From data to decisions: The role of machine learning in precision ecological restoration

Abstract

The United Nations Decade on Ecosystem Restoration (2021–2030) highlights the importance of ecological restoration in tackling global challenges such as biodiversity loss, habitat degradation, and the decline of vital ecosystem services. As traditional restoration approaches often face limitations in scale, data analysis, accuracy, and adaptability, there is a growing need for more precise, data‐driven solutions. Machine learning (ML) has emerged as a powerful tool in ecological restoration, offering the potential to optimize restoration strategies, improve decision‐making, and monitor long‐term outcomes with greater precision and helps in minimizing potential failures. This paper explores the role of ML in the design and implementation of ecological restoration projects, focusing on its use in precision planning, predictive modeling, species reintroduction, and integration of remote sensing and image recognition. ML techniques, such as random forests, decision trees, support vector machines, and neural networks, aid in analyzing large‐scale ecological data, uncovering complex relationships, and optimizing restoration strategies, resource allocation, and ecological risk assessments. Moreover, ML also empowers real‐time decision‐making by analyzing live data to detect threats (illegal lodging, habitat loss, poaching, etc.) and predict risks from environmental/human activity shifts, enabling dynamic actions for ecosystem protection and conservation. However, challenges persist, including data quality issues, relevance of features, and the trade‐off between model interpretability and performance. Inaccurate or biased data from remote areas, feature redundancy, and the black‐box nature of certain ML algorithms can hinder the effectiveness of the model. These challenges can be addressed by improving data quality, improving model transparency, leveraging local knowledge, and fostering collaboration between academia, industry, and government. Although the application of ML in ecological restoration is still in its early stages, overcoming current challenges will significantly enhance its reliability as a tool for sustainable environmental management.

INTRODUCTION

The deterioration of ecosystems poses a significant barrier to achieving sustainable development globally. Recent decades have seen rapid population growth, urban expansion, intensive agriculture, and climate change that accelerate environmental degradation (Marques et al., 2019). In response, researchers and policy makers have turned to ecological restoration (trying to return an ecosystem to its original or historical state and ecological processes), focusing on rehabilitation by improving productivity, stability, or services of a degraded ecosystem, even if it does not fully return to the original/reference state. Different regions prioritize different aspects of ecological restoration based on their unique circumstances. Europe emphasizes the restoration of mining areas, North America targets forests and water bodies, while Australia and New Zealand focus on grasslands (Fu et al., 2019). China has implemented the Three North Shelter Forest Program and Africa initiated the Great Green Wall project (Crouzeilles et al., 2016). The Three North Shelter Forest Program started in 1978, aimed to combat desertification in northern China by planting billions of trees to establish a vast network of shelterbelts across the country's arid and semi‐arid regions (Cao et al., 2011). The Great Green Wall Project aimed at combating desertification and land degradation in the Sahel region. It seeks to restore 100 million hectares of land by 2030, involving over 20 African nations (Mirzabaev et al., 2022). These efforts are integral to addressing the severe impacts of desertification, improving food security, and increasing biodiversity in the affected areas (Crouzeilles et al., 2016). In 2021, the General Assembly of the United Nations launched the Decade on Ecosystem Restoration (2021–2030) to promote restoration activities across diverse ecosystems, from forests to wetlands, to combat global ecosystem loss and mitigate climate change. However, many restoration projects have seen limited success due to poor design, inaccurate assessments, and neglect of local environmental conditions (Cao et al., 2011; Wang et al., 2024). Recently, several departments have identified the pitfalls of mass tree planting in drylands and are now advocating for precision restoration, a framework that deliberately employs tailored techniques to address site‐specific ecological challenges, moving beyond one‐size‐fits‐all approaches (Kildisheva et al., 2016; Qi et al., 2024). However, implementing precision restoration at large scales remains difficult because conventional methods struggle to integrate heterogeneous environmental data, capture nonlinear interactions among biophysical factors, and predict site‐specific restoration outcomes under changing conditions. Machine learning (ML) offers a promising solution to these gaps; it can synthesize diverse data sources (remote sensing, soil, climate), identify complex drivers of restoration success, and optimize spatially explicit intervention strategies (Zhu et al., 2023). Despite this potential, the application of ML in restoration planning remains underexplored. Therefore, researchers stress the need to develop and test precision restoration methods that leverage ML to improve restoration success and long‐term cost‐effectiveness.

Precision restoration is vital for rehabilitating fragile ecosystems such as drylands, characterized by limited resources and harsh conditions (Stavi & Lal, 2015). Unlike mass planting approaches that indiscriminately increase tree density, precision restoration uses tailored strategies that consider local climate, soil properties, hydrology, and native species (Castro et al., 2021). Such precision aims to optimize ecological outcomes and ensure the long‐term sustainability and resilience of restored habitats. Precision restoration offers significant benefits by reducing resource waste and environmental impact (Copeland et al., 2021). In arid areas, large‐scale tree planting can lead to adverse effects, including depletion of scarce water, alterations in soil composition, and increased land degradation from invasion of non‐native species (Cao et al., 2011). By integrating a diverse array of shrubs and grasses better suited to drylands, precision restoration improves soil health, reduces erosion, and creates habitats for local wildlife, fostering a more resilient ecosystem (Ci et al., 2024). Precision restoration is a sustainable and scientific approach to land recovery, preserving biodiversity, and supporting communities dependent on healthy ecosystems amidst global environmental challenges (Copeland et al., 2021). Traditional restoration approaches often face limitations in scale, data analysis, accuracy, and adaptability, which necessitate more precise, efficient, and cost‐effective data‐driven solutions (Yu et al., 2021). For instance, during the initial phases of China's Three North Shelter Forest Program, the extensive planting of fast‐growing poplar and elm trees on semi‐arid land led to significant dieback. This was due to a lack of detailed soil and groundwater data, an inability to accurately predict survival rates at specific sites, and a failure to adjust planting strategies to accommodate local microclimatic variations (Cao et al., 2011). Consequently, such limitations necessitate more precise, efficient, and cost‐effective data‐driven solutions. Therefore, recent advances in data‐driven technologies, especially ML, offer new opportunities for enhancing precision in ecological restoration.

The rise of ML in ecological studies represents a transformative shift in how researchers analyze and interpret complex environmental data for precision restoration planning (Stupariu et al., 2022). Traditional ecological research often relied on manual data collection and statistical methods, which, while effective, had limitations in handling vast datasets and uncovering intricate patterns (Pichler & Hartig, 2023). ML, a subset of artificial intelligence (AI), contains algorithms that improve predictive accuracy by learning from data rather than relying on rule‐based programming (Zhong et al., 2021). In the context of precision ecological restoration, ML techniques facilitate the extraction of insights from extensive and complex ecological data, spanning remote sensing, biodiversity monitoring, soil health, and climate impacts (Willcock et al., 2018). As restoration projects increasingly involve varied datasets from satellite imagery, sensors, and climate records to on‐ground biodiversity and soil health assessments, ML can streamline the processing and integration of these datasets, enabling more accurate ecosystem assessments and enhancing decision‐making across multiple restoration stages (Cui et al., 2023). One of the core benefits of ML in ecological restoration is its ability to identify patterns and correlations hidden within vast datasets that could be undetectable through traditional methods (Reichstein et al., 2019). For example, remote sensing and ML models together enable mapping of ecosystem degradation and recovery trajectories with high spatial and temporal accuracy, supporting restoration practitioners in setting specific, measurable targets (Funk et al., 2019). Furthermore, ML algorithms can process real‐time data to generate actionable insights for restoration managers and simulate various scenarios, such as changes in climate or land use, providing recommendations for restoration strategies under different conditions, allowing decision‐makers to optimize restoration plans based on potential outcomes (Huntingford et al., 2019).

The application of ML in ecological research, though powerful for handling large and complex datasets, is constrained by several key limitations (Christin et al., 2019). Fundamentally, ML models are driven by statistical correlations and identify patterns in data, but do not inherently comprehend the underlying ecological processes (Cui et al., 2023). This theoretical gap restricts the generalizability of ML inferences, confining their validity to the parameter space of the input data (Lucas, 2020). Consequently, predictive models require rigorous validation using independent data, and hypotheses generated from descriptive ML must be framed with their limited transferability in mind (Spake et al., 2017).

An additional, widely cited challenge is the black‐box nature of many ML algorithms (Zednik, 2021), which can obscure the logic behind their predictions, making it difficult to understand how a result was achieved (Ryo et al., 2021). This challenges scientific interpretation and trust. To ensure the robustness of ML‐based research, it is imperative to prioritize methodological transparency. This entails making input data openly available and providing comprehensive documentation of all model settings, software, and code used, thereby facilitating critical evaluation and reproducibility (Scowen et al., 2021).

With the advent of ML, ecologists can now leverage advanced algorithms to process large volumes of data, allowing for more nuanced insights into ecological systems (Yu et al., 2021). Integration of ML in ecological restoration goes beyond technical advances to encompass collaborative research and policy development. As restoration goals become increasingly ambitious in scope, the role of ML in managing restoration at scale becomes essential (Scowen et al., 2021). However, challenges remain in aligning ML‐driven restoration techniques with ecological principles, maintaining transparency in algorithmic decisions, and ensuring that local knowledge and conservation ethics guide ML applications in restoration. This review article aims to explore the multifaceted role of ML in ecological restoration, detailing its applications, potential benefits, and challenges along with future research directions and policy integration. In doing so, it provides a comprehensive perspective on how ML can transform data into actionable decisions, driving more precise, efficient, and sustainable ecological restoration practices.

OVERVIEW OF ML TECHNIQUES

The core idea of ML is to build models that identify patterns in data, enabling systems to adapt to changes and handle complex tasks beyond the scope of human capability or traditional programming (Jordan & Mitchell, 2015). With the rise of big data and increasing computational power, ML has become more accessible, leading to breakthroughs in automation and predictive analytics. It is classified into several types (Figure 1), including supervised learning (SL), unsupervised learning (USL), reinforcement learning (RL), and deep learning (DL), each with unique approaches and applications (Sarker et al., 2020). The process of developing the ML application involves several key steps (Figure 1) and can utilize a variety of common algorithms (Table 1).

FIGURE 1.

Various types of machine learning (ML) algorithms and the fundamental steps of ML and deep learning (DL) application include: (a) defining the problem, (b) gathering data, (c) preprocessing the data, (d) splitting the data, (e) training the models, (f) optimizing the models, (g) evaluating the models, (h) interpreting the results, and (i) deploying the models.

TABLE 1.

Key features, strengths and weaknesses of commonly used machine learning algorithms.

Algorithm	Type	Key features	Strengths	Weaknesses	References
Random forest (RF)	Supervised	Bagging, decision trees, feature randomness, reduces overfitting	High accuracy, robustness, feature importance	Computationally intensive, lower interpretability than a single tree	Breiman (2001), Sarker et al. (2019).
Support vector machine (SVM)	Supervised	Optimal hyperplane, support vectors, kernel functions (linear, RBF, polynomial)	Effective in high‐dimensional spaces, strong generalization	Struggles with noisy or overlapping classes	Cortes and Vapnik (1995), Mountrakis et al. (2011).
Decision tree (DT)	Supervised	Recursive splitting using Gini impurity or information gain, intuitive structure (flowchart)	Easy to interpret, works with both numerical and categorical data	Overfitting, sensitive to noise	Bansal et al. (2022)
K‐nearest neighbor (KNN)	Supervised	Lazy learning, no training phase, non‐parametric, classifies based on nearest neighbors	Simple, adaptable, effective for various data types	Computationally expensive prediction, poor for high‐dimensional data	Deng et al. (2016), Wang et al. (2022)
K‐means clustering (K‐means)	Unsupervised	Partitions data into K clusters based on Euclidean distance, centroid recalculation	Efficient, scalable, widely used in market segmentation and pattern recognition	Sensitive to initial centroids, assumes spherical clusters	Ahmed et al. (2020), Kodinariya and Makwana (2013)
Adaptive boosting (AdaBoost)	Supervised (ensemble)	Combines weak learners (typically decision trees), adapts to misclassified data	Increases accuracy of weak models, versatile	Sensitive to noise and outliers	Freund and Schapire (1997), Zhao et al. (2019)
Gradient boosting (GB)	Supervised (ensemble)	Sequential decision trees, iterative error correction, minimizes loss function	High accuracy, captures complex data patterns	Computationally intensive, prone to overfitting without tuning	Bentéjac et al. (2021), Natekin and Knoll (2013)
Naive bayes	Supervised	Probabilistic, assumes feature independence, based on Bayes' Theorem	Fast, simple, works well with high‐dimensional data (e.g., text)	Struggles with correlated features, zero probability for unseen data	Ting et al. (2011), Wickramasinghe and Kalutarage (2021)
Logistic regression (LR)	Supervised	Linear combination of features, sigmoid function for binary classification	Simple, interpretable, good for linear relationships	Struggles with complex, non‐linear data	Stoltzfus (2011)
Artificial neural networks (ANNs)	Supervised	Interconnected layers of nodes (neurons), backpropagation, non‐linear activation functions	Powerful, can model complex relationships	Requires large data, computationally intensive, prone to overfitting	Abiodun et al. (2018)
Convolutional neural networks (CNNs)	Supervised (deep learning)	Designed for grid‐like data (images), uses convolutional layers, filters for feature extraction	Excellent for image recognition, efficient via local connectivity	Needs a lot of data and computation power	Yamashita et al. (2018)
Long‐short‐term memory (LSTM)	Supervised (deep learning)	Type of RNN designed for sequential data, uses input, output, and forget gates to manage long‐term dependencies	Excellent for sequential data, time series, and language tasks	Computationally intensive, outperformed by newer models (e.g., transformers)	Yu et al. (2019), Zhao et al. (2017)

Supervised learning

SL uses algorithms that require external guidance. The input data is split into training and testing data sets (Crisci et al., 2012). The training data set is used for model training to accurately predict or classify the output variable. During training, the algorithms learn patterns or relationships from the data and then apply these patterns to the test data set, resulting in predictions or estimations. Commonly used SL algorithms include random forest (RF), decision trees (DTs), linear regression, support vector machines (SVMs), logistic regression (LR), and neural networks (Kotsiantis et al., 2007).

Unsupervised learning

USL is a type of ML that discovers underlying patterns, structures, or relationships within the data. When presented with new data, it uses the characteristics previously learned to identify patterns or organize the data into classes. It is commonly used for tasks like clustering and feature reduction (Solan et al., 2005).

Reinforcement learning

In RL, an agent develops decision‐making ability by exploring and interacting with its environment. The agent carries out actions guided by specific rules or strategies and gets feedback in the form of rewards or penalties depending on the results of its actions. The ultimate goal is to maximize the cumulative reward over time by refining the rules or strategies to make better decisions. RL operates on a trial‐and‐error basis, where the agent continuously explores and exploits its environment to discover the most effective actions (Sutton, 1992).

Deep learning

DL belongs to a broader class of ML methods based on artificial neural networks (ANNs), distinguished by its ability to perform representation learning (Lecun et al., 2015). It employs a multilayer computational architecture including input, hidden, and output layers to extract patterns from data. Compared with traditional ML approaches, DL offers superior performance in many applications, particularly when large datasets are available (Sarker et al., 2020). The most widely used DL algorithms include multilayer perceptron (MLP), convolutional neural network (CNN), and long short‐term memory recurrent neural network (LSTM‐RNN) (Sarker, 2021).

PROGRESS IN ML APPLICATIONS FOR PRECISION ECOLOGICAL RESTORATION

ML is increasingly applied in ecological restoration to optimize decision‐making, predict ecosystem behavior, and monitor environmental changes (Figure 2). It aids species selection, biodiversity assessment, and sustainable management, improving restoration effectiveness (Ahmed, 2026b).

FIGURE 2.

Application of machine learning (ML) in major research areas including ecological factor identification and prediction, remote sensing and image recognition, and ecological risk assessment.

Predictive modeling and restoration planning

In restoration ecology, project planning often uses reference sites, historical conditions, or regional values to define desirable outcomes for an ecosystem (White & Walker, 1997). However, the perception of what constitutes a healthy and functional ecosystem can change over time due to changing climatic, environmental, and social factors, a phenomenon known as shifting baselines (Steen & Jachowski, 2013). Emerging ecosystems, with new species compositions due to habitat degradation, further complicate using the traditional reference site (Woodrich & Pape, 2024). Predictive modeling for ecological restoration leverages advanced computational techniques, including ML, to forecast the outcomes of various restoration strategies, allowing decision‐makers to simulate how ecosystems will respond to different interventions (Caughlin et al., 2019). This reduces the risk of project failure and ensures that restoration efforts align with evolving environmental realities. A study by Nyengere et al. (2025) used the SVM algorithm and remote sensing to evaluate the impact of a community‐led forest restoration initiative in central Malawi from 2003 to 2023. Land cover changes were quantified through an SVM classification of satellite imagery (2003–2023), with ecological recovery indicated by trends in the normalized difference vegetation index (NDVI). The analysis revealed a dramatic landscape transformation, characterized by a 61.2% reduction in bare land and a 57.8% decline in grassland, coupled with a remarkable 305.6% increase in tree cover, accompanied by a significant shift in NDVI values from negative to positive, indicating successful ecosystem revitalization. The high accuracy of SVM classifications demonstrated that previously degraded land transitioned to grassland and tree cover, underscoring the profound effectiveness of participatory conservation strategies.

By analyzing historical data and environmental variables such as climate, soil composition, species distribution, and water availability, these models help identify the most effective restoration approaches to improving ecosystem health (Brudvig, 2017). They allow planners to test multiple restoration scenarios, optimize resource allocation, and predict the potential impact of specific interventions on biodiversity, habitat quality, and ecosystem services such as carbon sequestration or water filtration (Rydgren et al., 2019). By combining potential natural vegetation (PNV) and potential normalized difference vegetation index (PNDVI) through multiple ML algorithms (RF, MaxEnt, K‐nearest neighbor [KNN], LR, CART, and SVMs), Qu et al. (2024) mapped restoration patterns in the Lower Tarim River Basin of Northwest China. The model, trained on data including LULC, Sentinel 2A imagery (NDVI), climate, soil, topography, and hydrological properties, accurately identified suitable restoration locations, areas, and species. RF model outperformed all other ML models in simulating PNV and PNDVI with higher accuracy. Findings revealed that potential habitats for trees and grass are near the riverbank, covering 1.88 and 2.96 km², respectively, while shrubs occupy a larger area (25.12 km²) further from the course. The analysis also indicated that currently, 2.56% and 1.54% of the existing area is suitable for conversion to trees and grasses, respectively, providing a critical basis for precise, effective dryland vegetation restoration. Furthermore, predictive models can integrate climate projections to ensure that strategies remain resilient under future environmental conditions, helping to future‐proof restoration efforts (Huntingford et al., 2019). These models also help to manage uncertainty by providing a range of possible outcomes and performing risk assessments, allowing adaptive management through ongoing monitoring and adjustments to strategies as conditions change (Maris et al., 2018). By improving the efficiency, sustainability, and effectiveness of restoration projects, predictive modeling plays a crucial role in improving the success of ecological restoration and ensuring that ecosystems can recover and thrive over the long term, even in the face of environmental stressors or climate change.

Reintroduction of species and habitat suitability

Effective reintroduction planning often demands more than just empirical field data, as gathering the comprehensive data needed to accurately predict how reintroduced species will respond to various management strategies is typically impractical (Duffy et al., 2007). Furthermore, experimental manipulations are usually feasible only in controlled and simplified environments (Giometto et al., 2015). ML plays a transformative role in species reintroduction and ecosystem restoration by using data‐driven insights to optimize decision‐making, improve precision, and improve success rates (Crisci et al., 2012). ML integrates diverse datasets, including satellite imagery, ecological surveys, and behavioral tracking, to analyze habitat suitability, population dynamics, dispersal process, and species interactions, predict species survival, and guide reintroduction efforts (Malone et al., 2018). ML algorithms identify optimal reintroduction sites by evaluating factors like climate, soil quality, and vegetation, and ensuring species are introduced into areas where they are most likely to thrive (Beery et al., 2021). For example, a ML classifier called Bagging (Bootstrap Aggregating) was used to map potential areas for the introduction of Barbary sheep (Ammotragus lervia) in eastern Morocco (Naimi et al., 2024). Based on 88 recorded locations and three topographical factors, the high‐performing model (AUC = 0.929) found that suitable habitat, covering 13.8% of the region, is predominantly in the western area. These optimal zones are characterized by mountainous terrain, steeper slopes, and higher altitudes. The research findings provided valuable information for decision‐makers, suggesting a strategic plan to reintroduce these species to improve local biodiversity in the region (Naimi et al., 2024). Similarly, the ML model MaxEnt was successfully used for habitat suitability and reintroduction of Carterocephalus palaemon, butterflies in Northamptonshire after extinction in England. Another study that aimed at conserving the threatened Elaeocarpus serratus (rudraksh) employed the MaxEnt ecological niche model to identify suitable habitats for reinforcement (Baruah et al., 2019). First, they standardized the macro propagation techniques via seed germination and air‐layering. Then, they reintroduced 1050 saplings into the wild habitats identified by the model. This process resulted in a high survival rate of 68%, demonstrating the effectiveness of combining predictive modeling with active propagation for the successful conservation of a threatened species. Rather et al. (2020) used the ML algorithm RF to model multiscale habitat suitability using camera trap and scat data and predicted the impacts of future climate change on tiger and leopard distributions, revealing a 23% habitat loss for tigers under the RCP 8.5 scenario by 2050. Furthermore, RF has proven to be effective in predicting species richness, significantly reducing errors compared to traditional methods used in ecology, such as estimating richness distributions of fish, spiders, and small mammals (Tuia et al., 2022). The potential distribution of the critically endangered Gymnocladus assamicus tree in (Arunachal Pradesh, India) has been effectively mapped using MaxEnt, Generalized Linear model, Bioclim, and RF models (Sarma et al., 2022). Using 23 environmental variables (bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data), the MaxEnt model predicted a significantly larger suitable habitat (1884 km²) compared to the other models, which showed highly restricted ranges. By addressing uncertainties, optimizing resource allocation, and integrating conservation and climate data, ML improves the success of species reintroduction and fosters sustainable and adaptive ecosystem restoration.

ML for remote sensing and GIS data integration for ecosystem monitoring

ML has become a transformative tool in remote sensing and image recognition, ensuring accurate, efficient, and scalable analysis of vast and complex datasets (Belgiu & Drăguţ, 2016). It is widely recognized as the standard approach for classifying remote sensing images due to its efficiency and robustness in processing high‐dimensional data and capturing the intricate characteristics of various classes in geoscientific studies (Maxwell et al., 2018). In remote sensing, ML algorithms analyze satellite imagery, aerial photos (man and unmanned), and other geospatial data to extract meaningful insights, such as land cover classification, vegetation health, urban growth, and disaster impact assessments (Lary et al., 2016). Techniques such as RF, SVM, ANN, and CNN are widely used for tasks such as identifying patterns, detecting anomalies, and mapping changes over time. CNNs have transformed image recognition through automated feature extraction, leading to high accuracy in identifying objects, textures, and boundaries within images (Belgiu & Drăguţ, 2016). ML also enables multitemporal analysis, tracking dynamic processes like deforestation, glacial retreat, or urban sprawl (Wolff et al., 2015). Furthermore, its ability to integrate diverse datasets, such as spectral, thermal, and elevation data, enhances the depth and accuracy of the analysis. ML significantly reduces human effort and error by automating traditionally labor‐intensive tasks and making predictions from incomplete or noisy data. Buthelezi et al. (2024) conducted a study to develop a framework for natural forest mapping on the municipal scale, combining remote sensing with ML. Using Landsat imagery and five spectral indices, forest distribution was modeled with LightGBM, CatBoost, and XGBoost. LightGBM exhibited an accuracy of 90.76% with an F1 score, making it the most robust method for mapping natural forests in the Thekwini Municipality.

The effectiveness of four ML algorithms (KNN, RF, SVM, and ANN) for classifying land use/cover in Dengkou Oasis was evaluated. Landsat‐8 OLI data, spectral indices, and auxiliary variables that were derived from a digital terrain model to classify seven different land cover categories were used. All models achieved high accuracy, with ANN performing best (97.16%), closely followed by RF (96.92%) and SVM (96.20%). The RF algorithm demonstrated high stability, ease of use, and efficiency attributes during parameter tuning, making it an excellent initial choice for land use/cover classification. Zhang et al. (2016) used HyMap (advanced hyperspectral sensor) hyperspectral imagery and LiDAR (light detection and ranging; remote sensing technology that uses laser light to measure distances and create 3D maps and models) data to classify urban forest tree species, improving accuracy through the reduction of the minimum noise fraction (MNF) band. Using 20 optimal bands, MCC achieved 88.9% accuracy and RF 87.0%, outperforming results with the 118 bands. The findings highlight MNF's effectiveness and MCC's robustness for high‐dimensional datasets in tree species classification helpful in precise urban forestation. Similarly, ML was used in remote sensing data for land use and ecosystem services change (Mahmoud & Gan, 2018), carbon storage estimation (de Oliveira Silveira et al., 2019), and several other ecological predictions.

Ecological risk assessment

Ecological risk assessment is becoming increasingly sophisticated with the integration of ML, particularly in the context of precision restoration. ML algorithms can process vast and complex datasets, making it possible to model and predict the type of pollutants, assess ecosystem vulnerabilities, and identify areas most in need of restoration (Yu et al., 2021). By analyzing patterns in historical and real‐time environmental data, ML can uncover insights into how ecosystems respond to various stressors, such as pollution, climate change, and habitat destruction (Brieuc et al., 2018). Traditional risk assessments often rely on nonprobabilistic methods, which can limit their ability to incorporate uncertainty and variability. On the contrary, ML models, such as Bayesian networks, offer powerful tools for probabilistic and causal modeling, seamlessly integrating qualitative and quantitative data. These models excel in combining multiple lines of evidence, allowing for a more comprehensive and nuanced understanding of ecological risks (Carriger et al., 2016). Compared to weight‐of‐evidence approaches, Bayesian networks reduce biases and improve the prediction of causal relationships by explicitly modeling uncertainty and interdependencies among variables. This makes them particularly valuable for assessing ecological risks, where complex interactions and uncertainties are inherent (Cui et al., 2023). In South Korea, an ensemble model combining RF and Maxent was developed to create a national‐scale risk map for the occurrence of pine wilt disease (PWD). Overall, 149 sites of PWD occurrence and 149 sites with absence of PWD in South Korea were selected (Lee et al., 2021). A dataset consisting of seven environmental variables (elevation, slope, and distance to road as geographical variables, and annual mean temperature, minimum and maximum temperatures and annual precipitation, as meteorological variables), including seven land use types (urban area, agriculture land, forest, grassland, wetland, barren area, and water) was used. The model predicted most of the territory to be at risk, with potential expansion under climate change. The findings highlight the utility of the model for PWD surveillance and monitoring, enabling inference of spread pathways and directions (Lee et al., 2021). A diffusion mapping approach using ML can analyze ecosystem monitoring data to reconstruct biological traits, track changes in functional biodiversity over time, and improve ecological risk assessment of these changes (Ryabov et al., 2022). ML models are also effective tools for predicting the environmental fate and biological effects of emerging pollutants. These models can forecast the distribution and behavior of pollutants in various environmental media (Li et al., 2018; Zhang, Li, et al., 2021; Zhang, Xie, et al., 2021). For example, ML has been applied to predict the sorption of active pollutants in soils and sediments using chemical descriptors, enabling the assessment of ecological risk at the landscape scale (Li et al., 2021). A high‐quality dataset for 54 active pharmaceutical ingredients (APIs) across 13 soils/sediments was used to develop sorption models, comparing three ML methods (ANN, RF, SVM) and linear regression. Optimal performance was achieved by the RF model incorporating both chemical and solid descriptors, accurately predicting sorption coefficients for 90% of an independent test set within a factor of 10 (Li et al., 2021). This model provided a valuable tool for assessing the environmental risk of both existing and new pharmaceuticals. Additionally, ML has been used to explore the relationship between new pollutants, such as nanoparticles and microplastics, and their impacts on soil and marine microbial communities under varying ecological conditions. For instance, Godasiaei (2025) used advanced neural networks (GRU, long short‐term memory [LSTM], recurrent neural network [RNN], CNN) to analyze microplastic sorption of organic pollutants. The models were trained on a curated dataset featuring fundamental compound descriptors, including the n‐octanol/water partition coefficient (Log D), covalent acidity, covalent basicity, and molecular polarizability. The RNN model was the most accurate, achieving a 0.967 accuracy, in predicting how factors like the n‐octanol/water coefficient (Log D), acidity, and molecular polarizability affect sorption. The findings highlight RNN's potential as a fast and effective tool for understanding microplastic pollution dynamics and associated environmental risks. By integrating ecological risk assessment with ML, restoration efforts can be guided by data‐driven decisions, improving their effectiveness and sustainability. This approach ensures that resources are allocated efficiently, allowing ecosystem recovery while mitigating risks to biodiversity, human well‐being, and long‐term ecological health. Ultimately, the combination of ML and ecological risk assessment represents a transformative tool to advance ecosystem restoration and promote resilience in natural systems.

ML/AI FOR REAL‐TIME DECISION‐MAKING IN ECOLOGY

ML/AI are revolutionizing ecological management and restoration by transforming the approach to predicting and responding to environmental challenges. The shift from static, historical data‐driven forecasts to dynamic, real‐time decision support systems enables adaptive management, allowing for continuous adjustments in decision‐making based on incoming data (Jordan & Mitchell, 2015). These capabilities are directly applicable to precision restoration, which requires site‐specific, adaptive interventions tailored to local environmental heterogeneity, ensuring that the right interventions are applied to the right locations at the right time. The integration of ML and AI equips researchers and managers with powerful tools (Figure 3) to process large datasets and make informed, timely decisions (Yu et al., 2025), effectively tackling pressing issues such as biodiversity loss, habitat degradation, and resource management. Precision restoration via ML and AI ensures that restoration efforts are not only more effective but also adaptive, enabling ecosystems to recover in a more targeted, efficient, and sustainable manner.

FIGURE 3.

Machine learning (ML)/artificial intelligence (AI) for real time decision‐making in ecology. Created in BioRender; Ahmed (2026a), CC BY 4.0.

Advanced modeling for ecosystem simulation and restoration decision‐making

AI is emerging as a pivotal tool for advancing precision restoration, enabling a shift from broad, one‐size‐fits‐all approaches to targeted, data‐driven interventions (Woodrich & Pape, 2024). By integrating diverse datasets, AI significantly enhances ecosystem modeling and the prediction of restoration outcomes. This capability allows practitioners to test hypotheses and develop credible counterfactuals, moving beyond simple monitoring to actively forecasting the effects of specific policy decisions and biodiversity responses under different intervention scenarios (Leclère et al., 2020). This represents a core tenet of precision restoration, using predictive power to allocate effort where it will be most impactful, thereby overcoming the limitations of resource‐intensive traditional models (Newbold et al., 2020). For example, AI models have been used in North American prairies to predict how grazing management and replanting of native species would affect biodiversity and ecosystem function over time. These predictions are used in precision decision‐making about where and when to deploy restoration efforts most effectively (Huang et al., 2021).

The development of ecological digital twins, virtual replicas of real‐world ecosystems, exemplifies this shift. These models integrate data across different domains to create a dynamic testing ground for precise restoration planning (Tzachor et al., 2023). The CAPTAIN model (AI agent) is an example of ecological digital twins that integrates data from fields like ichthyology and seabird ecology to simulate and predict restoration efforts in marine ecosystems (Keith et al., 2022). This approach aligns with precision restoration by allowing ecosystem managers to test and optimize strategies in a virtual environment before physical actions are taken, ensuring interventions are both targeted and effective (Silvestro et al., 2025).

A fundamental application of AI in precision restoration is the spatial prioritization of efforts. ML guides restoration initiatives by analyzing complex layers of data including habitat degradation, species distributions, climate change projections, and human activity to pinpoint locations where interventions will yield the most significant returns for habitat recovery (Han et al., 2023). This ensures that limited resources are invested with precision. In the Amazon rainforest, AI has been used to prioritize areas for restoration by identifying regions at high risk of deforestation and those with the highest biodiversity potential (Leal et al., 2020). This spatial prioritization ensures that restoration efforts focus on areas with the greatest ecological value and vulnerability, maximizing conservation outcomes (Han et al., 2023).

The precision of restoration actions is further refined by AI‐enhanced Species Distribution Models (SDMs). These models are vital for making informed decisions about land use changes and their implications for biodiversity (Booth et al., 2014). By synthesizing species data with remote sensing and ML, SDMs achieve greater accuracy in predicting how species will respond to a restored landscape (Ryo et al., 2021). A study in Northwest China demonstrated how AI enhanced SDM accuracy for endangered species conservation. By developing a semi‐supervised AI model, researchers accurately predicted suitable habitats for Populus euphratica in China's Tarim River basin with only 443 samples, significantly outperforming conventional SDMs like MaxEnt (Ci et al., 2023). The AI‐powered SDM achieved 85% accuracy and revealed that 13.49% of the study area offers suitable habitat, highlighting substantial restoration potential. AI is particularly adept at managing the noisy and incomplete data common in ecology, revealing intricate patterns and overcoming limitations like spatial biases. Emerging methods like physics‐inspired AI and explainable AI (xAI) are crucial for building trustworthy models that can effectively leverage citizen science data, ensuring restoration plans are both ecologically sound and precisely tailored to future species needs (Reynolds et al., 2024).

AI dramatically accelerates the foundational science that guides precision restoration. The current process for assessing species extinction and ecosystem collapse risk is a manual, slow endeavor, creating a bottleneck for conservation prioritization (Zizka et al., 2022). AI can automate this by systematically scanning global scientific literature and monitoring real‐time land cover changes to extract pertinent data (Branco et al., 2023). This not only speeds up assessments and reduces costs but also enables a more dynamic and precise understanding of threats. The future of restoration planning lies in amalgamating these AI‐derived parameter estimates with spatially explicit predictive models, creating a continuously updated evidence base to ensure restoration resources are directed toward the species and ecosystems at greatest and most immediate risk (Silva et al., 2022).

Automated image collection and interpretation for species identification, monitoring, and environmental compliance

ML/AI‐driven monitoring is revolutionizing precision restoration by providing the high‐resolution, real‐time data essential for targeted ecosystem management. Platforms like iNaturalist, Pl@ntnet, Google Lens, and Merlin (Goëau et al., 2013; van Horn et al., 2018), through automated species recognition, enable precise mapping of existing populations, tracking range extensions, monitoring invasive species, and detecting illegal imports at customs (Pirotta et al., 2022). It can also issue immediate alerts for specific species, population densities, or emerging threats (Chalmers et al., 2019), which are essential for adaptive management in restoration projects.

The advancement of automated recognition processes also enhances the analysis of movement patterns and home ranges through methodologies such as mark‐recapture, facilitating a deeper understanding of species behavior and dynamics. Such insights are pivotal in precision restoration, as they allow restoration practitioners to assess population sizes, identify priority areas for intervention, and refine strategies based on ongoing monitoring (de Lorm et al., 2023). By continuously tracking species behavior in real time, restoration strategies can be adjusted dynamically to achieve the best outcomes.

Another significant contribution of ML/AI is their ability to identify novel species through image analysis (Zhao et al., 2024), supporting the documentation of dark diversity, species that are underreported or unidentified. By combining this with DNA analysis, AI enhances species identification in both field‐captured images and museum collections (Lu et al., 2024). This capability is essential for restoration efforts as it ensures that all species, including hard‐to‐detect ones, are considered, enhancing the precision and accuracy of biodiversity assessments for comprehensive ecosystem restoration.

AI camera systems powered by ML are significantly enhancing the capability to monitor environmental compliance in real time and facilitate precision restoration efforts. These systems can track biodiversity gains, analyze water quality data from treatment plants, and detect illegal deforestation via satellite imagery, thereby reducing the reliance on human observers (Shivaprakash et al., 2022). A key application is in monitoring commercial fishery bycatch, which is driving some seabird species toward extinction. These systems can detect bycatch incidents in real time, track mitigation measures, and assess compliance with regulations set by regional fisheries management organizations (Da Rocha et al., 2021). By leveraging ML, this technology enhances predictive capabilities, reduces human error, and contributes to the sustainable management of endangered seabird species and overall fishing practices.

Audio‐based species monitoring and environmental sensing

ML/AI are transforming ecological restoration from a broad‐stroke endeavor into a precise, data‐driven science. By leveraging audio recordings, ML/AI technologies enable accurate species identification and behavior tracking, which are essential for precision restoration efforts in complex environments. Tools like BirdNET can identify approximately 3000 bird species (Kahl et al., 2021), with expanding potential to include other vocalizing species such as invertebrates. The integration of autonomous recording units with localized identification algorithms allows for continuous, non‐invasive monitoring, providing real‐time insights into species presence and abundance (Znidersic & Watson, 2022). Egwumah et al. (2025) used various AI‐driven ML models (DT, SVM, RF, deep neural network [DNN]) to predict bird presence or absence across various habitats (degraded areas, wetlands, forestland, and bare ground), integrating environmental factors like air quality, temperature, and tree height. Models such as RF and DNNs depicted high accuracy (94.3% and 86.3%, respectively), with key predictors including species activity and pollutants. Such AI‐driven predictions can help identify critical bird habitats threatened by habitat loss, prioritizing areas with tall trees and low pollution levels for conservation.

Beyond species identification, ML/AI also enables the recognition of individual animals through their vocalizations, supporting detailed population studies (Djokic, 2025). This is particularly beneficial for monitoring species in restored habitats, where tracking population size, movement, and dispersal patterns can inform the success of restoration strategies and guide future management interventions (Linhart et al., 2022). In marine environments, distributed acoustic sensing (DAS) technologies, powered by AI, provide critical tools for monitoring marine fauna such as cetaceans and seals. These technologies also enable the assessment of human impacts on marine ecosystems, including activities like shipping and deep‐sea mining, which may hinder restoration efforts (Reynolds et al., 2024). Additionally, low‐powered ML/AI audio classifiers integrated into hydrophones offer the potential for continuous monitoring of marine species in remote locations, a significant advantage for precision restoration in expansive marine ecosystems (Sanguineti et al., 2021).

Satellite and airborne sensing for tracking and restoration

Through the integration of satellite and airborne sensing with ML/AI, restoration efforts can be conducted with greater precision, ensuring that interventions are tailored to specific ecological needs (Chen et al., 2018). ML/AI‐driven interpolation of satellite sensor data enables advanced classification of ecosystems and detection of land use changes, which are crucial for evaluating restoration progress. By identifying patterns and trends not easily recognized through traditional techniques, DL methods enhance the detection of subtle shifts in habitat quality and land cover (Helber et al., 2019). For example, CNNs reflect changes in grassland degradation by recognizing complex relationships in the data, such as changes in vegetation cover, soil composition, and land use (Lou et al., 2023). This integration not only facilitates the rapid analysis of large‐scale datasets but also allows continuous monitoring, so that small changes and trends can be detected (Yan et al., 2022). These insights help direct restoration interventions to the areas that need more attention, improving the precision of restoration strategies.

Factors such as cloud cover, illumination angle, and atmospheric conditions can severely impact satellite imagery quality, complicating ecosystem assessments. However, AI has shown superior effectiveness in correcting these issues compared to conventional methods, enabling more reliable data for restoration planning (Duporge et al., 2021). This capacity for image correction and enhancement ensures that restoration efforts are based on the most accurate environmental data available, even in challenging conditions.

In addition to land cover monitoring, AI has made strides in identifying species from space, facilitating remote monitoring of biodiversity across vast landscapes (Helber et al., 2019). For instance, Wu et al. (2023) developed a deep learning pipeline using very high‐resolution satellite imagery (38–50 cm) to detect and count nearly 500,000 migratory ungulates in the Serengeti‐Mara ecosystem, achieving an F1 score of 84.75%. This demonstrates that satellite‐based ML can monitor individual species, not just habitats, in remote areas. This ability to track species distribution, particularly in remote or difficult‐to‐access areas, is crucial for precision in restoration efforts aimed at reintroducing or conserving specific species.

AI's ability to integrate different data modalities such as optical, radar imagery, and time‐series analysis further refines land cover classification, threat detection, and habitat monitoring (Ortega Adarme et al., 2022). A study by López et al. (2020) combined Sentinel‐1 radar and Sentinel‐2 optical time series (SITS) to map natural vegetation in Pendjari National Park, Benin. The findings revealed that combining dense optical time series with radar data improves classification accuracy and yields better‐quality maps than using optical data alone. Dense time series enhance vegetation discrimination, offering more reliable maps for environmental management.

Maintaining up‐to‐date, near real‐time data is critical for monitoring ongoing restoration activities. AI/ML methods enable the automation of Earth observation datasets, facilitating continuous monitoring and evaluation of restoration progress. Indices like the Human Footprint Index and Biodiversity Intactness Index are enhanced by AI, offering real‐time insights into land use changes and ecosystem health (Mittelstadt et al., 2023). Keys et al. (2021) developed a ML‐based human footprint index (ml‐HFI) using satellite imagery and AI methods to automate routine updates of human pressure globally (via land use/cover change, infrastructure, settlements). It supports monitoring of the UN's SDG 15 on “Life on Land,” identifying countries that are making progress while facing increased human pressure. These tools provide key metrics to guide adaptive management and ensure that interventions are on track to achieve long‐term sustainability.

Integration of data for new ecological insights

ML/AI enhances precision restoration by integrating diverse datasets, improving ecosystem monitoring and management through new ecological insights (Brown et al., 2022). One example of this is the merging of photographic catalogs with remote sensing imagery, which has resulted in advanced land cover change products like Dynamic World. Such integrations allow for more precise tracking of land use changes over time, which is essential for assessing restoration progress. Similarly, the CAPTAIN project (Silvestro et al., 2022) employs RL to integrate species distribution, anthropogenic disturbances, population densities, phylogenetic diversity, land use data, and climate projections, ultimately informing conservation priorities and guiding precision restoration efforts (Silva et al., 2022).

Federated AI learning (FL) offers another promising approach for precision restoration, particularly when dealing with sensitive information such as endangered species locations and illegal hunting activities (Zhang, Xie, et al., 2021). FL is a decentralized ML approach that enables multiple nodes or devices to collaboratively train a shared model without sharing raw data (Yurdem et al., 2024). By leveraging techniques from healthcare, FL allows for the development of distributed models that provide real‐time insights into biodiversity trends while ensuring that sensitive data remains protected. However, the implementation of robust security measures is essential to guard against attacks that could compromise confidential training data (Reynolds et al., 2024).

AI also improves the understanding of species' Areas of Habitat (AoH) by addressing limitations in traditional ecological data and sampling methods (Wicaksono et al., 2019). Through the integration of occurrence data, habitat preferences, remote sensing, and environmental datasets, AI refines AoH maps and enhances the accuracy of species distribution and population predictions. This leads to better conservation planning and more targeted restoration efforts (O'Malley et al., 2024).

Furthermore, ML/AI‐powered surveillance systems are currently being used to monitor wildlife, such as elephants and tigers, in conservation areas. These systems not only alert communities to wildlife presence but also track human activities that could interfere with restoration efforts (Dertien et al., 2023). By combining short‐ and long‐term data on human–wildlife interactions, AI improves conflict prediction and enables proactive interventions, ensuring better outcomes for both species and local communities (Tuia et al., 2022). However, it is crucial to consider the ethical implications and privacy concerns to avoid exacerbating tensions between restoration goals and local community needs.

CHALLENGES AND LIMITATIONS IN ML APPLICATIONS FOR PRECISION RESTORATION

Although ML has contributed significantly to restoration ecology research, there are still various limitations and challenges that need to be addressed. Key concerns include data quality, the relevance of selected features, the balance between interpretability and performance, and the development and applicability of models. These factors must be carefully considered before integrating ML into restoration studies.

Establishment of robust and reliable data sets

The effective application of ML in ecology is constrained not by the availability of data, but by the specific and demanding data requirements of ML models themselves. The performance of ML is contingent on large volumes of robust, consistently formatted, and meticulously labeled data for training (Bochenek & Ustrnul, 2022). Although several valuable ecological databases are built by aggregating literature and legacy sources, with high‐quality data acceptable for traditional analytical tools, they can present challenges for ML. These challenges include data gaps, inconsistencies, and imbalanced representations that arise from the collaborative and long‐term nature of ecological data collection (Scowen et al., 2021). Furthermore, the dynamic and spatially heterogeneous nature of ecosystems means that data can be sparse or biased, particularly from remote areas (Pichler & Hartig, 2023). This inherent “scruffiness” of real‐world ecological data which experts can navigate can undermine ML model effectiveness when it leads to incomplete or unrepresentative training sets (Yu et al., 2021). Therefore, the central challenge is a mismatch of data structure, not a lack of scientific effort. As a result, ensuring that the data are robust, comprehensive, and accurately reflect ecological realities is a critical first step for any ML application in ecology. Additionally, the establishment of ecological datasets often involves significant challenges in data preprocessing and feature selection (Frey, 2020). In many cases, ecological data are noisy, with missing values, outliers, or redundant information that can affect model performance (Penone et al., 2014). Deciding which variables to include in the dataset requires a deep understanding of the ecological system being studied, as well as the specific research question at hand (Farley et al., 2018). Furthermore, ecological systems are inherently complex, and feature engineering can require extensive domain knowledge to identify the most relevant factors that influence ecological outcomes (Pichler et al., 2020). Balancing the inclusion of diverse, high‐dimensional data while ensuring the interpretability and accuracy of the ML model can be a delicate process. Therefore, careful consideration of data quality, relevance, and representativeness is crucial to successfully applying ML in ecological research.

Data scarcity

Another significant limitation in applying modern ML techniques to ecology and evolution is the lack of necessary data required for ML model (Pichler & Hartig, 2023). This blind restoration problem (insufficient data) can be overcome by leveraging advanced ML techniques like transfer learning (Wang et al., 2020). This technique reduces the need for large datasets by enabling models to generalize from limited data. In transfer learning, knowledge is acquired from a data‐rich source domain to enhance learning in a data‐scarce target domain, thereby overcoming the challenge of limited labeled data (Khan et al., 2022). In the case of ecological restoration, transfer learning can be used to fine‐tune pre‐trained models for specific tasks, particularly for image‐based data (Ma et al., 2024). However, for structured tabular data, which is common in restoration, transfer learning is less effective due to the task‐specific nature of the relationships between features (Pichler et al., 2020). A hybrid approach that combines traditional ML methods with pre‐trained DL models for feature extraction could help mitigate data scarcity. Additionally, incorporating expert knowledge and domain‐specific insights could further enhance the model's predictive capabilities, even when data are limited (Lu et al., 2025).

Feature extraction and selection

Feature extraction and selection in ML present several challenges that can affect model performance. Feature extraction is a pivotal step in ML, aimed at identifying the most informative features from raw data to improve classifier performance (Chandrashekar & Sahin, 2014). Feature selection, or dimensionality reduction (DR), is a method in both supervised and USL that reduces the dimensions of the data set while preserving key discriminative information (Feng et al., 2022). Feature selection involves strategically identifying and selecting a subset of the most relevant features, resulting in improved model simplicity and efficiency. It helps mitigate the challenges of high dimensionality, often used before building classification or regression models (Ding et al., 2024). The presence and intricate correlations among thousands of features can lead to redundancy issues, leading to computational inefficiencies and overfitting (Li et al., 2017). Redundancy and multicollinearity, where features are highly correlated or linearly dependent, further complicate the process by introducing unnecessary complexity and instability in the model (Senawi et al., 2017). Additionally, reducing the dimensions can cause the loss of important information, limiting the model's ability to capture the underlying patterns. Some feature interactions may also be overlooked, as traditional selection methods often fail to account for features that are only meaningful when considered together (Zhu et al., 2023). Overfitting is another risk, as feature selection methods may inadvertently choose too many specific features that do not generalize well to new data (Srivastava et al., 2014). Scalability is also a concern, as many methods struggle to handle large datasets efficiently. Furthermore, selecting the right feature selection technique, whether filter‐based, wrapper‐based, or embedded, can be difficult and often requires domain knowledge to identify the most relevant features. Without this expertise, the process becomes trial and error, potentially leading to suboptimal results (Zhu et al., 2023). Addressing these challenges requires a combination of careful preprocessing, appropriate DR techniques, and domain‐specific strategies to retain the most informative features for model training.

Interpretability and black‐box nature of ML algorithms

Interpretability in ML models presents a significant challenge in ecological restoration studies, where understanding the connection between variables is crucial for accurate analysis and decision‐making (Yu et al., 2021). Ecological data are often complex and involve a multitude of interacting factors such as species diversity, environmental conditions, soil properties, and climate variables (Lucas, 2020). These datasets can be high‐dimensional, noisy, and sparse, making it difficult for ML models to provide clear and understandable insights into how specific variables influence ecosystem processes (Pichler & Hartig, 2023). The black‐box nature of many ML algorithms, particularly DL and ensemble models, makes it challenging to interpret how the model arrives at its predictions (Rudin, 2019). This deficiency of transparency is problematic because ecological studies often aim to uncover cause‐and‐effect relationships, identify ecological drivers, and inform policy or conservation strategies (Ryo et al., 2021). Without interpretability, it becomes difficult to validate model predictions against ecological knowledge or to understand the real‐world mechanisms driving ecological patterns (Lucas, 2020). In addition, ecological study models are typically used by stakeholders with limited technical expertise, such as conservationists, land managers, and policymakers, who need to understand the rationale behind model outcomes to make informed decisions (Pichler & Hartig, 2023). If a model cannot provide interpretable insights, it reduces its value for practical application and can hinder effective communication of results. Additionally, ecosystem studies are often influenced by uncertainty, such as fluctuating environmental conditions or incomplete data, and without interpretability, it is challenging to assess the reliability of model predictions in the face of such uncertainty (Murdoch et al., 2019). To address these challenges, there is an increasing emphasis on developing interpretable ML techniques in ecological studies. This may involve using simpler models that provide more transparency, applying interpretability tools such as Local Interpretable Model‐agnostic Explanations (LIME) or Shapely Additive Explanations (SHAP) to assess feature importance, or designing hybrid approaches that connect the predictive power of complex models with the transparency of simpler ones (Molnar et al., 2018; Zhao & Hastie, 2019). Enhancing interpretability not only increases the credibility and trustworthiness of ML in ecosystem studies but also helps ecologists and decision‐makers better understand the underlying processes governing ecosystems, leading to more effective and informed management practices.

Uncertainty and error in ML simulations

ML techniques for ecological restoration have shown potential but often overlook the uncertainties in predictions. These uncertainties stem from dynamic environmental factors such as weather patterns, species interactions, and habitat conditions (Lürig et al., 2021). Without uncertainty quantification, ML models can lead to overfitting or underfitting, resulting in unreliable outcomes. Relying on predictions without considering the uncertainty in the input data or model can lead to biased decisions (Willcock et al., 2018). For instance, if the input data falls outside the model's training distribution, the model may produce unrealistic recommendations. However, by including CIs in predictions, decision‐makers can better understand the uncertainty of the model and make more informed restoration choices. Thus, accounting for uncertainties is essential to ensure reliable and effective ecological restoration decisions (Scowen et al., 2021). Uncertainty in ML can stem from both the input data and the modeling procedures. There are two main types of uncertainty: (1) epistemic uncertainty, which arises from the choice of ML architecture and model parameters, and (2) aleatory uncertainty, which is caused by the intrinsic randomness of the physical system (Hüllermeier & Waegeman, 2021). Modeling both types of uncertainty improves the predictive performance of the models. Moreover, distinguishing between them helps identify the sources of errors that can potentially be reduced. This differentiation also improves transparency in decision‐making, enabling more informed choices, as the remaining uncertainties are clearly understood and acknowledged (der Kiureghian & Ditlevsen, 2009).

Uncertainty measurement methods fall into four major categories: single‐deterministic methods, Bayesian approximations, ensemble learning, and test‐time data augmentation. Bayesian methods, such as Monte Carlo dropout, MCMC optimization, Bayesian model averaging (Samadi et al., 2020), and ensemble approaches like deep and Bayesian deep ensembles (Basora et al., 2025), are widely used. Data uncertainty, particularly for normally distributed uncertainty, is typically computed by forecasting parameters such as the mean and SD (Lakshminarayanan et al., 2017). These methods, when applied to ML‐based ecological restoration models, can minimize errors and uncertainty during both training and testing periods.

Generalization of ML

In ML applications, ensuring generalization and understanding of the model application domain (AD) are crucial for reliable performance (Caro et al., 2022). AD defines the space where predictions are reliable and should be mathematically formulated. Model testing, typically performed using a random subset of data not involved in training, helps to assess generalization (Cui et al., 2023). However, without validation on an independent dataset outside the parameter space of the training‐testing data, models may become overfitted and fail to generalize to new contexts or time periods (Scowen et al., 2021). Overfitting is a common issue when dealing with small datasets and complex algorithms, where models perform well on training data but fail to predict new samples. This results in poor accuracy when applied to new unseen data (Peters & Schuld, 2022). To mitigate this, the training dataset can be split for both training and generalizability of the test, or the model can be tested on a dataset beyond the space of learned parameters. Furthermore, techniques such as cross‐validation and methods like feature selection and model simplification can help to minimize model overfitting (Roberts et al., 2017). This combination of strategies ensures that the models are robust, generalizable, and applicable to a wide range of data.

FUTURE RESEARCH PERSPECTIVES

1. Improving data infrastructure and access is crucial for effective ecological restoration using ML. Research should focus on expanding open‐access databases that provide standardized data on species, soil health, and climate conditions, and fostering collaborative data platforms. Additionally, advancing remote sensing technologies, such as satellite imagery, drones, and hyperspectral sensors, will provide detailed real‐time data, which ML can analyze to uncover critical ecological patterns.

2. Developing XAI tools, such as DTs or Shapley values, to make ML outcomes more transparent to ecologists is urgently needed. In addition, methods to quantify the uncertainty of the model and highlight the importance of characteristics can help practitioners understand which ecological factors drive the predictions.

3. Ecological restoration is a complex challenge that benefits from an interdisciplinary approach that combines ecological science, social science, and policy frameworks. Research should focus on integrating ecological data with social science insights (e.g., community participation, land use practices) to better understand restoration needs. Furthermore, ML can be used to model policy scenarios and evaluate the socioeconomic outcomes of different restoration strategies.

4. Developing scalable ML solutions for ecological restoration is crucial due to the variability of ecosystems and restoration contexts. Research should focus on transfer learning, enabling models to adapt across diverse environments with minimal data, and creating generalizable restoration models that can apply universally across ecosystems. Additionally, context‐sensitive algorithms should be developed to adapt restoration strategies to local conditions.

5. Local knowledge about ecosystems, their history, and traditional restoration practices can be invaluable in guiding the restoration process. ML tools should be designed to integrate community input and local expertise into restoration decisions, making ML tools more inclusive and locally relevant. Develop participatory ML tools that allow communities to contribute data and knowledge to restoration projects. These tools can be used to gather feedback on species selection, restoration goals, and on‐the‐ground challenges. This collaboration will ensure that restoration practices are culturally appropriate and are more likely to succeed in the long term.

6. Collaboration between academia, industry, and governments is essential to harness the full potential of ML for large‐scale ecological restoration. Research should explore public–private partnerships to fund ML tools, policy frameworks that support data‐driven restoration, and cross‐sector knowledge sharing to ensure practical, real‐world applications. Key recommendations include establishing multisectoral working groups for collaborative projects, aligning ML research with global restoration goals (e.g., UN Decade on Ecosystem Restoration), and increasing funding from governments and industries to accelerate the development and adoption of scalable ML solutions.

CONCLUSION

Ecological restoration has become increasingly important for the management of ecosystems in response to growing environmental challenges around the world. The integration of ML in ecological studies offers a transformative approach to analyze complex data, enhancing restoration planning and decision‐making for better ecological restoration outcomes. This paper examined frequently used ML algorithms and their applications in various fields of ecological restoration. It also identified the challenges faced by ML and suggested future research avenues to enhance its application for precision restoration and sustainable environmental management. RF, DT, and SVMs were the most used ML algorithms in ecological studies. These ML algorithms facilitate the analysis of large ecological datasets, highlight complex interactions, and improve strategies for restoration, resource management, and ecological risk assessments. Predictive modeling in ecological restoration uses ML to forecast the impact of restoration strategies, helping decision‐makers simulate ecosystem responses. By analyzing data on climate, soil, species, and water, it identifies the most effective approaches to improve ecosystem health and reduce project risk. Predictive models can incorporate climate projections, ensuring that restoration strategies remain resilient to future environmental changes. ML integrates diverse data, including satellite imagery and ecological surveys, to assess habitat suitability and guide species reintroduction. By evaluating factors such as climate, soil, and vegetation, ML algorithms analyze satellite imagery and geospatial data to extract insights such as land cover classification, vegetation health, and disaster impact. ML models (RF, SVM, ANN, and CNN) are used to identify patterns, detect anomalies, and map changes, excelling in automated feature extraction and high‐precision image recognition. ML enables real‐time ecological decisions by analyzing live data (e.g., satellite, sensors, drones, AI cameras, audio data) to predict species distributions, detect rare populations, and identify threats like habitat loss or illegal activity. It guides adaptive restoration by prioritizing areas, modeling intervention impacts, and monitoring environmental compliance by instantly converting data into insights to align restoration and conservation with policy and ecosystem resilience. ML integration in ecological restoration faces certain challenges such as poor data quality, black‐box nature of models, lack of model interpretability, and difficulty scaling from small to large ecosystems. Integrating traditional ecological knowledge is also challenging, and model uncertainty due to data gaps and environmental unpredictability complicates predictions. These challenges can be addressed by improving data quality, enhancing model transparency, developing XAI tools (DTs or Shapley values), leveraging local knowledge, and fostering collaboration across academia, industry, and government.

AUTHOR CONTRIBUTIONS

Zeeshan Ahmed conceived the review paper idea. Zeeshan Ahmed wrote and finalized the manuscript. Ghulam Murtaza, Sikandar Ali and Qi Liu helped in arranging data and preparing figure and tables. Zeeshan Ahmed and Dongwei Gui validated and approved the final manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

Ahmed, Zeeshan , Gui Dongwei, Liu Qi, Murtaza Ghulam, and Ali Sikandar. 2026. “From Data to Decisions: The Role of Machine Learning in Precision Ecological Restoration.” Ecological Applications 36(6): e70305. 10.1002/eap.70305

DATA AVAILABILITY STATEMENT

Data (Ahmed 2026b) are available in Figshare at https://doi.org/10.6084/m9.figshare.31345312.v1.