Saving Ecology

The molecular identification of species can revolutionize ecology, making it more automated, scalable, accurate, precise, and robust.

There are three main components that characterize an ecosystem: abiotic and biotic factors; energy flow and nutrient cycling. These components are organized hierarchically: abiotic factors are simpler than biotic factors, which in turn are simpler than energy flows and nutrient cycles.

It’s a widely recognized principle in engineering and systems design that “a system is only as fast as its slowest component.” Similarly, “A system’s reliability is often determined by its most uncertain component.”

For ecosystems, this uncertain component are the biotic factors, or biodiversity.

The inability to measure biodiversity robustly has crippled our understanding of both the biotic components of ecosystems and, consequently, the energy flow and nutrient cycles that pass through these compartments.

Our insistence on using species as the unit of biodiversity and relying on morphology for species identification has limited our understanding of ecosystems. Limited identification of organisms results in an incomplete picture of biodiversity, obscuring food web dynamics and affecting our understanding of energy flow, nutrient cycling, and ecological niches.

To save ecology, we must advance our methods of identifying and measuring biodiversity.

Create robust biodiversity understanding

“You cannot control what you cannot measure”

Peter Drucker

To understand biodiversity and the biotic components of ecosystems, we need data.

When you contrast the robustness of conclusions in ecological modeling with those in physics, one notable reason emerges: the volume of data. Physics often relies on large, precise datasets collected through controlled experiments or observations, with methods that are often more standardized and less subject to environmental fluctuations. In contrast, ecological studies frequently operate small datasets, due to the complexity and scope of natural ecosystems and data may be subject to greater variability and noise, impacting the reliability of models.

Small datasets are more than a shortcoming, they fail to adequately represent the broader population or phenomena under study leading to biased or skewed conclusions. Small datasets are also sensitive to outliers and lack the statistical power required to detect significant effects or relationships, thereby increasing the risk of Type II errors (false negatives) and overfitting.

Molecular data, obtained via scalable and automated DNA sequencing technologies, can address these gaps. High-throughput sequencing offers quantitative data with the precision found in physics. As sequencing costs decline, larger projects become feasible, which in turn leads to better data collection across spatial and temporal scales. Such high-throughput technologies generate datasets comparable to those used in complex physics models.

Besides the benefits of Large datasets mitigate to reduce the impact of noise and enhancing statistical power and estimate stability, molecular data allows for the cross-validation and independent replication that Robustness requires.

The transition from small to large datasets has revolutionized various sectors. In healthcare, big data analytics sift through extensive medical records to provide accurate diagnoses. In finance, it alters the way risk is assessed by analyzing numerous variables in real-time. In retail, it optimizes stock levels, thereby reducing costs.

Biodiversity as the ultimate energy and information storage

In his seminal 1988 article “Self-Organization, Transformity, and Information” published in Science, H.T. Odum highlights that the study of ecosystems has generated concepts applicable to all complex systems. He argues that self-organization in nature shows how energy relates to hierarchy and information. During this process, certain species and relationships are selectively reinforced, leading to designs that maximize the rate of useful energy transformation.

“During the trials and errors of self-organization, species and relations are being selectively reinforced as more energy becomes available to those designs that feed products back into increased production.”

Odum elaborates that systems aim for designs that maximize power—the rate of useful transformation of available energy—for given resource conditions. These goals are not unique to ecosystems; they are common to various systems including chemical reactions, turbulence, social systems, and celestial bodies. Because of that, these systems can be said to have energetic-mathematic goals. The maximum power principle.

“In designs that prevail after self-organization, it takes much energy of lower type to generate a small amount of higher type (the exergy concept I’ll talk about soon), preventing use energy as a measure of work where more than one type of energy is concerned.”

As Odum reminds us, engineering practice already recognizes that it takes 4J from coal to make an electrical joule.

To compare different types of energy, he introduces the concept of Emergy (spelled with an “M,” derived from ‘energy memory’). It is defined as the amount of one type of energy required to generate a flow or storage of another type. Ultimately, all types of energy can be equated to the amount of solar energy that generated them.

“As one passes through the energy transformation spectrum, energy decreases, but the information and its processing often increase (feedbacking as control elements). By recognizing that information has high transformity, we have a new useful measure with which to study various kinds of information in relation to energy.

If the essence of self- organization is automatic reinforcement of available choices, in ecosystems the multiple choices (possible types of pathways to be tried out) are the information in the species and their genetic variation.

Therefore, understanding biodiversity at the molecular level, not just the species level, is crucial for improving our understanding of ecology and ecosystems.

The emergy and exergy of biodiversity

In 1997, Bastianoni and Marchettini proposed the ratio between emergy and exergy as a measure of the level of organization in systems. Exergy represents the “useful work potential” of a system, essentially quantifying its ability to perform work when reaching equilibrium with a reference environment.

“Emergy and exergy have been developed as complementary goal functions. By definition, emergy is the solar energy directly and indirectly required to generate a flow or a storage. Exergy is a property of a system, measuring the maximum work that can be extracted from a system when it goes towards the thermodynamic equilibrium with a reference state. The concept of emergy contains the history, the time and all the different processes involved up to the present state of the system, while exergy is a measure of the actual state, of the level of organization and of the information content. These two approaches are very suitable for describing self-organizing systems such as ecosystems. The ratio of the emergy flow to the exergy can give further information on the state of a system, showing what concentration of solar energy equivalent, space and time (emergy) is required to maintain or create a unit of organization (exergy).”

Exergy helps distinguish the useful energy within a system from its total energy, contributing to the system’s efficiency and sustainability. High-quality energy will possess higher exergy, which translates into a greater ability to perform work.

In real-world ecosystems, measuring exergy can be complicated due to the intricate interplay of biotic and abiotic factors. However, biomass, for example, offers a straightforward way to gauge stored chemical exergy in these systems.

A more advanced measure of exergy can be found in the high-level information contained in the DNA of species. Meyer and Jorgensen (1979) included a term in their exergy calculations linked to the number of genes that organize an organism’s DNA. This approach assumes a clear correlation between gene count and organismal complexity.

Bastianoni and Marchettini tested this emergy/exergy concept in both theoretical and real-world aquatic ecosystems comprising phytoplankton, zooplankton, fish, and detritus. Introducing new plant and animal species to an area effectively injects initial genetic information, which with their own production and selection costs that are hard to quantify.

Exergy density then serves as a metric for a system’s distance from equilibrium or its level of complexity. In an ecosystem, it reflects the degree of natural selection acting on available resources over time. Higher levels of organization can only be achieved if the self-organization process has had adequate time to optimize species selection and their interconnections, considering existing environmental conditions and resource availability.

The new ecology is the old ecology

Biodiversity can serve as a tangible measure of the more abstract thermodynamic concept of exergy. By examining an ecosystem through the lens of biodiversity, we can assess the amount of usable energy present. This, in turn, provides valuable insights into the ecosystem’s health, resilience, and sustainability.

This perspective aligns with the broader ecological principle that increased biodiversity enhances ecosystem robustness and functionality. These aspects can be related back to the thermodynamic principles of exergy and emergy. Biodiversity, Exergy and Emergy are intimately connected.

The Evolution of DNA Sequencing and Its Impact on Ecology

When Odum formulated the ‘Maximum Power Principle’ and introduced the concept of emergy, and when Bastianoni and Marchettini later expanded upon this using the emergy/exergy ratio, DNA sequencing was a tedious, manual process, the Human Genome Project was a massive global effort and the notion that large DNA datasets could inform ecology at a foundational level—based on DNA-encoded evolutionary information—likely seemed distant.

Today, however, molecular biodiversity can serve as the ultimate measure of a system’s exergy. Ecosystems with higher biodiversity levels often contain more organized, usable energy. These ecosystems tend to feature more complex networks of interactions, which can be interpreted as a form of stored exergy. This stored exergy represents the potential for various ecological processes and functions to occur.

Molecular biodiversity has the potential to inaugurate a new era of ecology, one that materializes the field’s original ideals.