Introduction
The contemporary crisis of natural capital demands new forms of understanding, management, and collective action. In this article, natural capital is understood as the set of biotic and abiotic components that sustain life and ecological processes, while biodiversity refers specifically to the variety of genes, species, communities, and ecosystems that comprise it. Although the two concepts are closely related, they are not equivalent: biodiversity is a central dimension of natural capital and possesses, in addition to its ecological and social functions, an intrinsic value that does not depend exclusively on its economic usefulness.
Within this framework, the term living infrastructure refers to the set of ecological systems that sustain territorial processes such as water regulation, soil formation, pollination, ecological connectivity, food provision, climate stability, and the cultural reproduction of communities. This infrastructure does not manifest itself uniformly. Biota assumes different forms, densities, relationships, and vulnerabilities across tropical rainforests, dry forests, páramos, wetlands, coastlines, marine environments, savannas, deserts, and urban systems. Any digital strategy must therefore recognize the differential expression of life across the planet’s major ecological settings and avoid applying the same grid, indicator, or model as though all territories were comparable.
The study of nature has relied on technology for decades. Cartography, aerial photography, remote sensing, databases, geographic information systems, telemetry, and ecological modeling are all part of contemporary scientific practice. The novelty of the new digital era does not therefore lie in digitizing what was previously entirely analog, but in automating the capture, classification, integration, and interpretation of large volumes of information. Artificial intelligence, remote sensors, environmental DNA, automated bioacoustics, digital twins, and reporting platforms expand the spatial and temporal scales of observation, but they also introduce new biases, technological dependencies, and interpretive challenges.
A decisive paradox emerges in Latin America: the region contains extraordinary biological richness, yet much of its information remains dispersed, undigitized, affected by taxonomic gaps, characterized by limited interoperability, and marked by unequal access. These disparities are evident between urban and rural areas, between institutions with different financial capacities, between intensively monitored ecosystems and remote territories, and between dominant languages and local knowledge that does not always find a place within digital platforms. Democratizing biotic information therefore requires connectivity, public infrastructure, specialized training, common standards, recognition of authorship, and protection mechanisms for sensitive data (Economic Commission for Latin America and the Caribbean [ECLAC], 2024).
These tensions raise unavoidable questions: Who controls natural capital data? Who decides what is measured and at what scale? How can nature be prevented from being reduced to financial indicators? Are biogeographical, human, and ecological boundaries respected when life is represented through digital grids? What happens to the concept of the minimum mapping unit when algorithms aggregate or simplify heterogeneous territories? What role is assigned to local communities, Indigenous Peoples, and territorial knowledge? These questions do not challenge the use of technology itself; rather, they challenge the assumption that automation necessarily amounts to understanding.
From Territorial Characterization to Assisted Ecological Intelligence
Biodiversity conservation has historically relied on biological inventories, scientific collections, protected areas, ecological restoration, population monitoring, and environmental regulation. These strategies remain essential. Before automating, it is necessary to know the territory: to visit it, describe it, identify species, record interactions, understand ecological cycles, and compare data with the experience of those who inhabit it. No algorithm can replace fieldwork, taxonomic validation, or the situated interpretation of the researcher.
Scientific modernization requires explicit statistical methods, traceability, and evidence-based decision-making. Nevertheless, this requirement should not come at the expense of basic characterization. Models depend on source data, and data depend on observations made under specific conditions. When samples are scarce, geographically biased, or derived from poorly defined categories, automation does not eliminate the problem; it amplifies it and gives it an appearance of precision.
Within this context, digital ecological intelligence should not be understood as an autonomous quality of machines, but as a human, scientific, and institutional capacity supported by computational tools. Systems commonly referred to as artificial intelligence are trained to recognize patterns, classify records, or estimate probabilities. They may emulate certain cognitive operations, but they do not possess the biological, historical, ethical, and territorial understanding that guides ecological research. Their role should be to assist, not replace.
These tools can support species identification, image and sound analysis, detection of land-cover changes, species-distribution modeling, and the generation of early warnings. Their results, however, must be subjected to field validation, uncertainty analysis, bias assessment, and evaluation of transferability across regions. A model trained in a humid forest, an urban collection, or a group of well-documented species cannot automatically be extrapolated to other contexts. Ecological intelligence emerges from the articulation of data, natural history knowledge, quantitative methods, territorial experience, and public deliberation (Cañas et al., 2025).
Natural Capital as an Information System, Not as a Commodity
In the digital era, ecosystem signals can be transformed into data: images, sounds, genetic sequences, occurrence records, climatic variables, soil properties, water flows, and vegetation changes. Sensors, satellites, drones, camera traps, environmental DNA, and geospatial platforms make it possible to construct layers of information that were previously difficult to integrate. Nevertheless, data are not nature; they are partial representations produced through decisions concerning scale, sampling, resolution, categories, and validation methods.
This distinction is important because natural capital cannot be reduced to a collection of assets, risks, or opportunities. Life possesses intrinsic, relational, cultural, and ecological values that are neither ordinal nor fully interchangeable. An endemic species, a sacred wetland, a biological corridor, or an ecological relationship cannot be automatically replaced through accounting-based compensation. Recognizing corporate impacts and dependencies may be useful, but it should not displace the right of ecosystems to persist or collective obligations of care (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services [IPBES], 2022).
The LEAP approach developed by the Taskforce on Nature-related Financial Disclosures can help organizations locate, evaluate, assess, and prepare responses to nature-related issues. Its application, however, requires meaningful consultation, early participation, and data governance. Applying it in Indigenous or community territories without consent, without agreements concerning ownership and use of information, or without benefit-sharing mechanisms may turn monitoring into a form of data extractivism. TNFD guidance itself recognizes that engagement with Indigenous Peoples, local communities, and affected stakeholders should be embedded throughout the assessment process rather than limited to subsequent validation (Taskforce on Nature-related Financial Disclosures [TNFD], 2023a, 2023b).
Anticipatory Technologies with Ecological Limits and Common Standards
One of the most significant possibilities offered by automation is the strengthening of anticipatory management. Rather than intervening only after damage has occurred, digital tools can detect early signs of deforestation, fragmentation, species loss, hydrological alteration, fires, pollution, or phenological change. These capabilities can support governments, universities, communities, and organizations, provided that early-warning systems are connected to concrete decisions, resources, and responsibilities.
The usefulness of these tools depends on the quality, representativeness, and comparability of the data. Latin America remains far from achieving full interoperability: undigitized records persist, standards are applied unevenly, metadata are incomplete, geographical and taxonomic gaps remain, and information from different countries, institutions, and ecosystems is difficult to integrate. Global platforms have helped mobilize data, but they do not by themselves correct source biases or guarantee nomenclatural and ecological validation (Global Biodiversity Information Facility [GBIF], 2020; Bloom et al., 2021).
The regional agenda should move toward comparable sampling protocols, controlled vocabularies, persistent identifiers, complete metadata, reference collections, taxonomic validation, system interoperability, and multilingual access. Democratization does not mean indiscriminate publication. Some locations of threatened species, traditional knowledge, or genetic data require differentiated levels of access. Openness must be combined with protection, attribution, consent, and accountability.
Nature Positive: A Political Horizon, Not a Technological Guarantee
The concept of nature positive proposes halting and reversing biodiversity loss, restoring ecosystems, and reorganizing economic activities so that they do not destroy the ecological foundations on which they depend. As a political horizon, it may mobilize relevant action, but it becomes problematic when presented deterministically, as though technology could unilaterally reverse complex processes or restore any degraded system.
Ecosystems respond to thresholds, historical dependencies, nonlinear interactions, and recovery periods that do not necessarily correspond to investment or public-policy cycles. Some losses are irreversible; others require decades and cannot be compensated for by an equivalent increase elsewhere. The notion of nature positive therefore becomes operationally meaningful only when connected to inventories, baselines, reference areas, ecological limits, continuous monitoring, field verification, community participation, and adaptive management.
The Kunming-Montreal Global Biodiversity Framework established four goals for 2050 and 23 action targets for 2030, accompanied by a monitoring framework to guide implementation. Its scope should not be interpreted as permission to simplify nature into a dashboard of indicators, but as an invitation to strengthen the scientific, institutional, and social capacities required to conserve, restore, and sustainably use biodiversity (Convention on Biological Diversity [CBD], 2022). Measurement is necessary, but it cannot replace deliberation about what should be protected, for whom, and within what limits.
The Material and Biogeographical Footprint of Digital Technology
Not everything digital is ecologically sustainable. Technological infrastructure consumes energy, water, minerals, land, and materials; generates electronic waste; and requires storage and processing networks whose footprint often remains invisible to users. Even Earth observation produces impacts associated with satellites, data centers, transmission, storage, and cloud computing (Anderson et al., 2024). The expansion of the technology sector also increases dependence on energy, water, critical minerals, and physical infrastructure, potentially creating additional pressures on ecosystems (World Economic Forum, 2025).
These footprints are not territorially equivalent. A data center located in a region affected by water stress, high ecosystem sensitivity, or a carbon-intensive energy matrix does not produce the same impacts as a facility located in a different context. Industrial policy frequently treats water or energy consumption as an aggregate figure and overlooks basic biogeography: seasonal availability, hydrological connectivity, endemic species, aquifer vulnerability, and cultural uses of territory.
The same contradiction appears in the critical minerals that support devices, batteries, and networks. The extraction of coltan and other minerals along Amazonian frontiers, as well as lithium extraction in high-Andean salt flats, may degrade fragile ecosystems, affect water sources, and intensify conflicts with local communities. Geographical precision is essential: the point is not to claim that all these minerals are extracted from the same environments, but to recognize that digital infrastructure connects distant territories through material supply chains whose ecological costs remain concealed.
Studies conducted in high-Andean wetlands and salt-flat environments also show that each basin has specific hydrogeological and ecological characteristics and cannot be treated as directly comparable with other territories or projects. Lithium extraction may alter water balances, modify relationships between surface water and groundwater, and increase pressure on ecosystems that depend on highly specific environmental conditions (García-Sanz et al., 2021; Vera et al., 2023).
Governance, the Nagoya Protocol, and Data Sovereignty
Digitization can turn genetic information, species records, ecological maps, and traditional knowledge into circulating assets. This process raises questions concerning appropriation, validation, intellectual property, and benefit sharing. Can natural capital data be commercialized without taxonomic validation? Who authorizes their use when they originate in collectively governed territories? How are the contributions of local researchers, communities, and collection custodians recognized?
The Nagoya Protocol provides a framework for access to genetic resources, prior informed consent, mutually agreed terms, and the fair and equitable sharing of benefits arising from their utilization. Its implementation is essential to prevent digitization from reproducing extractive forms of bioprospecting. The circulation of sequences, models, and databases, however, creates additional challenges that require national regulations, community agreements, and traceability mechanisms adapted to the digital environment (Convention on Biological Diversity, 2011).
Environmental data sovereignty cannot be reduced to storing information on national servers. It requires the capacity to produce, interpret, validate, protect, and make decisions about the use of data. Without biological inventories, scientific collections, taxonomists, field ecologists, public infrastructure, territorial connectivity, and communities with decision-making power, digital sovereignty becomes an empty aspiration. Knowing the territory is a condition for conserving it; digitizing without knowing may reinforce the same asymmetries that digitization is intended to overcome.
Latin America and Colombia: From Biological Abundance to Scientific Autonomy
Latin America occupies a central place in this debate because of the diversity of its rainforests, dry forests, páramos, savannas, wetlands, coastlines, marine environments, and high-mountain systems, as well as the cultural plurality of its territories. This richness coexists with deforestation, mining, urban expansion, fragmentation, water conflicts, and profound scientific and technological inequalities. The region does not face a shortage of life, but rather a relative shortage of interoperable information, distributed capacities, and public control over the infrastructures that transform life into data.
Colombia has an opportunity to build a situated agenda for ecological intelligence. Such an agenda should begin by strengthening inventories, collections, field monitoring, and taxonomic training; articulating universities, research institutes, communities, the state, and businesses; developing public infrastructure for biological data; adopting interoperable standards; protecting traditional knowledge; and promoting computational models validated within the country’s ecosystems.
The objective should not be to transform biodiversity into a new digital raw material, but to increase autonomy in understanding, caring for, and governing territory. The region can move from being a provider of resources and data to becoming a producer of its own knowledge, technology, and decisions, provided that innovation remains subordinate to ecological limits, collective rights, and public responsibilities. The sequence matters: first territory, inventory, and understanding; then automation.
Conclusion
The new digital era is transforming the way natural capital is observed and managed, but it does not alter a fundamental truth: life is not a database, and ecosystems are not devices that can simply be restarted. Technologies can expand observation, accelerate analysis, and support early-warning systems; they cannot replace fieldwork, scientific validation, territorial experience, or ethical deliberation.
Responsible digital ecological intelligence must recognize biogeographical heterogeneity, the intrinsic value of nature, the limits of automation, the need for common standards, community participation, and the material footprint of technological infrastructure itself. It must also prevent biological information from being extracted, commercialized, or processed without consent, traceability, and fair benefit sharing.
The main danger of digital reductionism is the tendency to imagine nature as a simple “Ctrl + Z”: a system in which all damage could be undone through data, automated restoration, or compensation. This technocratic illusion disregards irreversibility, ecological thresholds, and the uniqueness of each territory. The task of the twenty-first century is not to digitize biodiversity until it becomes abstract, but to govern technology from the standpoint of life, territory, and ecological limits.
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