丹参在保护区生态压力下的种子发芽预测:一种人工智能建模方法。

IF 2.2 2区 环境科学与生态学 Q1 Agricultural and Biological Sciences
Maryam Saffariha, Ali Jahani, Daniel Potter
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引用次数: 0

摘要

背景:丹参(Salvia)是唇形科(Lamiaceae)中一个庞大、多样和多态的属,包括约 900 个观赏和药用物种,几乎分布在世界各地。丹参种子萌发的成功与否取决于多种生态因素和压力。我们旨在应用多重线性回归(MLR)和多层感知器(MLP)等人工智能建模技术,分析在盐度、干旱、温度和 pH 值四种生态胁迫下丹参种子的萌发情况。在不同的非生物条件组合下测试了 S.limbata 种子的萌发情况。测试了 10、15、20、25 和 30 °C 五种不同温度,0、-2、-4、-6、-8、-10 和 -12 bars 七种干旱处理,0、50、100.150、200、250、300 和 350 mM NaCl 八种盐度处理,以及 4、5、6、7、8 和 9 六种 pH 值处理。共测试了 228 种组合,以确定模型开发所需的发芽率:与 MLR 相比,MLP 模型在训练数据集(0.95)、验证数据集(0.92)和测试数据集(0.93)中的 R2 值都很显著。灵敏度分析结果表明,干旱、盐度、pH 值和温度分别是影响翅果种子萌发的最重要变量。土壤水分含量高、盐分含量低的地区对翅果种子的萌发具有较高的潜力。此外,温度为 18.3 °C、pH 值为 7.7 的地区可获得最多的发芽率:结论:多层感知器模型有助于管理者确定在农业或自然生态系统中种植林蛙种子的成功率。所设计的图形用户界面是一种环境决策支持系统工具,可帮助农业或牧场管理者预测在不同生态限制条件下林蛙种子发芽的成功率(百分比)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.

Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach.

Background: Salvia is a large, diverse, and polymorphous genus of the family Lamiaceae, comprising about 900 ornamentals, medicinal species with almost cosmopolitan distribution in the world. The success of Salvia limbata seed germination depends on a numerous ecological factors and stresses. We aimed to analyze Salvia limbata seed germination under four ecological stresses of salinity, drought, temperature and pH, with application of artificial intelligence modeling techniques such as MLR (Multiple Linear Regression), and MLP (Multi-Layer Perceptron). The S.limbata seeds germination was tested in different combinations of abiotic conditions. Five different temperatures of 10, 15, 20, 25 and 30 °C, seven drought treatments of 0, -2, -4, -6, -8, -10 and -12 bars, eight treatments of salinity containing 0, 50, 100.150, 200, 250, 300 and 350 mM of NaCl, and six pH treatments of 4, 5, 6, 7, 8 and 9 were tested. Indeed 228 combinations were tested to determine the percentage of germination for model development.

Results: Comparing to the MLR, the MLP model represents the significant value of R2 in training (0.95), validation (0.92) and test data sets (0.93). According to the results of sensitivity analysis, the values of drought, salinity, pH and temperature are respectively known as the most significant variables influencing S. limbata seed germination. Areas with high moisture content and low salinity in the soil have a high potential to seed germination of S. limbata. Also, the temperature of 18.3 °C and pH of 7.7 are proposed for achieving the maximum number of germinated S. limbata seeds.

Conclusions: Multilayer perceptron model helps managers to determine the success of S.limbata seed planting in agricultural or natural ecosystems. The designed graphical user interface is an environmental decision support system tool for agriculture or rangeland managers to predict the success of S.limbata seed germination (percentage) in different ecological constraints of lands.

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来源期刊
BMC Ecology
BMC Ecology ECOLOGY-
CiteScore
5.80
自引率
4.50%
发文量
0
审稿时长
22 weeks
期刊介绍: BMC Ecology is an open access, peer-reviewed journal that considers articles on environmental, behavioral and population ecology as well as biodiversity of plants, animals and microbes.
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