A guest post by Dr Robin Whytock, CEO of our partner Okala, about measuring and monitoring biodiversity. 

The biodiversity debate

Biodiversity - and how we measure, monitor, and restore it - has become a mainstream topic in the past five years. It's widely accepted that the global climate and biodiversity crises are inseparable, and we can't tackle one without addressing the other. 

This has been an exciting period for biodiversity scientists, with increased funding opportunities from the public and private sectors (dampened somewhat by recent politics) and even multi-million-pound competitions to develop scalable biodiversity measurement technologies.

Yet, despite the mainstream enthusiasm, the word "biodiversity" has become divisive. There is no universally accepted definition of biodiversity or how to measure it, and even recent peer-reviewed papers will use the term loosely to describe individual species (Figure 1), taxonomic groups, or broader ecological communities. It's not surprising many are confused. 

Figure 1. Two chimpanzees Troglodytes troglodytes captured on one of Okala’s camera traps in Gabon, central Africa. Biodiversity research is often biased towards charismatic species like mammals and birds, but arguably, other taxa like insects and gastropods are even more important indicators of ecosystem health.

Measuring biodiversity in a perfect world

In my quest to measure biodiversity, I often wonder what the perfect measure would look like. This way, we can more easily understand why such a vast number of summarised metrics and definitions exist and how to choose the right approach for our needs.

So, what would perfect biodiversity measurement look like? In a perfect world, we would know where every living organism was on earth at any given time, from every bacterium in the human gut to the jellyfish in the sea, bird eggs hatching in the forest, and lichen growing on a rocky outcrop. We could also track the unique genetic code of each organism and its clones and understand its life stage, behavior, and response to environmental stimuli.

With that perfect information streaming in real time to our imaginary supercomputer, we could track populations of every species on Earth, monitor extinctions and invasions, watch evolution happen before our eyes, and understand the complex relationships that make up the web of life.

Since we're far from achieving this perfection, biodiversity scientists make compromises, usually big ones, and we shouldn’t be shy about it!

A practical approach

When setting out to measure biodiversity, the first question to answer is the "why?". For biodiversity restoration projects, we want to know if we have succeeded in restoring a habitat, species, or ecosystem. One challenge is that it can take decades or even centuries for restoration to succeed because of ecological time lags. For example, woodland species can be slow to naturally re-colonize a newly planted woodland. This is something I explored during my PhD. Although the long-term outcomes of woodland planting were challenging to predict, land managers can look for indicators – like the arrival of woodland generalists - that suggest restoration is going in the right direction (Watts et al. 2020). 

Once we know the “why,” we can get to the “how”. There are almost infinite ways we can measure and monitor biodiversity change. In reality, the decision about what to measure and how to do it is based on (1) our own personal or compliance objectives, which might be to monitor a mammal community in response to land-use change or improve soil fertility to improve crop yields, (2) what is feasible in terms of methods, technology, and statistical power to detect change, and (3) what is the budget? In reality, the budget often comes first, further limiting what is feasible, but new technologies are making biodiversity monitoring more scalable (Figure 2). 

Figure 2. Camera traps and eDNA can be used to study biodiversity. Along with other methods like bioacoustics and remote sensing, these tools can generate exciting insights into biodiversity at scale. However, like any technology, these approaches should be used carefully and with an awareness that all methods have their strengths and weaknesses.

Once we’ve resolved these three questions, we can design a biomonitoring plan, the most critical step. We can monitor fish using traps, termites captured in test tubes (Figure 3), mammals using automated camera traps, or a combination of many methods summarised by an index, but once we’ve decided, the key to success in long-term biomonitoring is to stick to the plan. Even if imperfect, repeating the same methods year after year will, over time, deliver results that detect change. Some of the most successful long-term biodiversity monitoring projects are based on what would now be considered outdated or imperfect methods. Still, the commitment to sticking to the plan over decades has delivered hugely exciting insights into biodiversity dynamics.

Figure 3. Taken from Evouna Ondo et al. 2023. Termite communities were used to understand how biodiversity responds to different human-managed fire regimes over time. The termite community changed along a gradient of fire management (left to right on the x-axis) from closed-canopy forest (LF) to annually burned savanna (LBS). In this system, termite species were an excellent choice to represent “biodiversity” and its responses to fire management. 

Biodiversity pragmatism

At Okala, we look at each project individually. What kind of impacts do we expect, and how can we measure biodiversity change within the budget? There is no single biodiversity metric or “best answer,” the key to success is being pragmatic, acknowledging that we can’t measure everything, and accepting the complex trade-offs that often exist. Suppose we embrace this complexity and commit to pragmatic, long-term biodiversity monitoring approaches. In that case, we will be in a much stronger place to measure progress towards ambitious goals, like the Kunming-Montreal Global Biodiversity Framework’s goal to protect 30% of land for nature by 2030.

References

Evouna Ondo et al. 2023, Journal of Biogeography https://doi.org/10.1111/jbi.14671

Watts et al. 2020, Nature Ecology & Evolution https://doi.org/10.1038/s41559-019-1087-8