Biodiversity in the Digital Age Part 3

Welcome to part three in a series of five articles exploring the place of biodiversity in an increasingly digital society. The aim of this series is to investigate the process of facilitating meaningful transfers of value between nature and the digital world in order to conserve and restore biodiversity.

In the first article I wrote about biodiversity credit markets and outlined the key drivers behind the demand side of the market. In the second article I expanded upon this by exploring the role that digital assets and the growing digital economy can play in facilitating high integrity demand in biodiversity credit markets. In this article I tackle some principles of ecology while exploring the foundations for creating digital biodiversity credits. Let’s dig in!

To recap, biodiversity credits are units representing some quantity of maintenance or improvement in biodiversity occurring as a result of conservation or restoration activities. The objective of biodiversity credits is to facilitate greater investment in biodiversity conservation and restoration programs and activities. They can do this by operating as financial instruments representing biodiversity gains.

Whilst currently still a nascent space, with standards and structures still under development, a recent global review of biodiversity credit schemes has identified that some key trends for designs and features are starting to emerge.

Principal amongst these is that a majority of the current schemes adopt a similar structure for unitising biodiversity credits as outlined in Figure 1.

As identified above a biodiversity credit is comprised of three components: an outcome (measuring outcomes rather than activities is preferred as I explain below), an area and a time. Unsurprisingly this structure mirrors that associated with monitoring environmental outcomes in the context of Environmental Impact Assessment (EIA), as well as guidance for monitoring and evaluating natural assets under environmental and natural resource management programs.

The reason for this is that although a biodiversity credit represents a financial instrument with application in the financial sector, the fundamental requirement of a biodiversity credit is to ensure that capital flows are achieving meaningful and tangible benefits for nature.

To ensure that this is the case the basis of a biodiversity credit needs to be grounded in the principles of biodiversity management and ecological restoration, and specifically the fundamentals of outcomes-based monitoring and management.

Outcomes based monitoring and management in ecology is vital for fostering accountable and adaptive conservation practices. By emphasising measurable ecological outcomes, this approach ensures a more transparent and evidence-driven strategy, fostering adaptive practices that directly contribute to the health and resilience of ecological systems.

Outcomes in conservation are defined by the observable improvements or the preservation of ecological conditions over time, something that is only achievable with knowledge of the commencing condition, also referred to as the baseline state. The baseline state is crucial in outcomes-based monitoring as it serves as a reference point, providing a clear understanding of the initial conditions before implementing conservation or management interventions. Comparing measurements of biodiversity over time to the baseline state enables precise assessment of the effectiveness of strategies, facilitating informed decision-making and adaptive management practices.

The consideration of area in outcomes-based monitoring is essential as it provides spatial context to ecological changes, allowing for a comprehensive understanding of the distribution and extent of impacts within ecosystems. Evaluating outcomes based on spatial dimensions enhances the precision of conservation strategies, addressing ecological challenges at varying scales and facilitating effective management decisions in diverse landscapes.

A biodiversity credit therefore takes these principles and aligns them with a spatial and temporal framework, usually fixed, that allows for outcomes to be unitised and delivered to market.

Archetypes relate to the types of outcomes that biodiversity credit schemes support and can be broadly classified as the overarching objectives for biodiversity management. These objectives serve as the management goals to guide conservation efforts, defined by the status and requirements of biodiversity for a given area (the baseline condition) and are used to outline the desired ecological outcomes. The achievement of these objectives is then measured through observed outcomes, which are the tangible, measurable changes in biodiversity and ecosystem conditions resulting from the implemented management strategies (Figure 2).

In their review Pollination identified four distinct archetypes for biodiversity credit schemes, outlining the different classes of activities that are currently deemed creditable. These include:

1) Protection — providing a verified designation of protected-area status through recognised instruments including conservation covenants, Indigenous Protected Areas, conservation easements and private protected area programs and agreements.

2) Regeneration — undertaking activities that result in an improvement of ecological value and condition over time

3) Stewardship — undertaking activities that result in the maintenance of ecological value and condition over time

4) Adaptation — undertaking activities that result in the ongoing resilience of ecological systems in the context of climate change related threats

Based on the prevailing status and condition of a project area there is potential for multiple archetypes to be considered, each requiring distinct management activities to be implemented and differentiated outcomes to be demonstrated in order to be achieved. This in turn offers opportunities for multiple classes of biodiversity credits to be recognised, also referred to as ‘stacking’, both within a single project area and also across the life of a project as biodiversity condition changes (i.e. the transition of a regeneration project into a stewardship project over time).

Another point to note with the archetypes is the recognition of stewardship as a creditable class of activity within biodiversity credit schemes and the opportunity that this raises for distinction against carbon credits. The primary objective of nature-based carbon credits is to mitigate climate change by enhancing carbon sequestration, with a secondary objective of promoting sustainable land use practices. Broadly speaking this has resulted in 2 classes of biodiversity enhancing carbon credits being recognised; carbon sequestration via a regeneration class of activities (afforestation, reforestation, biodiverse plantings, human induced regeneration etc) and emissions prevention through a habitat protection class of activities (avoided deforestation).

The regeneration class of nature-based carbon credits activities are primarily focused upon restoring degraded landscapes (i.e. not targeting the protection of pristine habitats) as these areas offer a greater opportunity for carbon sequestration over time. Nevertheless, there is an upper bound to the credit duration associated with these projects, as the sequestration rate typically decreases as the ecosystem matures and approaches climax conditions.

Avoided deforestation projects require that an area which otherwise would have been cleared is protected. In this way these projects align with the protection archetype for biodiversity credits but can also include aspects of stewardship if active management is being undertaken.

In biodiversity conservation habitat protection typically provides superior outcomes than habitat restoration, as it can be achieved at lower costs, with greater fidelity to the structure of natural systems and without the time delay required for restoration (admittedly it is a bit more nuanced than this so for further reference it is worth reading this). What’s more, protecting and managing what remaining habitat there is is critical to achieving aspirational goals such as nature positive and 30×30 regardless of whether a direct clearing impact exists. Of course on top of this we then need to build connectivity through wildlife corridors and increase the total habitat area, all of which will require active regeneration of degraded lands.

For this reason stewardship biodiversity credits offer an opportunity to pay for the management and protection of pristine habitats, that need not be directly threatened by clearing, and over timeframes that are unbound with respect to carbon sequestration requirements. As a result, the potential economic and social impacts of stewardship biodiversity credits for Indigenous Peoples and Local Communities (IPLC) is immense.

Measurement and metrics play a pivotal role in evaluating and quantifying ecological outcomes by providing objective and standardised tools to assess the effectiveness of conservation and management efforts. Through the use of indicators, these measurements enable scientists, policymakers and now financial markets to track changes in biodiversity, habitat health, and ecosystem services. Expectedly, the effectiveness of ecological measurement hinges on:

• Thoughtful consideration of what aspects to measure and the selection of appropriate methodologies;
• Ensuring a comprehensive understanding of ecological dynamics;
• Available resources and technologies; and
• Requirements to inform strategies for biodiversity conservation and management.

However before exploring the details of what to measure when considering biodiversity it is important to recognise that nature is inherently complex, characterised by intricate interactions among diverse living organisms and dynamic relationships between biotic and abiotic components. As a result, and despite our efforts to comprehend the complexity of nature, our understanding is inherently limited by the sheer intricacy of the system. The interwoven relationships, feedback loops, and myriad factors influencing ecological dynamics make it challenging to grasp the entirety of nature in its holistic form, let alone define metrics that adequately represent biodiversity as a whole. As a result much of what we use to describe and define biodiversity will necessarily always be somewhat reductionist and incomplete.

Despite this abstraction from the true, complex state of nature, careful and well-designed monitoring approaches can be adopted to capture essential aspects of biodiversity and used to quantify outcomes associated with conservation and management strategies. For biodiversity credit schemes the focus of measurement, as identified through various classes of metrics, falls into four distinct categories:

1) Ecosystem — a flexible approach focusing upon the selection of multiple metrics deemed most suitable to represent the holistic condition and integrity of the ecosystem type (i.e. terrestrial, marine, aquatic)

2) Habitat — metrics representing habitat condition, structure and integrity with respect to the requirements of a specific fauna species

3) Vegetation — metrics representing vegetation condition, structure and integrity as a proxy for condition of an ecosystem (principally terrestrial at this stage)

4) Indicator Species — metrics based on the presence of selected species as an indicator of functional ecosystems (eg. Savimbo Biodiversity Methodology Version 1.1-C)

In addition to distinction across the various classes of metrics it is important to recognise the interplay between metrics, area and time (the three components of a biodiversity credit identified in Figure 1).

For biodiversity credit schemes the ‘project area’ represents the entirety of the area from credits are derived, based on the outcomes as defined by the class of metrics deemed appropriate to the ecosystem type and the defined archetype (Figure 2). Within a project area a credit represents a sub-unit of the project area, typically fractionalised into a standard area. This approach to crediting works well for metrics representing sessile components of biodiversity (plant species, vegetation etc) but becomes more difficult for impermanent and mobile components of biodiversity (particularly long ranging animals) or more dynamic ecosystems (marine). This undoubtedly will have influence on the selection of metrics (species presence/absence over population dynamics, and the distribution/condition of plants over the distribution/condition of animals) as well as the focus of biodiversity credit schemes (preferencing terrestrial over marine).

Time is also an interesting consideration as the frequency of monitoring profoundly influences the understanding of ecological outcomes by determining the temporal resolution of observed changes. High-frequency monitoring, conducted at short intervals, offers a detailed understanding of rapid fluctuations, seasonal patterns, and responses to episodic events, facilitating adaptive management strategies and the detection of rare occurrences. In contrast, low-frequency monitoring, conducted at longer intervals, provides a broader perspective on long-term trends, ecosystem stability, and successional changes. Consideration of monitoring interval varies for metrics and across ecosystems and is also influenced by the archetype and the management activities that are being undertaken.

Unfortunately, these elements of complexity with respect to representing biodiversity operate in tension with the requirements of financial markets, which prefer simplified metrics and homogeneity to create standardised, interchangeable products and facilitate efficient and liquid trading. That is not to say that biodiversity credits are too complex to be achievable, it is to identify that a trade-off will always exist between the integrity of the outcome (or at least a demonstration of it) and concession towards convenience for the market.

Deciding how to measure biodiversity outcomes involves a nuanced consideration of both the methodological approach and the available technology (obviously there is an intersection between the two), with an added emphasis on scientific principles including proper practice in sampling design and statistical analysis. However before exploring these aspects in more detail it is worth making a minor detour through the world of monitoring versus modelling.

Monitoring and modelling are essential techniques employed to assess biodiversity outcomes, with monitoring involving direct observation and data collection, while modelling utilises mathematical frameworks to simulate and predict ecological patterns and changes. Both approaches are complementary, with monitoring providing empirical data for model parametrisation and validation, and modelling offering a tool to explore ecological processes beyond what can be directly observed in the field.

When considering the requirements for biodiversity credit schemes to provide meaningful outcomes for nature as measured via tangible outcomes there is a clear preference for adopting monitoring approaches over modelling approaches. The reason for this is monitoring provides direct, empirical data from the field, and actual on-the-ground verification of biodiversity outcomes, offering a real-word validation of biodiversity condition and changes. Monitoring also allows for the assessment of site-specific variability and is better suited to capture dynamic and unpredictable outcomes in circumstances where unexpected changes have occurred. In contrast models rely on assumptions, data inputs, and algorithms, and their accuracy is contingent on the quality of these inputs, leading to potential questions on their credibility if based on limited on-the-ground verification.

However, this is not to say that there isn’t a place for modelling in biodiversity credit schemes, particularly as it relates to their ability to provide predictions of species distributions, enhance the understanding of ecosystem dynamics, and contribute to more informed conservation and management decisions.

Although several monitoring designs are used in ecology to quantify change over time, it is anticipated that the BACI (Before-After-Control-Impact) approach, a foundational methodology for evaluating the impact of environmental interventions or disturbances, will be one of the more widely adopted within biodiversity credit schemes. By collecting data before and after the implementation of an intervention in both treatment (impact) and control areas, BACI can be used to discern causal relationships between the intervention and observed ecological changes, by controlling for natural variability and allowing for differentiation between human-induced impacts and natural fluctuations in ecosystems.

The BACI approach aligns with many of the archetypes for biodiversity credits, specifically those relying upon management actions and interventions to achieve biodiversity outcomes such as regeneration and stewardship. The approach also regularly forms the basis of accredited methods for the measurement, reporting and verification of natural assets, such as in certifiable environmental accounting frameworks like Accounting For Nature. The BACI approach is also highly versatile, with a systematic and statistically rigorous framework that is adaptable to various ecological systems and scales.

Additional methods integrate spatial and temporal dimensions and employ methods such as space-time cube analysis and remote sensing. These approaches, which are particularly relevant for studying phenomena such as habitat fragmentation, migration patterns and climate change impacts, enable the continuous monitoring of biodiversity across landscapes, helping quantify patterns, trends, and responses to environmental changes. Other approaches include time series analysis, repeated measures designs and mark-recapture studies.

Rigorous survey designs and data analysis methods are essential for objective assessment of whether biodiversity outcomes have been achieved and to support integrity in biodiversity credit markets. Careful consideration of sampling design, randomisation, and statistical power is crucial during monitoring strategy development, whilst appropriate statistical analyses are required to interpret the collected data and draw conclusions about the observed changes in monitored metrics.

ost and practicality have always been significant factors influencing the choice of ecological monitoring methods and the level of effort applied to quantifying biodiversity outcomes. In the commercial sphere, field monitoring approaches for non-research focused biodiversity assessments are typically pared back, with requirements to mobilise experienced and expert personnel to remote locations also impacting on the frequency with which monitoring is undertaken.

Practically it is not feasible for field teams to monitor every individual plant or animal, habitat or area, particularly for ecological systems that are vast and complex. As a result, there has historically been a preference for conducting sampling approaches that yield detailed information on selected variables, from largely accessible locations, but offering only limited spatial coverage for the data that is collected.

In recent years there has been a move towards the use of technology enabled monitoring techniques in ecology (nature tech), including the adoption of remote sensing data, eDNA analysis and artificial intelligence for field camera and acoustic sensor data, over singularly human labour-based approaches. Much of this has been propelled off the back of technological advancements and increased accessibility for a range of digital data platforms and solutions including, but not limited to, satellites, UAVs, connected IoT sensor networks, mobile devices, DNA extraction, amplification, and sequencing technologies, high performance computing, neural networks, machine learning models and open data initiatives. An overview of the maturity and potential impact of existing nature tech solutions is given in Figure 3.

Importantly many of these technologies influence the temporal and spatial resolution at which monitoring can be undertaken, typically offering opportunities for more frequent data capture and at scales that can encompass entire project areas and their surrounds (the latter of which is important for understanding connectivity). In some instances this allows for a move away from more traditional sampling type monitoring approaches to more comprehensive evaluation techniques using remote sensing data and time series analysis.

As digital technologies continue to evolve and interact, the overall cost of implementing digital monitoring approaches for biodiversity is likely to decrease, making these tools more widely available and applicable for ecological monitoring. This in turn will positively impact upon the availability of data to quantify and verify outcomes associated with biodiversity credit markets, resulting in higher integrity verification of outcomes at lower overall cost. Importantly the ascent of these technologies will also continue the transition of biodiversity data towards a natively digital format that can be integrated into frameworks for the creation of digital biodiversity credits.

To round out this exploration into the foundations for creating digital biodiversity credits it is worth considering the management actions and on-ground activities required to produce biodiversity credits.

Firstly, it is important to recognise that biodiversity management and ecological restoration is a complex subject for which desired outcomes are difficult to achieve. Indeed the underlying complexity of nature and the intricacy of interrelationships in ecological systems has undoubtedly contributed to a history of poor performance with respect to biodiversity and threatened species management, biodiversity protection and the implementation of biodiversity offset schemes. Layered on top of this is the fact that biodiversity protection and ecological restoration is an expensive activity (see also this and this) meaning that constraints in funding at a project level are likely to have a significant influence on whether successful outcomes are achieved. Finally, seasonality and stochastic variability (including aspects such as drought, flood and fire) are also considerable constraints to implementing effective management programs as these factors lend themselves to increased requirements for responsiveness, flexibility and irregularity, all of which act in conflict with the ideals for planning.

Nevertheless, despite these constraints biodiversity protection and ecological restoration are achievable particularly if some key principles and considerations are adhered to. First of these is an understanding that the practice of biodiversity management and ecosystem restoration is foundationally local due to unique ecological characteristics, species compositions and environmental dynamics. Understanding these unique ecological characteristics and addressing the local threatening processes necessitates context-specific strategies that acknowledge the intricacies of each locale. This in turn highlights that community engagement and understanding of local ecological contexts remains a crucial component for the design and implementation of successful conservation and restoration initiatives.

Once a local and regional understanding of biodiversity has been gained practices such as strategic conservation planning can be employed. Strategic conservation planning is a systematic and science-based approach used to guide the allocation of resources and efforts for conservation purposes. It involves the identification of conservation goals, the assessment of ecological and socioeconomic factors and the development of prioritised actions to achieve those goals. Strategic conservation planning aims to maximise the effectiveness of conservation efforts by focusing resources where they will have the greatest impact. For ecological restoration, documents such as the National standards for the practice of ecological restoration in Australia should be considered as these not only detail principles underpinning restoration philosophies and methods, but they also outline the steps required to plan and implement a restoration project to increase the likelihood of its success.

Given the complexity of nature there should be preference towards approaches that embrace uncertainty, encourage learning and promote flexibility as championed by frameworks such as adaptive management. Adaptive management is a dynamic and iterative process designed to enhance the effectiveness of biodiversity conservation and ecological restoration efforts. It involves continuously monitoring and assessing the outcomes of interventions, learning from the results, and adjusting management practices accordingly. By incorporating feedback from real-world outcomes, adaptive management allows practitioners to refine their approaches over time, adapting to changing ecological conditions and uncertainties whilst remaining responsive to the effectiveness of management actions.

The final aspect to identify is that human intervention plays a necessary and crucial role in achieving biodiversity protection and ecological restoration. While natural processes can contribute to ecosystem recovery to some extent, natural processes alone are largely insufficient for achieving biodiversity protection and ecological restoration. This is particularly the case where threatening processes, such as habitat destruction, invasive species and the impacts of climate change are now outpacing the ability of ecosystems to adapt.

As a result relying on the concept of leaving natural areas unmanaged is unlikely to achieve optimal biodiversity protection and ecological restoration outcomes, as this approach fails to acknowledge and address the contemporary challenges that global ecosystems face.

Congratulations on making it to the end of Part 3 of the Biodiversity in the Digital Age series. The next article in the series will tie everything together by looking at a system for funding community led digital biodiversity credits and linking this to the digital economy, i.e. a system for facilitating meaningful transfers of value between nature and the digital world.

Mint this article as an NFT!