Journal of Agricultural Biological and Environmental Statistics最新文献_第3页

Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation 基于核估计的存在-缺失图物种丰度估算

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-12-01 DOI: 10.1007/s13253-023-00589-4

Ya-Mei Chang, Ying-Chi Huang

{"title":"Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation","authors":"Ya-Mei Chang, Ying-Chi Huang","doi":"10.1007/s13253-023-00589-4","DOIUrl":"https://doi.org/10.1007/s13253-023-00589-4","url":null,"abstract":"We present a novel method for estimating species abundance using presence–absence maps. Our approach takes the spatial context into consideration, distinguishing it from conventional methods. The proposed method is built upon a well-known kernel estimation for point pattern intensity, with the addition of a new parameter representing the mean abundance in each occupied cell. The parameter estimate is obtained through maximum likelihood estimation. The expected abundance corresponds to the integral of the intensity over the study area, which can be estimated by taking the Riemann sum of the intensity. The implementation of our method is straightforward, using existing packages in the R software. We compared various bandwidth selection methods within our approach and assessed the estimation performance against some approaches based on the random placement model or negative binomial model through the simulation study and an empirical forestry data in Barro Colorado Island (BCI), Panama. The results of the simulation and the application demonstrate that our method, with a carefully chosen bandwidth, outperforms the alternatives for highly aggregated data and improves the issue of underestimation. Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Type of LASSO Regression Model with Cauchy Noise 一类新的柯西噪声LASSO回归模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-28 DOI: 10.1007/s13253-023-00583-w

Amir Hossein Ghatari, Mina Aminghafari, Adel Mohammadpour

引用次数: 0

Assessing Spatial Stationarity and Segmenting Spatial Processes into Stationary Components 评估空间平稳性和分割空间过程到平稳组件

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-21 DOI: 10.1007/s13253-023-00588-5

ShengLi Tzeng, Bo-Yu Chen, Hsin-Cheng Huang

引用次数: 0

Spatial Confounding and Spatial+ for Nonlinear Covariate Effects 非线性协变量效应的空间混杂和空间+

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-18 DOI: 10.1007/s13253-023-00586-7

Emiko Dupont, Nicole H. Augustin

{"title":"Spatial Confounding and Spatial+ for Nonlinear Covariate Effects","authors":"Emiko Dupont, Nicole H. Augustin","doi":"10.1007/s13253-023-00586-7","DOIUrl":"https://doi.org/10.1007/s13253-023-00586-7","url":null,"abstract":"Regression models for spatially varying data use spatial random effects to reflect spatial correlation structure. Such random effects, however, may interfere with the covariate effect estimates and make them unreliable. This problem, known as spatial confounding, is complex and has only been studied for models with linear covariate effects. However, as illustrated by a forestry example in which we assess the effect of soil, climate, and topography variables on tree health, the covariate effects of interest are in practice often unknown and nonlinear. We consider, for the first time, spatial confounding in spatial models with nonlinear effects implemented in the generalised additive models (GAMs) framework. We show that spatial+, a recently developed method for alleviating confounding in the linear case, can be adapted to this setting. In practice, spatial+ can then be used both as a diagnostic tool for investigating whether covariate effect estimates are affected by spatial confounding and for correcting the estimates for the resulting bias when it is present. Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the Temperature-Driven Development of Stage-Structured Insect Populations with a Bayesian Hierarchical Model 用贝叶斯层次模型预测阶段结构昆虫种群的温度驱动发育

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-16 DOI: 10.1007/s13253-023-00581-y

Kala Studens, Benjamin M. Bolker, Jean-Noël Candau

{"title":"Predicting the Temperature-Driven Development of Stage-Structured Insect Populations with a Bayesian Hierarchical Model","authors":"Kala Studens, Benjamin M. Bolker, Jean-Noël Candau","doi":"10.1007/s13253-023-00581-y","DOIUrl":"https://doi.org/10.1007/s13253-023-00581-y","url":null,"abstract":"The management of forest pests relies on an accurate understanding of the species’ phenology. Thermal performance curves (TPCs) have traditionally been used to model insect phenology. Many such models have been proposed and fitted to data from both wild and laboratory-reared populations. Using Hamiltonian Monte Carlo for estimation, we implement and fit an individual-level, Bayesian hierarchical model of insect development to the observed larval stage durations of a population reared in a laboratory at constant temperatures. This hierarchical model handles interval censoring and temperature transfers between two constant temperatures during rearing. It also incorporates individual variation, quadratic variation in development rates across insects’ larval stages, and “flexibility” parameters that allow for deviations from a parametric TPC. Using a Bayesian method ensures a proper propagation of parameter uncertainty into predictions and provides insights into the model at hand. The model is applied to a population of eastern spruce budworm (Choristoneura fumiferana) reared at 7 constant temperatures. Resulting posterior distributions can be incorporated into a workflow that provides prediction intervals for the timing of life stages under different temperature regimes. We provide a basic example for the spruce budworm using a year of hourly temperature data from Timmins, Ontario, Canada. Supplementary materials accompanying this paper appear on-line.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Principal Component Analysis of Two-dimensional Functional Data with Serial Correlation 具有序列相关性的二维函数数据的主成分分析

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-16 DOI: 10.1007/s13253-023-00585-8

Shirun Shen, Huiya Zhou, Kejun He, Lan Zhou

引用次数: 0

Testing Correlation in a Three-Level Model 三层模型的相关性检验

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-16 DOI: 10.1007/s13253-023-00575-w

Anna Szczepańska-Álvarez, Adolfo Álvarez, Artur Szwengiel, Dietrich von Rosen

引用次数: 0

Integrating Different Data Sources Using a Bayesian Hierarchical Model to Unveil Glacial Refugia 利用贝叶斯层次模型整合不同数据源揭示冰川避难所

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-15 DOI: 10.1007/s13253-023-00582-x

Mauricio Campos, Bo Li, Guillaume de Lafontaine, Joseph Napier, Feng Sheng Hu

{"title":"Integrating Different Data Sources Using a Bayesian Hierarchical Model to Unveil Glacial Refugia","authors":"Mauricio Campos, Bo Li, Guillaume de Lafontaine, Joseph Napier, Feng Sheng Hu","doi":"10.1007/s13253-023-00582-x","DOIUrl":"https://doi.org/10.1007/s13253-023-00582-x","url":null,"abstract":"Rapid anthropogenic climate change has elevated the interest in studying the biotic responses of species during the Last Glacial Maximum. During this period, species retreated to highly spatially restricted geographic regions where survival was possible, known as glacial micro-refugia, from which they migrated and expanded when conditions became more suitable. Several distinct sources of evidence have contributed to developing a new understanding of how these regions might have impacted the sustainability of the natural populations of many species. Pollen records in Eastern Beringia have been used to explore the possibility that the region harbored glacial refugia for several plants from the arctic tundra and/or the boreal forest biomes common to the region. Our study focuses on Alnus viridis and Picea glauca, two predominant species of arcto-boreal vegetation. We propose to integrate genomic, SDM, and existing fossil data in a hierarchical Bayesian modeling (HBM) framework to determine whether multiple refugia existed in isolated geographic areas. This study demonstrates how the flexibility of HBMs makes the formal synthesis of such disparate data sources feasible. Our results highlight the regions of plausible refugia that can guide future investigations into studying the role of glacial refugia during climate change. Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sample Size for Estimating Disease Prevalence in Free-Ranging Wildlife Populations: A Bayesian Modeling Approach 估计自由放养野生动物种群疾病流行的样本量:贝叶斯建模方法

4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-13 DOI: 10.1007/s13253-023-00578-7

James G. Booth, Brenda J. Hanley, Florian H. Hodel, Christopher S. Jennelle, Joseph Guinness, Cara E. Them, Corey I. Mitchell, Md Sohel Ahmed, Krysten L. Schuler

引用次数: 0

The Third Competition on Spatial Statistics for Large Datasets 第三届大型数据集空间统计竞赛

4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-11-10 DOI: 10.1007/s13253-023-00584-9

Yiping Hong, Yan Song, Sameh Abdulah, Ying Sun, Hatem Ltaief, David E. Keyes, Marc G. Genton

引用次数: 0