Changing Relationship between Specific Leaf Area and Leaf Matter Dry Content of Moso Bamboo Phyllostachys pubescens syn. edulis (Poales: Poaceae) under the Stress of Pantana phyllostachysae (Lepidoptera: Lymantriidae)

Bamboo forests play a crucial role in global forest ecosystems, exhibiting substantial carbon sequestration capabilities and widespread distribution on the Earth. Du et al. [1] estimated the total distribution area of bamboo forests worldwide to be 3053.84 × 10⁴ ha by using multi-source remote sensing data. “2021 China’s Forest and Grassland Ecology Comprehensive Monitoring and Evaluation Report” indicated that the area of bamboo forest in China in 2021 was 7,562,700 ha, accounting for 3.31% of the total forest area. Its carbon storage significantly contributes to achieving the “double carbon” goal. The carbon storage of bamboo forests contributes a lot to the realization of the goal of “double carbon”, which is conducive to the ecological security and socioeconomic development of society. Among them, Moso bamboo, Phyllostachys pubescens syn. edulis (Carrière) J. Houz. (Poales: Poaceae) is an important bamboo plant that is widely distributed in tropical and subtropical regions of Asia, Africa, and the Americas, and has attracted much attention globally for its rapid growth and diverse uses [2]. However, Pantana phyllostachysae Chao (Lepidoptera: Lymantriidae), as a leaf-feeding pest, feeds on the leaves of Ph. pubescens, leading to symptoms such as deficiencies, yellowing, and lesions on the leaves [3,4]. This not only affects the growth status of bamboo forests but also reduces the carbon storage capacity and biomass of bamboo forests, which has a serious impact on the yield and quality of bamboo products [5]. Every year, P. phyllostachysae causes huge ecological and economic losses to bamboo forests. Therefore, it is of great significance to study the leaf characteristics of Ph. pubescens forests and their intrinsic relationship patterns under the stress of the P. phyllostachysae, which can help to understand the leaf functional attributes and the embodiment of the intrinsic mechanism and adaptive capacity of bamboo forests.

The vitality of vegetation is often gauged by the physiological condition of leaves [6], and leaf functional traits serve as key indicators of a plant’s adaptive response to its environment at the leaf level. Over recent years, physiological ecologists have increasingly focused on unraveling plant adaptation strategies through the examination of leaf functional traits [7,8]. These traits, intricately linked to plant growth responses and resource utilization efficiency, encompass specific leaf area (SLA), leaf dry matter content (LDMC), leaf thickness, leaf area, leaf longevity, leaf nitrogen content (N), phosphorus content (P), chlorophyll content (Chl), and so on [9]. A comprehensive analysis of these indicators provides valuable insights into the adaptation mechanisms and resource utilization strategies of plants in diverse environments. While current studies on plant traits predominantly concentrate on a large-scale level [10], there is a noticeable dearth of attention towards changes in leaf functional traits during the developmental stages of individual plants—an essential aspect for the in-depth exploration of individual plant growth and development [11]. Hence, there is a pressing need for intensified systematic research on the dynamics of leaf functional traits at the individual plant level to foster a more comprehensive understanding of the mechanisms driving plant adaptation, growth, and development. Leaf functional traits serve as reflections of the physiological and morphological adaptations undertaken by plants in response to environmental changes [12,13,14]. Within this array of traits, SLA and LDMC emerge as pivotal indicators, encapsulating a plant’s ability to harness resources and adapt to its environment for survival [15,16,17,18]. SLA, denoted as the ratio of leaf unifacial area to leaf dry weight, provides insights into a plant’s adaptive condition in diverse environmental conditions [15,19]. It serves as a fundamental leaf trait within the global leaf economic phenotype spectrum, offering a key component in plant carbon harvesting strategies and indicating the efficiency of plants in utilizing light, temperature, and water resources [20,21,22]. In contrast, LDMC is intricately linked to the nutritional status of plants and the establishment of carbon pools [16,23]. The study of leaf functional traits is indispensable for comprehending and predicting plant responses to environmental changes amid the global shift.

The examination of trait variations in plants under stress situations is an important area in plant ecology and environmental biology [24]. Stressors encompass biological stresses (e.g., pathogen infection), physical stresses (e.g., heat, drought, and salinity), and chemical stresses (e.g., soil contamination) [25,26,27], which may induce a range of physiological, morphological, and molecular trait changes in plants. Currently, scholars’ studies on plants under various natural environmental conditions and different elemental stresses primarily focus on physical and chemical stresses. For instance, in a study of 12 species of broadleaf evergreen eucalypts in Victoria, Liz et al. [28] observed that SLA and leaf area decreased with increasing tree height and age, while leaf thickness increased. Similarly, when examining North American redwoods in temperate forests of northern California, Koch et al. [29] found that leaf area decreased with increasing tree height, and specific leaf mass increased. Canham et al. [22] noted that LDMC serves as a predictor of resource acquisition stability, reflecting a plant’s nutrient conservation capability. Wilson et al. [30] demonstrated that leaf water content influences LDMC, with dry matter content being less affected by leaf thickness. Other studies have indicated an elevation-related increase in LDMC [31], and research by Hu et al. [32] suggested that the LDMC of evergreen tree species tends to rise with elevation. Cui et al. [33] illustrated that a higher LDMC benefits plants in nutrient storage, aiding adaptation to arid and infertile environments, thus thriving in challenging conditions. Conversely, analyses of the correlation between SLA and LDMC traits under biotic stresses, particularly concurrent pest stresses, have yielded fewer discernible patterns. Understanding plant survival strategies and predicting ecosystem stability are integral to comprehending ecosystem responses to environmental shifts, facilitating the design and implementation of biodiversity conservation measures. There is a lot of research related to predicting and monitoring plants through modeling. For instance, a decision tree regression model was applied to build the extraction model of plants [34], for wetland information extraction [35], etc.; the RF regression model was applied for the identification of bamboo forest patches [36], and to build the model for estimating the chlorophyll of winter wheat canopy [37]; the XGBoost regression model was applied to build the prediction model of the five metabolites in mustard leaves [38], to build the inversion model of the concentration of phytoplankton pigments [39], to identify bamboo species [40], and for the detection of P. phyllostachysae, etc. [41]; and the Catboost regression model was used in the study of the estimation of cedar monocotyledonous storage, etc. [42].