scholarly journals Migratory Seasonality and Phenology by Birds in a Temperate Forest with Two Disturbance Conditions

2021 ◽  
Author(s):  
Yessenia Cruz-Miranda ◽  
Luis A. Tarango-Arámbula ◽  
Jonathan G. Escobar-Flores ◽  
Genaro Olmos-Oropeza ◽  
Leonardo Chapa-Vargas
Keyword(s):  
Generalized Linear Model ◽  
Temperate Forest ◽  
Linear Models ◽  
Dry Season ◽  
Pine Forest ◽  
Forest Condition ◽  
Fixed Radius ◽  
Colaptes Auratus ◽  
Aphelocoma Ultramarina ◽  
Special Protection

Objective: The objective was to infer the effect of the variables phenology (migration-non-migration), seasonal (rainfall-dry season), sex and forest condition on the abundances of birds (resident-migratory) in a semi-preserved and disturbed oak pine forest. Design/methodology/approach: It was carried out in Monte Tlaloc, State of Mexico, under two conditions of apparent disturbance, semi-preserved oak pine forest and disturbed oak pine forest. Ten bird samplings were carried out with "count on point" with a fixed radius of 25 m, covering the 4 seasons of the year and migratory periods. With the previous data, the Relative Abundance Index (RAI) was estimated. To infer the effect of the variables phenology, seasonality, sex and forest condition on the abundances of birds, generalized linear models were elaborated. Results: The IAR of the birds registered in the semi-considered pine forest indicates that the species with the lowest presence was Aphelocoma ultramarina (0.002) and with the highest frequency Empidonax sp. (0.13), unlike to that found in the disturbed pine forest where the lowest IAR corresponded to Colaptes auratus (0.003) and with the highest appearance was Ptiliogonys cinereus (0.23). The Generalized Linear Model suggested that forest condition and phenology are significantly related to the frequency of species. Limitations on study/implications: In this study it was found that the abundance of birds was affected by the condition of the forest and that the phenology (migration-non-migration), seasonal (rain-dry season), sex and condition of the forest were related to the abundance of birds. Four species classified as under Special Protection and two Threatened according to NOM-059 were registered as well as the presence of four endemic species which highlights the importance of conserving these ecosystems. Findings/conclusions: The fauna communities present in Monte Tláloc highlight the importance of conserving the pine-oak forests since this site is part of the Eje Neovolcanico Transversal.

Posterior contraction in sparse generalized linear models

Biometrika ◽  
2020 ◽  
Author(s):  
Seonghyun Jeong ◽  
Subhashis Ghosal
Keyword(s):  
Generalized Linear Models ◽  
Bayesian Methods ◽  
Generalized Linear Model ◽  
Linear Models ◽  
Continuous Distribution ◽  
Fractional Power ◽  
Regression Coefficients ◽  
Link Function ◽  
Convergence Properties ◽  
Set Up

Summary We study posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity. A mixture of a point mass at zero and a continuous distribution is used as the prior distribution on regression coefficients. In addition to the usual posterior, the fractional posterior, which is obtained by applying Bayes theorem with a fractional power of the likelihood, is also considered. The latter allows uniformity in posterior contraction over a larger subset of the parameter space. In our set-up, the link function of the generalized linear model need not be canonical. We show that Bayesian methods achieve convergence properties analogous to lasso-type procedures. Our results can be used to derive posterior contraction rates in many generalized linear models including logistic, Poisson regression and others.

An analysis of error structure in modeling the stock–recruitment data of gadoid stocks using generalized linear models

2004 ◽  
Vol 61 (1) ◽  
pp. 134-146 ◽  
Author(s):  
Yan Jiao ◽  
David Schneider ◽  
Yong Chen ◽  
Joe Wroblewski
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Linear Models ◽  
Model Error ◽  
Future Research ◽  
Error Structure ◽  
Gamma Distributions ◽  
Error Distributions ◽  
Acceptable Model ◽  
Stock Recruitment

When modeling the stock–recruitment (S–R) relationship, the Cushing, Ricker, and other S–R models are fitted to the observed S–R data by estimating parameters with assumptions made concerning the model error structure. Using a generalized linear model approach, we explored and identified the appropriate model error structure in modeling S–R data for gadoid stocks. The S–R parameter estimation was found to be influenced by the choice of error distributions assumed in the analysis. In modeling S–R data for gadoid stocks, the Beverton–Holt model was found to be more sensitive to the assumption of model error distribution than the Cushing and Ricker models. The lognormal and gamma distributions had higher probability of being acceptable model error distributions. Cluster analyses and summary statistics of error distributions in S–R modeling did not show consistent patterns in the identification of an acceptable model error structure among species, geographic distributions, and sample sizes. A better understanding of the factors and mechanisms resulting in differences in the choice of appropriate model error distributions for different populations is needed in future research. We recommend that the generalized linear model be used to identify acceptable model error structures in quantifying S–R relationships.

A simulation study of impacts of error structure on modeling stock–recruitment data using generalized linear models

2004 ◽  
Vol 61 (1) ◽  
pp. 122-133 ◽  
Author(s):  
Yan Jiao ◽  
Yong Chen ◽  
David Schneider ◽  
Joe Wroblewski
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Linear Models ◽  
Least Squares Method ◽  
Estimation Of Parameters ◽  
Estimation Errors ◽  
Error Structure ◽  
Stock Recruitment ◽  
Data Points ◽  
The Impact

Stock–recruitment (S–R) models are commonly fitted to S–R data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of error structure on S–R modeling. The generalized linear model, which can readily deal with different error structures, was used in estimating parameters. This study suggests that the quality of S–R parameter estimation, measured by estimation errors, can be influenced by the realism of error structure assumed in an estimation, the number of S–R data points, and the number of outliers in modeling. A small number of S–R data points and the presence of outliers in S–R data could increase the difficulty in identifying an appropriate error structure in modeling, which might lead to large biases in the S–R param eter estimation. This study shows that generalized linear model methods can help identify an appropriate error distribution in S–R modeling, leading to an improved estimation of parameters even when there are outliers and the number of S–R data points is small. We recommend the generalized linear model be used for quantifying stock–recruitment relationships.

scholarly journals Linear and generalized linear models for the detection of QTL effects on within-subject variability

2007 ◽  
Vol 89 (4) ◽  
pp. 245-257 ◽  
Author(s):  
Dörte Wittenburg ◽  
Volker Guiard ◽  
Friedrich Liese ◽  
Norbert Reinsch
Keyword(s):  
Generalized Linear Model ◽  
Linear Models ◽  
Repeated Measurements ◽  
Link Function ◽  
Test Statistics ◽  
Special Aspect ◽  
Subject Variability ◽  
The Mean ◽  
Daughter Design ◽  
Qtl Effects

SummaryQuantitative trait loci (QTLs) may affect not only the mean of a trait but also its variability. A special aspect is the variability between multiple measured traits of genotyped animals, such as the within-litter variance of piglet birth weights. The sample variance of repeated measurements is assigned as an observation for every genotyped individual. It is shown that the conditional distribution of the non-normally distributed trait can be approximated by a gamma distribution. To detect QTL effects in the daughter design, a generalized linear model with the identity link function is applied. Suitable test statistics are constructed to test the null hypothesis H0: No QTL with effect on the within-litter variance is segregating versus HA: There is a QTL with effect on the variability of birth weight within litter. Furthermore, estimates of the QTL effect and the QTL position are introduced and discussed. The efficiency of the presented tests is compared with a test based on weighted regression. The error probability of the first type as well as the power of QTL detection are discussed and compared for the different tests.

scholarly journals Soil–atmosphere exchange flux of total gaseous mercury (TGM) at subtropical and temperate forest catchments

2020 ◽  
Vol 20 (24) ◽  
pp. 16117-16133
Author(s):  
Jun Zhou ◽  
Zhangwei Wang ◽  
Xiaoshan Zhang ◽  
Charles T. Driscoll ◽  
Che-Jen Lin
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Solar Radiation ◽  
Open Field ◽  
Temperate Forest ◽  
Subtropical Forest ◽  
Pine Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest ◽  
Soil Hg

Abstract. Evasion from soil is the largest source of mercury (Hg) to the atmosphere from terrestrial ecosystems. To improve our understanding of controls and in estimates of forest soil–atmosphere fluxes of total gaseous Hg (TGM), measurements were made using dynamic flux chambers (DFCs) over 130 and 96 d for each of five plots at a subtropical forest and a temperate forest, respectively. At the subtropical forest, the highest net soil Hg emissions were observed for an open field (24 ± 33 ng m−2 h−1), followed by two coniferous forest plots (2.8 ± 3.9 and 3.5 ±  4.2 ng m−2 h−1), a broad-leaved forest plot (0.18 ±  4.3 ng m−2 h−1) and the remaining wetland site showing net deposition (−0.80 ± 5.1 ng m−2 h−1). At the temperate forest, the highest fluxes and net soil Hg emissions were observed for a wetland (3.81 ± 0.52 ng m−2 h−1) and an open field (1.82 ± 0.79 ng m−2 h−1), with lesser emission rates in the deciduous broad-leaved forest (0.68 ± 1.01 ng m−2 h−1) and deciduous needle-leaved forest (0.32 ± 0.96 ng m−2 h−1) plots, and net deposition at an evergreen pine forest (−0.04 ± 0.81 ng m−2 h−1). High solar radiation and temperature during summer resulted in the high Hg emissions in the subtropical forest and the open field and evergreen pine forest at the temperate forest. At the temperate deciduous plots, the highest Hg emission occurred in spring during the leaf-off period due to direct solar radiation exposure to soils. Fluxes showed strong positive relationships with solar radiation and soil temperature and negative correlations with ambient air TGM concentration in both the subtropical and temperate forests, with area-weighted compensation points of 6.82 and 3.42 ng m−3, respectively. The values of the compensation points suggest that the atmospheric TGM concentration can play a critical role in limiting TGM emissions from the forest floor. Climate change and land use disturbance may increase the compensation points in both temperate and subtropical forests. Future research should focus on the role of legacy soil Hg in reemissions to the atmosphere as decreases in primary emissions drive decreases in TGM concentrations and disturbances of climate change and land use.

scholarly journals Renewable processes under maturing coniferous stands crown in Vologda region

2021 ◽  
Vol 25 (2) ◽  
pp. 10-18
Author(s):  
L.V. Zarubina ◽  
◽  
A.A. Karbasnikov ◽  
D.A. Peshin ◽  
◽  
...  
Keyword(s):  
Growth And Development ◽  
Pine Forest ◽  
Vologda Region ◽  
Forest Condition ◽  
Forest Conditions

Evaluation of renewable processes was carried out on the territory of Totma area in Vologda region. The objects of study were six sites of flourishing coniferous stockings in different forest condition. The laying of test plots was carried out taking into account the requirements of OST 56-69–83. Undergrowth accounting was carried out taking into account the height and state of life. The processing of field materials was carried out by methods generally accepted in forestry. According to the results, we can make a conclusion that provision for growth and development of spruce staddle in flourishing pine stockings in different forest conditions are unpleasant. The pine staddle is absent at all. There is enough amount of coniferous staddle under spruce canopy for formation spruce-deciduous or spruce planting after logging works. As recommendation for saving aboriginal forest and reducing expenses on the reforestation works in pine forest crop after logging works, we offer to hold alternating gradual fell with intensity of 30 % and implementation of measures in assistance for natural renewal as soil mineralization in processes of main executed logging works. We think that implementing fell is necessary to time to seed year.

scholarly journals Comparison and Bias Correction of TMPA Precipitation Products over the Lower Part of Red–Thai Binh River Basin of Vietnam

2018 ◽  
Vol 10 (10) ◽  
pp. 1582 ◽  
Author(s):  
Hung Le ◽  
Jessica Sutton ◽  
Duong Bui ◽  
John Bolten ◽  
Venkataraman Lakshmi
Keyword(s):  
River Basin ◽  
Bias Correction ◽  
Linear Models ◽  
Dry Season ◽  
Linear Scaling ◽  
Wet Season ◽  
Ground Measurement ◽  
High Bias ◽  
Precipitation Analysis ◽  
Seasonal Analysis

As the limitation of rainfall collection by ground measurement has been widely recognized, satellite-based rainfall estimate is a promising high-resolution alternative in both time and space. This study is aimed at exploring the capacity of the satellite-based rainfall product Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), including 3B42V7 research data and its real-time 3B42RT data, by comparing them against data from 29 ground observation stations over the lower part of the Red–Thai Binh River Basin from March 2000 to December 2016. Various statistical metrics were applied to evaluate the TMPA products. The results showed that both 3B42V7 and 3B42RT had weak relationships with daily observations, but 3B42V7 data had strong agreement on the monthly scale compared to 3B42RT. Seasonal analysis showed that 3B42V7 and 3B42RT underestimated rainfall during the dry season and overestimated rainfall during the wet season, with high bias observed for 3B42RT. In addition, detection metrics demonstrated that TMPA products could detect rainfall events in the wet season much better than in the dry season. When rainfall intensity was analyzed, both 3B42V7 and 3B42RT overestimated the no rainfall event during the dry season but underestimated these events during the wet season. Finally, based on the moderate correlation between climatology–topography characteristics and correction factors of linear-scaling (LS) approach, a set of multiple linear models was developed to reduce the error between TMPA products and the observations. The results showed that climatology–topography-based linear-scaling approach (CTLS) significantly reduced the percentage bias (PBIAS) score and moderately improved the Nash–Sutcliffe efficiency (NSE) score. The finding of this paper gives an overview of the capacity of TMPA products in the lower part of the Red–Thai Binh River Basin regarding water resource applications and provides a simple bias correction that can be used to improve the correctness of TMPA products.

Monitoring multinomial logistic profiles in Phase I using log-linear models

2018 ◽  
Vol 35 (3) ◽  
pp. 678-689 ◽  
Author(s):  
Hamidreza Izadbakhsh ◽  
Rassoul Noorossana ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Phase I ◽  
Generalized Linear Model ◽  
Control Charts ◽  
Design Methodology ◽  
Linear Models ◽  
Multinomial Logistic Regression ◽  
Test Method ◽  
Ratio Test ◽  
Content Type ◽  
Log Linear

Purpose The purpose of this paper is to apply Poisson generalized linear model (PGLM) with log link instead of multinomial logistic regression to monitor multinomial logistic profiles in Phase I. Hence, estimating the coefficients becomes easier and more accurate. Design/methodology/approach Simulation technique is used to assess the performance of the proposed algorithm using four different control charts for monitoring. Findings The proposed algorithm is faster and more accurate than the previous algorithms. Simulation results also indicate that the likelihood ratio test method is able to detect out-of-control parameters more efficiently. Originality/value The PGLM with log link has not been used to monitor multinomial profiles in Phase I.

scholarly journals Capturing multiple timescales of adaptation to second-order statistics with generalized linear models: gain scaling and fractional differentiation

2019 ◽  
Author(s):  
Kenneth W. Latimer ◽  
Adrienne L. Fairhall
Keyword(s):  
Generalized Linear Models ◽  
Cortical Neurons ◽  
Generalized Linear Model ◽  
Single Neuron ◽  
Statistical Approach ◽  
Linear Models ◽  
Fractional Differentiation ◽  
Multiple Timescales ◽  
Second Order Statistics ◽  
Gain Adaptation

AbstractSingle neurons can dynamically change the gain of their spiking responses to account for shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects. The GLM describes spiking as a Poisson process whose rate depends on a linear combination of the stimulus and recent spike history. The GLM successfully replicates gain scaling observed in Hodgkin-Huxley simulations of cortical neurons that occurs when the ratio of spike-generating potassium and sodium conductances approaches one. Gain scaling in the GLM depends on the length and shape of the spike history filter. Additionally, the GLM captures adaptation that occurs over multiple timescales as a fractional derivative of the stimulus variance, which has been observed in neurons that include long timescale after hyperpolarization conductances. Fractional differentiation in GLMs requires long spike history that span several seconds. Together, these results demonstrate that the GLM provides a tractable statistical approach for examining single-neuron adaptive computations in response to changes in stimulus variance.

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