Venerable trees of tropical Chinese Wuzhishan city: Distribution patterns and drivers

Self Cite

Despite their relatively small numbers, large old trees play disproportionately important roles in global biodiversity and ecosystem functions. There is a lack of systematic reviews and quantitative analyses of the accumulated literature. Understanding the research context and evolution could pump prime research and conservation endeavors. Using the comprehensive Web of Science, we applied VOSviewer (1.6.19) and CiteSpace (6.1R2) bibliometric software to examine the large old tree research field in 2000–2022. The queries of the bibliographic database generated quantitative–visual depictions in the form of knowledge maps. The nodes denote research intensity, and inter-node linkages denote the pathways and frequencies of collaborative activities. The research outputs differed significantly in terms of regions, countries, institutions, high-citation articles, productive researchers, hot topics, and research frontiers. Conspicuous spatial disparities were displayed, with the U.S.A., China, and Australia leading in publication counts and a cluster of European countries making considerable collective contributions. The research collaboration demonstrated a dichotomy: European countries networked more by geographical propinquity, and the top three countries connected by long-distance leap-frog jumps. The entrenched discrepancies between the endowed developed domains vis-à-vis the deprived developing domains were clearly expressed. The research productivity progressed through three stages: initial, growth, and flourishing. The leading institutions, researchers, and highly cited papers were recognized. The keyword analysis pinpointed diverse research hotspots: growth dynamics, conservation and management, ecological functions, and environmental response. This study informs recommendations for future research directions and cooperation on longevity mechanisms, evolutionary adaptation, dynamic monitoring, and temporal–spatial patterns. The integrated application of GIS, machine learning, and big data technologies could strengthen research capability.

Section: Main Researchersmentioning

confidence: 99%

Section: Research Frontiersmentioning

confidence: 99%

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

Xie,

Liu,

Liu

et al. 2024

Self Cite

“…The relationship between TRWi and climatic variables was modeled by applying a sequential combination of Random Forest (RF)-which belongs to the group of machine learning algorithms-and Generalized Linear Models (GLMs), two methods that have been successfully used in growth ring analyses (e.g., [77][78][79][80]). A diagram with the analysis flow is presented in Figure 3.…”

Section: Modeling Approachesmentioning

confidence: 99%

Climate–Growth Relationships in Laurus azorica—A Dominant Tree in the Azorean Laurel Forest

Pavão

Jevšenak

Silva

et al. 2023

Forests on oceanic islands, such as the Azores archipelago, enable interesting dendroclimatic research, given their pronounced climatic gradients over short geographical distances, despite the less pronounced seasonality. The Lauraceae play an essential ecological role in Macaronesian natural forests. An example is Laurus azorica (Seub.) Franco, a relevant species given its high frequency and physiognomic dominance in Azorean laurel forests. This study aims to quantify climate–growth relationships in L. azorica using a dendroecological approach. We sampled four stands at São Miguel and two stands at Terceira islands, for a total of 206 trees. Following standard dendrochronological methods and rigorous sample selection procedures, we obtained relatively low rbar values and high temporal autocorrelation. Using a stepwise Random Forest analysis followed by Generalized Linear Models calculation, we found prominent effects of present and previous year temperature, but a low precipitation signal on growth rings, with some model variation between stands. Our results agreed with previous observations for broad-leaved species with diffuse porous wood, contributing to increase the baseline dendroecological knowledge about Azorean forests. Due to the high levels of within- and between-stand variation, and to refine the climatic signal analysis, complementary approaches should be explored in the future.

“…However, based on the traditional ecological speciesarea hypothesis, ancient trees and their factors in the area of administrative regions or cities are quite different, resulting in deviation. Furthermore, some research scales are small or medium based on the available ancient tree data, such as for one city or one county [41,42]. Due to the area limitations of the cities and counties where the latitude and longitude spans are small, the differences in internal topography, climate, history and culture, and other conditions are not significant, so it is challenging to distinguish the main drivers of ancient trees in different regions, which is not conducive to the protection of the ancient tree resources in accordance with the local conditions.…”

Section: Introductionmentioning

confidence: 99%

What Drives the Spatial Variation of Interregional Ancient Trees? A Geoinformatics–Based Approach in Henan, Central China

Hou,

Ren,

Wang

et al. 2024

In the context of ecological civilization construction in China, interregional ancient trees are important natural and cultural resources, and it is vital that they are effectively protected and utilized according to their spatial distribution and regional conditions in order to provide a balance between local economic development and the ecological environment. This article analyzes the spatial heterogeneity of 26,842 ancient trees and explores the underlying natural and human factors by using geoinformatics–based techniques (i.e., the nearest neighbor index, kernel density, spatial autocorrelation, and the geographically weighted regression model) in Henan Province, Central China. The research findings conclude that: (1) The interregional ancient trees show positive spatial autocorrelation, and the aggregation effect in Western Henan is the most significant. (2) The growing environment and elevation exert a more obvious influence on the spatial pattern of interregional ancient trees. (3) The main influencing factors per region are different, for example, historical culture in Western Henan, water source and elevation in Southern Henan, and the growing environment in Eastern Henan, while ancient trees in Northern Henan have a weak relationship with each factor. This research facilitates political strategy making for sustainable development and the protection of interregional ancient trees.