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Environmental and Ecological Statistics
Journal Prestige (SJR): 0.594
Citation Impact (citeScore): 1
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-3009 - ISSN (Online) 1352-8505
Published by Springer-Verlag Homepage  [2469 journals]
  • Testing environmental effects on taxonomic composition with canonical
           correspondence analysis: alternative permutation tests are not equal

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      Abstract: Abstract After applying canonical correspondence analysis to metagenomics data with hugely different library sizes (site totals) it became evident that Canoco and the R-packages ade4 and vegan can yield (at least up to 2022) very different P-values in statistical tests of the relationship between taxonomic composition (species composition) and predictors (environmental variables and/or treatments). The reason is that vegan and Canoco up to version 5.12 apply residualized response permutation (but ignore the model intercept), whereas ade4 applies predictor permutation. Predictor permutation, when extended to residualized predictor permutation, is applicable in partial constrained ordination. This paper shows by simulation that residualized response permutation can yield a very inflated Type I error rate, if the abundance data are both overdispersed and highly variable in site total. In contrast, residualized predictor permutation controlled the type I error rate and had good power, also when the predictors were skewed or binary. After square-root or log transformation of the abundance data, the differences between the permutation methods became small. Residualized predictor permutation is recommended, particularly in testing trait–environment relationships using double constrained correspondence analysis, because this method also critically depends on the species totals, which are generally highly variable. It is implemented in Canoco 5.15 and the R-code of this paper.
      PubDate: 2022-09-13
       
  • Some efficient closed-form estimators of the parameters of the generalized
           Pareto distribution

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      Abstract: Abstract In this paper, we consider several families of closed-form estimators of the two parameters of the Generalized Pareto Distribution (GPD). These estimators are easy to compute and have high efficiency when compared to previously proposed methods. We also consider some estimators which are not of closed-form. All methods are based on certain order statistics. The proposed procedures are best for extreme values of the shape parameters and sample sizes of 100 or larger. Monte Carlo simulations are conducted to investigate the performance of the proposed parameter estimation procedures. Our findings suggest that the proposed estimation methods are competitive compared to the existing methods. We provide a real data application to illustrate the utilization of the proposed methods in estimating the GPD parameters.
      PubDate: 2022-09-12
       
  • Assessing the effects of multivariate functional outlier identification
           and sample robustification on identifying critical PM2.5 air pollution
           episodes in MedellĂ­n, Colombia

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      Abstract: Abstract Identification of critical episodes of environmental pollution, both as a outlier identification problem and as a classification problem, is a usual application of multivariate functional data analysis. This article addresses the effects of robustifying multivariate functional samples on the identification of critical pollution episodes in Medellín, Colombia. To do so, it compares 18 depth-based outlier identification methods and highlights the best options in terms of precision through simulation. It then applies the two methods with the best performance to robustify a real dataset of air pollution (PM2.5 concentration) in the Metropolitan Area of Medellín, Colombia and compares the effects of robustifying the samples on the accuracy of supervised classification through the multivariate functional DD-classifier. Our results show that 10 out of 20 methods revised perform better in at least one kind outliers. Nevertheless, no clear positive effects of robustification were identified with the real dataset.
      PubDate: 2022-09-07
       
  • Fast estimation and choice of confidence interval methods for step
           regression

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      Abstract: Abstract In this paper we propose a new fast grid search algorithm for finding the least square estimators of a step regression model. This algorithm makes it practical to compute resampling-based confidence intervals for step regression models. We introduce five data generating models, including one where the mean model is a step model (model correctly specified) and four where the mean models are not step models (model misspecified), and use them to study the coverage probabilities of two new types of resampling-based confidence intervals for step regression: symmetric percentile bootstrap confidence intervals and subsampling confidence intervals using a new set of rules-of-thumb to select block size. Our results show that when the model is correctly specified, the symmetric percentile Efron bootstrap confidence intervals provide close-to-nominal coverage and have shorter intervals than the subsampling methods; when the model is misspecified, the subsampling method using the rules-of-thumb provides good coverage and shorter confidence intervals than the symmetric percentile Efron bootstrap method and the subsampling method using a double bootstrap-like procedure for block size selection. Finally, we apply the proposed methods to a real world environmental dataset on the relationship between grassland productivity, soil moisture anomalies and other hydro-climatic and land use variables to provide inference for the threshold in soil moisture anomalies, across which there is a jump in grassland productivity.
      PubDate: 2022-09-06
       
  • Bayesian multi-species N-mixture models for unmarked animal communities

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      Abstract: Abstract We propose an extension of the N-mixture model that enables the estimation of abundances of multiple species as well as the correlations between them. Our novel multi-species N-mixture model (MNM) is the first to address the estimation of both positive and negative inter-species correlations, which allows us to assess the influence of the abundance of one species on another. We provide extensions that permit the analysis of data with excess of zero counts, and relax the assumption that populations are closed through the incorporation of an autoregressive term in the abundance. Our approach provides a method of quantifying the strength of association between species’ population sizes and is of practical use to population and conservation ecologists. We evaluate the performance of the proposed models through simulation experiments in order to examine the accuracy of both model estimates and coverage rates. The results show that the MNM models produce accurate estimates of abundance, inter-species correlations and detection probabilities at a range of sample sizes. The MNM models are applied to avian point data collected as part of the North American Breeding Bird Survey between 2010 and 2019. The results reveal an increase in Bald Eagle abundance in south-eastern Alaska in the decade examined.
      PubDate: 2022-09-05
       
  • Fiducial inference on gamma distributions: two-sample problems with
           multiple detection limits

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      Abstract: Abstract The problems of finding confidence limits for the difference between two gamma means and the difference between two upper percentiles based on samples with multiple detection limits are considered. Simple methods for constructing confidence intervals and upper tolerance limits are developed based on cube root transformation and fiducial inferences. The performances of the proposed methods are evaluated by Monte Carlo simulations and are compared with parametric bootstrap and the method of variance estimates recovery. Computational results indicate that the proposed methods provide more satisfactory results even for small samples with high proportion of nondetects. The approaches are illustrated with some practical datasets.
      PubDate: 2022-09-01
       
  • Correction: Nonparametric conditional density estimation in a deep
           learning framework for short-term forecasting

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      PubDate: 2022-08-26
       
  • Exploring land use determinants in Italian municipalities: comparison of
           spatial econometric models

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      Abstract: Abstract This study sets up a spatial econometric framework to explore the factors that best describe land consumption in Italy at the municipal level. By modelling the different types of spatial interactions and geographical proximity between all Italian municipalities, the direct effects of land use drivers are assessed together with spillover effects. Land use data are drawn from the ISPRA-SNPA 82/18 Report and cover all 7,998 Italian municipalities. The results highlight the existence of endogenous and exogenous interaction effects and the crucial role of the demographic, socio-economic and institutional structure on land use intensity. Hence the need for a planning policy aimed at: i) strengthening institutional cooperation to deal with excessive administrative fragmentation; ii) improving institutional and governmental quality to trigger virtuous mechanisms for sustainable land use management.
      PubDate: 2022-07-17
      DOI: 10.1007/s10651-022-00541-8
       
  • Evaluation of ecological security for the Association of Southeast Asian
           Nations-5 countries: new evidence from the RALS unit root test

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      Abstract: Abstract The present study seeks to determine the convergence of the ecological footprint pressure index for the Association of Southeast Asian Nations (ASEAN-5) countries over the period of 1961–2017. For this purpose, traditional unit root tests in conjunction with residual augmented least squares (RALS) type unit root tests have been applied to examine the convergence of all countries under investigation. RALS type tests were chosen due to showing a significantly improved power over conventional tests that do not use information on non-normal errors. The traditional unit root results do not show support for the ecological footprint pressure index convergence of the highlighted ASEAN-5 blocs. However, the RALS type and nonlinear unit root tests results reveal that the ecological footprint pressure index became convergent. Thus, governments and policymakers need to adopt stricter policies to protect the environment. These results have a more far-reaching effect on energy and environmental security for the study region.
      PubDate: 2022-07-14
      DOI: 10.1007/s10651-022-00540-9
       
  • Distribution-free changepoint detection tests based on the breaking of
           records

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      Abstract: Abstract The analysis of record-breaking events is of interest in fields such as climatology, hydrology or anthropology. In connection with the record occurrence, we propose three distribution-free statistics for the changepoint detection problem. They are CUSUM-type statistics based on the upper and/or lower record indicators observed in a series. Using a version of the functional central limit theorem, we show that the CUSUM-type statistics are asymptotically Kolmogorov distributed. The main results under the null hypothesis are based on series of independent and identically distributed random variables, but a statistic to deal with series with seasonal component and serial correlation is also proposed. A Monte Carlo study of size, power and changepoint estimate has been performed. Finally, the methods are illustrated by analyzing the time series of temperatures at Madrid, Spain. The R package RecordTest publicly available on CRAN implements the proposed methods.
      PubDate: 2022-07-06
      DOI: 10.1007/s10651-022-00539-2
       
  • Extending null scenarios with Faddy distributions in a probabilistic
           randomization protocol for presence-absence data

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      Abstract: Abstract Navarro and Manly (Popul Ecol 51:505–512, 2009) (NM) have proposed a randomization protocol for null model analysis of species occurrences at discrete locations based on probability distributions and generalized linear models. In the NM method, presences-absences are governed by independent Bernoulli random variables. In addition, a non-observable non-negative random variable (“quasi-abundance”) from either Poisson, Binomial or Negative Binomial distributions are log-linearly related to the qualitative effects of species and location. By connecting the probability of occurrence of each species on each location and the quasi-abundance distributions, one generalized linear model for the observed presences-absences is selected by profile deviance, and the resulting fitted probabilities of the null model with minimum deviance is used to generate random matrices via parametric bootstrap. This work contributes with a unified theoretical formulation of the NM method, based on Faddy distributions, to allow general distributions of over-dispersed and under-dispersed discrete random variables. For a subset of the Faddy models, the log concave property of the inverse link function guarantees convergence to a global minimum deviance thus providing unique estimates for the linear parameters of the models. The method is illustrated using presence-absence data of island lizard communities. Interpretations of this combined GLM-parametric bootstrap protocol are discussed, highlighting the way fitted probabilities under the chosen null model are related to the row and column totals of the observed table. Additional properties of the probabilistic NM protocol, with possible avenues of future research, are also discussed.
      PubDate: 2022-07-06
      DOI: 10.1007/s10651-022-00537-4
       
  • Switching state-space models for modeling penguin population dynamics

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      Abstract: Abstract Tracking individual animals through time using mark-recapture methods is the gold standard for understanding how environmental conditions influence demographic rates, but applying such tags is often infeasible due to the difficulty of catching animals or attaching marks/tags without influencing behavior or survival. Due to the logistical challenges and emerging ethical concerns with flipper banding penguins, relatively little is known about spatial variation in demographic rates, spatial variation in demographic stochasticity, or the role that stochasticity may play in penguin population dynamics. Here we describe how adaptive importance sampling can be used to fit age-structured population models to time series of point counts. While some demographic parameters are difficult to learn through point counts alone, others can be estimated, even in the face of missing data. Here we demonstrate the application of adaptive importance sampling using two case studies, one in which we permit immigration and another permitting regime switching in reproductive success. We apply these methods to extract demographic information from several time series of observed abundance in gentoo and Adélie penguins in Antarctica. Our method is broadly applicable to time series of abundance and provides a feasible means of fitting age-structured models without marking individuals.
      PubDate: 2022-06-21
      DOI: 10.1007/s10651-022-00538-3
       
  • Spatio-temporal analysis of air pollution in North China Plain

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      Abstract: Abstract Accompanying China’s rapid industrialization, a vast area of the country, particularly the Beijing–Tianjin–Hebei (BTH) region, has significantly experienced concerning levels of air pollution over the past decade. Exposure to severe particulate matter (PM), \(PM_{2.5}\) in particular, it raises a crucial public health concern, but quantifying \(PM_{2.5}\) accurately across large geographic areas and across time poses a great challenge. To investigate \(PM_{2.5}\) concentration in the BTH region, we utilize a spatio-temporal mixed effects model that includes geographic information system-based time-invariant spatial variables and time-varying meteorological covariates. Our kriging results find that \(PM_{2.5}\) concentration is hazardous in the North China Plain (NCP), where major iron, steel, and cement industries are located. More importantly, our analysis of the impact of wind finds that the severity of air pollution highly depends on the direction of the wind. That is, a northerly wind can considerably reduce the level of \(PM_{2.5}\) in the NCP, while a southerly wind generally does not alleviate air pollution and sometimes even increases it. Using prediction error as a proxy for the level of local emissions, we find that Shijiazhuang and Tangshan produce the most significant local emissions, which coincides with a heavier industry in these two cities. During the winter heating period, we find that the two densely populated cities of Beijing and Tianjin have dramatic increases in local emissions because of the massive coal consumption during this period.
      PubDate: 2022-06-01
      DOI: 10.1007/s10651-021-00521-4
       
  • Tests for aggregated dispersion: Van Valen’s test and a new
           competitor

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      Abstract: Abstract Van Valen’s test is usually applied as a two sample test for equality of dispersion for multivariate data. Motivated by a comment of Manly (Van Valen’s test. Encyclopedia of Statistical Sciences, 2006) that “Little is known about the properties of Van Valen’s test” we develop an alternative test and compare the Van Valen test with our alternative robust test in an extensive simulation study. We find that Van Valen’s test does not actually test for equality of variance sums; however, for that null hypothesis it still performs well in terms of closeness to the nominal significance level. Due to testing the correct null hypothesis and the excellent adherence to the nominal significance level, we recommend the use of the robust test as a permutation test.
      PubDate: 2022-06-01
      DOI: 10.1007/s10651-021-00517-0
       
  • New methods of life expectancy estimation

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      Abstract: Abstract Two novel methods of life expectancy estimation, applied to various annual reported demographic datasets, are proposed. First, for datasets that fully recorded birth date and death date of all dead individuals, we rely on the well-known Kaplan–Meier estimation method to provide an accurate estimation framework of life expectancy. Our proposed method can be used as a gold standard in the accuracy investigation of other life expectancy estimation methods. The method can be applied for small areas, where complete mortality data are regularly produced by routine annual surveys. The second new created method, called as local parametric method, based on the theoretical background of survival process with local parametric Weibull distributions, estimates life expectancy using abridged survival data. Experiments on real longitudinal datasets show the new method provides very exact life expectancy estimations for 10 among 15 one-year datasets, whilst the method of Chiang often yields overestimations.
      PubDate: 2022-05-13
      DOI: 10.1007/s10651-022-00536-5
       
  • Modeling Dinophysis in Western Andalucía using a autoregressive
           hidden Markov model

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      Abstract: Abstract Dinophysis spp. can produce diarrhetic shellfish toxins (DST) including okadaic acid and dinophysistoxins, and some strains can also produce non-diarrheic pectenotoxins. Although DSTs are of human health concern and have motivated environmental monitoring programs in many locations, these monitoring programs often have temporal data gaps (e.g., days without measurements). This paper presents a model for the historical time-series, on a daily basis, of DST-producing toxigenic Dinophysis in 8 monitored locations in western Andalucía over 2015–2020, incorporating measurements of algae counts and DST levels. We fitted a bivariate hidden Markov Model (HMM) incorporating an autoregressive correlation among the observed DST measurements to account for environmental persistence of DST. We then reconstruct the maximum-likelihood profile of algae presence in the water column at daily intervals using the Viterbi algorithm. Using historical monitoring data from Andalucía, the model estimated that potentially toxigenic Dinophysis algae is present at greater than or equal to 250 cells/L between< 1% and>10% of the year depending on the site and year. The historical time-series reconstruction enabled by this method may facilitate future investigations into temporal dynamics of toxigenic Dinophysis blooms.
      PubDate: 2022-05-04
      DOI: 10.1007/s10651-022-00534-7
       
  • Estimating change in annual timber products output using a stratified
           sampling with certainty design

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      Abstract: Abstract A key aspect in understanding patterns in wood demand and harvesting activities is monitoring of timber products output by wood processing facilities. Estimation of change from year-to-year is necessary but is complicated due to shifts in the population as well as changing strata over time. Taking independent samples each year eases complexity, yet suffers from relatively large sampling error in comparison to other designs that take advantage of the covariance arising from correlated samples. In this study, a design intended to maximize the precision of the change estimate by retaining the initial sample to the extent possible was analyzed. Several approaches to estimating the covariance, with the primary challenge being that sometimes only a single sample unit occurred in both samples within a given stratum. Variance underestimation and overestimation were encountered depending on the covariance method. The best outcome was attained using a measure-of-size variable at the population level to approximate the covariance. However, this approach overestimated the variance by 11% in a Monte Carlo simulation. The simulation results suggested a 14% reduction in the standard error of the estimate was attainable from correlated samples relative to independent samples. Due to the challenges introduced for estimating the covariance for changing populations and strata over time, the value of relatively small reductions in sampling error need to be considered in the context of introducing complex and potentially unreliable covariance estimation methods.
      PubDate: 2022-03-23
      DOI: 10.1007/s10651-022-00533-8
       
  • Inference and model determination for temperature-driven non-linear
           ecological models

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      Abstract: Abstract This paper is concerned with a contemporary Bayesian approach to the effect of temperature on developmental rates. We develop statistical methods using recent computational tools to model four commonly used ecological non-linear mathematical curves that describe arthropods’ developmental rates. Such models address the effect of temperature fluctuations on the developmental rate of arthropods. In addition to the widely used Gaussian distributional assumption, we also explore Inverse Gamma-based alternatives, which naturally accommodate adaptive variance fluctuation with temperature. Moreover, to overcome the associated parameter indeterminacy in the case of no development, we suggest the zero-inflated Inverse Gamma model. The ecological models are compared graphically via posterior predictive plots and quantitatively via marginal likelihood estimates and Information criteria. Inference is performed using the Stan software and we investigate the statistical and computational efficiency of its Hamiltonian Monte Carlo and Variational Inference methods. We also explore model uncertainty and employ Bayesian Model Averaging framework for robust estimation of the key ecological parameters.
      PubDate: 2022-03-19
      DOI: 10.1007/s10651-022-00531-w
       
  • Dynamic impacts of energy use, agricultural land expansion, and
           deforestation on CO2 emissions in Malaysia

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      Abstract: Abstract This study empirically investigates the nexus among energy use, agricultural land expansion, deforestation, and carbon dioxide (CO2) emissions in Malaysia. Time series data from 1990 to 2019 were utilized using the bounds testing (ARDL) approach followed by the Dynamic Ordinary Least Squares (DOLS) method. The DOLS estimate findings show that the energy usage coefficient is positive and significant with CO2 emissions, indicating a 1% increase in energy consumption is related to a 0.91% rise in CO2 emissions. In addition, the coefficient of agricultural land is positive, which indicates that agricultural land expansion by 1% is associated with an increase in CO2 emissions by 0.84% in the long run. Furthermore, the forested area coefficient is negative, which means that decreasing 1% of the wooded area (i.e., deforestation) has a long-term effect of 5.41% increased CO2 emissions. Moreover, the pairwise Granger causality test results show bidirectional causality between deforestation and energy use; and unidirectional causality from energy use to CO2 emissions, agricultural land expansion to CO2 emissions, deforestation to CO2 emissions, agricultural land expansion to energy use, and deforestation to agricultural land expansion in Malaysia. The empirical findings reveal that increased energy use, agricultural land expansion, and deforestation have a negative impact on environmental quality in Malaysia. Thus, the effective implementation of policy measures to promote renewable energy, climate-smart agriculture, and sustainable management of forest ecosystems could be useful for reducing environmental degradation in Malaysia.
      PubDate: 2022-03-17
      DOI: 10.1007/s10651-022-00532-9
       
  • Revisiting the carbon emissions hypothesis in the developing and developed
           countries: a new panel cointegration approach

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      Abstract: Abstract Since global warming worsens with economic development and emitted CO2 is one of the main greenhouse gases, it is important to understand the relationship between CO2 emissions and economic growth. The paper applies a new panel cointegration test with cross-sectional dependence and structural breaks to examine this relationship in developed and developing countries, respectively. The results indicate that the “Environmental Kuznets Curve” does not hold in either group. For developing countries, there is neither linear nor quadratic long-term equilibrium relationship between CO2 emissions and economic growth. For developed countries, the quadratic relationship does exist between CO2 emissions and economic growth, whereas the linear one does not. A half of these countries have inverted U-shaped curves, while the other half have U-shaped curves. Besides, most of these countries are still on the rising stage of the curve. This paper gives new insights for policymakers to keep a balance between sustainable economic growth and suitable environmental quality.
      PubDate: 2022-01-04
      DOI: 10.1007/s10651-021-00526-z
       
 
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