Random forest model used to predict the medical out-of-pocket costs of hypertensive patients.

Background: Precise prediction of out-of-pocket (OOP) costs to improve health policy design is important for governments of countries with national health insurance. Controlling the medical expenses for hypertension, one of the leading causes of stroke and ischemic heart disease, is an important issue for the Japanese government. This study aims to explore the importance of OOP costs for outpatients with hypertension. Methods: To obtain a precise prediction of the highest quartile group of OOP costs of hypertensive outpatients, we used nationwide longitudinal data, and estimated a random forest (RF) model focusing on complications with other lifestyle-related diseases and the nonlinearities of the data. Results: The results of the RF models showed that the prediction accuracy of OOP costs for hypertensive patients without activities of daily living (ADL) difficulties was slightly better than that for all hypertensive patients who continued physician visits during the past two consecutive years. Important variables of the highest quartile of OOP costs were age, diabetes or lipidemia, lack of habitual exercise, and moderate or vigorous regular exercise. Conclusion: As preventing complications of diabetes or lipidemia is important for reducing OOP costs in outpatients with hypertension, regular exercise of moderate or vigorous intensity is recommended for hypertensive patients that do not have ADL difficulty. For hypertensive patients with ADL difficulty, habitual exercise is not recommended.

Temporal variation in the structure, abundance, and composition of Laminaria hyperborea forests and their associated understorey assemblages over an intense storm season.

Kelp species function as important foundation organisms in coastal marine ecosystems where they provide biogenic habitat and ameliorate environmental conditions, often facilitating the development of diverse understorey assemblages. The structure of kelp forests is influenced by a variety of environmental factors, changes in which can result in profound shifts in ecological structure and functioning. Intense storm-induced wave action in particular, can severely impact kelp forest ecosystems. Given that storms are anticipated to increase in frequency and intensity in response to anthropogenic climate change, it is critical to understand their potential impacts on kelp forest ecosystems. During the 2021/22 northeast Atlantic storm season, the United Kingdom (UK) was subject to several intense storms, of which the first and most severe was Storm Arwen. Due to the unusual northerly wind direction, the greatest impacts of Storm Arwen were felt along the northeast coast of the UK where wind gusts exceeded 90 km/h, and inshore significant wave heights of 7.2 m and wave periods of 9.3 s were recorded. Here, we investigated temporal and spatial variation in the structure of L. hyperborea forests and associated understorey assemblages along the northeast coast of the UK over the 2021/22 storm season. We found significant changes in the cover, density, length, biomass, and age structure of L. hyperborea populations and the composition of understorey assemblages following the storm season, particularly at our most north facing site. We suggest continuous monitoring of these systems to further our understanding of temporal variation and potential recovery trajectories, alongside enhanced management to promote resilience to future perturbations.

Machine learning based on biological context facilitates the identification of microvascular invasion in intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a rare disease associated with a poor prognosis, primarily due to early recurrence and metastasis. An important feature of this condition is microvascular invasion (MVI). However, current predictive models based on imaging have limited efficacy in this regard. This study employed a random forest model to construct a predictive model for MVI identification and uncover its biological basis. Single-cell transcriptome sequencing, whole exome sequencing, and proteome sequencing were performed. The area under the curve of the prediction model in the validation set was 0.93. Further analysis indicated that MVI-associated tumor cells exhibited functional changes related to epithelial-mesenchymal transition and lipid metabolism due to alterations in the NF-kappa B and MAPK signaling pathways. Tumor cells were also differentially enriched for the IL-17 signaling pathway. There was less infiltration of SLC30A1+ CD8+ T cells expressing cytotoxic genes in MVI-associated ICC, whereas there was more infiltration of myeloid cells with attenuated expression of the MHC II pathway. Additionally, MVI-associated intercellular communication was closely related to the SPP1-CD44 and ANXA1-FPR1 pathways. These findings resulted in a brilliant predictive model and fresh insights into MVI.

When is lethal deceptive pollination maintained? A population dynamics approach.

BACKGROUND AND AIMS: Not all plant-pollinator interactions are mutualistic, and in fact, deceptive pollination systems are widespread in nature. The genus Arisaema has a pollination system known as lethal deceptive pollination, in which plants not only attract pollinating insects without providing any rewards, but also trap them until they die. Many Arisaema species are endangered from various disturbances including reduction in forest habitat, modification of the forest understory owing to increasing deer abundance, and plant theft for horticultural cultivation. We aimed to theoretically investigate how lethal deceptive pollination can be maintained from a demographic perspective and how plant and pollinator populations respond to different types of disturbance. METHODS: We developed and analysed a mathematical model to describe the population dynamics of a deceptive plant species and its victim pollinator. Calibrating the model based on empirical data, we assessed the conditions under which plants and pollinators could coexist, while manipulating relevant key parameters. KEY RESULTS: The model exhibited qualitatively distinct behaviours depending on certain parameters. The plant becomes extinct when it has a low capability for vegetative reproduction and slow transition from male to female, and plant-insect co-extinction occurs especially when the plant is highly attractive to male insects. Increasing deer abundance has both positive and negative effects because of removal of other competitive plants and diminishing pollinators, respectively. Theft for horticultural cultivation can readily threaten plants whether male or female plants are frequently collected. The impact of forest habitat reduction may be limited compared to that of other disturbance types. CONCLUSIONS: Our results have emphasised that the demographic vulnerability of lethal deceptive pollination systems would differ qualitatively from that of general mutualistic pollination systems. It is therefore important to consider the demographics of both victim pollinators and deceptive plants to estimate how endangered Arisaema populations respond to various disturbances.

Reconciling the EU forest, biodiversity, and climate strategies.

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non-wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process-based ecosystem modeling and robust multi-criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi-functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests.

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

Predicting Conversion Time from Mild Cognitive Impairment to Dementia with Interval-Censored Models.

Background: Mild cognitive impairment (MCI) patients are at a high risk of developing Alzheimer's disease and related dementias (ADRD) at an estimated annual rate above 10%. It is clinically and practically important to accurately predict MCI-to-dementia conversion time. Objective: It is clinically and practically important to accurately predict MCI-to-dementia conversion time by using easily available clinical data. Methods: The dementia diagnosis often falls between two clinical visits, and such survival outcome is known as interval-censored data. We utilized the semi-parametric model and the random forest model for interval-censored data in conjunction with a variable selection approach to select important measures for predicting the conversion time from MCI to dementia. Two large AD cohort data sets were used to build, validate, and test the predictive model. Results: We found that the semi-parametric model can improve the prediction of the conversion time for patients with MCI-to-dementia conversion, and it also has good predictive performance for all patients. Conclusions: Interval-censored data should be analyzed by using the models that were developed for interval- censored data to improve the model performance.

Automated system utilizing non-invasive technique mammograms for breast cancer detection.

In order to increase the likelihood of obtaining treatment and achieving a complete recovery, early illness identification and diagnosis are crucial. Artificial intelligence is helpful with this process by allowing us to rapidly start the necessary protocol for treatment in the early stages of disease development. Artificial intelligence is a major contributor to the improvement of medical treatment for patients. In order to prevent and foresee this problem on the individual, family, and generational levels, Monitoring the patient's therapy and recovery is crucial. This study's objective is to outline a non-invasive method for using mammograms to detect breast abnormalities, classify breast disorders, and identify cancerous or benign tumor tissue in the breast. We used classification models on a dataset that has been pre-processed so that the number of samples is balanced, unlike previous work on the same dataset. Identifying cancerous or benign breast tissue requires the use of supervised learning techniques and algorithms, such as random forest (RF) and decision tree (DT) classifiers, to examine up to thirty features, such as breast size, mass, diameter, circumference, and the nature of the tumor (solid or cystic). To ascertain if the tissue is malignant or benign, the examination's findings are employed. These features are mostly what determines how effectively anything may be categorized. The DT classifier was able to get a score of 95.32%, while the RF satisfied a far higher 98.83 percent.

Ecosystem engineers cause biodiversity spill-over: Beavers are associated with breeding bird assemblages on both wetlands and adjacent terrestrial habitats.

The influence of ecosystem engineers on habitats and communities is commonly acknowledged in a site-bounded context, i.e. in places directly affected by the presence of the focal species. However, the spatial extent of the effects of such engineering is poorly understood, raising the question as to what impact they have on ecosystems situated beyond the species' direct influence. Beavers Castor spp., iconic ecosystem engineers, are capable of significantly transforming aquatic ecosystems. Their presence boosts biodiversity in adjacent aquatic and riparian habitats, but as a result of cascading processes, beavers may affect terrestrial habitats situated beyond the range of their immediate activity. Our study investigates the breeding bird assemblage along a spatial gradient from the water to the forest interior on central European watercourses modified and unmodified by beavers. The results show that beaver sites are characterized by a higher species richness and abundance of breeding birds than unmodified watercourses. Such sites also host a different species pool, as 27 % of the recorded bird species occurred exclusively on the beaver sites. The effect of the beaver's presence on the bird assemblage extended to adjacent terrestrial habitats located up to 100 m from the water's edge, where the species richness and abundance was higher and the species composition was substantially modified. We also found a positive correlation between the total area of beaver wetland and the numbers of bird species and individuals recorded. Our study adds to the general understanding of the spatial context of the ecosystem engineering concept, as the changes brought about by engineers have an influence beyond the area of their immediate occurrence. Our work also has implications for landscape planning and management, where existing beaver sites with terrestrial buffer zones may constitute a network of biodiversity hotspots.

Influence of plant community on Aedes albopictus (Diptera, Culicidae) oviposition behaviour: Insights from a Spanish botanical garden.

Mosquitoes are capable of transmitting pathogens of both medical and veterinary significance. Addressing the nuisance and vector roles of Aedes albopictus through surveillance and control programs is a primary concern for European countries. Botanical gardens provide suitable habitats for the development of Ae. albopictus and represent typical points of entry of invasive species. To assess the oviposition preferences alongside various biotic parameters (plant species community, shade index, and flowering), we conducted a study in a botanical garden of Sóller (Mallorca, Balearic Islands, Spain). A total of 6,368 Ae. albopictus eggs were recorded in 36 ovitraps positioned and revised every 15 days in seven different habitats over six months in 2016. Zero-inflated and generalized linear mixed-effects models were used to analyse Ae. albopictus habitat preferences. The number of eggs increased throughout the sampling period, peaking in September. The oviposition rates showed a patchy distribution, with Ae. albopictus showing preference for oviposition in laurel forest and cropland habitats. A positive effect of large leaves and presence of flowers on the oviposition of Ae. albopictus were also recorded. This study provides valuable information into the behaviour of Ae. albopictus in botanical gardens, which is essential data for informing surveillance and control programs.

Machine learning-based corrosion rate prediction of steel embedded in soil.

Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m2 and an R2 value of 0.987). In light of the feature selection method, the electrical resistivity is identified as the most significant feature. The combination of three features (resistivity, exposure time, and mean total organic carbon) is the optimal scenario for predicting the corrosion current density of soil-buried steel.

Development and validation of novel interpretable survival prediction models based on drug exposures for severe heart failure during vulnerable period.

BACKGROUND: Severe heart failure (HF) has a higher mortality during vulnerable period while targeted predictive tools, especially based on drug exposures, to accurately assess its prognoses remain largely unexplored. Therefore, this study aimed to utilize drug information as the main predictor to develop and validate survival models for severe HF patients during this period. METHODS: We extracted severe HF patients from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database and local hospital (as external validation cohorts). Three algorithms, including Cox proportional hazards model (CoxPH), random survival forest (RSF), and deep learning survival prediction (DeepSurv), were applied to incorporate the parameters (partial hospitalization information and exposure durations of drugs) for constructing survival prediction models. The model performance was assessed mainly using area under the receiver operator characteristic curve (AUC), brier score (BS), and decision curve analysis (DCA). The model interpretability was determined by the permutation importance and Shapley additive explanations values. RESULTS: A total of 11,590 patients were included in this study. Among the 3 models, the CoxPH model ultimately included 10 variables, while RSF and DeepSurv models incorporated 24 variables, respectively. All of the 3 models achieved respectable performance metrics while the DeepSurv model exhibited the highest AUC values and relatively lower BS among these models. The DCA also verified that the DeepSurv model had the best clinical practicality. CONCLUSIONS: The survival prediction tools established in this study can be applied to severe HF patients during vulnerable period by mainly inputting drug treatment duration, thus contributing to optimal clinical decisions prospectively.

Machine Learning Identification of Modifiable Predictors of Patient Outcomes After Transcatheter Aortic Valve Replacement.

Background: Transcatheter aortic valve replacement (TAVR) is an important treatment option for patients with severe symptomatic aortic stenosis. It is important to identify predictors of excellent outcomes (good clinical outcomes, more time spent at home) after TAVR that are potentially amenable to improvement. Objectives: The purpose of the study was to use machine learning to identify potentially modifiable predictors of clinically relevant patient-centered outcomes after TAVR. Methods: We used data from 8,332 TAVR cases (January 2016-December 2021) from 21 hospitals to train random forest models with 57 patient characteristics (demographics, comorbidities, surgical risk score, lab values, health status scores) and care process parameters to predict the end point, a composite of parameters that designated an excellent outcome and included no major complications (in-hospital or at 30 days), post-TAVR length of stay of 1 day or less, discharge to home, no readmission, and alive at 30 days. We used recursive feature elimination with cross-validation and Shapley Additive Explanation feature importance to identify parameters with the highest predictive values. Results: The final random forest model retained 29 predictors (15 patient characteristics and 14 care process components); the area under the curve, sensitivity, and specificity were 0.77, 0.67, and 0.73, respectively. Four potentially modifiable predictors with relatively high Shapley Additive Explanation values were identified: type of anesthesia, direct movement to stepdown unit post-TAVR, time between catheterization and TAVR, and preprocedural length of stay. Conclusions: This study identified four potentially modifiable predictors of excellent outcome after TAVR, suggesting that machine learning combined with hospital-level data can inform modifiable components of care, which could support better delivery of care for patients undergoing TAVR.

Assessing the impact of voluntary sustainability standards in Amazonian enterprises involved in the açaí value chain.

The diversity of sustainable certifications raises questions about the credibility, intentions, and impacts of Voluntary Sustainability Standards (VSS) on Global Value Chains (GVC). Few studies show the impacts of VSS on different sustainable dimensions in sectors such as the non-timber forest product (NTFP) sector. This paper aims to investigate in the value chain of the most important NTFP in the Amazon, açaí, whether VSS contributes to sustainable outcomes in the Governance, Environmental, Economic, and Social dimensions. Using case studies in enterprises of the açaí chain and the use of tools and indicators was possible to generate information that is currently scarce for NTFPs in the Amazon from the VSS perspective. The results show that there is a great distance that the weakest links of the GVC (Micro, Small, and Medium Enterprises - MSMEs) must walk to adopt VSS and be inserted into the global market. The requirements are based on bureaucratic management activities, which are extraordinarily complex and involve many issues and indicators. The VSS lacks supplements that evaluate and validate the results reported by the companies as sustainable. Finally, the VSS is still far from ensuring an inclusive and fully sustainable chain by itself.

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency.

PURPOSE: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency. METHODS: A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF. RESULTS: The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: -0.13 [-0.24, -0.01] and -0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect. CONCLUSION: In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.

Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model.

Purpose: To identify the main variables affecting the academic adaptability of hospital nursing interns and key areas for improvement in preparing for future unpredictable epidemics. Methods: The importance of academic resilience-related variables for all nursing interns was analyzed using the random forest method, and key variables were further identified. An importance-performance analysis was used to identify the key improvement gaps regarding the academic resilience of nursing interns in the case hospital. Results: The random forest showed that five items related to cooperation, motivation, confidence, communication, and difficulty with coping were the main variables impacting the academic resilience of nursing interns. Moreover, the importance-performance analysis revealed that three items regarding options examination, communication, and confidence were the key improvement areas for participating nursing interns in the case hospital. Conclusions: For the prevention and control of future unpredictable pandemics, hospital nursing departments can strengthen the link between interns, nurses, and physicians and promote their cooperation and communication during clinical practice. At the same time, an application can be created considering the results of this study and combined with machine learning methods for more in-depth research. These will improve the academic resilience of nursing interns during the routine management of pandemics within hospitals.

Cylindrocladiella species from Cunninghamia lanceolata plantation soils in southwestern China.

Species of Cylindrocladiella are saprobic or plant pathogenic, and widely distributed in soil in tropical and sub-tropical regions of the world. Limited information is available about the species diversity and distribution of Cylindrocladiella in China. The aim of this study was to identify the Cylindrocladiella isolates from soils collected in a Cunninghamia lanceolata plantation in the Yunnan Province of southwestern China. Species identification was based on DNA phylogeny of his3, ITS, tef1 and tub2 regions, combined with morphological characteristics. Isolates obtained were identified as Cylindrocladiella longistipitata and a novel species, described here as C. yunnanensis sp. nov. Further studies are required, however, to elucidate the lifestyles of these taxa. Citation: Liu Y, Chen SF (2024). Cylindrocladiella species from Cunninghamia lanceolata plantation soils in southwestern China. Fungal Systematics and Evolution 13: 143-152. doi: 10.3114/fuse.2024.13.08.

The first emerging records of a dragonfly in the dark zone of subterranean ecosystems: Exuviae and newly emerged adults of Hyrcanian Goldenring, Cordulegaster vanbrinkae in Danial Cave, northern Iran.

Riverine caves are special habitats that are home to many aquatic and terrestrial species. Some Odonata species and their emerging are recorded at the entrance and in the twilight zones of subterranean habitats around the world. However, the emergence of any Odonata species has not been recorded in the dark zones of caves or other subterranean habitats. We report the first evidence of the emerging of the Hyrcanian Goldenring, Cordulegaster vanbrinkae Lohmann, 1993, as an endemic species of the Hyrcanian biogeographical region, in the dark zone of Danial Cave, in the World Heritage-listed Hyrcanian Forests, northern Iran. During 2020-2023, three newly emerged and three exuviae of the species were recorded in the entrance zone (25 m) and the dark zone of the cave (200-280 m). The main hypothesis of the study is the entry and exit of adults from the cave entrance. However, we still do not know if the newly emerged will leave the cave or not. We still need more study on the biology and ecology of the species inside and around the cave. Danial Cave, with its high biodiversity, is one of the most important caves in the Middle East, and is urgently in need of conservation as a national natural monument.

Tree regeneration and ontogenetic strategies of northern European hemiboreal forests: transitioning towards closer-to-nature forest management.

Background: Tree ontogeny is the genetic trajectories of regenerative processes in trees, repeating in time and space, including both development and reproduction. Understanding the principles of tree ontogeny is a key priority in emulating natural ecological patterns and processes that fall within the calls for closer-to-nature forest management. By recognizing and respecting the growth and development of individual trees and forest stands, forest managers can implement strategies that align with the inherent dynamics of forest ecosystem. Therefore, this study aims to determine the ontogenetic characteristics of tree regeneration and growth in northern European hemiboreal forests. Methodology: We applied a three-step process to review i) the ontogenetic characteristics of forest trees, ii) ontogenetic strategies of trees for stand-forming species, and iii) summarise the review findings of points i and ii to propose a conceptual framework for transitioning towards closer-to-nature management of hemiboreal forest trees. To achieve this, we applied the super-organism approach to forest development as a holistic progression towards the establishment of natural stand forming ecosystems. Results: The review showed multiple aspects; first, there are unique growth and development characteristics of individual trees at the pre-generative and generative stages of ontogenesis under full and minimal light conditions. Second, there are four main modes of tree establishment, growth and development related to the light requirements of trees; they were described as ontogenetic strategies of stand-forming tree species: gap colonisers, gap successors, gap fillers and gap competitors. Third, the summary of our analysis of the ontogenetic characteristics of tree regeneration and growth in northern European hemiboreal forests shows that stand-forming species occupy multiple niche positions relative to forest dynamics modes. Conclusions: This study demonstrates the importance of understanding tree ontogeny under the pretext of closer-to-nature forest management, and its potential towards formulating sustainable forest management that emulates the natural dynamics of forest structure. We suggest that scientists and foresters can adapt closer-to-nature management strategies, such as assisted natural regeneration of trees, to improve the vitality of tree communities and overall forest health. The presented approach prioritizes ecological integrity and forest resilience, promoting assisted natural regeneration, and fostering adaptability and connectivity among plant populations in hemiboreal tree communities.

Arbuscular mycorrhizal diversity increases across a plant productivity gradient driven by soil nitrogen availability.

Arbuscular mycorrhizal fungi (AMF) are widespread obligate symbionts of plants. This dynamic symbiosis plays a large role in successful plant performance, given that AMF help to ameliorate plant responses to abiotic and biotic stressors. Although the importance of this symbiosis is clear, less is known about what may be driving this symbiosis, the plant's need for nutrients or the excess of plant photosynthate being transferred to the AMF, information critical to assess the functionality of this relationship. Characterizing the AMF community along a natural plant productivity gradient is a first step in understanding how this symbiosis may vary across the landscape. We surveyed the AMF community diversity at 12 sites along a plant productivity gradient driven by soil nitrogen availability. We found that AMF diversity in soil environmental DNA significantly increased along with the growth of the host plants Acer rubrum and A. saccharum., a widespread tree genus. These increases also coincided with a natural soil inorganic N availability gradient. We hypothesize photosynthate from the increased tree growth is being allocated to the belowground AMF community, leading to an increase in diversity. These findings contribute to understanding this complex symbiosis through the lens of AMF turnover and suggest that a more diverse AMF community is associated with increased host-plant performance.