Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

doi:10.1093/jpe/rtaa092

J Plant Ecol ›› 2021, Vol. 14 ›› Issue (2): 241-256 .DOI: 10.1093/jpe/rtaa092

• Research Articles • Previous Articles Next Articles

Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

Da-Ju Wang^1,2, Hai-Yan Wei^2,
*, Xu-Hui Zhang^1,2, Ya-Qin Fang^1,2 and Wei Gu^1,3,
*

¹ National Engineering Laboratory for Resource Development of Endangered Crude Drugs in Northwest of China, Shaanxi Normal University, Xi’an 710119, China, ²Department of Geographical Sciences, School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China, ³ Department of Biology, College of Life Sciences, Shaanxi Normal University, Xi’an 710119, China

^*Corresponding author. E-mail: weihy@snnu.edu.cn (H.-Y.W.); weigu@snnu.edu.cn (W.G.)

Received:2020-03-03 Revised:2020-05-25 Accepted:2020-11-08 Online:2020-11-29 Published:2021-03-01

Abstract

Abstract:

Aims

Remote sensing (RS) is a technical method for effectively capturing real-world data on a large scale. We aimed to (i) realize the time synchronization of species and environmental variables, and extract variables related to the actual growth of species based on RS in habitat suitability modeling, and (ii) provide a reference for species management.

Methods

Taking invasive species Ambrosia artemisiifolia in China as an example for habitat suitability modeling. Temperature and precipitation variables were calculated from the land surface temperature provided by the moderate-resolution imaging spectroradiometer (MODIS), and climate station data, respectively. Besides, other variables that directly affect the growth or reproduction of A. artemisiifolia were also included, such as the relative humidity of the previous year’s flowering period (RHPFP), and the effective UV irradiance reaching the Earth’s surface (UVI). The random forest method was selected to model the habitat suitability. The environmental variables and samples were divided into four-time periods (i.e. 1990–2000, 2001–2005, 2006–2010 and 2011–2016) based on sampling time. Variables from the long-time series of RS (1990–2016) and WorldClim (1960–1990) were also modeled.

Important Findings

It was feasible to extract environmental variables from RS for habitat suitability modeling, and was more accurate than that based on the variables from WorldClim. The potential distribution of A. artemisiifolia in 1990–2000 and 2006–2010 was smaller than that in 2001–2005 and 2011–2016. The precipitation of driest months (bio14), precipitation coefficient of variation (bio15), RHPFP and UVI were the important environmental variables that affect the growth and reproduction of A. artemisiifolia. The results indicated that the time synchronization of species and environmental variables improved the prediction accuracy of A. artemisiifolia, which should be considered in habitat suitability modeling (especially for annual species). This study can provide an important reference for the management and prevention of the spread of A. artemisiifolia.

Key words: remote sensing, time synchronization, habitat suitability, Ambrosia artemisiifolia, prevention

摘要：
基于遥感实现物种和环境变量时间同步性的生境适宜性建模
遥感是一种有效获取大规模现实数据的技术方法。我们旨在生境适宜性建模中基于遥感实现物种与环境变量之间的时间同步性，并提取与物种实际生长相关的变量，为物种管理提供更有效的参考。本研究以入侵中国的豚草(Ambrosia artemisiifolia)为例，开展生境适宜性建模，温度和降水变量分别依据中分辨率成像光谱仪(MODIS)提供的地表温度和气象站点数据计算；此外，本研究还包括直接影响豚草生长或繁殖的其他变量，如前一年花期的相对湿度和有效紫外辐射。选择随机森林模型开展生境适宜性建模，根据采样时间，把环境变量和样本分为4个时间段(1990–2000、2001–2005、2006–2010和2011–2016)，同时对基于RS (1990–2016)和WorldClim (1960–1990)的长时间序列的变量也进行建模。结果显示，从遥感提取环境变量开展生境适宜性建模是可行的，而且比基于WorldClim变量预测结果更准确。1990–2000年和2006–2010年豚草的潜在分布面积小于2001–2005年和2011–2016年，影响豚草生长和繁殖的重要环境变量包括最干旱月降水量(bio14)、降水变异系数(bio15)、前一年花期的相对湿度和有效紫外辐射。我们的研究结果表明，实现物种与环境变量的时间同步性提高了豚草潜在分布的预测精度，在生境适宜性建模(尤其为一年生物种)中应予以考虑。本研究为管理和预防豚草入侵扩散提供了重要参考。

关键词: 遥感, 时间同步性, 生境适宜性, 豚草, 预防

Da-Ju Wang, Hai-Yan Wei, Xu-Hui Zhang, Ya-Qin Fang and Wei Gu. Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables [J]. J Plant Ecol, 2021, 14(2): 241-256.

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Habitat suitability modeling based on remote sensing to realize time synchronization of species and environmental variables

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