Modelling Suitable Habitat and Ecological Characteristics of Old Trees Using DIVA-GIS in Anhui Province, China

Abstract

Old trees as a natural heritage are significant components of terrestrial biodiversity, and they also play an important role in the ecosystem. An overall understanding of the distribution patterns and ecological characteristics of old trees in China's Anhui Province will provide valuable insights into old tree conservation and scientific planning. Based on the data from 617 field collection and historical sample documents, we applied DIVA-GIS combined with the BIOCLIM model to qualitatively and quantitatively analyse the distribution patterns of old trees and their dominant climate factors for the first time. The model assessment results indicated that the area under the receiving operator curve (AUC) and kappa statistics reached 0.969 and 0.921, respectively, which indicated that the data fit the model very well and that the prediction was highly reliable. According to principal component analysis (PCA), the main bioclimatic variables influencing the distribution of old trees were as follows: water requirements, drought tolerance, preference temperature, heat resistance, cold tolerance and others. The cumulative frequency curves further confirmed that the ecological characteristics of the geographical distributing area for old trees in the study area were annual mean temperature (10.90~16.30ºC), maximum temperature of warmest month (25.60~32.40ºC), mean temperature of coldest quarter (0.30~4.90ºC), annual precipitation (665.00~1701.00 mm), precipitation of driest month (14.00~47.00 mm) and precipitation of driest quarter (45.00~173.00 mm). The current distribution pattern of old trees was mainly affected by precipitation factors. Furthermore, this study demonstrates a new method for the conservation of old trees by virtue of GIS software and predictive models. All of these results could help establish conservation plans for old trees.

Charting the Research Terrain for Large Old Trees: Findings from a Quantitative Bibliometric Examination in the Twenty-First Century

“…It covered specific issues such as carbon sequestration, climate impacts, genetic diversity, and the emergence of new topics [30][31][32]. The research literature is projected to grow continually, with Chinese scholars showing a particular interest [1,30,33]. ical studies.…”

Spatial Distribution and Driving Factors of Old and Notable Trees in a Fast-Developing City, Northeast China

“…A total of 10 administrative districts comprises the whole city, including Chaoyang District (CYD), Changchun New Area (CNA), Nanguan District (NGD), Kuancheng District (KCD), Lvyuan District (LYD), Automobile Economic and Technological Development Zone (ADZ), Jingyue High-tech Industrial Development Zone (JDZ), Erdao District (EDD), Economic and Technological Development Zone (EDZ), and Lianhuashan Eco-tourism Resort (LER) [31]. The built-up area is 543 km 2 , with a population of 4.468 million at the end of 2020. Changchun is located in the warm temperate zone, which is continental monsoon climate area, with an annual average temperature of 7.1 • C, annual precipitation of 662 mm, and annual sunshine duration of 2688 h in 2020 [31].…”

“…Old and Notable Trees (ONTs) are precious natural resources in all natural landscapes [1][2][3][4]. ONTs refer to trees more than 100 years old or that have important historical, cultural, scientific, and landscape values as well as commemorative significance [5].…”

“…The findings are suitable for the cities suffering from or that will experience rapid urbanization who wish to maintain sustainable development. Specifically, the three objectives of this study are: (1) to determine the current distribution of ONTs in Changchun, (2) to explore the reasons of ONTs' abundance in different environments, and (3) to reveal the potential driving factors of ONT patterns by comparing Changchun's data with nearby cities.…”

Distribution pattern of large old Ginkgo biloba in China under climate change scenarios

“…To better understand and protect these irreplaceable and vulnerable resources, comprehensive LOT studies have been conducted worldwide to map distributions, assess threats, and develop conservation strategies (Le Roux et al., 2014 ; Lindenmayer & Laurance, 2017 ; Malliarou et al., 2023 ). Among various research methods, the species distribution model (SDM) has emerged as an essential tool with diverse applications in LOT studies and conservation planning (Xie et al., 2020 , 2022 ). SDM utilizes tailor‐made computer algorithms to predict potentially suitable habitats based on known occurrence records and environmental variables (Abdelaal et al., 2019 ; Zhang et al., 2023 ).…”