Data and models from: A novel design method for customized visual delimiting surveys for plant pests based on transects and scouting

Models were used in the manuscript "A novel design method for customized visual delimiting surveys for plant pests based on transects and scouting," by Barney P. Caton, Godshen R. Pallipparambil, and Hui Fang. This paper describes a novel approach for designing custom visual delimitation surveys, called ‘Delimitation via Transect Data and Scouting,’ or DTDS.
To evaluate the methodology, we identified published delimitation survey plans or results of activities for six species. One is a plan for a pest plant (or weeds) [which has no corresponding model]; the other five species have host plants. Using available information, we created customized DTDS plans for each species, and then compared the sampling effort required to complete the original and novel plans, for localized populations. We used simulations of each case study to compare the results under uncertainty and to evaluate outcomes when mapped spatially. See text for more information about published plans.
Simulation specifications
Overview. Models were built to emulate the survey conditions (e.g., areas, host densities, infestation rates) and survey plan specifications (areas and hosts inspected). Outputs were number of infected/infested hosts detected, by plan or scenario. We also estimated the inspection time required per host and the total time taken, again by plan or scenario.
Functions and parameters. Because of the number of models and survey plans created for evaluations, we cannot exhaustively present the functions or parameters used. Parameters came from the source or were standardized: only survey specifications differed in simulations, not situational details. For parameters with a single, mean estimate (e.g., trees per km2), in every case we added uncertainty by using lower and upper values that were ten percent different from the mean, and used a uniform distribution to sample values (i.e., every value equally likely). For example, if authors estimated 10,000 hosts per km2, the lower limit was 9,000 and the upper limit was 11,000.
Nearly all functions used were basic arithmetic, such as calculating infestation densities (no. per unit area), area sizes (e.g., π × R2), or widths. One exception was the binomial process. In this process, n independent, identical trials are run, each one with the same probability of success, p, producing some number of successes, s (Vose 2000): s = RiskBinomial(N, p).
This function was used, for example, to find the number of infested cells or hosts detected, where N was the number inspected and p was the infestation rate. We assumed perfect detection (i.e., sensitivity = 1.0) for simplicity.
General specifications. The models were all coded in spreadsheets and run using @Risk ver. 7.5.1 Professional Edition (Palisade Corporation, 31 Decker Road, Newfield, NY 14867), a Microsoft Excel add-in. Unless otherwise specified below, simulation settings were as follows: number of iterations = 100,000; sampling type = Latin Hypercube; and random seed = 101.
See the README for descriptions of each data file. Resources in this dataset:Resource Title: Probabilistic models for evaluating visual delimiting survey designs for Asian longhorned beetle (ALB). File Name: Caton et al 2023 ALB Models.xlsxResource Title: Probabilistic models for evaluating visual delimiting survey designs for glassy-winged sharpshooter (GWSS). File Name: Caton et al GWSS model.xlsxResource Description:
Resource Title: Probabilistic models for evaluating visual delimiting survey designs for Phyllosticta citracarpa. File Name: Caton et al Phyllosticta models.xlsxResource Title: Probabilistic models for evaluating visual delimiting survey designs for pinewood nematode (PWN). File Name: Caton et al PWN Models.xlsxResource Title: Probabilistic models for evaluating visual delimiting survey designs for tomato brown rugose fruit virus (TBRFV). File Name: Caton et al 2023 TBRFV survey model v1.xlsxResource Title: README. File Name: README_Caton.txt

{'manuscript': '在Barney P. Caton、Godshen R. Pallipparambil和Hui Fang所著的《基于横断面和巡查的植物病虫害定制视觉界定调查的新设计方法》一文中，采用了模型。该论文详细阐述了一种创新的方法，即‘基于横断面数据和巡查的界定’，简称DTDS，用于定制视觉界定调查的设计。', 'evaluation': '为评估该方法，我们确定了六种物种已发表的界定调查计划或活动结果。其中一种为害草（或杂草）[无相应模型对应]；其余五种物种具有寄主植物。利用现有信息，为每种物种创建了定制的DTDS计划，然后比较了完成原始和新型计划所需的采样工作量，针对局部种群进行评估。我们通过每个案例研究的模拟来比较不确定性下的结果，并评估空间映射时的结果。更多关于已发表计划的详细信息，请参阅正文。', 'simulation_specs': {'overview': '构建了模型以模拟调查条件（例如，区域、寄主密度、侵染率）和调查计划规格（检查的区域和寄主）。输出结果为根据计划或场景检测到的感染/侵染寄主数量。我们还估计了每个寄主所需的检查时间和总时间，同样根据计划或场景。', 'functions_params': {'functions': '由于评估中创建的模型和调查计划数量众多，我们无法详尽地展示所使用的函数或参数。参数来自原始数据或进行了标准化处理：模拟中仅调查规格不同，而非情境细节。', 'estimation': '对于具有单一均值估计（例如，每平方公里树木数量）的参数，在所有情况下，我们通过使用比均值低或高10%的下限和上限来增加不确定性，并使用均匀分布来采样值（即，每个值等可能）。例如，如果作者估计每平方公里有10,000个寄主，则下限为9,000，上限为11,000。', 'exceptions': '几乎所有使用的函数都是基本的算术运算，例如计算侵染密度（单位面积内数量）、面积大小（例如，π × R2）或宽度。一个例外是二项过程。在这个过程中，运行n个独立的、相同的试验，每个试验都有相同的成功概率p，产生一定数量的成功次数s（Vose 2000）：s = RiskBinomial(N, p)。', 'assumptions': '为了简化，我们假设检测完美（即，灵敏度=1.0）。'}, 'general_specs': {'coding': '所有模型均使用电子表格编写，并使用Palisade Corporation（31 Decker Road, Newfield, NY 14867）的@Risk ver. 7.5.1专业版（Microsoft Excel插件）运行。除非以下指定，否则模拟设置如下：迭代次数=100,000；采样类型=拉丁超立方；随机种子=101。'}}, 'resources': {'title': '用于评估亚洲长角象（ALB）视觉界定调查设计的概率模型', 'files': [{'title': 'Caton等人2023年ALB模型', 'name': 'Caton et al 2023 ALB Models.xlsx'}, {'title': 'Caton等人2023年GWSS模型', 'name': 'Caton et al GWSS model.xlsx'}, {'title': 'Caton等人Phyllosticta模型', 'name': 'Caton et al Phyllosticta models.xlsx'}, {'title': 'Caton等人松材线虫（PWN）模型', 'name': 'Caton et al PWN Models.xlsx'}, {'title': 'Caton等人番茄棕色皱缩果病毒（TBRFV）调查模型v1', 'name': 'Caton et al 2023 TBRFV survey model v1.xlsx'}, {'title': 'README', 'name': 'README_Caton.txt'}]}}