos National Park Directorate, sponsored by various international non-governmental organizations, initiated a comprehensive marine and terrestrial spatial planning process to improve the management effectiveness of Galapagos’ protected areas. This effort aimed to reconfigure management areas by expanding or redistributing no-take zones to protect Key Biodiversity Areas and ensure the conservation of at least 30% of terrestrial and marine ecosystems in the Galapagos. However, this process encountered significant challenges, notably the erosion of fishers' trust following the implementation of a "Marine Sanctuary". This top-down decision resulted in socio-political tension, compelling the Ecuadorian government to delay the implementation of the new marine zoning until there was conclusive scientific data on its socio-economic impact on local small-scale fishers’ livelihoods. This paper examines the achievements and shortcomings of the Galapagos zoning update process, highlighting the successful integration of scientific zoning criteria and the enhanced protection of critical biodiversity areas, together with the reduced support from fishers and legal disputes. To move forward, we recommended a bottom-up approach centered on rebuilding trust among the fishing community via innovative engagement strategies and the introduction of market incentives to promote sustainable practices. Assessing the long-term impact of no-take zones is essential for refining management approaches. Additionally, the application of cutting-edge technology within an adaptive co-management model is recommended to improve fisheries and ecological data gathering and analysis, facilitating proactive and well-informed management decisions. Our suggestions aim to reconcile conservation objectives with the needs and aspirations of the local communities in this iconic archipelago.

Volunteer researchers in modern science: analysing “Nature” publications

The article presents a scientometric analysis of publications on citizen science, i.e., conducted with the participation of non-professional scientists, posted on the portal of the journal Nature, in order to identify the main directions and thematic trends in the field of citizen science over the last 5 years. The article identifies the key thematic clusters to which the reviewed publications belong, and analyses the thematic trends in each of the presented clusters. The search was carried out in English among the publications posted on the portal in the period from 2019 to 2023. The result is presented using computer visualisation and a table of key topics of the reviewed publications; in the future it is planned to expand the study and build a thematic map and scheme of key trends both abroad and in Russia. The analysis shows that the key topics in which volunteer researchers have been involved over the last 5 years have been environmental protection and conservation, publications on biodiversity and climate change, and data analysis in ecology. The authors note a widespread and growing interest in finding scientific volunteers to conduct research, as well as the growing role of volunteer researchers. The participation of non-professional scientists encourages a shift towards interdisciplinary research and promotes the use of different research methodologies. There are trends in the transformation of the role of volunteer researchers from the status of ordinary field researchers to full-fledged participants in research work.

Dynamics of Perceived Social Isolation, Secondary Conditions, and Daily Activity Patterns Among Individuals With Stroke: A Network Analysis of Ecological Momentary Assessment Data

ObjectiveTo assess contemporaneous and temporal dynamics of perceived social isolation (PSI), secondary conditions, and daily activity patterns in individuals post-stroke. DesignLongitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of an individual's lived experiences. We conducted dynamic network analyses to examine longitudinal associations among EMA variables. SettingsHome and Community. Participants202 individuals with mild-to-moderate chronic stroke (median age=60 years; 45% women; 44% black; 90% ischemic stroke; median NIHSS score=2; N=202). InterventionNot applicable. Main Outcome MeasuresEMA questions measured PSI, secondary conditions (pain, tiredness, stress, anxiety, worthlessness, difficulty concentrating, and cheerfulness), and daily activity patterns (being at home, being alone, and participating in productive activities). ResultsThe median EMA response rate was 84%. The contemporaneous model showed that PSI was associated with being home, alone, and all symptoms except pain. The temporal model revealed a pathway indicating that feelings of worthlessness predicted PSI (regression coefficient=0.06, P=.019), followed by stress (regression coefficient=0.06, P=.024), and then by being not at home (regression coefficient=-0.04, P=.013). ConclusionImplementing dynamic network analyses on EMA data can uncover dynamic connections among PSI, secondary conditions, and daily activity patterns after stroke. This study found a significant temporal association between PSI and negative emotions. Feeling isolated was followed by feeling stressed, which was followed by a tendency to be out of home, indicating adaptive behaviors in individuals with stroke. These findings highlight the importance of engaging in out-of-home or outdoor activities to mitigate PSI and negative emotions.

Nature-Based Management of Lawns—Enhancing Biodiversity in Urban Green Infrastructure

Urban green areas have multifunctional benefits that may mitigate unfavorable health and ecological effects. Green areas represent important biodiversity hideouts in anthropogenic regions. Lawns are the most common elements of urban greenery, covering a considerable number of green areas in cities. The aim of this analysis was to gain knowledge on urban greenery and elaborate recommendations related to management that favors grass lawn biodiversity. The established working hypothesis is that the limited intensity of management in urban grass areas is reflected in the modification of their species composition, as well as their potential ecological functions. An experiment on the management of city lawns was conducted in 2010 (still ongoing). There are three lawn variants under different management methods: ornamental, city, and permaculture lawns. Vegetation was assessed using the method of phytocoenological relevés. The coverage values of the individual plant species were processed using multidimensional analysis of ecological data. The results showed that human decisions and activities affected the species composition of these grassy areas. There were 46 plant taxa found during the monitoring: 12 in ornamental lawn variants, 24 in city lawn variants, and 31 in permaculture lawn variants. Permaculture lawns with extensive management represent the most environmentally friendly variant with respect to biodiversity and soil moisture content. However, changes in species composition have raised questions regarding the extent to which they may perform other ecosystem functions. Increasing the intensity of lawn management has resulted in lower plant diversity. Extensive management alters the aesthetic value of lawns and creates spaces for species that may spread in urban environments.

Mortality from major causes and lifestyles by proportions of public assistance recipients among 47 prefectures in Japan: Ecological panel data analysis from 1999 to 2016

ObjectiveComprehensive investigations of correlations between subnational socioeconomic factors and trends in mortality and lifestyle are important for addressing public health problems. MethodsForty-seven prefectures in Japan were divided into quartiles based on the proportion of public assistance recipients (PPAR). Age-standardized mortality from all causes, cancer, heart disease, and stroke in each prefecture were averaged for these quartiles in 2000, 2005, 2010, and 2015. Data from the National Health and Nutrition Survey were obtained for the following periods: 1999–2001, 2003–2005, 2007–2009, 2012, and 2016. Body mass index (BMI), intake of total energy, vegetable and salt, step count, and prevalence of current smoking and drinking for individuals aged 40–69 years age range were standardized for each prefecture and averaged by quartile. A two-way analysis of variance was used to assess differences in mortality and lifestyle across different years or periods, and quartiles. ResultsMortality rates decreased, with the first (lowest) quartile showing the lowest rates, across all causes, cancer and heart diseases in both sexes. BMI exhibited an increase in men, whereas, BMI in women and other lifestyle factors in both sexes, excluding smoking and drinking in women, exhibited a decrease. BMI, vegetable and salt intake, total energy intake in men, and smoking in women varied across quartiles. Lower quartiles exhibited lower BMI and smoking prevalence but higher energy, vegetables, and salt intake. ConclusionsPPAR exhibited favorable trends and significant differences in mortality related to all causes, cancer and heart disease across both sexes, along with BMI among women.

Designing of the software for atmospheric environment condition monitoring

In current article a process of development of an application for ecological data analysis was researched, particularly pollutants concentration measurements using wavelet transform apparat. To improve the ecological situation and preventing harmful ecological event it is needed to monitor the condition of environment. Ecological monitoring contains a complex system of observations of current environment conditions, observations’ results processing and analysis, creating ecological prognosis including natural and anthropogenic factors. Monitoring data serves as a main source of information for making ecologically significant decisions. Ecological data is stochastic and are extremely random, are known by extraordinary dissipation, directly or indirectly depend on numerous parameters. This data is hard to analyze with methods of classical mathematic, sometimes, when the randomness of the data is high, it could be even impossible. Pollutant’s concentration measurements data makes a time series. In current paper for the analysis of the time series wavelet transform tools were used, either the discrete version or the continuous one, based on those a mathematical model of the process was created. Object-oriented modelling of project’s architecture was created based on provided mathematical model, particularly, classes, components and activity diagrams were built. The application was developed as a cross-platform stand-alone app. After the analysis it was proved to be reasonable to write the application in Python programming language using libraries for mathematical and statistical functions, particularly NumPy, Math, Statistics, and additionally PyWv for wavelet usage assistance. Current application allows to process ongoing and historical data from multiple sources to analyze tendencies of changes in atmosphere condition

LMdist: Local Manifold distance accurately measures beta diversity in ecological gradients.

Differentiating ecosystems poses a complex, high-dimensional problem constrained by capturing relevant variation across species profiles. Researchers use pairwise distances and subsequent dimensionality reduction to highlight variation in a few dimensions. Despite popularity in analysis of ecological data, these low-dimensional visualizations can contain geometric abnormalities such as "arch" and "horseshoe" effects, potentially obscuring the impact of environmental gradients. These abnormalities appear in ordination but are in fact a product of oversaturated large pairwise distances. We present Local Manifold distance (LMdist), an unsupervised algorithm which adjusts pairwise beta diversity measures to better represent true ecological distances between samples. Beta diversity measures can have a bounded dynamic range in depicting long environmental gradients with high species turnover. Using a graph structure, LMdist projects pairwise distances onto a manifold and traverses the manifold surface to adjust pairwise distances at the upper end of the beta diversity measure's dynamic range. This allows for values beyond the range of the original measure. Not all datasets will have oversaturated pairwise distances, nor will capture variation that resembles a manifold, so LMdist adjusts only those pairwise values which may be undervalued in the presence of a sampled gradient. The adjusted distances serve as input for ordination and statistical testing. We demonstrate on real and simulated data that LMdist effectively recovers distances along known gradients and along complex manifolds such as the Swiss roll dataset. LMdist enables more powerful statistical tests for gradient effects and reveals variation orthogonal to the gradient. Available on GitHub at https://github.com/knights-lab/LMdist.

The Influence of Community Participation on Health Trends in the Mangrove Forest of Labuhan Maringgai District, East Lampung Regency

The health trend of mangrove forests is influenced by community participation around the forest. Communities have a role in managing mangrove forests. The purpose of this study was to determine the effect of community participation on the health trend of mangrove forests. The stages of this research are: Assessment of forest health based on ecological indicators and data analysis using the FHM method; collection of participation data with questionnaires to the public; and data analysis using the multiple linear regression method. Based on the results of the study, the final value of forest health at CL 1 was 4.11 (bad), CL 2 was 6.95 (good), CL 3 was 6.69 (good), and CL 4 was 7.4 (good). The average value for forest health is 6.29 in the moderate category. The significant values obtained are.023* (preparation),.321 (implementation), and.851 (evaluation). The planning aspect has an influence on the health condition of mangrove forests. Community participation affects the condition of forest health from an ecological perspective. So the role of the community is very important to support the preservation of mangrove forests.

The Deforestation and Biodiversity Risks of Power Plant Projects in Southeast Asia: A Big Data Spatial Analytical Framework

Ecosystem destruction and biodiversity loss are now widespread, extremely rapid, and among the top global anthropogenic risks both in terms of likelihood and overall impact. Thorough impact evaluation of these environmental abuses—essential for conservation and future project planning—requires good analysis of local ecological and environmental data in addition to social and economic impacts. We characterized the deforestation and biodiversity impacts of energy investments in Southeast Asia using multiple geospatial data sources related to forest cover and loss data from 2000 to 2018, other landcover data, and the location, type, and characteristics of energy investments. This study paid particular attention to different types of power plants and financing sources. We identified critical buffer zones and forest structures impacted by these projects in accordance with IUCN criteria and spatial ecology. The paper introduces a novel, replicable analytical framework that goes beyond earlier studies in which all forests are treated as equivalent. It characterizes forests based on spatial morphological structures such as core forest, edges, islands, and bridges, allowing for a more nuanced understanding of deforestation and its impacts on biodiversity. Preliminary findings suggest that projects financed by Chinese development banks pose different risks compared to non-Chinese financing. The study also reveals significant differences in biodiversity impacts based on the type of energy source, be it coal or hydro. The study offers critical insights into the trade-offs between energy development and biodiversity conservation. It provides actionable metrics and strategies for policymakers, conservationists, and development banks to prioritize forest and habitat preservation in Southeast Asia and globally.

Spatio-temporal variability in macroinvertebrate community structure and ecological health status of a tropical dynamic river-estuarine system, India: An integrated approach of multivariate analysis

River-estuarine ecosystems are under severe anthropogenic threat due to resource exploitation, transportation, sewage/industrial discharges, and pollutants from surrounding areas. Monitoring the water quality and biological communities is essential for assessing ecosystem health and sustainability. Present study integrated the ecological community data along with water quality analysis to understand the impact of anthropogenic pressures on benthic macroinvertebrates. Samples were collected from 10 locations (comprising of both rural and urban areas) for Benthic macroinvertebrates, physico-chemical and microbiological parameters along the lower stretch of the Bhagirathi-Hooghly river-estuarine (BHE) system during the post-monsoon seasons of 2020, 2021, and 2022. During the entire study period, a total of 5730 individuals from 54 families in 19 orders of 3 phylum of macroinvertebrate were recorded. Among them Thiaridae (27.1%) and Chironomidae (22.8%) were found to be the most abundant families. Based on the water quality data Cluster analysis and nMDS indicated two distinct groups of locations: Group-I with rural settings and Group-II with urban settings. Alpha diversity metrics showed higher diversity (2.817) and evenness (0.744) in rural locations (Group-I) compared to urban locations (Group-II). The overall saprobic score of the macroinvebrate data revealed Group-I (5.09) to be in good condition, while Group-II (4.95) showed moderately polluted conditions. Redundancy analysis (RDA) highlighted the correlation of pollution-tolerant species (Chironomidae, Culicidae) with high organic loads i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD) in Group-II. In contrast, Group-I locations exhibited positive correlations with Dissolved Oxygen (DO) and supported less pollution-tolerant organisms (Coenagrionidae, Dytiscidae). The study emphasizes the importance of integrated analysis of ecological community data and water quality parameters to assess the health status of river-estuarine ecosystems.

On the use of prior distributions in bayesian inference applied to Ecology: an ecological example using binomial proportions in exotic plants, Central Chile

BackgroundThe use of Bayesian inference (BI) is a common methodology for data analysis in Ecology and Evolution. This statistical approach is particularly useful in cases which information is scarce, because allows formalizing sources of information, other than sampling data (priors), obtained from technical reports, expert opinions and beliefs. Recent reviews detected that most ecological studies use non-informative priors without any justification, ignoring other sources of independent information available to construct informative priors. In this study, we examined how the selection of informative or non-informative priors, affects hypothesis testing. We compared the proportion of occupied sites (occupancy) in four exotic plant species living in two contrasting environments in Central Chile. Given that occupancy is related to binomial proportions, we developed a statistical procedure based on beta distribution, to compare occupancies using Bayes factor.ResultsBayes factor obtained from different non-informative priors led to similar inferences relative to H0. The use of informative prior drastically changed our decisions about H0 in three of four plant species.ConclusionsThe selection of priors is critical because they determine hypothesis testing. The use of independent information will improve our inferences, which is precisely the strength of BI. We hypothesize that the reluctance to use informative priors in ecological studies reflects extreme positivism and the use of non-informative priors is a strategy to avoid subjectivity; by doing that, ecologists depart from the philosophy of BI which accepts that the subjective knowledge is a valid, and sometimes the only alternative, to know the world.

Trends in mortality from major causes and lifestyle factors by per capita prefectural income: Ecological panel data analysis from 1995 to 2016 in Japan

In Japan, trends in mortality and lifestyle have not been fully investigated according to subnational socioeconomic factors. Forty-seven prefectures (subnational units) were divided into quartiles by annual per capita prefectural income. Age-standardized mortality from all causes, cancer, heart disease, and stroke was averaged by quartile in 1995, 2000, 2005, 2010, and 2015. Data from the National Health and Nutrition Survey were obtained for periods 1 (1995–1997), 2 (1999–2001), 3 (2003–2005), 4 (2007–2009), 5 (2012), and 6 (2016). Body mass index (BMI), the intake of vegetables and salt, the number of steps, and the prevalence of current smoking and drinking for the 40–69-year age range were standardized by 10-year age groups in the 2010 Japanese population and were averaged by quartile. Differences in mortality and lifestyle by year and period, and quartile were tested using a two-way analysis of variance. Mortality decreased in both sexes and mortality in men from all causes, cancer, and stroke differed by quartile, with mortality highest in the first (lowest) quartile. BMI in men and smoking prevalence in women increased, whereas remaining lifestyle factors except for the prevalence of drinking decreased in women. BMI and the number of steps in both sexes and current smoking prevalence in women differed by quartile, with lower quartiles showing a higher BMI and fewer step counts. In conclusion, favorable trends and significant differences in mortality from all causes, cancer, and stroke in men and BMI in women were observed by per capita prefectural income level.

The EAT-Lancet Commission's Planetary Health Diet Compared With the Institute for Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis.

Background This article aimed to compare the EAT-Lancet Commission's "Planetary Health Diet" (PHD) with the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease Study 1990-2017 (GBD2017) dietary and other risk factor data. In the PHD/GBD comparison, we also intended to show the relevance of a new multiple regression analysis methodology with dietary and non-dietary risk factors (independent variables) for noncommunicable disease (NCD) deaths/100000/year in males and females 15-69 years old from 1990 to 2017 (NCDs, dependent variable). Methods We formatted worldwide GBD2017 dietary risk factors and NCDdata on 1120 worldwide cohorts to obtain 7846 population-weighted cohorts. Each cohort represented about one million people, totaling about 7.8 billion people from 195 countries. With an empirically derived methodology, we compared the PHD animal- and plant-sourced food recommended ranges (kilocalories/day=KC/d) with optimal dietary ranges (KC/d) from GBD cohort data. Using GBD data subsets with low and high animal food consumption cohorts, our new GBD multiple regression formula derivation methodology equated risk factor formula coefficients to their population-attributable risk percents (PAR%s). Results We contrasted PHD recommendations for the available 14 dietary risk factors (KC/dmeans and ranges) with our GBD analysis methodology's optimal ranges for each dietary variable (KC/dmean and range): PHD beef, lamb, and pork mean: 30 KC/d(range: 0-60 KC/d)/GBD processed meat: 8.86 (1.69-16.03)+GBD red meat: 44.52 (20.37-68.68), PHD fish: 40 (0-143)/GBD: 19.68 (3.45-35.90), PHD whole milk or equivalents: 153 (0-306)/GBD: 40.00 (18.89-61.11), PHD poultry: 62 (0-124)/GBD: 56.10 (24.13-88.07), PHD eggs: 19 (0-37)/GBD: 19.42 (9.99-28.86), PHD: saturated oils 96 (0-96)/GBD added saturated fatty acids (SFA): 116.55 (104.04-129.07), PHD all added sugars: 120 (0-120)/GBD sugary beverages: 286.37 (256.99-315.76), PHD tubers or starchy vegetables: 39 (0-78)/GBD potatoes: 84.16 (75.75-92.58)+GBD sweet potatoes: 9.21 (4.05-14.37), PHD fruits: 126 (63-189)/GBD: 63.03 (21.61-113.71), PHD vegetables: 78.32 (9.48-196.14)/GBD: 85.05 (66.75-103.36), PHD nuts: 291 (0-437)/GBD nuts and seeds: 10.97 (5.95-15.98), PHD whole grains: 811 (811/811)/GBD: 56.14 (50.53-61.76), PHD legumes: 284 (0-379)/GBD: 59.93 (45.43-74.43), and total animal foodPHD: (0/400)/GBD: 329.84 (212.49-447.19). Multiple regression low and high animal foodsubsets' (animal foods mean=147.09 KC/d versus animal foods mean=482.00 KC/d) formulas each with 28dietary and non-dietary risk factors (independent variables) accounted for 52.53% and 28.83% of their respective total formula PAR%s with NCDs (dependent variable). Conclusions GBD data modeling supported many but not all the PHD dietary recommendations. GBD data suggested that the amount of consumption of animal foods was the dominant determinate of NCDs of countries globally. Adding to the univariate associations, multiple regression risk factor formulas with risk factor coefficients equated to their PAR%s further elucidated dietary influences on NCDs. This paper and the soon-to-be-released IHME GBD2021 (1990-2021) data should help inform the EAT-Lancet 2.0 Commission's work.

Machine learning and deep learning—A review for ecologists

Abstract The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, failing to synthesise the wealth of ML algorithms with different advantages and general principles. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. We conclude that ML and DL are powerful new tools for predictive modelling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data‐dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in ecology and evolution, comparable to other traditional statistical tools.