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相关概念视频

What are Populations and Communities?00:30

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Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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    估计野生种群大小对于保护至关重要. 一种新的遗传方法,近亲标记回收神经网络 (CKMRnn),准确地估计了种群,即使有空间变化,也减少了估计的不确定性.

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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
    • 人口遗传学 人口遗传学

    背景情况:

    • 估计野生种群规模至关重要,但具有挑战性.
    • 基因方法为传统技术提供了不那么侵入性和潜在的更便宜的替代方案.
    • 现有的近亲标记重新捕获 (CKMR) 模型与空间异质性作斗争.

    研究的目的:

    • 开发一种新的CKMR方法,以考虑人口密度和采样努力的空间异质性.
    • 使用遗传数据创建一个可靠的方法来估计人口规模.

    主要方法:

    • 一种基于模拟的方法,集成空间显式基于个体的模拟.
    • 利用深层卷积神经网络 (CKMRnn) 来进行人口规模估计.
    • 通过广泛的模拟和对乌干达大象的实证研究来验证该方法.

    主要成果:

    • CKMRnn在人口大小估计方面表现出高准确度,即使存在空间异质性.
    • 该方法成功地解释了潜在的混因素,如未知的人口历史.
    • 对乌干达大象的实证应用产生了与传统方法可比的点估计,但对置信区间宽度减少了30%左右.

    结论:

    • CKMRnn代表了遗传种群估计的重大进步,克服了以前的CKMR模型的局限性.
    • 这种方法为生态学家和自然保护主义者提供了更精确,更可靠的工具.
    • 该方法处理空间复杂性和减少不确定性的能力提高了其在现实世界保护场景中的适用性.