Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

By inergency On Feb 17, 2024

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1. Introduction

Forests, which cover approximately 31% of the world’s terrestrial ecosystems [1] and constitute about 80% of the global vegetation mass, play a crucial role as essential ecosystems on Earth. Forests serve multiple vital functions, such as in timber production, hydrological regulation, soil conservation, climate change mitigation, and air quality regulation [2,3].

Accurate assessment and monitoring of forest dynamics are of paramount importance. Currently, dynamic monitoring of forests mainly includes monitoring of forest stand dynamics, forest climate, and forest fire prevention, among which forest stand dynamics is a key link in the monitoring process. Determination of forest stock volume, biomass, and carbon storage are largely based on the forest dynamics, such as tree growth, tree mortality, and human influences, such as thinning [4]. The integration of tree mortality into the study of forest stand quantity dynamics is vital, as it is a fundamental process within forest dynamics [5]. Additionally, tree mortality, productivity, and biodiversity play crucial roles in shaping forest ecosystem dynamics and, consequently, influencing forest carbon sequestration [6,7]

Tree mortality is a crucial ecological process in forest development, as dead and decaying trees play vital roles in maintaining a healthy forest ecosystem [8]. Tree mortality encompasses the entire process from the initial decline in vitality to the eventual death of a tree, influenced by both its intrinsic ecological characteristics and external conditions. Forest mortality drives changes in species composition and stand density [9,10], and plays a significant role in the coexistence of different communities [11]. Elevated tree mortality levels can significantly impact ecosystem structure and function, affecting the services that forests provide to people [12]. Even minor changes in the mortality rates can have profound effects on tree lifespan, biodiversity, and the cycling of carbon and nutrients. In fact, tree mortality rates are key drivers of forest community changes, leading to notable alterations in composition and structure [13].

Moreover, an increase in mortality rates reduces the residence time of carbon in both forests and soil [14,15] and may affect the carbon storage potential of forests [16]. Consequently, conducting mortality research can enhance the understanding of mortality causes [13], contribute to a deeper comprehension of the succession and diversity dynamics in future forest communities [17], facilitate precise evaluation and estimation of forest carbon storage [18], support sustainable forest resource management, and enable accurate monitoring of forest carbon sinks [19].

Predicting tree mortality requires classification from 0 to 1. Therefore, most of the research on an individual tree mortality model was developed using logistic regression [20]. Some researchers used generalized mixed-effect model [21,22]. Additionally, other modeling methods, such as classification regression trees [23], non-parametric Bayesian estimation [24], compound Poisson models [25], semi-parametric regression [26], multilevel logistic regression [27], and Cox proportional hazard models [28] have been attempted in individual tree-mortality-model research.

Vanclay (1994) [29] classified tree mortality into two categories: natural and non-natural mortality. Natural mortality occurs during the developmental stages of trees, arising from variations in maturity among tree species and differences in individual genetic factors. This leads to varying competitive abilities for nutrients, water, and sunlight among different tree species and between larger and smaller trees. Consequently, trees in a weaker competitive position gradually die off. Non-natural mortality refers to tree mortality caused by improper afforestation techniques or external disturbances such as fires, droughts, flash floods, windstorms, and snow disasters [30]. In our study, we only focus on natural mortality. In recent tree-mortality-modelling research, the relationship between soil characteristics, topography, and tree mortality were often neglected [31]. Soil characteristics (e.g., moisture content, pH, texture, nutrients, and their availability) also affect plant growth and death. Studies have shown that tree mortality rates in China’s forest–grassland ecotone are significantly influenced by soil properties, topography, and tree size [32,33]. Furthermore, some research proved a strong correlation exists between soil moisture content and tree mortality [23]. Existing tree mortality modeling has mainly focused on predictor variables related to tree size, such as diameter at breast height or tree height [8,34]; growth-related variables, such as DBH increment, annual ring width, or basal area increment [24]; crown-related variables, such as leaf area index and crown shedding [35,36]; ratios of crown-related variables to growth-related variables [37]; competition variables, divided into distance-related competition and distance-independent competition [38,39]; climate variables [40]; and site quality [35].

The Larix gmelinii var. principis-rupprechtii tree-mortality-modeling studies have not yet explored the impact of soil nutrients on tree mortality. Soil, as a key habitat factor for tree regeneration and survival, possesses numerous physical and chemical properties. Various soil factors are interconnected, and they exhibit significant scale effects, even showing noticeable spatial variations on a small scale [41]. We consider the main soil nutrient factors affecting tree mortality, including total soil moisture, pH value, soil carbon (Organic C), nitrate nitrogen (NO₃-N), ammonium nitrogen (NH₄-N), and available potassium (available K), available phosphorus (available P). Carbon, nitrogen, potassium, and phosphorus are closely related to plant growth, thereby affecting plant regeneration and survival [42,43].

The prediction of tree mortality is a complex task due to the multitude of factors that can influence a tree’s health and survival. Traditional statistical models often struggle with this complexity, as they are limited in their ability to handle non-linear relationships and interactions between variables. Machine learning models, on the other hand, excel in these situations. They can learn from the data, identifying complex patterns and relationships that can improve prediction accuracy.

In recent years, machine learning has emerged as a powerful tool in various fields, including forestry. Machine learning algorithms can learn from data and improve their performance with experience, which make them particularly useful for tasks where explicit programming is difficult [44]. In the context of forestry, machine learning can be used to predict tree mortality, growth, and other key forest dynamics. These predictions can be based on a variety of factors, including climate, soil nutrients [45], and other individual or stand-level variables. Machine learning models, such as logistic regression [46], support vector machines [47], random forests [48] gradient boosting [49], and naive Bayes [50], have been successfully applied in this field. These models can handle complex interactions and non-linear relationships between variables, making them more flexible and accurate than traditional statistical models.

To our knowledge, no tree-mortality-modeling studies has been carried out on the comparisons of different machine learning models. In this study, we applied several machine learning models, including logistic regression, support vector machines, random forests, gradient boosting, and naive Bayes, to predict tree mortality based on a variety of environmental factors. Our main aim is to develop a model to predict tree mortality, essentially a binary classification problem. This model categorizes the trees into two distinct classes: alive (0) and dead (1). Given the either live or dead nature of this problem, machine learning techniques are particularly well-suited for this task. Therefore, our main aim of this study is to (i) establish a prediction model of individual tree mortality prediction with machine learning methods; (ii) compare eight machine learning models and figure out the most suitable prediction model for individual tree mortality of the larch forests; (ⅲ) analyze the effects of different factors and determine which ones have strong influence on individual tree mortality and to provide a scientific foundation for larch forest sustainable development.

4. Discussion

Based on the performance metrics derived from both the training and test datasets, we observe nuanced insights into the predictive capabilities of the eight machine learning models employed in our study on tree mortality. The RF model showcased the best performance, with the highest precision and the highest accuracy, underscoring its robustness across various metrics. This model also demonstrated a high Kappa score, indicating a strong agreement beyond chance in its predictions, making it the most reliable model for predicting outcomes accurately.

In contrast, the LR and NB models showed foundational performance with reasonable metrics, indicating that they may struggle with complex data relationships compared to more sophisticated models. However, GBM exhibited superior performance, particularly in accuracy, and it had the highest F1 score, highlighting its capability in handling variable interactions and non-linear dynamics effectively. The SVM model also performed well, demonstrating high levels of accuracy and precision, suggesting it is effective in minimizing false positives. The K-NN model, while not achieving the highest scores, still provided a solid performance across all metrics, particularly in terms of its AUC-ROC curve, which suggests good classification ability.

In conclusion, the analysis underscores the RF and GBM models as the most promising in terms of accuracy, reliability, and overall performance. These models strike an excellent balance between precision and sensitivity, adeptly predicting outcomes most of the time. However, model selection should still consider specific project requirements, including computational costs and the implications of various types of prediction errors. Conversely, models like NB and LR, while offering solid foundational capabilities, display limitations in their predictive performance, likely due to their simpler nature and assumptions, which may not capture the intricate relationships within the data effectively.

The pivotal role of BAL as the most significant variable in predicting individual tree mortality underscores the intricate dynamics of forest stand structure and competition within ecosystems. This finding is consistent with ecological theories and empirical evidence suggesting that the spatial distribution and size hierarchy within a forest significantly affect individual tree growth, survival, and overall forest productivity [70].

The prominence of BAL within our analysis underscores the principle of competitive exclusion, illustrating that the trees within more densely populated stands surrounded by trees with greater basal areas, are at an increased risk of experiencing stunted growth and a higher likelihood of mortality. This struggle for vital resources like sunlight, water, and minerals intensifies when the basal area of neighboring trees surpasses that of the focal tree, resulting in increased stress and a potential rise in mortality. Essentially, trees that boast a larger basal area are better positioned to monopolize these resources, overshadowing their smaller counterparts and outperforming them for access to water and soil nutrients.

DBH is indicative of tree size, age, growth rate, and resilience [8] and is largely included as variable in tree mortality research [71,72,73] and emerged as a pivotal variable across several models with notable importance values such as 1.0000 in GAM, around 0.7 in the RF, SVM, KNN, and NB models. The prominence of DBH aligns with the understanding that trees with larger diameters are typically more resilient to environmental stressors [74]. However, the models also allude to intricate interactions, implying that specific conditions may challenge even trees with substantial DBH.

The mortality caused by competition for light, water, temperature, and nutrients is referred to as intrinsic mortality. Intrinsic mortality is influenced over the long term by the genetic and physiological characteristics of tree species, site conditions, and climatic factors [75]. Site conditions form the foundation of forest productivity and are closely tied to tree mortality. The present study primarily incorporates topography-related factors as site variables, encompassing elevation, aspect, position on slope, gradient, and microtopography. These factors predominantly influence hydrothermal factors and soil conditions directly associated with tree growth [76]. In this study, we applied slope and elevation as factors. Elevation, a factor influencing temperature, humidity, light, and soil characteristics, was accentuated in various models, particularly in the ANN model. This finding resonates with the ecological theories that particular altitudes may predispose certain tree species to mortality, underscoring the complex equilibrium between environmental parameters and tree vitality. In mountainous regions characterized by significant variations in elevation, distinct vegetation-vertical-zonation profiles are formed due to the undulating topography [77]. Slope, a determinant of soil erosion, moisture retention, and light exposure, was emphasized in models such as ANN, KNN and RF. While its ecological relevance in shaping tree growth and survival is recognized, slope was not uniformly significant across all models. This discrepancy invites further exploration to elucidate slope’s multifaceted role in forest ecology. Li Chunming et al. [78] also attempted to incorporate the influencing factors of aspect and elevation in their study on stand mortality in Mongolian oak forests. However, they found that the model outcomes indicated that these independent variables did not qualify for inclusion in the model. This result is different from our result. We attributed this outcome to the relatively low elevation (600–750 m) in their research and the high elevation (2079–2438 m) in our research.

CD, a measure of forest canopy cover, was highlighted in models like RF, SVM, KNN and NB. Within a given species, superior tree health is commonly linked to higher crown density values, reduced foliage transparency values, and diminished crown dieback values [79]. The models’ focus on CD reflects its critical influence on sunlight penetration, photosynthesis efficiency, and overall tree growth, emphasizing the intricate relationship between canopy architecture and arboreal survival.

Soil plays a pivotal role in tree growth by providing essential nutrients, moisture, and structural support. Among the spectrum of soil nutrients, NH₄-N and available P, assume a critical role in tree physiological processes. The soil’s NH₄-N content significantly influences plant health and growth by modifying nitrogen absorption efficiency, altering soil pH, and impacting the root environment’s microbial ecosystem. Too much NH₄-N can cause nitrogen toxicity, negatively affecting plant growth, while too little may hinder plant development and reduce productivity [80].

Phosphorus, being a fundamental constituent of ATP, nucleic acids, and phospholipids, exerts profound influence on tree development and growth when present in the form of available P [81]. In forest ecosystems, the concentration of available P within the soil can emerge as a constraining factor, especially within regions characterized by weathered or phosphorus-depleted soils [82]. The association between available P and tree vitality is intricate and multifaceted, often interacting with various other soil attributes and environmental variables. Grasping this relationship holds paramount importance in forest management and conservation, as it underscores the intricate equilibrium between soil fertility and tree well-being. The available P, denoting the available phosphorus in the soil, was underscored in models such as RF and ANN. As an essential nutrient for plant growth, the importance of the available P in these models suggests that phosphorus scarcity may constrain tree development. Although not uniformly significant, its ecological relevance merits further investigation.

In conclusion, these patterns of variable importance furnish invaluable insights into the mechanisms governing tree mortality, unveiling the synergistic interactions between tree attributes, soil nutrients, topographical variations, and tree mortality. The disparities in variable importance across models illuminate the unique attributes and sensitivities of each modeling approach, providing a road map for model selection tailored to specific ecological inquiries and management goals. This comprehensive assessment augments our understanding of individual tree characteristics and accentuates the significance of judicious model selection and feature engineering in advancing ecological research.

This study integrates machine learning insights with ecological theories and offers a multifaceted perspective on tree mortality factors. The prominence of variables such as BAL, BA, DBH, elevation, and CD across different models underscores their importance, while also highlighting the need for a nuanced understanding of other variables like slope, available P, and NH4-N. Future research should consider these complex interactions and the specific context of tree species, location, and environmental conditions.

Additionally, our study has some limitations. Firstly, our dataset may have biases as it comes from specific populations and regions. Secondly, the models might be influenced by the lack of data on dead trees or further influenced by data pre-processing methods. Future research can further improve model performance by using more diverse datasets and exploring different feature engineering techniques.

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