Underhyped Tech - Sensor Networks for Biodiversity
This is a deep dive into one of our nine underhyped frontier technologies for development.
The full exploration will be released on April 23rd. Sign up to the launch event here.
Scalable, real-time monitoring for conservation and climate resilience
Sensor networks for biodiversity monitoring are opening up new ways to understand and protect the natural world. These low-power, AI-enabled tools can track species, detect habitat change, and offer real-time insights into ecosystems—often in places people can’t easily reach. Unlike traditional methods, they work continuously, building a clearer picture of environmental change as it happens. While still underused, especially in low-resource settings, they hold huge promise for conservation, climate adaptation and beyond.
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Sensor Networks for Biodiversity Monitoring consist of low-power, autonomous sensors deployed across diverse ecosystems to track wildlife, climate variables, habitat changes, and environmental health. These networks use IoT-enabled devices, acoustic monitoring, motion detection, and AI-powered analysis to generate real-time biodiversity data at scale. They are autonomous as they can operate and make decisions without direct human intervention, using algorithms, machine learning and wireless communication.
Unlike traditional conservation methods, which rely on seasonal surveys and human observation, distributed sensor networks provide high-resolution, continuous datasets that improve species conservation, ecological modelling, and climate adaptation strategies.
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Wildlife Population Monitoring: Tracks species movements, migration patterns, and nesting behaviours through bioacoustics and motion sensors.
Ecosystem Health Assessment: Monitors forest, grassland, and wetland ecosystems by analysing environmental conditions.
Human-Wildlife Conflict Prevention: Detects animal movements in protected areas and near human settlements, helping mitigate conflicts.
Invasive Species Detection: Identifies non-native species through AI-driven audio and image recognition.
Poaching & Illegal Logging Detection: Uses acoustic and thermal cameras to track suspicious human activity in protected areas.
Pollination & Insect Monitoring: Collects real-time data on pollinators and insect populations, supporting agricultural biodiversity.
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Despite rapid advancements in sensor technology, AI, and the Internet of Things (IoT), nature and biodiversity monitoring remain heavily reliant on manual field surveys and sporadic data collection. Distributed sensor networks have the potential to revolutionise conservation by providing continuous, real-time insights into ecosystems, species behaviour, and habitat changes. However, lack of supply chains, limited connectivity, and fragmented implementation have slowed widespread adoption.
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Long-range access: Remote monitoring reduces the need for constant human presence in the field, allowing researchers and conservationists to allocate time and resources more efficiently.
AI-Driven Biodiversity Tracking: Machine learning and remote sensing technologies help map species populations and environmental events dynamically, offering deeper ecological insights.
Decentralised Monitoring: Real-time ecosystem tracking is possible even in remote or inaccessible areas where human observation is limited or impractical.
Multi-layered data collection: Sensors can be networked to provide a more complete view of ecosystems, layering different types of information to fill data gaps and improve analysis.
Cross-border conservation efforts: Supports the management of wildlife corridors and transboundary ecosystems, encouraging cooperation between nations and conservation groups.
Climate adaptation & ecosystem resilience: Predicts biodiversity shifts caused by environmental stressors, helping decision-makers plan conservation strategies accordingly.
Early warning systems: Detects forest degradation, habitat loss, ecosystem collapse risks, and changes in animal behaviour triggered by climate change.
Emergency communication potential: Sensor networks can be repurposed as alternative communication systems, providing voice or text-based alerts in the event of ecological disasters or emergencies.
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Data overload & processing complexity: High-resolution, continuous data streams require sophisticated analytics, cloud computing, and significant processing power to extract meaningful insights.
Data extractivism: When cloud services are used, companies may exploit collected environmental data without informed consent or fair compensation, raising concerns about ownership and ethical data governance.
Connectivity & power constraints: Many remote monitoring sites lack reliable energy infrastructure and network connectivity, limiting real-time data transmission and requiring alternative energy solutions.
Cybersecurity & data integrity risks: Sensor networks are vulnerable to hacking, data corruption, and misinterpretation, which could undermine the credibility of environmental monitoring efforts.
Calibration & sensor degradation: Long-term deployment in the field requires regular maintenance and recalibration, especially in extreme weather conditions that can degrade sensor performance over time.
Interoperability challenges: Different monitoring projects often use incompatible data formats and technologies, making it difficult to integrate diverse datasets into a cohesive environmental assessment.
Potential for surveillance misuse: While designed for ecological monitoring, these tools could be repurposed for unauthorised human surveillance, raising ethical and privacy concerns.
Tampering & data manipulation: Data may be intentionally altered or misrepresented to support specific narratives or exert control over policy and decision-making, threatening scientific integrity.
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Wireless Sensor Networks & IoT: Facilitate low-cost, real-time biodiversity monitoring, allowing for continuous data collection in remote areas.
Bioacoustic & Motion Detection Systems: AI-powered sound and movement tracking improves species identification and behavioural analysis.
Open Data & Citizen Science: Community-led biodiversity monitoring initiatives empower local conservation efforts, creating pathways for trained conservation professionals.
Self-Powered & Sustainable Sensors: Advances in bioelectric and solar-powered sensors extend deployment time, reducing maintenance needs.
Drone & Satellite Integration: Combines ground-based sensor networks with aerial and satellite observations, enhancing ecosystem monitoring with a broader perspective.
Public-Private Conservation Partnerships: Collaboration between governments, NGOs, and private sector stakeholders facilitates large-scale deployments and long-term sustainability.
Blockchain for Conservation Data: Ensures biodiversity data integrity, preventing tampering and enhancing transparency in conservation efforts.
Global Sensing Networks: Participation in international data-sharing initiatives provides local sensor networks with a broader context, improving long-term viability and scientific impact.
Global Sensing Standards: Unified standards for data collection and processing improve sensor interoperability, facilitate multi-layered datasets, and offer certification opportunities for trained sensor operators.
Online Dashboards & Cloud Processing: Cloud-based data processing reduces costs by charging only for storage and computational resources, making large-scale biodiversity monitoring more accessible.
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Limited Funding: Deploying large-scale monitoring networks requires substantial investment in hardware, data analytics infrastructure, and connectivity, which is often inaccessible for low-resource regions.
Fragmented Policy & Governance: Many biodiversity projects operate in isolation, lacking coordination across borders and jurisdictions, which limits large-scale conservation efforts.
Remote Deployment Challenges: Installing and maintaining sensors in dense forests, mountainous regions, or marine environments requires specialised equipment and expertise.
Community Buy-in & Ethical Concerns: Resistance can arise due to concerns over data ownership, local participation, and potential technological intrusion in indigenous and rural communities.
Regulatory & Compliance Issues: Varying national and regional environmental laws complicate global biodiversity monitoring and data-sharing agreements
Manufacturing Complexity: Advanced sensor components require specialised manufacturing, often necessitating imports, making procurement costly and logistically challenging.
Maintenance Complexity: With increased technological sophistication comes difficulty in acquiring replacement parts, especially in remote areas with limited access to global supply chains and online purchasing infrastructure.
Institutional Corruption: In some regions, biodiversity monitoring funds may be misallocated or diverted due to nepotism, or mismanagement, undermining long-term project sustainability.
Curious examples: what’s already happening?
Case Study no. 1
Nature 4.0 – A Smart Sensor Network for Environmental Monitoring in Marburg Open Forest - Prototype
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Conservation strategies require frequent and detailed observations of landscapes to monitor biodiversity and environmental changes. Traditional expert surveys provide in-depth data but are limited in spatial coverage and frequency. While remote sensing helps to address these gaps, it still requires expert interpretation and lacks real-time integration with ground-level environmental factors.
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The Nature 4.0 project aims to develop a modular environmental monitoring system that integrates expert-led surveys, remote sensing, and a network of autonomous environmental sensors. This network consists of:
✔️ Fixed and mobile sensors placed in the forest, attached to drones, rovers, or even animals to track environmental variables dynamically.✔️ Automated measurement stations that collect continuous, high-resolution data on species, habitats, and ecosystem processes.
✔️ Advanced data integration and analysis methods to process multi-layered environmental data efficiently.
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Enables real-time, high-resolution monitoring of biodiversity and ecosystem changes.
Supports early warning indicators for environmental degradation, allowing for rapid conservation action.
Promotes open science and collaboration among conservationists.
Strengthens collaboration between scientists, conservationists, policymakers, and the public.
Serves as a model for future large-scale biodiversity monitoring projects.
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Case Study no. 2
A Call for Remote Sensing Biodiversity Monitoring in Latin America - Review
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Latin America and the Caribbean (LAC) host 60% of the world’s biodiversity, yet biodiversity loss due to deforestation, climate change, and human activities is accelerating. Monitoring efforts remain fragmented, with limited investment, inconsistent methodologies, and a lack of local research capacity.
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A review of remote sensing applications for biodiversity monitoring in LAC highlights the need for a more coordinated approach. The study identifies key gaps and opportunities:
✔️ Enhanced Remote Sensing Techniques: Adoption of LiDAR, hyperspectral imaging, and machine learning for ecosystem and species-level monitoring.✔️ Capacity Building & Decolonised Research: Strengthening regional research networks and ensuring local knowledge integration.
✔️ Standardised Monitoring Protocols: Aligning remote sensing methodologies with global biodiversity frameworks for improved data comparability.
✔️ Investment in Local Research & Infrastructure: Addressing funding gaps and technical limitations to empower local research institutions.
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Improve the accuracy of conservation efforts across Latin America.
Enable early detection of deforestation, habitat degradation, and species decline.
Facilitate cross-border collaboration for transnational biodiversity protection.
Provide decision-makers with reliable, real-time ecological data to support policy actions.
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Other interesting cases
Artificial intelligence to save the Great Barrier Reef (Australia) - Research
A global real-time monitoring system to help save the world's coral reefs from further decline.
Biodiversity Sensor (Switzerland) - Product/Service
Drawing from artificial intelligence (AI) and machine-learning algorithms, the sensor locates and quantifies bee pollinator species in real-time.
Hula Earth (Germany) - Product/Service
Biodiversity Monitoring service with Satellites and IoT Sensors.
A framework to develop biodiversity monitoring programmes (Austria) - Framework/Method
For managers in protected areas, providing effective area-based conservation measures.
Privacy in Networks (France) - Framework/Method
A cryptographic standard for quantum sensor networks
Time-aware sensors to map chemical concentrations (USA) - Pilot
A network of thousands of sensors can collectively map out the entire concentration profile of previously inaccessible systems.
A network with modern seismological processing methods (American Samoa) - Product/Service
Informing an ongoing event response in a poorly monitored setting.
Global internet grid to detect earthquakes - Research
A new algorithm that takes the energy observed at receivers—either fibre optic cable channels and/or seismometers—and migrates that energy back through space and time to find a coherent peak in energy corresponding to a potential earthquake.
Future scenarios: what might happen in 2035?
As part of our exploration of these nine underhyped technologies, our partners at Pluriversa conducted a foresight exercise to consider possible futures and anticipate the challenges and opportunities within each technology. The exercise produced four scenarios for the year 2035, which you can read here, along with speculative use cases - with different potential outcomes - for each technology. Read on to explore potential future scenarios involving sensor networks for biodiversity.
2035: Environmental Knowledge Database
By now, most local governments own their network system. This can serve many purposes, but one in particular stands out: the IoT system for observation and healing.
Using cheaply produced and decent enough quality chips and sensors from a nearby provider, it is possible to track real-time metrics like particulate matter, loss of biomass, and soil composition, to name a few. This system feeds a huge open-source database.
Since the network system is a publicly owned natural monopoly, the local government can profit from renting its database. The majority of clients come from privately owned AI systems, making the processing of data a sensitive subject that is highly regulated. The same happens with robot bees that need the data to bioengineer environments.
2035: Bio-political Population Control
Equipped with the right sensor networks, any sufficiently large organisation can now create surveillance systems that can track employees' activities at a level never seen before. This has allowed a new form of micromanagement.
Easily adaptable from networks used for observing and healing nature, this version offers organizations a vast array of information like biometric identification, behavioural pattern tracking, and threat sentiment analysis. They are particularly useful to exercise control and coercion through bio-data.
Even government agencies use the system to spy on political opponents. With the use of AI, the systems can even be used to detect potential threats before they occur by using advanced sentiment and hormonal analysis, making it the perfect tool for predictive law enforcement.
Final thoughts
If you’re working on conservation, agriculture, climate resilience, or land management, there could be powerful ways to integrate sensor networks into your work. The real opportunity lies in applying them creatively, in context, and alongside existing local knowledge and tools.
Some ideas to explore next:
Could low-cost acoustic sensors help track pollinators or detect early signs of forest degradation?
Might real-time environmental data improve ecosystem restoration or climate adaptation planning?
Are there ways to connect citizen science efforts with sensor-based monitoring?
Could shared infrastructure—like open data platforms—support collaboration across regions or sectors?
Sensor networks aren’t a one-size-fits-all solution—but they offer a flexible, scalable way to see and respond to changes in the natural world. As costs fall and technology improves, now’s a good time to explore how these tools might work for your context—especially in places where every insight can make a difference.
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Nature 4.0: A networked sensor system for integrated biodiversity monitoring
Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions
Nature 4.0: A sensor network for environmental monitoring in the Marburg Open Forest
Wireless Sensor Networks for Environmental Monitoring: A Review
Towards a multisensor station for automated biodiversity monitoring
How Can Sensor Networks Be Used To Monitor Biodiversity Loss?
Cross layer based biological inspired self-organized routing protocol for wireless sensor network
A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
A self-sustaining autonomous system for long-term Arctic monitoring
Temporally resolved concentration profiling via computationally limited, distributed sensor nodes
Color-Selective Printed Organic Photodiodes for Filterless Multichannel Visible Light Communication
A Survey on Random Access Protocols in Direct-Access LEO Satellite-Based IoT Communication
This is a deep dive into one of our nine underhyped frontier technologies for development.
The full exploration will be released on April 23rd. Sign up to the launch event here.