Emission characteristics of biogenic volatile organic compounds in a subtropical pristine forest of southern China.

Emission characteristics of biogenic volatile organic compounds (BVOCs) from dominant tree species in the subtropical pristine forests of China are extremely limited. Here we conducted in situ field measurements of BVOCs emissions from representative mature evergreen trees by using dynamic branch enclosures at four altitude gradients (600-1690 m a.s.l.) in the Nanling Mountains of southern China. Composition characteristics as well as seasonal and altitudinal variations were analyzed. Standardized emission rates and canopy-scale emission factors were then calculated. Results showed that BVOCs emission intensities in the wet season were generally higher than those in the dry season. Monoterpenes were the dominant BVOCs emitted from most broad-leaved trees, accounting for over 70% of the total. Schima superba, Yushania basihirsuta and Altingia chinensis had relatively high emission intensities and secondary pollutant formation potentials. The localized emission factors of isoprene were comparable to the defaults in the Model of Emissions of Gases and Aerosols from Nature (MEGAN), while emission factors of monoterpenes and sesquiterpenes were 2 to 58 times of those in the model. Our results can be used to update the current BVOCs emission inventory in MEGAN, thereby reducing the uncertainties of BVOCs emission estimations in forested regions of southern China.

Influencia de la edad en el contenido de fenoles y ligninas en Gmelina arborea (Lamiaceae) / The influence of age on the phenolic contents and lignins of Gmelina arborea (Lamiaceae)

Resumen Introducción: La melina (Gmelina arborea), es una especie de gran interés por su madera y propiedades medicinales. En Costa Rica, existen clones genéticamente superiores que se propagan sin el conocimiento de la edad ontogénica y fisiológica de los materiales. Objetivo: Evaluar la relación del contenido de fenoles y ligninas en hojas, peciolos, tallos y raíces de plantas con diferentes edades. Métodos: Los contenidos de fenoles y ligninas totales se determinaron mediante el método colorimétrico de Folin-Ciocalteu y el método de extracción alcalina, respectivamente. Para la investigación se eligieron plantas in vitro "año cero" y árboles de año y medio, cuatro, siete y 20 años. El muestreo se realizó en marzo y abril del 2021. Resultados: Se demostró que todas las partes de la planta analizadas contienen compuestos fenólicos y ligninas, independientemente de su edad. No hubo una correlación positiva entre la edad con el contenido de fenoles y ligninas para ninguna condición de desarrollo, pues los valores más altos no se obtuvieron en los árboles más longevos. Los extractos de hojas de las plantas in vitro y los árboles de siete años mostraron, respectivamente, los contenidos más altos de fenoles y ligninas para todas las condiciones (P < 0.05). Los valores promedio más bajos de compuestos fenólicos para todas las condiciones se obtuvieron en los árboles de cuatro años. Respecto a las ligninas, el contenido más bajo se presentó en las raíces más longevas, aunque la tendencia no se mantuvo para el resto de las partes de la planta. Conclusiones: La investigación muestra los primeros resultados del contenido de compuestos fenólicos y ligninas presentes en diferentes tejidos de una especie forestal de edades diferentes. Por lo tanto, son los primeros valores de referencia acerca del compromiso bioquímico para la síntesis fenólica según la edad y el estado de desarrollo específico de una planta leñosa.

Abstract Introduction: Melina (Gmelina arborea) is a tree species of great interest for its wood and medicinal properties. In Costa Rica, there are genetically superior clones that are propagated without knowledge of the ontogenic and physiological age of the materials. Objective: To evaluate how age influences the content of phenols and lignins in leaves, petioles, stems, and roots of melina plants. Methods: The total phenolic and lignins contents were determined using Folin-Ciocalteu colorimetric method and alkaline extraction method, respectively. Plants of five different ages were chosen for the investigation (in vitro plants "year 0" and trees of a year and a half, four, seven and 20 years). Sampling was done in March and April 2021. Results: All parts of the plant analyzed contain phenolic compounds and lignins, regardless of their age. There was no positive correlation between age and phenol and lignin content for any development condition, since the highest values were not obtained in the oldest trees. Leaf extracts from in vitro plants and seven-year-old trees showed, respectively, the highest phenol and lignin contents for all conditions (P < 0.05). The lowest average values of phenolic compounds for all conditions were obtained in four-year-old trees. Regarding lignins, the lowest content occurred in the oldest roots, although the trend was not maintained for the rest of the plant parts. Conclusions: This study provides the first results of the content of phenolic compounds and lignins present in different tissues of a forest species of different ages. Therefore, they are the first reference values about the biochemical commitment for phenolic synthesis according to the age and the specific developmental stage of a woody plant.

Forest fire size amplifies postfire land surface warming.

Climate warming has caused a widespread increase in extreme fire weather, making forest fires longer-lived and larger1-3. The average forest fire size in Canada, the USA and Australia has doubled or even tripled in recent decades4,5. In return, forest fires feed back to climate by modulating land-atmospheric carbon, nitrogen, aerosol, energy and water fluxes6-8. However, the surface climate impacts of increasingly large fires and their implications for land management remain to be established. Here we use satellite observations to show that in temperate and boreal forests in the Northern Hemisphere, fire size persistently amplified decade-long postfire land surface warming in summer per unit burnt area. Both warming and its amplification with fire size were found to diminish with an increasing abundance of broadleaf trees, consistent with their lower fire vulnerability compared with coniferous species9,10. Fire-size-enhanced warming may affect the success and composition of postfire stand regeneration11,12 as well as permafrost degradation13, presenting previously overlooked, additional feedback effects to future climate and fire dynamics. Given the projected increase in fire size in northern forests14,15, climate-smart forestry should aim to mitigate the climate risks of large fires, possibly by increasing the share of broadleaf trees, where appropriate, and avoiding active pyrophytes.

Tree drought-mortality risk depends more on intrinsic species resistance than on stand species diversity.

Increasing tree diversity is considered a key management option to adapt forests to climate change. However, the effect of species diversity on a forest's ability to cope with extreme drought remains elusive. In this study, we assessed drought tolerance (xylem vulnerability to cavitation) and water stress (water potential), and combined them into a metric of drought-mortality risk (hydraulic safety margin) during extreme 2021 or 2022 summer droughts in five European tree diversity experiments encompassing different biomes. Overall, we found that drought-mortality risk was primarily driven by species identity (56.7% of the total variability), while tree diversity had a much lower effect (8% of the total variability). This result remained valid at the local scale (i.e within experiment) and across the studied European biomes. Tree diversity effect on drought-mortality risk was mediated by changes in water stress intensity, not by changes in xylem vulnerability to cavitation. Significant diversity effects were observed in all experiments, but those effects often varied from positive to negative across mixtures for a given species. Indeed, we found that the composition of the mixtures (i.e., the identities of the species mixed), but not the species richness of the mixture per se, is a driver of tree drought-mortality risk. This calls for a better understanding of the underlying mechanisms before tree diversity can be considered an operational adaption tool to extreme drought. Forest diversification should be considered jointly with management strategies focussed on favouring drought-tolerant species.

Seasonal dynamics in leaf litter decomposing microbial communities in temperate forests: a whole-genome- sequencing-based study.

Leaf litter decomposition, a crucial component of the global carbon cycle, relies on the pivotal role played by microorganisms. However, despite their ecological importance, leaf-litter-decomposing microorganism taxonomic and functional diversity needs additional study. This study explores the taxonomic composition, dynamics, and functional role of microbial communities that decompose leaf litter of forest-forming tree species in two ecologically unique regions of Europe. Twenty-nine microbial metagenomes isolated from the leaf litter of eight forest-forming species of woody plants were investigated by Illumina technology using read- and assembly-based approaches of sequences analysis. The taxonomic structure of the microbial community varies depending on the stage of litter decomposition; however, the community's core is formed by Pseudomonas, Sphingomonas, Stenotrophomonas, and Pedobacter genera of Bacteria and by Aureobasidium, Penicillium, Venturia genera of Fungi. A comparative analysis of the taxonomic structure and composition of the microbial communities revealed that in both regions, seasonal changes in structure take place; however, there is no clear pattern in its dynamics. Functional gene analysis of MAGs revealed numerous metabolic profiles associated with leaf litter degradation. This highlights the diverse metabolic capabilities of microbial communities and their implications for ecosystem processes, including the production of volatile organic compounds (VOCs) during organic matter decomposition. This study provides important advances in understanding of ecosystem processes and the carbon cycle, underscoring the need to unravel the intricacies of microbial communities within these contexts.

Injuries and deaths due to tree failure in The Netherlands: analysis of observational data from 1998-2021.

Urban and roadside trees contribute to health and resilience. However, when trees or branches fall, it can cause injuries or deaths. This study examined trends and variations of injuries and deaths due to tree failure in The Netherlands from 1998 to 2021, considering urban-rural location, sex, age and traffic mode. This study is the first to describe long-term trends in injuries and deaths due to tree failure from 1998-2021. The standardised rate of injuries per 1,000,000 population increased from 0.14 (SE 0.10) in 1998 to 0.91 (SE 0.21) in 2021, with an annual percentage increase of 5.3% (p = 0.002). The data shows a strong increase for rural areas, contrary to urban ones. The annual percentage increase in rural areas was 13.2% (p < 0.001) while injuries in urban areas increased with 3.0% (p = 0.026), which revealed large urban-rural disparities. A trend was absent in the frequency of deaths. More attention needs to be given to investigating causes, drivers and stressors associated with tree failure-related injuries. In particular, efforts should be made to reduce the prevalence in rural areas. The increase in injuries over time makes it necessary to create awareness and share knowledge among residents and local governments about tree failure risks.

Sanitizer Resistance and Persistence of Listeria monocytogenes Isolates in Tree Fruit Packing Facilities.

The foodborne pathogen Listeria monocytogenes can persist in produce processing environments, which increases the risk for food contamination. Increased resistance to antimicrobials commonly used in cleaning and sanitizing procedures may contribute to L. monocytogenes' persistence in these environments. This study aimed to evaluate sanitizer resistance in L. monocytogenes isolates collected from three tree fruit packing facilities (F1, F2, and F3) during packing seasons 2020-2021 (Y1) and 2021-2022 (Y2), and to assess evidence of persistence based on the genomic similarity of isolates to historical isolates collected in previous years. L. monocytogenes isolates collected in 2020-2022 (n = 44) were tested for resistance to peroxyacetic acid (PAA) and a proprietary biofilm-removing agent using a broth microdilution assay. Further, L. monocytogenes isolates were whole genome sequenced and screened for the presence of antimicrobial resistance and virulence genes, as well as to assess the genomic similarity of isolates using the CFSAN SNP bioinformatic pipeline. Over half (57%) of the tested isolates had a PAA minimum inhibitory concentration (MIC) of 250 ppm, which was similar to the applied concentration of the PAA sanitizer in the three facilities (230 ppm). In contrast, 80% of tested isolates had a biofilm remover MIC of 0.13 ppm, which was substantially below the concentration applied in the facilities (137 ppm). Genomes of all tested isolates carried antimicrobial resistance (fosX, lin, mdrL, mprF, and norB) and virulence (inlA, inlB, plcA, plcB, prfA, hly, mpl, and iap) genes. L. monocytogenes isolates collected between 2020 and 2022 belonged to three distinct lineages, with 22 multilocus sequence types (MLSTs) belonging to 22 different clonal complexes. Genomic similarity analysis with historical isolates collected from the same facilities in 2016-2017 demonstrated a 5-year persistence of the genotypes ST 1003 and ST 554 in F2, which were no longer detected in 2022. Overall, our results highlight the need to re-evaluate sanitizer concentrations to effectively control persistent L. monocytogenes strains in tree fruit packing facilities.

A global dataset of forest regrowth following wildfires.

Wildfires result in forest loss or degradation and release substantial emissions into the atmosphere. Forest regrowth following these fires allows for ecosystem repair and carbon replenishment. However, there is a lack of datasets explicitly characterizing the forest regrowth after fires. Here we employed multiple remotely sensed datasets to generate the first global maps of forest structure regrowth including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) following wildfires at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratio and rate at 5-year intervals, primarily from 2000 to 2020. The dataset developed in this study provides detailed insights into the characteristics of global forest regrowth following forest fires in both spatial and temporal dimensions, contributing to the assessment of forest ecology equilibrium and the quantification of forest carbon dynamics.

Carbon sequestration potential of tree planting in China.

China's large-scale tree planting programs are critical for achieving its carbon neutrality by 2060, but determining where and how to plant trees for maximum carbon sequestration has not been rigorously assessed. Here, we developed a comprehensive machine learning framework that integrates diverse environmental variables to quantify tree growth suitability and its relationship with tree numbers. Then, their correlations with biomass carbon stocks were robustly established. Carbon sink potentials were mapped in distinct tree-planting scenarios. Under one of them aligned with China's ecosystem management policy, 44.7 billion trees could be planted, increasing forest stock by 9.6 ± 0.8 billion m³ and sequestering 5.9 ± 0.5 PgC equivalent to double China's 2020 industrial CO2 emissions. We found that tree densification within existing forests is an economically viable and effective strategy and so it should be a priority in future large-scale planting programs.

Identification of urban street trees for green belt development for optimizing pollution mitigation in Delhi, India.

The current study evaluated the effects of air pollution on selected street trees in the National Capital Territory during the pre- and post-monsoon seasons to identify the optimally suitable tree for green belt development in Delhi. The identification was performed by measuring the air pollution tolerance index (APTI), anticipated performance index (API), dust-capturing capacity (DCC) and proline content on the trees. The APTI of street trees of Delhi varied significantly among different tree species (F11,88.91 = 47.18, p < 0.05), experimental sites (F3,12.52 = 6.65, p < 0.001) and between seasons (F1,31.12 = 16.51, p < 0.001), emphasizing the relationships between trees and other types of variables such as the climate and level of pollution, among other factors. This variability emphasizes the need to choose trees to use for urban greening in the improvement of air quality in different environments within cities. Ascorbic acid (AA) concentration and relative water content (RWC) had a strong influence on APTI with an extremely significant moderate positive correlation between AA concentration and APTI (r = 0.65, p < 0.001) along with RWC and APTI (r = 0.52, p < 0.001), indicating that higher levels of AA concentration and RWC are linked to increased air pollution tolerance. The PCA bi-plot indicates AA has poor positive loading coefficients with PC1 explaining 29.49% of the total variance in the dataset. The highest APTI was recorded in Azadirachta indica (22.01), Leucaena leucocephala (20.65), Morus alba (20.62), Ficus religiosa (20.61) and Ficus benghalensis (19.61), irrespective of sites and seasons. Similarly, based on API grading, F. religiosa and F. benghalensis were identified as excellent API grade 6 (81-90%), A. indica and Alstonia scholaris as very good API grade 5 (71-80%), M. alba, Pongamia pinnata and Monoon longifolium as good API grade 4 (61-70%) and Plumeria alba as moderate API grade 3 (51-60%) in different streets of Delhi. As these plants are indigenous to the region and hold significant socio-economic and aesthetic significance in Indian societies, they are advisable for avenue plantations as part of various government initiatives to support environmental sustainability.

Lithium ore tailings harm the vegetative development, photosynthetic activity, and nutrition of tree species.

Lithium (Li) exploitation promotes socioeconomic advances but may result in harmful environmental impacts. Thus, species selection for recovering environments degraded by Li mining is essential. We investigated the tolerance and early growth of four tree species to Li ore tailings (LOT), Enterolobium contortisiliquum and Handroanthus impetiginosus with wide geographic distribution and Hymenaea courbaril and H. stigonocarpa with restricted geographic distribution. The plants grew in LOT and soil for 255 days to evaluate photosynthesis, growth, and mineral nutrition. LOT negatively affected species growth, reducing the length of stems, roots, and biomass through structural and nutritional impoverishment. LOT favored the accumulation of Mg and decreased the absorption of K. The species presented a reduction in potential quantum efficiency and the chlorophyll index (b and total). E. contortisiliquum was the least tolerant species to LOT, and H. courbaril and H. stigonocarpa maintained their mass production in LOT, indicating greater tolerance to tailings. Furthermore, H. courbaril presented a translocation factor > 1 for Li and Mn, indicating the potential for phytoextraction of these metals. Our results offer first-time insights into the impacts of LOT on the early development of tree species with different geographic distribution ranges. This study may help in the tree species selection with a phytoremediation role, aiming at the recovery of areas affected by Li's mining activity.

A reduced-scale canyon street to study tree climate benefits: summer 2020 data with well-watered apple trees.

An outdoor reduced-scale canyon street was set-up in Angers, France, to study the impact of well-watered trees on urban microclimate and human comfort, with an integrated approach of the soil-plant-atmosphere continuum. Data were acquired during 26 days in summer 2020. The street is oriented north-south, with an aspect ratio of 1. It is organized in three zones: two zones with a central alignment of 5 ornamental apple trees, and one zone without trees. The water inputs are controlled through a drip-irrigation system. Each zone is instrumented to characterise the local microclimate and energy fluxes, the soil water status, and tree leaf temperature. To allow a better understanding of the physical mechanisms at stake in tree services, tree transpiration as well as crown light interception are also quantified, and the trees are characterised in terms of leaf area and crown dimensions. The data can benefit to researchers in urban meteorology and environmental physics. It can also provide reference data to run and evaluate microclimate models, especially regarding plant-atmosphere interactions.

Potential for large losses of carbon from non-native conifer plantations on deep peat over decadal timescales.

Peatland drainage is a large source of anthropogenic CO2 emissions. While conversion to agriculture is widely acknowledged to lead to "irrecoverable" carbon (C) losses, in contrast the C impacts of peatland forestry are poorly understood, especially in intensively managed plantations. Losses of C from peat oxidation are highly variable and can be compensated for by gains of C in trees, depending on the lifecycle of the timber and timescale considered. Here, we used ITRAX scanning to enable rapid detection of the Hekla 4 cryptotephra layer as a reliable chronological marker above which peat properties and C stocks could be compared between open and afforested blanket bog cores in the Flow Country of Northern Scotland. At one site, Bad a' Cheò, we combine replicated core pair comparisons (n = 19) with timber extraction data to derive net ecosystem C balance over the lifetime of the plantation. Here the reduction in peat C carbon storage above Hekla 4 in afforested samples (67 t C ha-1) is only partially compensated by tree C sequestration (47 t C ha-1), leading to a net ecosystem C balance indicating a loss of 20 t C ha-1 over the 50 years since the plantation was established. At that site, ∼65 % of tree C rapidly returned to the atmosphere, as it was primarily used for heat and power generation. Across the wider Flow country region, a simplified paired sampling method was adopted at eight further sites, finding a either a loss or negligible change in peat C storage above Hekla 4 in afforested samples with a mean loss of 86 t C ha-1 and median loss of 50 t C ha-1. This study suggests that potentially substantial C losses have been an unintended consequence of non-native conifer afforestation over deep blanket bogs.

Cold threat and moisture deficit induced individual tree mortality via 25-year monitoring in seminatural mixed forests, northeastern China.

Accurately predicting tree mortality in mixed forests sets a challenge for conventional models because of large uncertainty, especially under changing climate. Machine learning algorithms had potential for predicting individual tree mortality with higher accuracy via filtering the relevant climatic and environmental factors. In this study, the sensitivity of individual tree mortality to regional climate was validated by modeling in seminatural mixed coniferous forests based on 25-year observations in northeast of China. Three advanced machine learning and deep learning algorithms were employed, including support vector machines, multi-layer perceptron, and random forests. Mortality was predicted by the effects of multiple inherent and environmental factors, including tree size and growth, topography, competition, stand structure and regional climate. All three types of models performed satisfactorily with their values of the areas under receiving operating characteristic curve (AUC) > 0.9. With tree growth, competition and regional climate as input variables, a model based on random forests showed the highest values of the explained variance score (0.862) and AUC (0.914). Since the trees were vulnerable despite their species, mortality could occur after growth limit induced by insufficient or excessive sun radiation during growing seasons, cold threat caused thermal insufficiency in winters, and annual moisture constraints in these mixed coniferous forests. Our findings could enrich basic knowledge on individual tree mortality caused by water and heat inadequacy with the negative impacts of global warming. Successful individual tree mortality modeling via advanced algorithms in mixed forests could assist in adaptive forest ecology modeling in large areas.

Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP 'coefficient of variation' (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed 'MangoYOLO', for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting.

Patterns of early post-disturbance reorganization in Central European forests.

Disturbances catalyse change in forest ecosystems, and a climate-driven increase in disturbance activity could accelerate forest reorganization. Here, we studied post-disturbance forests after the biggest pulse of tree mortality in Central Europe in at least 170 years, caused by drought and bark beetle (Scolytinae) outbreaks in 2018-2020. Our objectives were to characterize the early state of tree regeneration after mortality, quantify patterns of reorganization relative to undisturbed reference conditions and assess how management and patch size affect forest reorganization after disturbance. We surveyed 1244 plots in 120 patches under managed (salvage-logged, often planted) and unmanaged (deadwood remaining on site, no planting) conditions in Germany. We found that regeneration density on disturbed sites was high (median 11 897 stems ha-1), resulting from a cohort of advance regeneration. Disturbances were strong drivers of change, with indications for resilience on only 36.3% of patches. Reassembly (i.e. a change in species composition) was the dominant pattern of reorganization (61.5%), and Picea abies forests changed most strongly. Post-disturbance management facilitated forest change, particularly promoting a change in species composition. The strength of reorganization increased with patch size. We conclude that the recent wave of tree mortality will likely accelerate forest change in Central Europe.

Phosphorus limitation promotes soil carbon storage in a boreal forest exposed to long-term nitrogen fertilization.

Forests play a crucial role in global carbon cycling by absorbing and storing significant amounts of atmospheric carbon dioxide. Although boreal forests contribute to approximately 45% of the total forest carbon sink, tree growth and soil carbon sequestration are constrained by nutrient availability. Here, we examine if long-term nutrient input enhances tree productivity and whether this leads to carbon storage or whether stimulated microbial decomposition of organic matter limits soil carbon accumulation. Over six decades, nitrogen, phosphorus, and calcium were supplied to a Pinus sylvestris-dominated boreal forest. We found that nitrogen fertilization alone or together with calcium and/or phosphorus increased tree biomass production by 50% and soil carbon sequestration by 65% compared to unfertilized plots. However, the nonlinear relationship observed between tree productivity and soil carbon stock across treatments suggests microbial regulation. When phosphorus was co-applied with nitrogen, it acidified the soil, increased fungal biomass, altered microbial community composition, and enhanced biopolymer degradation capabilities. While no evidence of competition between ectomycorrhizal and saprotrophic fungi has been observed, key functional groups with the potential to reduce carbon stocks were identified. In contrast, when nitrogen was added without phosphorus, it increased soil carbon sequestration because microbial activity was likely limited by phosphorus availability. In conclusion, the addition of nitrogen to boreal forests may contribute to global warming mitigation, but this effect is context dependent.

Fine-scale monitoring of catkins reveals an association between catkin concentration and plant community characteristics and microclimate.

Catkins, as a significant source of plant-caused pollution, disrupts daily human activities and industrial processes. Despite their impact, catkins have not been included in official environmental quality monitoring indicators, leading to a deficiency in scientifically rigorous collection and monitoring methodologies, as well as a lack of ecological prevention and management strategies. In this study, we introduced a fine-scale monitoring approach for catkins. Qualitative and quantitative relationships between catkin concentrations, plant community characteristics and microclimate factors were elucidated by analyzing on-site catkin concentration data from 33 representative plant communities in Beijing. Furthermore, we summarized the ecological strategies for the prevention and management of these catkins. The results indicated that (1) TS (three-dimensional green volume of trees in the catkin source layer), SB (three-dimensional green volume of shrubs in the catkin barrier layer), GB (three-dimensional green volume of ground cover plants in the catkin barrier layer), T (three-dimensional green volume of trees in the whole plant community), W (three-dimensional green volume of the whole plant community), species diversity, and relative air humidity were key plant community characteristics and microclimate factors influencing catkin concentration. Among these factors, TS, T, W, and relative air humidity showed a significant positive correlation with catkin concentration, while SB, GB, and species diversity exhibited a significant negative correlation with catkin concentration. (2) All seven key factors exhibited nonlinear relationships with catkin concentration. (3) TS served as the primary deciding factor for catkin concentration within the plant community. When TS > 744.0755 m3, the secondary decision factor for catkin concentration was GB. Otherwise, the determinants were SB and species diversity. The results showed that enhancing tree species diversity, enhancing the three-dimensional green volume of shrubs and ground cover plants, and increasing air humidity were practical means to facilitate the sedimentation of catkins. The measures used to obstruct catkins vary depending on the TS. When catkin source plants are abundant within a plant community, it is advisable to prioritize increasing ground cover plants. Conversely, when fewer sources of such plants exist, emphasis can be placed on augmenting mid-layer shrubs and diversifying plant species. These findings provide a scientific foundation for the planting design and stock optimization of communities containing catkin source plants.