Journal of Agricultural Biological and Environmental Statistics最新文献_第9页

A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships. 多种植物相互作用对草地生物多样性和生态系统功能关系影响的混合模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00505-2

Jack McDonnell, Thomas McKenna, Kathryn A Yurkonis, Deirdre Hennessy, Rafael de Andrade Moral, Caroline Brophy

{"title":"A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships.","authors":"Jack McDonnell, Thomas McKenna, Kathryn A Yurkonis, Deirdre Hennessy, Rafael de Andrade Moral, Caroline Brophy","doi":"10.1007/s13253-022-00505-2","DOIUrl":"https://doi.org/10.1007/s13253-022-00505-2","url":null,"abstract":"In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study.Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00505-2.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10708620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models. 空间变化混合模型降水数据产品的分布验证。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00515-0

Lynsie R Warr, Matthew J Heaton, William F Christensen, Philip A White, Summer B Rupper

{"title":"Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models.","authors":"Lynsie R Warr, Matthew J Heaton, William F Christensen, Philip A White, Summer B Rupper","doi":"10.1007/s13253-022-00515-0","DOIUrl":"https://doi.org/10.1007/s13253-022-00515-0","url":null,"abstract":"The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00515-0.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9908693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10705114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models. 复制计数和批量标记隐藏人口模型的计算效率和精度。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00509-y

Matthew R P Parker, Laura L E Cowen, Jiguo Cao, Lloyd T Elliott

{"title":"Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models.","authors":"Matthew R P Parker, Laura L E Cowen, Jiguo Cao, Lloyd T Elliott","doi":"10.1007/s13253-022-00509-y","DOIUrl":"https://doi.org/10.1007/s13253-022-00509-y","url":null,"abstract":"We address two computational issues common to open-population N-mixture models, hidden integer-valued autoregressive models, and some hidden Markov models. The first issue is computation time, which can be dramatically improved through the use of a fast Fourier transform. The second issue is tractability of the model likelihood function for large numbers of hidden states, which can be solved by improving numerical stability of calculations. As an illustrative example, we detail the application of these methods to the open-population N-mixture models. We compare computational efficiency and precision between these methods and standard methods employed by state-of-the-art ecological software. We show faster computing times (a <math><mrow><mo>∼</mo> <mn>6</mn></mrow> </math> to <math><mrow><mo>∼</mo> <mn>30</mn></mrow> </math> times speed improvement for population size upper bounds of 500 and 1000, respectively) over state-of-the-art ecological software for N-mixture models. We also apply our methods to compute the size of a large elk population using an N-mixture model and show that while our methods converge, previous software cannot produce estimates due to numerical issues. These solutions can be applied to many ecological models to improve precision when logs of sums exist in the likelihood function and to improve computational efficiency when convolutions are present in the likelihood function. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00509-y.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9249755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality 修正:用连续易出错暴露估计因果暴露反应函数:细颗粒物和全因死亡率的研究

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-12-26 DOI: 10.1007/s13253-022-00526-x

K. Josey, P. deSouza, Xiao Wu, D. Braun, R. Nethery

引用次数: 0

Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology 用时间优先抽样来推断物候的动态种群模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-12-10 DOI: 10.1007/s13253-023-00552-3

Michael R. Schwob, M. Hooten, Travis McDevitt-Galles

引用次数: 0

Correction to: Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series 修正:空间相关功能时间序列的异步变更点估计

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-12-02 DOI: 10.1007/s13253-022-00524-z

Mengchen Wang, Trevor Harris, Bo Li

引用次数: 0

Spatiotemporal Exposure Prediction with Penalized Regression 基于惩罚回归的时空暴露预测

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-11-17 DOI: 10.1007/s13253-022-00523-0

Nathan A. Ryder, Joshua P. Keller

引用次数: 0

Modeling Community Dynamics Through Environmental Effects, Species Interactions and Movement 通过环境影响、物种相互作用和运动建模群落动态

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-11-07 DOI: 10.1007/s13253-022-00520-3

Becky Tang, J. Clark, P. Marra, A. Gelfand

引用次数: 2

Review of Handbook of Graphs and Networks in People Analytics: With Examples in R and Python by Keith McNulty 回顾《人际分析中的图和网络手册:用R和Python举例》，作者:Keith McNulty

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-11-03 DOI: 10.1007/s13253-022-00521-2

Sa Ren

引用次数: 0

Spatiotemporal Event Studies for Environmental Data Under Cross-Sectional Dependence: An Application to Air Quality Assessment in Lombardy 横断面依赖下环境数据时空事件研究:在伦巴第地区空气质量评价中的应用

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2022-10-24 DOI: 10.1007/s13253-023-00564-z

Paolo Maranzano, M. Pelagatti

引用次数: 1