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How can the ‘elegant’ logic of physics be applied to the ‘messy’ reality of biology? A team of 51ÉçÇøºÚÁÏ (SFU) scientists have used their interdisciplinary expertise to develop new, more accurate ways to estimate how groups of living organisms disperse.

Led by biological sciences PhD graduate Ramis Rafay, with postdoctoral fellow Eric Jones, under the supervision of environmental microbiology professor Jane Fowler and physics professor David Sivak, the research was published in , a high-impact, multidisciplinary journal.

The movement of organisms—known as dispersal—is an important ecological process underpinning the diversity in the biosphere. Understanding dispersal is important for many areas of ecology, including climate change, disease spread, ecosystem health, and conservation.

However, dispersal is difficult to measure directly, particularly in complex communities with many interacting species. The Neutral Community Model (NCM) has been used to estimate the influence of individuals arriving from a larger surrounding community on a local ecosystem. However, the accuracy and reliability of the NCM’s quantitative estimates had not been previously assessed.

In this study, the researchers developed two new methods to estimate dispersal using the NCM and compared them with the existing method. Using computer simulations that mimic real microbial communities, they found that all three methods could estimate dispersal accurately, typically within 10 per cent of the true value.

Additionally, the two new methods required less data to achieve this level of accuracy, reducing sample size needs and labour demands. The researchers also found the methods produced consistent results when applied to real‑world data from wastewater treatment systems, tropical forests, and coral reefs.

Their research expands the NCM’s capabilities towards quantifying dispersal. After exploring the sampling and methodological conditions required for accurate estimation, the team was then able to write a ‘how-to’ guide for researchers.

The study, , provides researchers with a more efficient and reliable way to measure how biological communities are connected across diverse ecosystems worldwide.

We spoke to the researchers about their work.

What motivated you to develop the new methods for estimating dispersal? Was there a limitation of the Neutral Community Model (NCM) you were interested in addressing?

Directly measuring the movement of organisms into a community is surprisingly difficult, so scientists often infer dispersal rates indirectly using mathematical models and community compositional data—information describing which organisms are present at a given location and in what relative amounts. The NCM has mainly been used qualitatively to determine whether its statistical predictions are broadly consistent with the patterns we see in nature, rather than to quantify dispersal.

We were interested in determining whether this model could be used to provide accurate estimates of dispersal rates, and if so, how much data that would take. In the process, we realized that the standard method to fit the NCM uses only a small part of the information available in compositional data. This motivated us to develop two new methods that make greater use of this information. Then, by comparing estimates across the three methods, we were able to infer more accurate and reliable estimates of dispersal rates.

How do you apply the science of dispersal to real‑world ecological challenges or questions?

Dispersal can have important effects on the health of an ecosystem, due to the way that organisms affect their environment. For example, in wastewater treatment, microbes are the workhorses involved in purifying water and removing pollutants.

Because wastewater constantly flows into these treatment plants, they receive a continuous influx of microbes, and these incoming microbes influence the microbiome in the treatment system. Some of these microbes may be beneficial, such as those with the ability to degrade toxic pollutants, and these are the kinds of organisms we would like to recruit or retain.

Others may be harmful, contributing to decreased effectiveness of the treatment process, and these we’d like to avoid. By understanding dispersal more quantitatively, we can begin to design and operate these systems in ways that better engineer their microbiomes to provide beneficial outcomes, ultimately leading to cleaner, safer water and more sustainable processes.

How does collaboration across the disciplines of biology and physics shape the way you approach ecological questions?

We feel that collaboration between biology and physics is powerful because it brings together top-down and bottom-up ways of thinking. Biology often deals with the complexity of real ecosystems—the messy, contingent and context-dependent patterns we observe in nature—while physics seeks minimal, elegant principles that can explain or predict those patterns.

In ecology, this bridge is especially valuable because it helps us move from complexity to mechanism, generating insights and strategies to address pressing challenges such as improving ecosystem health, supporting human wellbeing, and conserving biodiversity in the face of climate change.

How can other researchers access and apply your models?

Anyone can access our models through a , where we have made our code openly available, including examples, and a guide for applying the methods. We wrote the paper with accessibility in mind so that it could serve as a practical guide for both computational and field researchers.

For more:

• Jane Fowler’s faculty web page and the
• David Sivak’s faculty web page and the Sivak Group
• and the