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相关概念视频

Distribution and Dispersion00:54

Distribution and Dispersion

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To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.0K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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相关实验视频

Updated: Jul 24, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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具有不完美的检测的联合物种分布模型,用于高维空间数据.

Jeffrey W Doser1,2, Andrew O Finley2,3, Sudipto Banerjee4

  • 1Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA.

Ecology
|July 10, 2023
PubMed
概括

一个新的空间因素多种占用模型通过考虑物种相关性,不完美的检测和空间自相关性来有效估计物种分布. 与忽视这些因素的模型相比,这种方法改善了生态预测.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语最接近的邻居是高斯过程.隐藏因素是一个隐藏因素.占用模式 占用模式

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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

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相关实验视频

Last Updated: Jul 24, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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科学领域:

  • 生态生态学 生态生态学
  • 保护生物学 保护生物学
  • 计算生物学 计算生物学

背景情况:

  • 估计物种分布和生物多样性对于生态和保护至关重要.
  • 联合物种分布模型 (JSDMs) 分析了多个物种的检测-不检测数据,但面临着诸如物种相关性,不完美的检测和空间自相关性等挑战.
  • 很少有方法可以同时解决JSDM中的所有三种复杂性.

研究的目的:

  • 开发一个空间因素多物种占用模型,同时考虑物种相关性,不完美的检测和空间自相关性.
  • 为了确保大量物种和空间位置的大型数据集的计算效率.
  • 为分析复杂的生态数据提供一个用户友好的工具.

主要方法:

  • 开发了一个空间因子多种占用模型,使用空间因子维度减小和最近邻居高斯过程.
  • 将拟议模型的性能与解决复杂性子集的五种替代模型进行了比较.
  • 实现了开源R包spOccupancy中的模型.

主要成果:

  • 模拟表明,忽视物种相关性,不完美的检测或空间自相关性导致预测性能较差.
  • 拟议的空间因素多种占用模型在对美国大陆98种鸟类的案例研究中表现出卓越的预测性能.
  • 忽视复杂性的影响因研究目标而异.

结论:

  • 开发的空间因子多种占用模型为了解物种分布和生物多样性的空间变化提供了强大的框架.
  • spOccupancy R包为生态学家提供了一个可访问的工具,可以应用先进的占用模型技术.
  • 同时处理多个数据复杂性对于准确的生态推断和保护规划至关重要.