AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management

Summary

Global biodiversity decline demands advanced technological solutions. This review synthesizes artificial intelligence (AI)-driven methodological innovations across data, algorithmic, and system layers for biodiversity conservation. At the data layer, multimodal sensing technologies including environmental DNA, acoustic monitors, and satellite imagery, coupled with data fusion techniques, enhance monitoring resolution. At the algorithmic layer, models ranging from convolutional neural networks to Transformers and reinforcement learning enable species identification, habitat assessment, and conservation optimization. At the system layer, federated learning, digital twins, and edge computing build scalable, secure conservation platforms. These advances restructure conservation practice from reactive monitoring toward proactive, predictive management across real-time individual tracking, precise habitat assessment, proactive threat detection, and optimized conservation prioritization. Despite challenges of data bias, algorithmic opacity, and socio-ethical tensions, responsible AI development under interdisciplinary governance offers a critical pathway toward achieving global conservation targets.

Graphical abstract

Ecology; Environmental science; Nature conservation

Introduction

Biodiversity, as the cornerstone of Earth’s life-support systems, sustains ecosystem functions and services, providing essential material resources and underpinning cultural, spiritual, and aesthetic values. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) assessment reports that the global average species abundance has declined by over 20% since 1900, with extinction rates being tens to hundreds times above natural background levels and over one million species facing extinction risk.¹ This declining trend manifests not only in species loss but also in the erosion of functional traits and phylogenetic diversity, undermining ecosystem resilience and adaptive capacity.

To address this crisis, the Kunming-Montreal Global Biodiversity Framework (GBF) encourages “innovative technologies such as artificial intelligence, remote sensing, and distributed sensors” to achieve the “30 × 30” target: conserving at least 30% of terrestrial and marine ecosystems by 2030.² However, only approximately 17% of land and 8% of ocean areas are currently protected,¹ highlighting the substantial challenges remaining.

Although biodiversity-related data have expanded, their application in conservation faces numerous bottlenecks. Traditional approaches reliant on static protected area designations, manual surveys, and experience-based decision-making exhibit pronounced limitations under rapid environmental change. Data fragmentation, insufficient spatiotemporal resolution, and difficulties in standardizing heterogeneous sources impede effective biodiversity information utilization. Since the 1980s, the ecological community has recognized a fundamental knowledge gap in understanding large-scale biodiversity patterns—a gap that is difficult to bridge through conventional techniques alone.³

The rapid advancement of artificial intelligence (AI) presents a transformative opportunity for biodiversity research and practice.^4,5 AI encompasses computational methodologies enabling machines to perform tasks requiring human intelligence. Machine learning (ML) allows systems to learn patterns without explicit programming, while deep learning uses multi-layered neural networks to extract hierarchical features from complex inputs. These methodologies suit biodiversity applications where data are high-dimensional, heterogeneous, and characterized by complex non-linear relationships. Fueled by increased computational power, methodological breakthroughs in machine and deep learning,⁶ and enhanced availability of multi-source data (e.g., satellite remote sensing, drones, acoustic monitoring, and environmental DNA [eDNA]), AI is reshaping conservation operations,^7,8,9 driving a shift from reactive to proactive prediction, experience-driven to data-driven decisions, and localized to global coordination. This revolution is regarded as crucial for achieving the GBF’s “30 × 30” target.³

Despite considerable potential, AI application faces multifaceted challenges. Data bias is pervasive; uneven geographic monitoring distribution and limited generalization for rare taxa cause systematic errors.¹⁰ Computational resources required for large-scale data processing are often inaccessible in regions with weak infrastructure that exacerbates technological inequality.¹¹ AI also raises ethical concerns regarding data privacy, algorithmic power concentration, global data divides, and potential erosion of Indigenous people’s rights.¹² Addressing these challenges requires interdisciplinary collaboration, open-source governance, and robust ethical frameworks to ensure that AI genuinely empowers conservation, rather than becoming technocratic formalism.^13,14

This article provides a comprehensive discussion of the methodological innovations driven by AI in biodiversity conservation, along with its transformative impact on application scenarios, practical case studies, and associated ethical challenges. Our review distinguishes itself by (1) providing a systematic three-layer framework (data, algorithmic, and system) that integrates technological advances with conservation practice; (2) synthesizing the latest AI methodologies including emerging approaches like foundation models and federated learning (FL); and (3) critically examining implementation challenges and governance frameworks. It aims to elucidate how AI, through its core capabilities in multi-source data fusion, intelligent analytical decision-making, and system optimization management, can fundamentally transform biodiversity conservation from traditional reactive models toward an intelligent, proactive paradigm.

Figure 1 provides a visual overview of this three-layer framework, illustrating the hierarchical relationship and interaction among data acquisition innovations, algorithmic advancements, and system-level integrations that collectively drive the transformation of conservation practices.

Figure 1.

The AI-driven biodiversity conservation: A layered integration framework

Methodology

Literature search and inclusion criteria

We searched Web of Science (https://www.webofscience.com), Scopus (https://www.scopus.com), and PubMed (https://pubmed.ncbi.nlm.nih.gov) for peer-reviewed articles published up to October 31, 2025, using combinations of AI and biodiversity/conservation terms. We included studies that have applied AI methods to biodiversity conservation or ecological management and excluded purely technical AI papers without a clear conservation context, as well as non-original contributions (e.g., editorials and commentaries).

Data extraction and categorization

Two authors independently extracted data on AI algorithms, data modalities, conservation scenarios, performance metrics, and geographical/taxonomic focus. Discrepancies were resolved by discussion with a third author. Studies were grouped into a three-layer framework (data, algorithmic, and system) and classified by the application scenario (e.g., species monitoring, habitat assessment, threat early warning, conservation prioritization, and effectiveness evaluation).

Synthesis approach

Due to heterogeneity in algorithms, ecosystems, and evaluation metrics, we performed a narrative synthesis rather than a quantitative meta-analysis. We identified cross-cutting themes, such as the alignment between algorithmic structure and ecological data properties, and summarize them with illustrative examples and tables. We also integrated literature on ethical and socio-technical challenges (e.g., data bias, algorithmic opacity, data sovereignty, and algorithmic colonialism) to provide a balanced perspective on both opportunities and risks.

Quality considerations

Although no formal risk-of-bias tool was applied, we evaluated each study for methodological transparency, data representativeness, robustness of performance evaluation, and attention to ethical issues. Key limitations were synthesized and are presented in the “limitations of the study” section.

Methodological innovations: From data acquisition to intelligent sensing

Methodological innovation in data layer

The acquisition and analysis of environmental and biological data form the foundation of biodiversity monitoring. While traditional methods are accurate, they are costly and difficult to scale.¹⁵ The need for AI technologies has driven biodiversity monitoring toward automation, real-time analysis, and predictive modeling. Traditional manual collection is gradually being superseded by integrated space-air-ground automated networks leveraging satellite remote sensing, drone surveys, infrared cameras, acoustic monitoring, and eDNA techniques,¹⁶ enabling multi-dimensional, high-throughput, non-invasive data collection on biological community structure and dynamics (Table 1).

Table 1.

Application of multimodal intelligent sensing technologies in biodiversity monitoring

Technology Type	Application scenario	Strengths	Limitations
Satellite remote sensing	large-scale habitats change monitoring17,18	wide coverage, periodic data collection	limited resolution, affected by cloud cover
Drone imaging	fine-scale biodiversity assessment,19,20	high resolution, flexible deployment	limited flight endurance, subject to airspace regulations
Acoustic monitoring	species behavior and distribution studies,21,22	not limited by light, enables continuous monitoring	susceptible to environmental noise
Infrared cameras	wildlife population monitoring23,24	operates 24/7, non-invasive	storage and battery limitations
Environmental DNA (eDNA)	aquatic biodiversity assessment25,26	high sensitivity, non-destructive	lacks real-time sensing, requires laboratory analysis

Infrared cameras and drones autonomously collect wildlife images around the clock, reducing monitoring costs by up to ∼80%.¹⁹ Acoustic recorders enable long-term, non-invasive monitoring of animal sounds, facilitating cost-effective studies for marine or nocturnal species.²¹ With AI-powered automated signal recognition, a single recorder can effectively monitor all bird species within wetlands covering up to 150 ha.²⁷ Breakthroughs in environmental genomics, particularly eDNA/eRNA technologies, are especially noteworthy; by collecting trace genetic material from water or soil and utilizing metabarcoding with AI classifiers, researchers can monitor species, reconstruct food webs, and detect invasives without disturbing organisms.^25,28,29 High-throughput nanopore sequencing now enables identification of up to 100,000 specimens in a single run, making large-scale species identification feasible,³⁰ and AI-combined eDNA achieves automated species identification exceeding 99% accuracy.³¹ This non-invasive approach is particularly suited for surveying elusive and aquatic species,³² expanding the scope and capability of biodiversity monitoring with unprecedented spatiotemporal resolution.

Biodiversity conservation often requires integrating multiple data sources. For example, comprehensive species status assessment may require combining wildlife image recognition, eDNA-detected species, and remote sensing habitat data.³³ Multimodal data fusion employs AI models to integrate heterogeneous data (images, sounds, text, and genetic data), yielding more comprehensive information than any single source, enhancing species distribution modeling and ecosystem monitoring precision.³⁴ Vision-language models like BioCLIP (bio-contrastive language-image pre-training) utilize cross-modal contrastive learning for semantic alignment between images and text, achieving superior zero-shot and few-shot classification performance. Data fusion techniques facilitate discovery of ecological patterns that are difficult to discern from single sources, thereby improving biodiversity assessment reliability.

Beyond species composition and distribution, biodiversity research increasingly focuses on species’ functional traits and ecological characteristics.³⁵ AI-driven trait quantification improves measurement accuracy and efficiency, making large-scale trait database construction feasible and providing rich foundational data for functional diversity studies and conservation strategy formulation.

Large-scale biodiversity datasets often suffer from incompleteness, heterogeneity, inaccuracy, and imbalance, which are challenging to address with traditional statistics.³⁶ AI innovations at the data layer also improve data quality and correct biases: by learning existing data distributions, they synthesize plausible virtual samples to fill gaps and balance categories. Transfer learning (TL) and domain adaptation mitigate disparities between data sources, allowing models trained on one region or taxon to be applied effectively to data-scarce contexts.³⁷ Adversarial training can detect and reduce systematic biases, enhancing model fairness across groups or environments. Through these techniques, researchers utilize non-standardized data more effectively, reducing bias impacts and enhancing biodiversity assessment credibility.

Methodological innovation in algorithmic layer

The ongoing development of AI algorithms offers versatile tools for biodiversity research, yet their performance depends fundamentally on whether algorithmic designs match the intrinsic properties of ecological data. Conventional biodiversity monitoring and analysis are constrained by challenges inherent to ecological data: high dimensionality, heterogeneity, complex non-linear relationships, and strong spatiotemporal variability. Algorithmic innovations address these constraints by tailoring computational approaches to these unique features. By establishing clear correspondences between analytical problems and appropriate algorithmic solutions, AI enables a shift from experience-based heuristics to robust, data-driven scientific inference.

To delineate these correspondences, this section first reviews the major classes of AI algorithms and their representative applications, followed by a comparative synthesis of their differences and problem-oriented advantages.

Traditional machine learning algorithms

Traditional machine learning algorithms include random forest (RF), maximum entropy model (MaxEnt), gradient boosting machine (GBM), and support vector machine (SVM). RF constructs multiple decision trees aggregated through bagging, effectively handling non-linear relationships and multi-source data while reducing overfitting. GBM employs sequential boosting where each tree corrects previous errors, achieving high accuracy in ecological parameter inversion with automated feature selection.^38,39 MaxEnt, based on maximum entropy principle, is widely used in species distribution modeling and predicting potential distributions, using occurrence points and environmental variables, with good stability in small-sample scenarios. SVM achieves classification by finding optimal hyperplanes in high-dimensional space, suited for pattern recognition and anomaly detection in ecological data. Traditional machine learning algorithms continue to play an important role in biodiversity data analysis due to their interpretability and robustness.

Convolutional neural networks

Convolutional neural networks (CNNs) excel at processing grid-structured data (e.g., images and spectrograms), achieving revolutionary success in visual recognition through hierarchical feature extraction.⁴⁰ In biodiversity, CNNs are extensively used for species image recognition and remote sensing interpretation, including insect diversity monitoring and behavior analysis,⁴¹ often matching or exceeding human accuracy while automating the “identification-counting-description” workflow.^42,43 CNNs also identify insect buzzes and woodpecker drumming sounds for monitoring seasonal and spatial ecological patterns.⁴⁴ In remote sensing, CNNs identify vegetation types, land cover changes, and habitat patterns from satellite or drone imagery.⁴⁵ Classic architectures like AlexNet and VGG (visual geometry group) achieve near-expert accuracy for bird and plant classification,⁴⁶ while residual networks (ResNet) introduce skip connections to mitigate vanishing gradients, enabling deeper networks for high-resolution interpretation and camouflaged species detection.^47,48

Object detection and segmentation models

In practical biodiversity monitoring, researchers often need not only to classify species in images but also to locate and count multiple objects. Object detection algorithms (e.g., the YOLO series, single shot multiBox detector/SSD) combine CNN recognition capabilities with localization functionality, automatically detecting and classifying animal positions in images or videos. Semantic segmentation (e.g., U-Net) and instance segmentation techniques (e.g., Faster R-CNN) can further precisely outline the contours of animals or plants in images for finer-grained analysis, such as measuring animal size or quantifying vegetation cover. For instance, U-Net models have been trained to accurately identify fish species in the Amazon River,⁴⁹ and combining aerial imagery with Faster R-CNN enables large-scale tree species identification and geolocation of individual trees in heterogeneous stands.⁵⁰ These models enable automated extraction of biological targets from complex backgrounds, holding vast potential in biodiversity monitoring and ecological surveys.

Recurrent neural networks and time-series analysis

Biodiversity data often contain substantial time-series information, such as population changes, phenological cycles, and sensor observations.⁵¹ Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) and gated recurrent unit (GRU) variants, excel at capturing temporal dependencies in sequential ecological data, such as population dynamics, migration patterns, and seasonal phenological changes.⁵² LSTM-based vegetation health index prediction models accurately predict vegetation stress timing and identify biodiversity hotspots.⁵³ RNN models with time-series environmental factors have successfully predicted arthropod population fluctuations in island ecosystems, demonstrating potential for ecological risk early warning.⁵⁴ These models support dynamic management and adaptive conservation strategies.

Graph neural networks

Species and environmental factors form complex interaction networks such as food webs, symbiotic networks, and migration pathways. Graph neural networks (GNNs) model entities and relationships at the node and edge levels, becoming a mainstream tool for graph-structured data.⁵⁵ Using multiple environmental elements as nodes and interaction strengths as edges, GNNs have successfully revealed predator-prey relationships, competition, and environmental driving effects among species.⁵⁶ GNNs applied to seagrass distribution modeling by integrating environmental, climatic, and anthropogenic factors achieved higher accuracy than traditional machine learning. GNNs can also connect different data sources into knowledge graphs, enabling the examination of ecosystems from a network science perspective and revealing system-level behaviors, informing strategies like prioritizing key species for network stability.

Generative models

Generative adversarial networks (GANs) and variational autoencoders (VAEs) show strong application potential for scenarios with data scarcity or requiring simulation.⁵⁷ VAEs construct latent representation spaces for generating virtual ecological data points useful for filling monitoring gaps or simulating responses under different scenarios. GANs can reconstruct soundscape components, synthesizing realistic soundscapes to accurately extract species diversity from complex acoustics.⁵⁸ Cross-modal generative models like CycleGAN can transfer data styles between environmental conditions, providing richer training samples.⁵⁹ Generative models provide new tools for simulation and prediction; researchers can simulate species appearance or distribution in new environments and predict potential ecosystem states under different interventions, supporting more scientific conservation plans.

Reinforcement learning

Reinforcement learning (RL) learns optimal strategies through trial-and-error interactions, reducing human input for enhancing model performance.⁶⁰ In conservation, RL addresses dynamic decision-making such as protected area site selection, patrol route optimization, and resource allocation. RL agents optimize patrol routes by selecting locations based on poaching hotspots, terrain, and weather, simulating poacher behavior to refine strategies.⁶¹ RL can also design adaptive management strategies, such as dynamically adjusting protected area access based on real-time monitoring data,⁶² and in ecological restoration, determining optimal timing or sequence for species reintroductions to maximize ecosystem function recovery.⁶³ By framing conservation as decision-making processes, RL offers potential for proactive and optimized management.

Unsupervised and self-supervised learning

Large amounts of biodiversity data are unlabeled, challenging supervised methods. Unsupervised learning’s core strength lies in analyzing intrinsic data structures, associations, and patterns without prior labels. SOM methods applied to fish community clustering revealed homogenization in human-impacted areas, enabling early detection of declining diversity.²⁶ Self-supervised learning learns feature representations from unlabeled data via pretext tasks for downstream applications,⁶⁴ applied to image and audio pre-trained models in biodiversity, such as automatically detecting and classifying animal vocalizations.⁶⁵ These approaches exploit vast unlabeled ecological data, extracting valuable information for conservation decisions.

Transformer models and multimodal language models

Transformers, based on the self-attention mechanism and originally achieving natural language processing (NLP) breakthroughs, are now being widely applied in computer vision and multimodal tasks. They effectively model long-range dependencies and are, thus, advantageous for problems requiring global context. In species identification, Transformers have demonstrated potential surpassing CNNs for complex fish classification.⁶⁶ Vision-language models based on Transformers (e.g., CLIP and BioCLIP) have opened new avenues for automated biodiversity knowledge discovery by combining image and text information, holding promise for building comprehensive knowledge bases. Given the success of Transformers in foundation models (e.g., GPT series), specialized biodiversity large models integrating image, text, audio, and gene sequence data offer significant future promise for global conservation decisions.

Table 2 summarizes the application of the major AI algorithms and models in biodiversity conservation, including algorithm categories, representative models/methods, key characteristics, and typical application scenarios.

Table 2.

Applications of AI algorithms and models in biodiversity conservation

Algorithm Category	Representative model	Key characteristics	Typical application scenarios
Random forest (RF)	CART + RF hybrid	resistant to overfitting; feature importance interpretable	species distribution modeling; aerial image denoising67,68
Maximum entropy model (MaxEnt)	MaxEnt	requires only species presence data; handles continuous and discrete variables	species geographic distribution prediction; habitat delineation69,70
Gradient boosting machine (GBM)	XGBoost, LightGBM, CatBoost	high accuracy; automatic feature selection	habitat suitability mapping; invasive risk prediction69,71
Support vector machine (SVM)	SVM (RBF kernel)	identifies optimal hyperplane in high-dimensional space for separating ecological regions	boundary delineation; outlier detection72
Convolutional neural network (CNN)	VGG, AlexNet	strong local feature capture; hierarchical feature abstraction	species image recognition; satellite image interpretation40,45
Residual network (ResNet)	ResNet-50/101	mitigates vanishing gradient problem; supports ultra-deep networks	high-resolution remote sensing image analysis; microscopic morphology analysis50,73
Transfer learning	ImageNet pre-trained CNN models	cross-domain knowledge transfer; alleviates small-sample limitations	small-sample species classification; rare species detection74,75
Object detection models	YOLO series	high real-time performance; end-to-end detection	real-time wildlife tracking; drone patrol inspection76,77
Graph neural network (GNN)	GAT, GCN, GraphSAGE	models relationships between entities; handles non-Euclidean data	ecosystem relationship modeling; species interaction networks,55,78
Generative adversarial network (GAN)	DCGAN, StyleGAN	adversarial training framework; generates high-quality synthetic data	data augmentation; cross-domain generation,58,79
Cross-modal generative models	CycleGAN	enables bidirectional cross-domain mapping without paired data	cross-environmental condition data augmentation; historical site restoration57,59
Variational autoencoder (VAE)	β-VAE, CVAE	probabilistic generation; interpretable latent space	missing data imputation; extinct species reconstruction,57,80
Recurrent neural network (RNN)	LSTM, GRU	models long-term dependencies; suitable for progressive state updates	population dynamics time-series prediction; acoustic signal analysis52,54
Long short-term memory (LSTM)	bidirectional LSTM (BiLSTM)	bidirectional information flow; captures contextual dependencies	phenological rhythm prediction; gene expression time-series modeling,53,81
Reinforcement learning (RL)	DQN, PPO	trial-and-error learning; adapts well to dynamic environments	protected area planning optimization; robotic sampling path design62,82
Unsupervised learning	k-means, SOM	clustering and dimensionality reduction; discovers hidden patterns	community clustering; environmental gradient delineation79,83
Self-supervised learning	SimCLR, MoCo	contrastive learning; builds general feature representations	feature extraction from unlabeled images; cross-modal association64,65
Transformer models	BERT, ViT	self-attention mechanism; models long-range dependencies	cross-modal data fusion; global-scale species census3,66
Multimodal language models	BioCLIP, FLAVA	image-text matching; semantic alignment	species discovery; automated species description generation; integrated knowledge base construction34,84

Note: The application scenarios listed represent typical use cases for each algorithm; in practice, different algorithms may exhibit overlapping applications.

While the algorithms above possess individual merits, their true scientific value lies in how their structural differences align with specific ecological data bottlenecks. To clarify these distinctions, we synthesize the comparative advantages of these algorithms regarding four common problems in biodiversity research.

Handling spatiotemporal dynamics vs. static spatial patterns

A fundamental difference lies in how algorithms process dimensionality. Traditional algorithms (e.g., MaxEnt and RF) and standard CNNs are highly effective for static spatial pattern recognition (e.g., current species distribution or land cover). However, when the research question involves spatiotemporal dynamics (e.g., phenological shifts and population fluctuations), RNNs (LSTM/GRU) demonstrate irreplaceable advantages due to their sequential memory architecture. For comprehensive spatiotemporal forecasting, emerging hybrid architectures (e.g., CNN-LSTM or spatiotemporal Transformers) are increasingly being preferred to single-paradigm models.

Coping with data scarcity vs. exploiting massive unlabeled data

The choice between supervised deep learning and generative/self-supervised models hinges on data availability. Supervised models (e.g., YOLO and Faster R-CNN) yield the highest precision in target extraction but require large, high-quality labeled datasets, which are often prohibitively expensive in ecology. When labeled data are scarce, generative models (GANs/VAEs) provide a distinct advantage by synthesizing training samples, whereas self-supervised learning avoids the labeling bottleneck entirely by learning generalizable representations from unlabeled sensor data, thereby proving more efficient for broad-scale acoustic or camera-trap surveys.

Modeling topological interactions vs. independent entity analysis

Most algorithms treat data points as independent. However, ecosystems are inherently interactive networks. The primary advantage of GNNs over traditional machine learning (ML) or CNNs is their ability to process non-Euclidean graph data. While an RF model might predict a species’ abundance based on climate variables alone, a GNN can explicitly integrate interspecific competition or mutualism as edge weights, making it the superior choice for systemic stability analysis and food-web modeling.

Predictive inference vs. sequential decision optimization

The majority of aforementioned algorithms (RF, CNNs, RNNs, and GNNs) are designed for predictive inference (mapping inputs to outputs). In contrast, RL operates on a fundamentally different mechanism of optimizing cumulative rewards through agent-environment interaction. When conservation problems transition from “predicting where species are” to “how to optimally allocate patrols or restore habitats under uncertain conditions,” RL becomes the mathematically appropriate tool, bridging the gap between passive analysis and active management.

Methodological innovation at system level

Beyond innovations at the data and algorithmic layers, the effective application of AI in biodiversity conservation relies critically on methodological transformations and supporting infrastructure at the system level. “System-level” innovations refer to “methodological” transformations and supporting infrastructure that integrate AI technologies with computational infrastructure, network architectures, and distributed systems to build efficient, reliable, and secure biodiversity conservation platforms. These innovations enable AI technologies to transition from the laboratory to practical field deployment, functioning effectively within complex natural environments. They provide novel solutions for data sharing, simulation-based decision support, real-time monitoring, and secure, verifiable record-keeping (Figure 2).

Figure 2.

AI-driven system-layer innovations in supporting biodiversity conservation

FL and privacy preservation

Biodiversity data are often distributed across institutions and regions. FL enables model training across multiple data sources without centralization, addressing data silos while preserving privacy.⁸⁵ This framework mitigates risks including unauthorized disclosure of sensitive species locations, potential data leakage among institutions, and non-compliance with data protection regulations. Participants train models locally and share only parameters (not raw data) for aggregation. However, FL has privacy risks: gradient leakage attacks can reconstruct training data, and membership inference attacks may reveal data usage. Mitigation strategies include differential privacy, secure aggregation protocols, and homomorphic encryption,⁸⁶ particularly important for endangered species location data or Indigenous knowledge systems. FL provides a scalable, trustworthy pathway for global biodiversity conservation, fostering international data collaboration and enhancing conservation outcomes.

Digital twin ecosystems

Digital twin (DT) technology creates high-fidelity virtual replicas of real-world systems that are dynamically updated with real-time data for interactive physical-virtual mapping. DT is not merely a monolithic model nor a simple aggregation of massive data and machine learning. In biodiversity conservation, integrating sensor data, remote sensing, and historical ecological data allows DTs to dynamically simulate ecosystem states and changes, creating virtual habitats supporting simulation, strategy evaluation, and management optimization.⁸⁷ DTs can forecast future evolution under different scenarios (e.g., species migration under climate change), reveal anthropogenic drivers of biodiversity loss, and assess conservation strategy effectiveness within virtual environments.⁸⁸ The practical significance of DTs lies in (1) enabling “virtual experimentation” without real-world risks; (2) providing early warning by detecting deviations from expected states; and (3) facilitating adaptive management through continuous prediction updates.⁸⁹ Figure 3 depicts this architecture. For example, DT platforms have simulated impacts of different fire management strategies on savanna biodiversity, revealing that different threats lead to distinct, sometimes opposing, extinction probabilities depending on biological traits.⁹⁰ Such “strategy sandboxes” help balance scientific uncertainty with precaution,⁹¹ leading to more scientifically grounded planning and adaptation, reducing costs of trial-and-error, and enhancing the targeting and effectiveness of conservation actions.

Figure 3.

Conceptual diagram of a digital twin for ecosystem management

Edge computing and internet of things sensor networks

Biodiversity monitoring involves numerous sensor nodes across remote areas, generating continuous data streams. Transmitting all data to central servers involves bandwidth and latency limitations. Edge AI pushes computational intelligence to the data collection edge, enabling localized, real-time processing where only meaningful events and metadata are uploaded, which drastically reduces the transmission volume and energy consumption.⁹² Edge computing frameworks reduce average task completion delays by approximately 21.6% and potentially halve the storage costs.^93,94 Tiny machine learning (TinyML) enables running deep learning models on ultra-low-power microcontrollers consuming milliwatts, ideal for long-term field deployments.⁹⁵ Internet of Things (IoT) sensor networks with edge computing achieve distributed perception and decision-making; nodes process data locally and transmit summaries to central systems,⁹⁶ enhancing real-time responsiveness and robustness, even during network outages.

Lightweight model deployment

Deploying AI models on resource-constrained field devices (e.g., camera traps, drones, and ranger handhelds) necessitates model lightweighting for real-time inference. Lightweighting primarily employs techniques like model pruning, quantization, and knowledge distillation to reduce parameter size and computational complexity while striving to maintain original accuracy.⁵⁷ To facilitate a direct comparison of these strategies across different field scenarios, we provide a structured summary in Table 3. For example, as shown in the table, deep CNNs pruned using genetic algorithms and fine-tuned with mean squared error distillation resulted in student models running on Raspberry Pi, with only a 4.7% accuracy drop while reducing computation.⁹⁷ Similarly, developers deployed an AI camera alert system for wild tiger conservation, embedding a lightweight poaching detection model capable of providing real-time alerts for potential poaching signs or human-wildlife conflict.⁹⁸ These lightweight AI tools empower front-line conservation personnel to directly benefit from AI technology, reducing reliance on remote servers and lowering the deployment costs. The efficiency of lightweight models enables the deployment of more sensor nodes across broader monitoring networks, yielding richer biodiversity data to support scientific research and policy formulation. Lightweight model deployment also enhances the autonomy and timeliness of conservation work, facilitating the genuine integration of AI into grassroot conservation practices.

Table 3.

Comparison of model lightweighting strategies for edge deployment in conservation

Strategy type	Representative method/study	Hardware context	Performance characteristics
Pruning and distillation	genetic algorithm pruning + MSE distillation97	Raspberry Pi	4.7% accuracy drop; significant reduction in computational load
Lightweight architecture	lightweight poaching detection model98	field camera trap system	real-time inference latency; trigger-based alerting
TinyML	ultra-low-power inference95	microcontrollers (MCU)	milliwatt-level power consumption; minimal memory footprint
General compression	pruning, quantization, knowledge distillation57	generic edge devices	reduced parameter size; maintained original accuracy baseline

Blockchain-enabled ecological data provenance

The integrity, authenticity, and traceability of biodiversity data are fundamental for effective conservation strategies and legal evidence.⁹⁹ Blockchain offers a decentralized, immutable, traceable solution for data management, with core value in establishing trusted infrastructure enhancing data credibility and transparency. It creates an unalterable record chain permanently documenting data origin, creation time, and modification history. Attribute-based encryption (ABE) enables granular access policies ensuring only authorized users can decrypt data, with all access actions immutably recorded.¹⁰⁰ When combined with FL, blockchain builds trusted data spaces; for example, some studies^89,101,102 have proposed a blockchain-based access control framework for genome-wide association study (GWAS), ensuring secure distributed training. Privacy-preserving computation enables joint analysis without exposing raw data.¹⁰³ Theoretical frameworks for blockchain-based wild bird data sharing have been proposed,¹⁰⁴ and researchers are exploring blockchain integration with IoT, UAVs (unmanned aerial vehicles), and AI for enhanced monitoring.¹⁰⁵ In green finance, blockchain’s transparency enables innovative ecological compensation mechanisms and incentivizes open data sharing.¹⁰⁶ While applications remain primarily at the proof-of-concept stage, blockchain’s advantages in data immutability and provenance present broad potential. Future research should focus on pilot implementation and impact evaluation.

Technology-enabled restructuring of application scenarios in conservation practice

Driven by the aforementioned technological advancements, biodiversity conservation is undergoing a fundamental restructuring of its application scenarios, powered by AI. AI technologies are now being extensively applied across various aspects, including species monitoring, habitat assessment, threat early warning, conservation decision support, and public engagement, thereby automating, precision-enabling, and intelligently transforming conservation work (Figure 4).

Figure 4.

Biodiversity conservation scenario re-engineering by AI

AI is comprehensively reshaping the application scenarios of biodiversity conservation, transitioning the field from reactive remediation toward proactive prevention. It demonstrates significant potential in enhancing monitoring precision, expanding the spatial and temporal coverage, and facilitating scientific decision-making processes.

AI-enabled biodiversity monitoring paradigms

Real-time individual identification

Leveraging computer vision and biometric technologies, AI enables real-time, individual-level recognition by analyzing unique animal markings, acoustic signatures, or behavioral patterns.^3,107,108 Figure 5 illustrates this end-to-end workflow. For marine species, deep learning facilitated rapid identification of “unknown” whales Carretta 2018 and individual Risso’s dolphins,⁴³ outperforming human judgment. The Snapshot Serengeti project achieved over 93% accuracy for 48 species across 3.2 million images, with 99.3% auto-classifiable.⁴² The MLWIC2 platform attained 96.8% species recognition across 58 species from 3 million images.²⁴ Research indicates that intraspecific individual variation substantially impacts ecosystem function, signifying a paradigm shift from species-level to individual-level focus.^109,110 This technology facilitates monitoring population changes, detecting anomalies, and developing targeted conservation strategies.

Figure 5.

Workflow of AI-enabled real-time individual wildlife identification

Population abundance estimation

Accurate estimation of population size is critical for evaluating conservation effectiveness. Traditional methods relying on manual counts or surveys are often labor intensive, time consuming, and prone to error. AI enables large-scale, rapid population estimation. For wildebeest across 4,000 km², counts achieved <10% error rate, while covering tens of times the area of ground surveys.¹⁷ Monitoring migratory wildebeest using U-Net and YOLOv8 yielded approximately 600,000 individuals—less than half of traditional estimates—suggesting potential systematic overestimation by conventional methods, validated through cross-validation. AI can also utilize Bayesian frameworks to post-process detection results, mitigating false-negative or -positive impacts. Acoustic spatial capture-recapture (ASCR) methods, combining species identity as a latent variable with ML detection outputs, achieve near-zero error in acoustic density estimates.¹¹¹ AI achieves efficient, objective, large-scale abundance estimation through automated acoustics, drone/satellite detection, and statistical calibration.

Migration tracking

AI’s capacity for multi-source data fusion provides novel solutions for tracking wildlife migration across vast distances. Transformers analyze animal movement patterns, extracting regularities from long-term tracking data to predict future directions; self-attention-based models successfully predicted cross-seasonal bison migration corridors.¹¹² AI also synergizes with eDNA, inferring species presence and movement from trace genetic material. Machine learning combined with eDNA metabarcoding could distinguish between the disturbed and undisturbed freshwater communities, with higher accuracy than traditional statistics. Automated image recognition enables rapid classification of millions of camera trap photos, spatially reconstructing encounter frequencies to infer corridor structure.⁴² This multi-source fusion approach monitors migratory species at lower cost and broader scales, supporting transboundary conservation and corridor planning.

Genetic diversity conservation

Genetic diversity underpins species’ long-term survival and adaptation. However, conservation often prioritizes population size, while overlooking intra-population genetic variation.⁴² Populations with low genetic diversity are more susceptible to genetic drift and inbreeding depression, leading to reduced fitness.¹¹³ DNA language models can predict lethal mutations without multiple sequence alignments, aiding identification of crucial alleles in crop wild relatives.¹¹⁴ Similar techniques capture high-load genomic segments in tigers, utilizing local ancestry for whole-genome kinship estimates, prioritizing retaining original genomic fragments during pairing and preventing exogenous gene introgression.¹¹⁵ In gene bank management, ML-based genome scanning rapidly identifies representative individuals for minimal core collections capturing full allelic diversity.¹¹⁶ Meta-population genetic management of Australian Macquarie perch, using whole-genome data and AI-predicted migration scenarios, doubled nucleotide diversity and boosted adaptive potential.¹¹⁷ AI has become a crucial aid in genetic diversity conservation.¹¹³

Combating illegal trade

The illegal trade in wildlife and their derivatives poses a major global threat to biodiversity. AI is emerging as a key technology in countering this illicit trade. On one front, NLP techniques mine transaction text on the dark web and social media, automatically identifying sensitive information related to wildlife trafficking, such as code words used by smugglers, species names, and quantities.¹¹⁸ By analyzing the surrounding environmental context within images, these systems can flag images depicting animals in unnatural settings, aiding law enforcement in intercepting endangered species smuggling.¹¹⁹ At border checkpoints, incrementally learning X-ray baggage inspection models enable real-time adaptation to new smuggling categories, avoiding the “catastrophic forgetting” typical of offline retraining, and maintaining stable recall rates for detecting various contraband, including wildlife products. Australia has piloted the use of real-time tomography technology, developing wildlife recognition algorithms deployed in postal/passenger baggage channels to curb transnational illegal wildlife transport.¹²⁰ Integrating these technologies, AI shifts the monitoring paradigm for illegal trade from “post-incident tracking” to “pre-incident warning.” Systems can continuously scan all channels for suspicious information and trigger automatic alerts, enhancing law enforcement efficiency. However, the collection and analysis of vast data volumes raise privacy and data security concerns, necessitating the establishment of clear legal and technological boundaries between protecting citizen privacy and safeguarding biodiversity. AI provides powerful tools to combat wildlife trafficking, thereby playing an increasingly vital role in deterring poaching and smuggling and protecting endangered species.

Habitat assessment and mapping

High-resolution habitat classification

Precise habitat mapping is fundamental for developing effective conservation strategies. Traditional habitat models often rely on single-scale variables and, thus, fail to reflect species’ multi-scale requirements, resulting in low prediction accuracy. Semantic segmentation methods (e.g., U-Net) enable efficient classification of high-resolution remote sensing imagery, automatically identifying land cover and delineating finer habitat units.¹⁸ Fusing multi-source data (optical, radar, and hyperspectral) helps overcome single-source spectral mixing limitations, enhancing classification accuracy.¹²¹ Terrestrial LiDAR (light detection and ranging) captures micro-scale canopy features; integrating LiDAR variables with XGBoost achieved 92% accuracy for Canada Lynx habitat at 30-m resolution, substantially outperforming RF models using only optical variables.¹²² Machine learning also enables large-scale habitat prediction across diverse ecoregions.¹²³ AI-driven high-resolution habitat classification and multi-scale habitat models provide spatially detailed and reliable habitat information. The combined application of machine learning (e.g., RF and XGBoost) and deep learning (e.g., U-Net) with multi-source data (GPS/global positioning system, LiDAR, optical, and radar) improves classification accuracy and predictive reliability. These technological advances furnish solid data support for formulating science-based conservation strategies, assessing climate change impacts, and optimizing the spatial arrangement of human activities, empowering managers to implement targeted conservation at finer spatial scales.

Landscape connectivity

Habitat fragmentation is a primary driver of biodiversity loss. Maintaining and restoring landscape connectivity are crucial for species migration and population persistence. AI offers unique advantages in landscape connectivity analysis, aiding in the identification and optimization of ecological corridors. Machine learning techniques can then be applied to rank node importance, and, thus, they help locate key ecological corridors.¹²⁴ This approach identified two functional corridor types (fast/slow), providing guidance for planning optimal corridor width and placement.¹²⁵ AI models like GANs can simulate landscape changes under different land-use scenarios, predicting their impacts on connectivity.⁷⁹ Feeding these scenarios into traditional connectivity models enables prediction of potential disruptions to network connectivity caused by urban expansion or farmland conversion, while also allowing pre-assessment of the effectiveness of different restoration strategies. RL-based agents can iteratively adjust land allocation in virtual landscapes through trial-and-error learning to maximize predefined connectivity metrics,¹¹¹ yielding optimized corridor designs. AI provides comprehensive modeling and optimization capabilities for landscape connectivity. It offers a quantifiable pathway for balancing human activities and ecological conservation, aiding decision-makers in rapidly identifying, evaluating, and implementing the ecologically most beneficial corridor solutions within dynamic environments to maintain ecosystem integrity and function.

Ecosystem function assessment

Biodiversity encompasses not only species richness but also the soundness of ecosystem functions. Vegetation structure and functional diversity are also identified as priority indicators within international consensus frameworks for satellite remote sensing of biodiversity.¹²⁶ AI, combined with satellite remote sensing, enables the assessment and monitoring of ecosystem functions, such as primary productivity, carbon storage, and nutrient cycling. Traditional ecological models are often parametrically complex and difficult to update in real time. AI models (e.g., RF and neural networks) can learn the relationships between environmental drivers and productivity from vast amounts of remote sensing and ground-based observation data, enabling rapid estimation of regional-scale net primary productivity (NPP).¹²⁷ AI can also build comprehensive vegetation functional maps through the synergistic inversion of multiple interrelated traits. The results of such functional assessments help identify ecosystem “health indices,” providing a scientific basis for conservation actions, such as prioritizing the protection of areas with high productivity and strong carbon sink capacity to enhance ecosystem services.¹²⁸ AI empowers more comprehensive monitoring and assessment of ecosystem functions, facilitating the preservation of biodiversity while safeguarding the healthy functioning of ecosystems.

Threat detection and early warning

Illegal logging/poaching monitoring

AI provides large-scale, around-the-clock monitoring capabilities. Utilizing infrared cameras, acoustic sensors, drones, and satellite imagery combined with computer vision detection algorithms, AI systems can detect anomalous activities in real time, with notifications reaching mobile applications within approximately 30 s.^98,129,130 In African protected areas, AI video analytics automatically identify poachers or illegal vehicles; in Southeast Asian rainforests, AI-powered drones detect illegal logging far more efficiently than manual patrols. ML models incorporating environmental variables and historical data can effectively predict deforestation risk, assisting strategic patrol deployment¹³¹ and applicable to any tropical forest region.¹³² AI combined with hyperspectral imaging enables non-destructive identification of illegally logged timber,¹³³ and ML tools can help identify shark and ray species from fin images, supporting CITES (Convention on International Trade in Endangered Species) enforcement.¹³⁴

Wildfires and pest/disease outbreaks early warning

Disasters such as forest fires and pest/disease outbreaks inflict severe damage to the biodiversity and ecosystems. Drone-AI technology enables real-time tracking of forest dynamics, effectively monitoring fire outbreaks and pest infestations for early warning and rapid response.¹³⁴ For fire monitoring, deep learning models analyzing drone-based smoke detection and satellite thermal infrared remote sensing can detect hotspots and issue alerts at the earliest stages of ignition.¹³⁵ Fine-scale fire prediction models combining RF with spatial autocorrelation features enable the prediction of major fires up to 1 week in advance, providing accurate assessments of burn intensity and occurrence probability (R² = 0.77).¹³⁶ Regarding pests and diseases, AI can detect early signs of infestation by analyzing spectral anomalies indicative of leaf chlorosis or dieback in remote sensing imagery. Another project deployed IoT sensors in forests, utilizing deep learning object detection algorithms to automatically count insects in pheromone traps with over 95% accuracy.⁷⁷ This allows real-time monitoring of pest population parameters, triggering automatic alerts once outbreak thresholds are reached. leveraging multi-source data from drones, satellites, and IoT, combined with deep learning models (e.g., YOLO, Detectron2, and Capsule Network) and temporal models (e.g., LSTM and RF), has achieved detection accuracies exceeding 90% and reduced response times for real-time monitoring, early warning, and risk prediction of forest fires and pest/disease outbreaks. The implementation of these technologies not only enhances disaster management efficiency but also provides robust technical support for conserving forest ecosystem biodiversity.

Prediction and assessment of climate extreme events

Global climate change has increased the frequency of extreme weather events (e.g., heatwaves, droughts, and floods), posing severe threats to species and ecosystems. AI technology offers unique advantages in predicting and assessing the impacts of extreme climate events on biodiversity. On one hand, deep learning is used for downscaling and bias correction of climate models, enhancing regional prediction accuracy for extreme weather. This provides finer-grained climate risk information for regional ecosystems, establishing a reliable baseline of extreme climate risks to inform biodiversity management decisions such as species distribution modeling, habitat suitability assessments, and protected area site selection. Machine learning integrated with species distribution models (SDMs) can help assess the impact of extreme events on species habitats and vulnerability. By inputting scenarios like extreme drought or cold snaps, AI-driven SDMs can predict which species are likely to lose suitable habitat or face elevated extinction risks. Distribution modeling and connectivity analysis for the fragmented mountain hare (Lepus timidus) in the Alps predicted an average loss of approximately 35% of suitable habitat by 2100 due to projected temperature increases, accompanied by significant habitat fragmentation.¹³⁷ AI also facilitates real-time monitoring of ecosystem impacts from extreme events. This information assists managers in implementing timely remedial actions, such as rescuing stranded animals or initiating habitat restoration. AI provides powerful tools for addressing the challenges posed by climate change to biodiversity, thereby enabling early warnings, scientific assessment, and effective mitigation of the ecological impacts caused by extreme climate events.

Invasive species prevention and control

Invasive alien species, second only to habitat loss in damaging native ecosystems, presents a major global challenge for biodiversity conservation. Traditional manual surveys are costly, inefficient, and often fail to detect low-density or cryptic invaders promptly. Computer vision and sensor networks enable AI to detect the presence of invasive species in real time and trigger alerts. Distributed underwater cameras paired with AI image recognition systems allow deep learning models to locate individual fish bodies in real time,⁷³ enabling extensive, continuous monitoring of invasive fish populations. Many invasive birds and amphibians reveal their presence through calls. Acoustic sensors deployed in forests, combined with AI sound recognition, can detect early signs of invasion. Models based on LSTM networks with attention mechanisms have achieved differentiation across 264 bird species.¹³⁸ Beyond monitoring and integrating existing technologies, AI could potentially assist in the eradication of invasive species in the future. Exploring the use of AI-integrated robotics for automated removal is also conceivable, such as drones equipped with AI vision identifying and spraying specific invasive plants, or underwater robots autonomously capturing invasive fish. While these technologies are not yet widely deployed, they show considerable promise. AI offers end-to-end support for invasive species management, holding the potential to increase control efficiency, reduce labor costs, and safeguard native species and ecosystem integrity.

AI-based conservation prioritization

Systematic conservation planning

Global ecosystem degradation is intensifying, yet resources for ecological restoration are limited. Identifying priority areas for protection represents a critical need in biodiversity conservation.^128,139 Traditional systematic conservation planning (SCP) relies on expert knowledge and heuristic algorithms,¹⁴⁰ often suffering from subjectivity and low precision, making it difficult to find optimal solutions under complex constraints. In conservation planning, researchers encode landscape features, species distributions, economic costs, and other factors into an environmental state. Decisions such as adding new protection units, implementing restoration measures, or allocating funds are defined as actions. An RL-trained agent learns the optimal strategy by repeatedly interacting within a simulated spatiotemporal environment to maximize cumulative reward.¹⁴¹ Through millions of iterations of learning within this simulated, dynamic environment, a deep RL agent can learn complex, non-intuitive strategies, identifying portfolio solutions with the highest “return on conservation investment,”¹⁴² providing scientific grounding for decision-makers. RL models incorporating dynamic state spaces can also adapt to changing conditions,¹⁴³ enabling the adjustment of protection strategies based on the latest species monitoring data for adaptive optimization of conservation plans. It is foreseeable that with increased data and computational resources, RL-driven conservation planning will assist nations globally in allocating limited conservation funds and resources more efficiently, thereby enabling rapid achievement of biodiversity conservation targets.

Multi-objective optimization

Biodiversity conservation often requires balancing multiple objectives, such as protecting the maximum number of species while considering ecosystem services (e.g., carbon storage and water regulation) and local community socio-economic costs and benefits.¹⁴⁴ AI provides powerful tools for multi-objective optimization, thus satisfying multiple ecological goals while reducing social conflict.¹⁴⁵ Research focusing on boreal caribou, biodiversity, and climate targets has demonstrated that multi-objective optimization can find compromise solutions balancing all three objectives when conservation hotspots do not overlap.¹⁴⁶ AI-based multi-objective optimization shifts the paradigm from single-objective focus to systematic trade-offs. By visualizing the Pareto front, it helps decision-makers intuitively understand conflicts and synergies between different objectives, enabling the formulation of more comprehensive and sustainable conservation strategies.¹⁴⁷ In contemporary conservation practice, characterized by limited resources and increasingly conflicting objectives, AI-driven multi-objective optimization is instrumental in developing holistic and sustainable conservation strategies.

Uncertainty quantification

Conservation decision-making frequently faces significant uncertainties, including incomplete species distribution data, future environmental changes, and model prediction errors. Ignoring these uncertainties can lead to ineffective or even counterproductive conservation measures. AI techniques, particularly Bayesian deep learning and methods like Monte Carlo dropout, provide pathways to quantify and manage these uncertainties. Bayesian deep learning incorporates probability distributions within neural networks, enabling the output of prediction confidence intervals. By incorporating prior information, the model reduced errors caused by uneven sampling effort, allowing decision-makers to pinpoint “high-risk, high-uncertainty” zones for more intensive field monitoring.⁷⁰ Monte Carlo dropout is a technique for approximating Bayesian inference in deep learning. By randomly “dropping out” neurons during prediction and performing multiple sampling passes, it generates slightly different predictions each time, allowing the estimation of prediction variance. In predicting future habitat for bull trout (Salvelinus confluentus) in the interior Columbia River Basin (USA), Monte Carlo sampling accounted for parameter uncertainty, model structural uncertainty, and climate scenario uncertainty. The results provided probability distributions of occurrence and their 95% intervals for each river segment, clearly indicating that climate model uncertainty was the primary source of error in habitat area predictions.⁷⁰ By quantifying uncertainty, AI helps build “robust” conservation strategies, such as implementing more precautionary measures or enhanced monitoring in high-uncertainty areas. Uncertainty quantification makes conservation decision-making more scientifically robust, thereby helping avoid failures caused by model bias or insufficient data and increasing the success rate and adaptability of conservation actions.

Conservation effectiveness evaluation

AI-assisted indicator automation

Assessing the effectiveness of biodiversity conservation requires scientific metrics, such as the species recovery index (SRI), habitat integrity index (HII), and global indices like the living planet index (LPI).¹⁴⁸ Traditionally, calculating these indicators relied on manual collection and analysis of vast datasets, representing a time-consuming and labor-intensive process. The integration of AI technologies has automated and streamlined indicator assessment. Machine learning models enable the automatic extraction of required information from multi-source data. Similarly, the HII, quantifying human disturbance levels, can be computed automatically by AI analyzing data on night-time lights, road density, and land use. Tasks previously requiring expert manual processing are now handled efficiently by AI models in bulk, ensuring faster processing, consistent standardization, and reduced human error. AI facilitates real-time updates of these indicators, enabling dynamic monitoring of conservation effectiveness. If an indicator shows a declining trend in a specific area, the system can issue timely alerts, prompting managers to implement remedial actions. AI-assisted indicator automation enhances the timeliness, objectivity, and transparency of conservation effectiveness evaluation, providing robust data support for the continuous improvement of conservation strategies.

Counterfactual inference

Evaluating the true impact of conservation interventions requires comparing the observed outcome after intervention with the hypothetical outcome without intervention—a statistical approach known as counterfactual inference. Traditional evaluations often rely on simple before-after comparisons and struggle to construct reliable counterfactual scenarios. Research underscores the importance of robust “no intervention” baselines, as different statistical strategies can yield divergent conclusions when unobserved biases exist.¹⁴⁹ It found that reducing pollutant concentrations to the minimum observed levels could double eel fecundity compared with current levels, far exceeding the benefits of other management measures, thereby identifying the most effective conservation pathway.¹⁵⁰ To enhance the practicality of counterfactual explanations, researchers developed DECE (decision explorer with counterfactual explanations), an interactive visualization platform that generates “minimal intervention” plans by finding the smallest modifications to a machine learning model’s inputs needed to achieve the desired outcome.⁸³ While DECE was not designed for ecological data, its framework is applicable to protected area efficacy assessment. DR-VIDAL (doubly robust variational information-theoretic deep adversarial learning) is a generative framework integrating VAEs, information-theoretic GANs (InfoGAN), and doubly robust estimation. It disentangles confounders in observational data, generates counterfactual scenarios, and ensures unbiased causal effect estimates, even if sub-models are misspecified.¹⁵¹ Such tools enable precise attribution of conservation net benefits, informing policy and resource allocation. As causal inference gains traction in ecology, it promises to scientifically answer the following question: “Does conservation work?”

Adaptive management loop

Conservation is a dynamic process requiring adaptive strategies. AI-supported adaptive management enables a real-time feedback loop, making actions more flexible and efficient. Online learning models continuously update parameters and strategies using new data.⁶² An RL algorithm embedded within a water resource allocation model, using real-time monitoring data, continuously updates strategies after each allocation decision, outperforming traditional static schemes and validating AI-driven adaptive management feasibility in large-scale aquatic ecosystems.⁷⁶ By integrating monitoring data with analytical results in an iterative feedback loop, conservation practitioners can continuously refine monitoring protocols and improve detection efficiency. As IoT and real-time analytics advance, this adaptive management loop is poised to become the new conservation paradigm.

Citizen science and education

Citizen science platforms

Public participation is crucial for biodiversity conservation. AI technologies are transforming citizen science platforms by enabling more accessible species monitoring and data collection. The “citizen science + AI” model outperforms manual annotation in terms of efficiency and accuracy for camera trap data processing.²³ AI-powered apps (e.g., PlantNet and iNaturalist) allow users to upload species photos; guiding as little as 1% of recorders to sample at high-information locations can enhance model performance.¹⁵² A Finnish bird platform registered 6 million observations in a single quarter,¹⁵³ and iNaturalist hosted over 200 million observations by 2025.^154,155 AI also serves for data quality control, automatically reviewing submissions to detect blurry images or misidentifications, and dynamically adjusting trust levels based on contributor history. This AI-expert collaborative mechanism ensures data reliability, invigorating public participation and expanding monitoring coverage.

Science education

In science communication and education, the integration of AI with technologies like augmented reality (AR) and virtual reality (VR) delivers immersive biodiversity experiences to the public. Beyond providing information and experiences, AI can design more effective interventions to encourage biodiversity-friendly behaviors. A frontier application involves leveraging large language models (LLMs) for personalized environmental messaging. LLMs possess strong NLP and generation capabilities, allowing the customization of communication content based on audiences’ characteristics and preferences, thereby enhancing message persuasiveness and impact.¹⁵⁶ Results showed that interactive AI agents increased participants’ pro-environmental behavioral intentions and sustainable product choices, with the beluga character, scoring high on empathy, proving most effective for behavior change.¹⁵⁷ Such personalized messaging resonates more deeply with users than generic campaigns, leading to higher conversion rates. Incorporating gamification and reward mechanisms into behavioral interventions has yielded positive results. It is crucial for behavioral interventions to adhere to ethical and privacy norms, thus ensuring that AI operates with respect for user consent and data security. AI-driven behavioral interventions offer tailored solutions for public environmental engagement, effectively motivating conservation actions at individual and community levels, and mobilizing collective power for biodiversity protection.

Challenges and mitigation strategies

Data-level challenges and countermeasures

Small sample sizes and data scarcity

Biological datasets face unique challenges including long-tailed distributions, “unknown” species, interspecific similarities, and intraspecific polymorphisms.¹⁵⁸ Solutions include metric-based few-shot learning frameworks for fish identification⁷⁵ and KI CLIP combining CLIP foundation models with expert descriptions to achieve >90% accuracy, using just 8–16 training samples.¹⁵⁹ TL further reduces sample requirements by fine-tuning cross-species pre-trained models.⁷⁴ However, long-tailed distribution remains challenging: abundant data for common species skew models and scarce data for rare species lead to poor performance.¹⁶⁰ Social media and deep learning can also detect rare megafauna in video surveys.¹⁶¹ Future work requires robust few-shot algorithms and active learning strategies to prioritize high-information samples for annotation.

Data bias and fairness

Biodiversity data often exhibit imbalances in geographical distribution and taxonomic coverage.¹⁶² Data are, typically, abundant for certain regions (e.g., protected areas and easily accessible research sites) and certain taxa (e.g., large mammals and birds), while being scarce for plants and fungi, remote areas, and small, inconspicuous species such as invertebrates.^163,164,165 Such data bias can result in AI models performing well for data-rich regions and species but poorly in data-scarce scenarios, leading to inequitable conservation outcomes. Research on African savanna camera trap monitoring confirmed higher missed detection rates for small species than for large mammals, contributing to insufficient conservation focus on smaller taxa.¹⁶⁶ Correcting such biases necessitates quantifying data imbalances, identifying bias sources, and implementing measures to balance datasets. Concurrently, incorporating fairness metrics during model evaluation is crucial to ensure that performance disparities across different regions and species remain within acceptable limits.

System robustness and environmental adaptability

AI monitoring systems deployed in the field frequently face challenges from harsh environments and unforeseen events, such as extreme temperatures, humidity, strong winds, and power outages, demanding systems to possess inherent self-healing mechanisms and adaptive capabilities. Edge computing endows sensor devices with a degree of intelligence, offering promising solutions for environmental adaptability. Machine learning models, for instance, can monitor statistical features of camera video streams; persistent anomalies like black screens or frozen frames trigger immediate alerts to maintenance personnel. To cope with drastic environmental changes (e.g., background shifts due to seasonal transitions), AI models also require online learning and adaptive capabilities, enabling continuous parameter adjustment using new data to maintain accurate target recognition. This can be achieved through continual learning (CL) or TL, which allows models to adapt to observation data under varying lighting, weather, and seasonal conditions. These directions focus more on hardware and software integration; dedicated publications within biodiversity research are relatively scarce. However, practical development is advancing rapidly. Edge AI on drones can utilize lightweight messaging protocols like MQTT (MQ Telemetry Transport) to push GPS locations and images to a command center upon detecting target animals, enabling reliable real-time alerts, even in remote areas with unstable network connectivity.¹⁶⁷ Enhancing system robustness in the wild requires integrated hardware-software solutions. Hardware improvements should focus on energy autonomy, physical protection, and resilience to environmental interference. Software enhancements should incorporate intelligent monitoring, adaptive algorithms, and leverage lightweight, reliable communication protocols to ensure the long-term field viability of AI systems.

Algorithm-level challenges and countermeasures

Algorithmic bias

Due to factors such as training data composition and model design, AI algorithms may exhibit bias, leading to inconsistent performance across different scenarios. Within biodiversity conservation, algorithmic bias can manifest as lower identification accuracy for certain species, genders, or environments. Similarly, some image-based individual recognition models may favor distinctively marked adult individuals while overlooking juveniles or individuals with subtle markings, introducing statistical bias. Algorithmic bias can also stem from models overfitting to implicit patterns within the training set. To mitigate algorithmic bias, developers need to incorporate fairness assessments during both model training and evaluation phases, ensuring balanced performance across diverse subsets of the data. Methods like ensemble learning can be employed that combine predictions from multiple models to reduce the influence of bias inherent in any single model.¹⁶⁸ Critically, before utilizing AI outputs for decision-making, a thorough understanding of the model’s limitations is essential. Integration with validation by human experts is often necessary to prevent unfair allocation of conservation resources or the overlooking of important species due to algorithmic bias.

Interpretability and policy compliance

Many high-performing AI models, particularly deep learning models, are often perceived as “black boxes.” Their decision-making processes are difficult to interpret directly, leading to challenges for decision-makers in understanding, trusting, and gaining insight into the underlying mechanisms of model outputs. This opacity can also raise ethical and accountability concerns due to potential hidden biases.^169,170 Data privacy and regulatory compliance are significant concerns. In biodiversity monitoring, if camera traps inadvertently capture images involving human activities, this constitutes personal data requiring protection. Similarly, if AI models are used for “high-risk” decisions influencing public policy, they may need to adhere to requirements for transparency and accountability. The development and deployment of AI systems must concurrently address model interpretability and compliance; on one hand, advancing explainable AI (xAI) techniques,¹⁷¹ such as attention visualization, feature importance analysis, and embedding domain knowledge or traditional physical models within machine learning frameworks, can elucidate key drivers behind model decisions.^84,172 Ensuring compliance of data usage with privacy regulations is paramount, for instance, through anonymization of sensitive data that retains only necessary biological information.¹⁷³ Enhancing transparency and adhering to regulations are essential for AI tools to gain acceptance among policymakers and the public and to be effectively integrated into conservation decision-making processes.

Model and concept drift

In real-world environments, data distributions can shift over time, causing the performance of trained AI models to gradually degrade—a phenomenon known as model drift. In biodiversity, concept drift (where the underlying relationships change) can arise from multiple sources: ecosystem changes themselves (e.g., species invasions and habitat alterations leading to shifts in target species’ appearance or behavior), environmental condition changes (e.g., climate change altering seasonal phenology), or changes in data quality due to sensor degradation. Failure to update models can result in predictions deviating from reality. To counter model drift, it is necessary to establish online adaptive learning mechanisms, enabling periodic fine-tuning or retraining of the model with newly collected data.¹⁷⁴ Implementing continuous annotation workflows is crucial, where experts validate outputs and correct errors (e.g., adding labeled samples or enabling self-learning for novel patterns) to maintain accuracy. Models could be endowed with self-learning capabilities, using unsupervised learning to detect emerging patterns of unknown species and prompting experts for annotation, enabling automatic adaptation to novel concepts and continuous improvement of accuracy. This human-AI collaborative closed-loop ensures that models remain synchronized with the evolving real world over time. Addressing model drift requires evolving AI systems from one-off training artifacts toward continuous learning processes, guaranteeing the reliability and timeliness of model outputs.

Causal inference limitations

Ecosystems constitute complex causal networks. While AI models excel at discovering correlations within large datasets, inferring causality from such data remains challenging.¹⁷⁵ Traditional statistical methods rely on strict assumptions and testing procedures for causal inference, whereas “black box” models like deep learning often struggle to provide clear explanations of causality. This limitation restricts the utility of AI in designing interventions—as we need to understand not just “what happened,” but also “why it happened” and “what would happen if we acted.” To overcome this limitation, researchers are exploring methods that integrate causal reasoning with machine learning. Explainable ML frameworks, highlighting key predictors and their directional impacts, provide causal clues. However, AI’s causal capabilities remain limited, necessitating synergy with ecological theory and experiments. Correlations identified by AI should be validated via controlled experiments or expert knowledge to avoid flawed causal assumptions.

System-level challenges and countermeasures

Computational power and energy consumption

The environmental costs (data center energy and carbon emissions from hardware production) and economic costs of training and deploying complex AI models cannot be overlooked.¹⁷⁶ Many conservation projects operate with limited funding, making expensive cloud computing difficult to afford. Model compression techniques, such as pruning (removing redundant network connections), quantization (reducing numerical precision), and knowledge distillation (transferring knowledge from large to compact models), enable deployment of efficient AI systems on resource-constrained field devices.^177,178 Using renewable energy for training and optimizing algorithms to reduce iterations are important green AI practices, ensuring AI application remains sustainable.

Networking and infrastructure

Many regions rich in biodiversity (e.g., rainforests and deep oceans) often lack stable network connectivity and robust computational infrastructure. This poses significant challenges for cloud-dependent AI systems, as the powerful AI capabilities of the cloud remain inaccessible if devices cannot transmit data for real-time processing. For situations with intermittent connectivity, utilizing lightweight messaging protocols mentioned earlier,¹⁶⁷ a strategy of “offline processing + cached uploading” can be adopted. Devices locally store and process data first, uploading results or updated models for aggregation when a network connection becomes available. This federated edge deployment model has been applied in environmental monitoring projects, such as multiple field stations training local models and periodically aggregating updates to a global model, which enable collaborative learning under low-bandwidth conditions.¹⁷⁹ Regarding infrastructure development, it may be necessary to equip remote areas with solar-powered small-scale data centers or edge servers to provide essential computational power and storage support. By enhancing local computing capabilities and optimizing data transmission strategies, AI systems can reliably operate, even in areas with weak networking and infrastructure, ensuring real-time analysis and feedback for biodiversity data.

Socio-ecological challenges and countermeasures

Community engagement and data sovereignty

AI technologies must consider community participation and data sovereignty, balancing ecosystem services with social equity¹⁸⁰ while supporting Indigenous-led conservation governance.^181,182 Indigenous people often possess invaluable traditional ecological knowledge, and conservation must respect local land tenure and resource practices to avoid inequitable outcomes.¹⁸³ Community representatives should participate in project design and co-develop data collection and use protocols. Data sovereignty is critical; AI projects collect environmental data that may include community land or cultural heritage information, requiring clear ownership and usage rights. Frameworks must emphasize respect for Indigenous knowledge, preventing “data colonialism.”¹⁸⁴ Only with genuine community buy-in can AI-driven conservation achieve lasting impact.

Sustainable funding and governance

The deployment and maintenance of AI systems require sustained financial investment and effective governance mechanisms; otherwise, projects struggle to operate in long term. Purchasing equipment, training personnel, processing data, and updating models all necessitate funding, which is often limited and unstable in the conservation sector, and, thus, exploring sustainable financing models to support AI in conservation is essential. Regarding governance, clear policies and standards need to be developed to ensure that AI technology use complies with the legal and ethical norms, evolving alongside technological advancements. Establishing AI ethics review boards to oversee major conservation AI projects, for instance, can prevent misuse and inappropriate decisions. Cultivating local technical expertise is vital for sustainability. Only when local capacity in AI technology exists can projects continue functioning after external funding ends. Achieving the long-term success of AI conservation projects requires exploring diverse funding sources while optimizing expenditures and establishing clear responsibilities and collaborative participation in governance. This ensures that the technological dividends continue to benefit biodiversity conservation effectively.

Policy and ethical challenges and countermeasures

Multi-departmental coordination

Biodiversity conservation involves multiple government departments, including environmental protection, forestry, agriculture, water resources, public security, and customs. The effective application of AI technologies often requires cross-departmental data sharing and collaboration. However, in practice, data silos and fragmented responsibilities between departments can impede the implementation of AI solutions. Establishing multi-departmental coordination mechanisms is essential to integrate AI outputs into existing legal and enforcement frameworks. Policies should clearly delineate departmental responsibilities in AI applications: data collection departments provide high-quality data, technical departments develop and maintain AI systems, and management departments execute actions based on AI alerts. Concurrently, cross-departmental data-sharing agreements should be formulated to enable data flow while ensuring security and privacy, allowing AI models to synthesize multi-source information for analysis. Policymakers should also issue guidelines encouraging collaborative innovation between institutions, such as establishing interdepartmental AI conservation task forces for regular information exchange and coordinated action. Through institutional design, AI can become a nexus connecting disparate departments that fosters synergistic conservation efforts, rather than fragmented initiatives. The true value of AI in conservation practice is realized only when its analytical results seamlessly translate into coordinated multi-departmental action.

Risk and accountability

As AI assumes increasingly critical roles in conservation decision-making, potential adverse consequences—such as algorithmic bias, privacy violations, technological dependency, and resource misallocation—necessitate establishing responsible AI frameworks.^185,186 If an AI model’s erroneous prediction leads to conservation failures (e.g., missed poaching incidents or incorrect habitat exclusions), who bears the responsibility? Under current legal frameworks, ultimate responsibility typically resides with the institutions or individuals deploying AI, though increasing AI autonomy complicates accountability attribution. To mitigate such risks, technical measures should prioritize enhancing model reliability and safety through rigorous validation testing and redundant systems to minimize critical errors. Policy-wise, high-risk AI systems should mandate “human-in-the-loop” protocols,¹²⁷ ensuring that AI outputs undergo expert review before implementation. This enables the timely correction of errors when they occur. Establishing ecological compensation mechanisms is also advisable. When AI decision errors cause ecological harm, responsible entities should conduct impact assessments and provide remediation. Such mechanisms provide ecological restitution while incentivizing developers to improve models. Ethically, maintaining prudent accountability is paramount; AI is an assistive tool, and ultimate conservation responsibility remains with humans. Only through robust risk management and accountability systems can we harness AI’s benefits while preventing irreversible ecological damage from technological failures.

Privacy infringement concerns

While AI-driven monitoring technologies protect wildlife, they raise significant human privacy concerns. Improper use or leakage of such imagery could violate personal privacy and provoke social controversy. First, data collection should minimize acquisition of non-conservation-related human information. Automatically filtering out human-containing frames during processing reduces privacy exposure.¹⁸⁷ Second, unavoidable human imagery must be anonymized (e.g., blurring faces/license plates), while retaining essential biological data,¹⁷³ with strict access controls limited to conservation purposes. Policies should reference privacy regulations to establish clear standards for handling personal data in wildlife monitoring, thereby prohibiting secondary uses. Concurrently, transparent community engagement—disclosing device locations and purposes—builds public understanding and trust. When sensitive technologies like facial recognition are essential for combating serious crimes (e.g., poaching), their use must undergo rigorous ethical review and legal authorization, avoiding misuse. Wildlife protection must not sacrifice fundamental human rights. Embedding privacy safeguards and ethical oversight into technological applications prevents the dilemma of “protecting animals while harming people,” ensuring the legitimacy and sustainability of AI monitoring.

Algorithmic colonialism: Data sovereignty deficits in the global south

AI development is unevenly distributed, with advantages concentrated in the Global North and large tech corporations. Many critical data sources reside in developing countries yet require Northern technology for analysis, fueling concerns about “algorithmic colonialism”¹⁸⁸: Southern data processed by Northern AI systems generate insights influencing Southern resource governance, while Southern nations retain limited control. Addressing this requires international advocacy for data sovereignty and equitable cooperation; Northern institutions must respect host-country data sovereignty through clear agreements, technology transfer should involve co-development and co-ownership with local partners, and AI capacity building in developing nations is crucial. International conventions can facilitate this by establishing guidelines for fair data and technology sharing.

Future prospects

Cutting-edge technology prospects

General biodiversity large models

As ecosystem monitoring data accumulate and secure data-sharing methods like FL advance, constructing a general biodiversity foundation model becomes feasible. Analogous to general AI models (e.g., GPT series), these general biodiversity large models (GBLMs) will integrate multimodal data (genomics, remote sensing imagery, bioacoustics, and climate variables) with ecosystem dynamics theory and be trained for biodiversity applications to process multimodal inputs including images, sounds, text, and gene sequences. GBLMs will possess three core capabilities. The first is generality and transferability, enabling rapid adaptation via fine-tuning or prompting to diverse specific tasks like species identification, ecological forecasting, and conservation policy analysis. The second is causal reasoning, integrating structural causal models with deep learning to infer causal relationships in ecological processes from correlative data. The third is adaptive learning, continuously optimizing prediction accuracy through real-time interaction with global monitoring networks. These models will not only enhance the spatiotemporal resolution of species distribution predictions but also simulate ecosystem resilience changes under complex disturbance scenarios, providing unprecedented scientific support for conservation decision-making.

Generative AI for “zero-shot” species discovery

Generative AI (e.g., GANs and diffusion models) holds the potential to aid scientists in discovering new species or predicting characteristics of unknown organisms. Traditionally reliant on field surveys and expert identification, zero-shot learning combined with generative models offers a novel approach.¹⁸⁹ Cross-modal generative models could also translate textual descriptions into images, helping researchers reconstruct possible appearances of extinct or undiscovered species based on historical accounts. This text-image-gene cross-modal retrieval and generation technology promises to expand the boundaries of human biodiversity knowledge. Generative AI could also simulate morphological variations under different environments, generating hypotheses for taxonomy. Results from generative AI require rigorous scientific validation, but as an assistive tool, it can greatly accelerate the process of species discovery and classification.

Quantum computing for ecological simulation

Quantum computing, considered the next-generation computational paradigm, offers immense potential for simulating complex systems due to its parallel processing capabilities. Simulating ecological networks involving millions of species and interactions is often computationally intractable for classical computers within reasonable time frames. While still nascent, breakthroughs could enable simulations that take supercomputers days or years to run to be completed time frames. This will allow scientists to run scenario analyses (e.g., predicting species extinction risks under different conservation strategies) more frequently, enabling timely adjustments to conservation measures. Challenges including error correction, noise mitigation, and algorithm design remain, but as a frontier direction, quantum computing promises a qualitative leap in computational power, offering novel possibilities for tackling complex ecological problems.

Ultra-low-power adaptive edge devices

The convergence of edge computing and TinyML will drive revolutionary advances in field monitoring devices, enabling ultra-low-power systems integrating “sensing-analysis-decision” capabilities. Next-generation devices will integrate nanoscale sensors, energy-harvesting modules, and quantum dot processors, which will enable complex AI inference tasks that require only microwatts of power. Future applications of flexible electronics and biocompatible materials will allow these devices to seamlessly integrate into the natural environment, forming an “invisible” biodiversity sensing network.

Expanded application scenarios

Deep sea and polar extreme environments

Deep-sea and polar environments, characterized by extreme conditions and limited human presence, harbor unique biodiversity. In the future, autonomous underwater vehicles (AUVs) capable of prolonged autonomous operation will be equipped with AI detection systems for biodiversity surveys in deep-sea and polar oceans. In polar seas, AUVs can navigate under ice floes, using sonar and cameras to track whales, seals, and other animals, with AI algorithms distinguishing biological targets from obstacles in sonar returns. This AUV + AI combination will enable unprecedented scale and frequency in monitoring deep-sea and polar ecosystems, filling critical knowledge gaps and informing global conservation strategies.

Urban biodiversity

As urbanization progresses, cities have become novel habitats for many wildlife species, presenting unique challenges for urban biodiversity conservation.¹⁹⁰ AI in “urban computing” optimizes green infrastructure layout and urban biodiversity. Specific applications may include intelligently selecting tree species and configurations for streetscapes to attract beneficial birds and optimizing the placement of urban wetlands and rain gardens to form wildlife corridors. With the integration of urban computing with AI, future cities can evolve from biodiversity “deserts” into ecological cities coexisting harmoniously with nature that would enable species protection while enhancing residents’ quality of life and environmental awareness.

Planet-scale DWs

Traditional biodiversity conservation often focuses regionally on select species and struggles to address global change.¹⁹¹ Effectively tackling the global biodiversity crisis requires integrating interactions between climate, ecology, and human society. A grand future vision is the creation of a planetary-scale DW system, deeply coupling climate models, biodiversity models, and socio-economic models into a high-fidelity simulation of Earth’s ecosystems. Achieving this requires global scientific collaboration and vast computational resources, but advances in cloud computing and AI are making it technically feasible. Once realized, this planetary-scale DW will serve as humanity’s “dashboard” for Earth system management, enabling more precise assessment and ensuring that human activities operate within planetary safe boundaries.¹⁹² Once this is realized, the insight directs us toward developing more effective and context-appropriate conservation strategies.

Governance and ethical framework

As AI applications deepen in biodiversity conservation, constructing a global governance and ethical framework will be paramount for ensuring responsible technological development. Current fragmented regulatory systems are ill-equipped for challenges like cross-border data flows, algorithmic bias, and the digital divide. At the national level, data sovereignty and sharing regulations, such as biodiversity data trust mechanisms, will be implemented. At the organizational level, algorithmic impact assessment (AIA) systems will mandate reviews for high-risk AI systems.

Core elements of the ethical framework will include the following: (1) algorithmic fairness, ensuring that AI systems perform equitably across regions and species without systemic bias; (2) data sovereignty, establishing FL governance models (“local data processing + global model sharing”); (3) technological inclusivity, bridging the AI capacity gap in developing nations through open-source platforms and South-South cooperation; (4) accountability and traceability, utilizing blockchain for full life-cycle traceability of AI decisions. Frameworks like the EU AI Act provide initial models, which will evolve into globally accepted biodiversity AI ethics guidelines.

Education and capacity building

The AI-driven revolution in biodiversity conservation necessitates corresponding human resource development and capacity-building systems. The current digital skill gap among conservation practitioners must be addressed through innovative educational models. Beyond formal education, AI skill training for in-service conservation professionals is vital. Developing nations need professionals skilled in both local ecology and AI; protected area staff require practical training in deploying edge devices, data collection, and basic model interpretation. Establishing regional AI computing centers in developing countries will provide local high-performance computing services, reducing dependence on foreign cloud resources. These measures will empower developing nations to cultivate their own AI talent and teams, fostering innovative local conservation projects, and contributing to global biodiversity goals while promoting technological and economic development.

Conclusions

AI is increasingly becoming a pivotal tool in biodiversity conservation and has been driving profound paradigm shifts across monitoring, assessment, and decision support. At the data layer, multimodal sensing technologies and advanced fusion techniques are revolutionizing biodiversity monitoring, with unprecedented spatiotemporal resolution. At the algorithmic layer, diverse models from traditional ML to deep learning and foundation models are unlocking new analytical capabilities. System-level innovations are constructing next-generation intelligent conservation platforms, facilitating a transition from localized management to global collaboration. However, technological progress introduces new challenges including data bias, algorithmic opacity, and socio-ethical dilemmas. Future success hinges not only on technological innovation but also on establishing effective governance frameworks, ethical guidelines, and collaborative mechanisms to ensure that AI development aligns with long-term biodiversity conservation goals. Interdisciplinary integration will serve as a key driver for advancing this field, while participation and capacity-building in the Global South are critical to avoid “algorithmic colonialism” and enable truly inclusive global conservation networks. AI is ushering biodiversity conservation into a data-driven, intelligent decision-making era, marked by three paradigm shifts: from discrete to continuous observation dimensions, from experience-driven to data-driven decision-making models, and from fragmented regional governance to globally coordinated architectures. While AI provides unprecedented tools, ultimate success depends on how responsibly and equitably these tools are wielded through interdisciplinary, transregional, and multi-stakeholder collaborations.

Limitations of the study

Although this review provides a comprehensive three-layer framework for AI-driven biodiversity conservation, several limitations inherent to this work must be acknowledged. First, as a narrative synthesis rather than a systematic meta-analysis, this work may be susceptible to selection bias, potentially overemphasizing successful implementations while underreporting null or negative results. Second, the rapid evolution of the field means that novel methodologies published after our literature search cutoff might have remained uncaptured. Third, the synthesis operates primarily at a conceptual level; while it provides architectural clarity, it lacks the standardized performance benchmarks required to quantitatively compare the efficacy of specific algorithms across diverse contexts. Fourth, the proposed three-layer framework (data, algorithmic, and system) risks oversimplifying the complex, non-linear feedback loops that characterize technological interdependencies in real-world conservation operations. Fifth, the socio-ethical discussion, particularly regarding data sovereignty and algorithmic colonialism, relies predominantly on English-language literature, which may inadvertently marginalize the distinct perspectives and governance challenges of the Global South. Finally, while this review highlights the transformative potential of AI, it lacks the quantitative cost-benefit evidence necessary to substantiate the economic viability of these technologies, leaving the financial sustainability of AI-driven conservation efforts uncertain without rigorous empirical validation.

Acknowledgments

This study was financially supported by the National Social Science Fund of China (21BZZ046), Humanities and Social Science Fund of Ministry of Education of China (20YJC810003), Fundamental Research Funds for the Central Universities (QNTD202502, BLX202375) and the National College Student Innovation and Entrepreneurship Training Program (202510022077).

Author contributions

Conceptualization, S.N.; methodology, S.G. and S.N.; formal analysis, S.N.; data curation, S.G.; investigation, S.G.; visualization, S.G. and S.N.; writing – original draft, S.G., Z.L., and S.N.; writing – review & editing, S.G., X.H., and S.N.; supervision, S.N.; project administration, S.G.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.