What Telecom Infrastructure Can and Can't Solve for Biodiversity Data

Conservation has a longstanding measurement problem. Knowing whether an intervention – a protected area, a community forest scheme, a certification standard – is actually working takes detailed, repeated, long-term surveys of the species living there, and those surveys are expensive and slow. A landmark Mongabay-led review of conservation effectiveness found that we still know surprisingly little about which of the most widely used conservation strategies actually work.

Bioacoustic monitoring has emerged as one of the more credible responses to that gap. Acoustic recorders can detect the calls of birds, mammals, amphibians and insects from hundreds of metres away, continuously and non-invasively, at a fraction of the cost of field surveys or camera-trap networks. The hardware problem is largely solved: open-source recorders like AudioMoth now cost a fraction of what dedicated conservation electronics used to, putting acoustic sensing within reach of far more projects than a decade ago.

But what comes after the recording?

A 2021 review of acoustic biodiversity monitoring using deep learning identifies two specific, unresolved constraints on turning sensor networks into usable, real-time monitoring systems. The first is reliable connectivity to transmit data for centralised analysis – it notes explicitly that many of the environments where habitat and species surveys happen lack mobile network access. The second is the rapidly escalating compute cost of processing that data as sensor counts scale. A separate synthesis of the field adds a third constraint: the absence of standardised reference sound libraries and shared audio-processing protocols across projects, which limits how well results from one deployment generalise to another.

Standardisation has moved faster than what has been captured. BirdNET, developed and maintained by Cornell's K. Lisa Yang Center for Conservation Bioacoustics, has become a de facto global standard for automated bird-sound identification, validated across dozens of independent studies from the Italian Alps to Mount Kenya to Hawaiʻi, with formal regional standards papers now emerging on top of it. What remains genuinely unresolved is narrower: regional and global south, fine-tuning to improve accuracy over the base model, and standardisation for non-avian taxa lags well behind birds. 

As I argued in an earlier piece on climate intelligence, bioacoustic monitoring has matured through the first two stages (perception and interpretation) and is now stalling on the third, which is action: the infrastructure that gets a detection off a sensor, processes it affordably, and carries it into a usable, comparable, financeable result.

What's already been tried

Rainforest Connection (RFCx), working with Huawei's TECH4ALL programme, has deployed acoustic monitoring across fifteen countries using a network of repurposed smartphones distributed through rainforest canopies, transmitting detections of chainsaws, vehicles and gunshots in near real time. In Indonesia, Indosat Ooredoo Hutchison, in partnership with GSMA and GiZ, combined acoustic and visual sensing with a mix of wired and wireless connectivity to support mangrove conservation monitoring in North Kalimantan, a project GSMA's Nature Tech Nexus report highlighted in 2024.

Both are genuine progress, but they illustrate the same underlying constraint: each project had to assemble its own connectivity solution – a repurposed handset network in one case, a bespoke mix of connectivity types in the other – simply because no shared infrastructure existed to plug into. 

That raises an obvious question: does infrastructure already exist that could remove the need to solve connectivity project by project?

Where mobile tower infrastructure fits

Mobile network operators already have exactly the combination the literature says is missing: reliable power, backhaul connectivity, and increasing edge compute capacity at tower sites. These sites are maintained on a recurring schedule and deployed specifically to reach population centres in rural and remote geographies that are otherwise expensive to serve. If tower sites can host edge inference for acoustic classification, instead of requiring every recording to be transmitted for centralised processing, that directly addresses the compute-scaling constraint the 2021 review flags. AI systems processing environmental data also need the kind of stable, high-bandwidth, low-latency transmission that telecom networks are built to provide. This is a connection that's still underused, and it's one of the reasons why fewer than 25% of countries currently have biodiversity monitoring aligned with the Kunming-Montreal Global Biodiversity Framework.

The honest limitation: coverage and connectivity are not the same problem

As per State of Mobile Internet Connectivity report, mobile broadband now reaches 96% of the world's population and the remaining 4%, roughly 300 million people, is concentrated in the least developed countries, landlocked developing countries and small island developing states where connectivity is hardest and most expensive to deliver, and where satellite backhaul remains costly. Towers are built to serve people, which means dense coverage tracks settlement and road networks, not necessarily the most ecologically intact, least-disturbed habitat interiors that biodiversity monitoring often cares most about. A tower-based approach is well suited to the agricultural frontier, forest-edge and buffer-zone geographies where human-wildlife conflict and habitat pressure are typically highest, which is not a small use case, but it is a narrower one than "biodiversity monitoring" as a whole. Telcos and tower companies in Africa and other emerging market economies have also located towers in rural areas where population densities are low (under 300 people per sq km) to ensure connectivity access. In Africa, for instance, GSMA Intelligence analysis indicates 40-45% of base stations sit in rural, which can be leveraged as real estate for sensors to achieve bioacoustic recordings and on-site analysis.  

Moreover, the remaining standardisation gap identified, doesn't go away with better connectivity. Solving transmission and compute makes it more affordable to run acoustic monitoring at scale; it doesn't by itself produce the shared reference and fine-tuned regional libraries and comparable protocols that would let results from a tower-based network be meaningfully compared against RFCx's or IOH's or anyone else's. That would need to be built deliberately alongside any infrastructure work.

Why this is still worth pursuing

Even in its current state, closing the connectivity and compute constraint matters for reasons beyond conservation science. Climate and nature finance increasingly depends on verifiable, continuous impact data, GSMA's work on digitally enabled climate finance points to mobile and digital channels as a way to generate exactly that kind of evidence for development finance institutions, whose funding criteria already reward verifiable, ongoing monitoring over one-off assessments. The same institutions are also acutely aware of unmet demand in adjacent markets, the global smallholder agricultural finance gap stood at just over $200 billion annually as of the most recent (2025) sector assessment which suggests an appetite for well-evidenced, digitally verifiable rural finance and resilience mechanisms that better biodiversity data could plausibly extend into.

Mobile infrastructure is not a ready-made answer to conservation monitoring's data problem. However, it's an answer to a well-documented constraint – connectivity and compute cost – that overlaps closely with infrastructure that already exists and is already maintained, in exactly the geographies where that constraint bites hardest. That overlap is worth testing properly, with the standardisation and coverage limitations built into the design from the outset.