Analysis Of Ecological Data Research Articles

Construction of Ecological Environment Information System Based on Big Data: A Case Study on Dongting Lake Ecological Area

With the rapid development of cloud computing, Internet of Things, and other technologies, the information technology trend led by “big data” has an impact on all fields. The application of big data technology in the field of ecological environmental protection enables accurate and comprehensive ecological information collection, data analysis, and mining, accurate ecological problem identification, and effective solution. Taking Dongting Lake Ecological Area as an example, this paper constructs an ecological environment information system based on big data and expounds its specific application in water, atmosphere, soil environment monitoring, and pollution control, aiming to provide a reference for the application of big data technology in the field of ecological environment protection in Dongting Lake Ecological Area and more effectively maintain the ecological environmental quality and safety in the area.

Measuring Primary Care Across 35 OECD Countries.

To examine the psychometric properties and scores of the Person-Centered Primary Care Measure (PCPCM) in 28 languages and 35 Organisation for Economic Co-operation and Development (OECD) countries. Using a paid online sampling service, we requested age- and sex-representative samples of 360 adults in each country. We administered the Person-Centered Primary Care Measure-a previously validated 11-item, patient-reported measure that was developed using what patients and clinicians said is most important about primary care. We also assessed construct validity through associations with demographics, the Patient-Enablement Instrument, number of years the person had been with their primary care physician and practice, whether the patient thought the doctor knowing the results would improve their care, and whether it was hard to complete the survey. We assessed the psychometric properties of the PCPCM in each country and report the summative and item-specific PCPCM scores for each country. The PCPCM exhibited solid psychometric properties across all languages and countries, with Cronbach's alphas ranging from 0.88 to 0.95, and corrected item-total correlations ranging from 0.47 to 0.81, with the vast majority of countries ranging from the low 0.50s to the high 0.70s. Multiple analyses showed strong evidence of concurrent validity. With a potential range from a low of 1 to a high of 4, the overall mean score was 2.74, with a standard deviation of 0.19. Mean PCPCM scores ranged from the lowest in Sweden (2.28) to the highest in Turkey (3.08), with Germany ranking second (3.01), and the United States third (2.99). The internal consistency and concurrent validity of the PCPCM across multiple countries provides strong evidence of the coherence of the breadth of primary care functions that patients and clinicians say are important. The diversity of total and item-specific scores across countries provokes interesting hypotheses about the influence of each different country's policies, practices, demographics, and culture on primary care, and provides a strong impetus for further ecological and individual data analyses using the Person-Centered Primary Care Measure. Annals "Online First" article.

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A comparative investigation of the anatomical characters through a microscopical examination of the prepared transverse sections of the stem was carried out. Six plates with 32 photomicrographs were provided to convincingly show the considerable variations of anatomical characters within the nine examined species. The matrix of 18 anatomical characters which included nine quantitative and nine qualitative was applied for the clustering analysis (CA) followed by the principal component analysis (PCA) using the Multivariate Analysis of Ecological Data, PC-ORD. The results exhibited significant variations among the species resulting in the construction of an artificial key; this key accurately represents a sufficient tool to display the considerable variation among the recognized species prominently. The distinction between Terminalia L., 1767 species based on significant variations in the elements of stem anatomy; axial parenchyma and ray characteristics were considered as important parameters, while vessel diameter, fiber wall thickness, etc. were considered minor characters to differentiate between the studied species. The potential usefulness of the differentiation of these species properly maintains a profound efficiency in pharmaceutical and traditional medicine.

Robust Two-Step Wavelet-Based Inference for Time Series Models

Latent time series models such as (the independent sum of) ARMA(p, q) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics. Inference on and/or prediction from these models can be highly challenging: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when the time series are extremely large; (iii) model selection adds another layer of (computational) complexity; and (iv) solutions that address (i), (ii), and (iii) simultaneously do not exist in practice. This paper aims at jointly addressing these challenges by proposing a general framework for robust two-step estimation based on a bounded influence M-estimator of the wavelet variance. We first develop the conditions for the joint asymptotic normality of the latter estimator thereby providing the necessary tools to perform (direct) inference for scale-based analysis of signals. Taking advantage of the model-independent weights of this first-step estimator, we then develop the asymptotic properties of two-step robust estimators using the framework of the generalized method of wavelet moments (GMWM). Simulation studies illustrate the good finite sample performance of the robust GMWM estimator and applied examples highlight the practical relevance of the proposed approach.

A new granite cave-dwelling Bent-toed Gecko from Vietnam of the Cyrtodactylus irregularis group (Squamata; Gekkonidae) and a discussion on cave ecomorphology

An integrative analysis of genetic, morphological, and ecological data recovered a new granite cave-adapted species, Cyrtodactylus raglaisp. nov., from the Song Giang River Valley, Khanh Hoa Province, Vietnam. Cyrtodactylus raglaisp. nov. is nested within one of two major clades within the irregularis species group where it forms a monophyletic group with C. cryptus and its sister species C. kingsadai. It differs from its sister species by an uncorrected pairwise sequence divergence of 16.5% and 16.8% based on the mitochondrial genes NADH dehydrogenase subunit 2 and its flanking tRNAs and the first subunit of cytochrome oxidase c (CO1), respectively. Cyrtodactylus raglaisp. nov. is a narrow-range endemic restricted to a riparian, granite cave microhabitat and its overall morphology bears that of other granite cave-dwelling ecomorphs in the genus. The Song Giang River Valley and its associated waterways are currently threatened by the construction of a hydropower station which will negatively impact the surrounding ecosystem. Urgent field surveys in this region are necessary in order to obtain critical data on its biodiversity and importance to conservation efforts in southern Vietnam.

Annotated Video Footage for Automated Identification and Counting of Fish in Unconstrained Seagrass Habitats

Technological advances are improving the collection, processing and analysis of ecological data. One of these technologies that has been adopted in recent studies by ecologists is computer vision (CV). CV is a rapidly developing area of machine learning that aims to infer image content at the same level humans can by extracting information from pixels (LeCun et al., 2015; Weinstein, 2018). CV in ecology has gained much attention as it can quickly and accurately process image from remote video imagery while allowing scientists to monitor both individuals and populations at unprecedented spatial and temporal scales. Automated analysis of imagery through CV has also become more accurate and streamlined with the implementation of deep learning (a subset of machine learning) models that have improved the capacity to processes raw images compared to traditional machine learning methods (LeCun et al., 2015; Villon et al., 2016). As the use of camera systems for monitoring fish abundances is common practice in conservation ecology (Gilby et al., 2017; Whitmarsh et al., 2017; Langlois et al., 2020), deep learning allows for the automated processing of big data from video or images, a step which usually creates a bottleneck when these data must be analyzed manually.

Using the right tool for the job: the difference between unsupervised and supervised analyses of multivariate ecological data.

Ecologists often collect data with the aim of determining which of many variables are associated with a particular cause or consequence. Unsupervised analyses (e.g. principal components analysis, PCA) summarize variation in the data, without regard to the response. Supervised analyses (e.g., partial least squares, PLS) evaluate the variables to find the combination that best explain a causal relationship. These approaches are not interchangeable, especially when the variables most responsible for a causal relationship are not the greatest source of overall variation in the data-a situation that ecologists are likely to encounter. To illustrate the differences between unsupervised and supervised techniques, we analyze a published dataset using both PCA and PLS and compare the questions and answers associated with each method. We also use simulated datasets representing situations that further illustrate differences between unsupervised and supervised analyses. For simulated data with many correlated variables that were unrelated to the response, PLS was better than PCA at identifying which variables were associated with the response. There are many applications for both unsupervised and supervised approaches in ecology. However, PCA is currently overused, at least in part because supervised approaches, such as PLS, are less familiar.

Spatial distribution, sampling efficiency and Taylor's power law. 2. Interpreting density‐dependent sampling efficiency

AbstractThe intimate relationship between sampling efficiency and Taylor's power law (TPL) was investigated with gypsy moth sample data. The data were used to compute sampling efficiency directly and indirectly by TPL. Comparison of TPLs and efficiency plots of male and female pupae confirmed the identities linking TPL with sampling efficiency. Divergence of sex‐specific TPL plots indicated local scale density‐dependent sex ratio. Egg mass sample data confirmed the sampling efficiency and TPL identities provided the same variance and mean vectors were used to compute TPLs. Small differences in sample numbers destroy the identities but approximate efficiency estimates are still obtainable from the TPLs. Sampling efficiency of timed walks, fixed area and variable area surveys were estimated and ranked. Rescaling moth catches per trap to number per unit volume changes slope, intercept and correlation coefficient while stretching the pattern of data points. Comparison of absolute density estimates over two different time intervals showed density‐dependent variation declining with increasing sample interval. Fitting power laws by ordinary dependent regression is less efficient than fitting by geometric mean regression and produces biased regression parameters. The significance of this for the analysis and interpretation of ecological sample data generally is discussed.

A brief survey of ecological data analysis techniques

It is emphasize in the paper, that because of qualitative change of ecological data and its complexity the analysis and estimation of population parameter is a complex process. Therefore, for ecological data collection, the sampling frame clearly defined distinct and identifiable. More often, sampling frames are imperfect, further different techniques measure of species diversity, evenness, community similarity, dominance indices has been given which can be useful for research scholars.

Strict allopatric speciation of sky island Pyrrhula erythaca species complex

Increasing evidence of post-divergence gene flow between taxa is shifting our understanding on the mode of speciation. A fundamental question arises concerning the circumstances under which strict allopatric speciation still holds true. Sky island populations might undergo reduced gene flow by niche conservatism to highland habitats and follow divergence in an allopatric manner. In this study, we tested this hypothesis in the sky island Grey-headed Bullfinch (Pyrrhula erythaca) species complex via statistical analyses of both genetic and ecological data. Results of coalescent-based analysis of multiple nuclear loci suggested that P. e. owstoni likely colonized Taiwan island during the severe mid-Pleistocene glacial climate followed by strictly allopatric divergence from P. e. erythaca distributed in Himalayas-Hengduan mountains and central North China. Results of ecological niche modeling suggested that their speciation may be attributed to the niche conservatism of these birds and the lack of a suitable ecological corridor during subsequent milder glacial episodes. In addition, we delimited the traditionally defined P. erythaca into two full species, P. erythaca in the Asian mainland and P. owstoni on the island of Taiwan, based on both genetic and behavioural evidences. These results suggest that ecology can have a dynamic role in allowing highland populations to expand their ranges and isolated by habitat barriers to diversify in a strictly allopatric manner.

Using artificial intelligence classification of videos to examine the environmental, evolutionary and physiological constraints on provisioning behavior

The use of artificial intelligence (AI) technologies can revolutionize how we approach data collection and analysis in behavioral ecology. One such example is in provisioning behavior. Parents of altricial species are selected to provide parental care (such as food provisioning) for their offspring, but there is substantial variation in the level of this care. Provisioning rate may be determined environmentally, by the physiological ability of parents and needs of nestlings, or by evolutionary incentives. We quantified provisioning rate in 20 purple martinProgne subisnests in the context of an experimental reduction of nest ectoparasites. 10 nests had a parasite reduction treatment, and 10 nests were controls. By using AI to automate the analysis of nest camera videos we were able to obtain nearly‐continuous provisioning rate information at a high temporal resolution for the first half of the nestling period. We used random forest modeling to assess the factors determining provisioning rate and found evidence for environmental, evolutionary and physiological constraints and incentives on provisioning. Birds appeared to be environmentally limited in their provisioning in cool, wet conditions, especially later in the breeding season; but adjusted their provisioning according to the changing physiological needs of nestlings. We found evidence for a compensatory response to increased parasite load, in which parents increased provisioning to more heavily parasitized nests.

Identifying and testing conservation decision thresholds in temperate montane grasslands

Conservation decision thresholds have most often been described within the context of triggering management actions necessary to maintain an ecological system within a desired state. Thresholds are also applied within conservation legislation, often as triggers to determine when certain regulations may or may not apply. In many applications, thresholds have been criticized for being subjective, inconsistent and open to bias and rather than being derived from sound and objective analysis of ecological data they can result from undocumented judgement and opinion. Although judgement and opinion may be necessary for setting decision thresholds in circumstances where appropriate data are lacking, a failure to use structured processes for obtaining, analysing and presenting these can result in contentious, unsubstantiated thresholds. This paper demonstrates how thresholds for identifying grassland conservation values, for the purposes of decision making under vegetation clearing legislation, can be estimated from simple floristic indicators after combining the judgment of multiple experts through hierarchical modelling. Using two stages of elicitation and modelling, the resulting conservation value states were found to be consistent and predictable, correlated with known paddock-level disturbance history and congruent with prior condition states that define an endangered grassland ecosystem. While the thresholds, and the underpinning coefficients, may not reflect the opinion of all grassland ecologists, they are transparent, testable and can be updated or modified to incorporate additional criteria and a greater diversity of stakeholders. The approach could be adopted to similar contexts in other vegetation types where policy or regulatory decision triggers are required.

A spatial community regression approach to exploratory analysis of ecological data

Abstract Ecological datasets tabulating hundreds or thousands of species are now becoming available. Identifying and understanding the underlying clustering or communities of species which comprise these datasets is difficult with existing joint species distribution models as they usually model dependence on covariates or spatial effects at the species level rather than at the community level. This study describes a novel Bayesian statistical model incorporating multiple model components from existing work designed to represent the communities of species and the dependence of each community on covariates. This model is also extended to produce per‐community maps of spatial residuals designed to help identify useful missing covariates. We assess this model via a simulation study and it is applied to analyse a subset of the Florabank1 plant species database with approximately 2,000 sampling units across 900 species. We present a streamlined workflow for interpreting clusters of co‐occurring species along with their salient environmental and spatial characteristics.

Dynamic Monitoring of RS and GIS Resources and Ecological Environment Based on High Temperature Materials

The dynamic monitoring and analysis of regional ecological environment is an important part of land use research, which can provide support for regional ecological environment and macro regulation of government departments. With the support of remote sensing technology (RS) and geographic information system (GIS), a land use dynamic monitoring system is established to obtain land use change information quickly, accurately and economically. In order to reduce the influence of thermal radiation, noise and vibration of high temperature material machinery on the measurement results in high temperature measurement environment, the paper proposed the measurement method of ultraviolet light high humidity thermal deformation and gray average method. In this paper, the algorithm of digital image correlation method and the establishment of high temperature measurement system are studied. A set of high temperature material mechanical digital image correlation system suitable for high humidity environment measurement is independently established and applied to the high humidity test of materials. The results show that the above comprehensive method can effectively improve the influence of thermal flow disturbance on system imaging in high humidity environment. Comprehensive analysis of regional ecological environment data shows that environmental quality is closely related to regional ecological environment. Meteorological conditions are one of the important factors affecting the environment, and the project of returning farmland to forest plays a vital role in improving the environment.

Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet-multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM-Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

Higher Density of Primary Care Facilities Is Inversely Associated with Ischemic Heart Disease Mortality, but Not with Stroke Mortality: A Japanese Secondary Medical Service Area Level Ecological Count Data.

Poor accessibility to physicians might be linked to the inadequate control of cardiovascular risk factors. The aim of this study was to investigate whether the accessibility of primary care physicians was related to a lower incidence of ischemic heart disease and stroke mortality via ecological data analyses of both primary care facility density and internal physician density. The unit of observation was the Japanese secondary medical service area, the basic unit for healthcare planning and administration. A primary care facility was defined as a clinic or medical institution with less than 200 inpatient beds, whose specialty included internal medicine. The number of primary care facilities per 10,000 population and the number of internal physicians per 10,000 population were used as explanatory variables. Bayesian hierarchical models were used to analyze the relative risks (RR) of primary care facility density and internal physician density using the socioeconomic confounders of designated emergency hospitals, natural log-transformed population density, birth rate, secondary and tertiary industrial workers, and taxable income. In multivariate models for ischemic heart disease mortality, primary care facility density was significantly related to the total population (RR = 0.986, 95% credible interval [CrI]: 0.979-0.993), men (RR = 0.988, 95% CrI: 0.981-0.996), and women (RR = 0.986, 95% CrI: 0.979-0.993). No significant results were obtained for internal physician density. In the multivariate models for stroke mortality, neither primary care facility density nor internal physician density showed any significant effects. Increasing primary care facility density may reduce ischemic heart disease mortality.

Ecological inference for infectious disease data, with application to vaccination strategies.

Disease surveillance systems provide a rich source of data regarding infectious diseases, aggregated across geographical regions. The analysis of such ecological data is fraught with difficulties, and, unless care and suitable data summaries are available, will lead to biased estimates of individual-level parameters. We consider using surveillance data to study the impacts of vaccination. To catalog the problems of ecological inference, we start with an individual-level model, which contains familiar parameters, and derive an ecologically consistent model for infectious diseases in partially vaccinated populations. We compare with other popular model classes and highlight deficiencies. We explore the properties of the new model through simulation and demonstrate that, under standard assumptions, the ecological model provides less biased estimates. We then fit the new model to data collected on measles outbreaks in Germany from 2005-2007.

Tools and a web server for data analysis and presentation in microbial ecology

The methods for data presentation are important in bioinformatics as data processing algorithms. The article describes the software package for the extensive analysis of tables with estimates of bacterial abundance levels in environmental samples. The package was designed to be executed in a distributed hardware environment, with powerful packages in Python in the backend and interactive front-end forms. Most of microbial ecology-specific functionality is implemented by the scikit-bio Python package, together with the other Python packages intended for big data analysis. Interactive visualisation tools are implemented by the D3.js software library, therefore, the software project is named D3b. The package is a suite of tools for the analysis of microbial ecology data implemented as a web-service and as a desktop application. It supports a substantial part of the graphical and analytical descriptions of microbial communities used in scientific publications. Source codes are available at github (sferanchuk/d3b_charts) and the on-line version of the system is accessible at d3b-charts.bri-shur.com.

The time-varying effect of alcohol use on cigarette smoking relapse risk

BackgroundAlcohol consumption promotes lapses to smoking among smokers trying to quit, perhaps particularly among smokers with lower dependence. We assessed how the role of alcohol in lapses varies over time. MethodsThis is a secondary analysis of ecological momentary assessment data collected from 159 daily smokers (mean age = 43.90 (SD = 10.41), 56.60% female) who drink alcohol. During the 4 weeks following initial cessation (quit >24 h), a logistic time-varying effect model (TVEM) modeled momentary assessments of lapses and temptations to smoke compared to randomly-selected moments as a function of concurrently-assessed recent alcohol use (past 15 min). Time was examined continuously. ResultsRecent alcohol use was associated with smoking lapses, particularly for less nicotine dependent individuals, and the association varied across time. For individuals who did not smoke within 5 min of waking, alcohol use became a significant predictor of lapse on Day 1 post-quit, increased in strength until Day 7, then decreased such that alcohol use was no longer associated with lapse by Day 25. For this subgroup, the associations between alcohol use and temptations were relatively stable and significant from Day 1 to 22 post-quit. Results were similar when dependence was assessed by the Nicotine Dependence Symptom Scale. ConclusionsThe association between drinking and smoking lapse and temptations varies over time, peaking early in smoking abstinence and declining thereafter. This could reflect progressive relapse of most vulnerable individuals or habituation to alcohol as a smoking cue. Interventions to prevent alcohol-related lapses are essential early in the quit period.

The effect of three soil tillage treatments on weed infestation in forage maize

This study assessed the impact of using soil tillage in maize crops on weed infestation intensity and weed species composition. A field experiment was established as a model example of livestock production management in drier climate conditions where maize was grown in seven-step crop rotation sequence: alfalfa – the first year, alfalfa – the second year, winter wheat, forage maize, winter wheat, sugar beet, and spring barley. Three soil tillage treatments were applied: conventional tillage (CT), minimum tillage (MT), and no-tillage (NT). An arithmetic method and multivariate analyses of ecological data were used. The highest weed infestation, mainly due to late spring species, was recorded in MT. Perennial and overwintering species were frequently observed in NT. Early spring weed species were abundant in CT. Different tillage treatments cause a significant change in the weed species spectrum in maize. A study of the relationship between tillage and the level of weed infestation requires long-term monitoring which will allow us to predict the intensity of weed infestation in particular locations.