#9 Why measurement is so important

The natural value chains we depend on for human survival are long and intertwined. We are most familiar with the ends of value chains, because we experience them directly. But we often don’t have sufficient information about their beginning or middle parts to make informed decisions about how to manage them responsibly.

One result is that market supply and demand for our ecosystems don’t self-adjust. As a result, ecosystems struggle to provide the amount of quality of services we need.

My belief is that modern science and technology will enable us to find the information we need to make informed decisions about how to manage value chains. This may allow us to avoid the major economic disruption that would follow from crossing tipping points attached to individual ecosystems.

Our ability to measure many aspects of the natural world is limited. But technology is already starting to provide some partial solutions. I talked recently to Clara Johnston of NatureMetrics about advances in the measurement of ocean biodiversity. (We are already working with NatureMetrics in the context of an ocean biodiversity project.)

Clara Johnston: Biodiversity is basically the whole tree of life. That’s why it's so complicated to measure and to manage. A lot of the information we have is focused on the larger “charismatic” species (e.g. whales). But they're usually also at the top of the food chain and only part of the picture. We're not recording smaller organisms that might be a better indicator of how well an ecosystem is functioning.

We have to measure everything from microbiomes through to those large charismatic species. Without getting data, for example, on what's happening to the planktonic communities that the whales are feeding on, we won’t understand the impact on the population or species distribution of different organisms that we're seeing.

One problem in getting data is that the different taxonomic groups or different types of organisms in the ocean will be of varying importance to the people and organisations measuring them.

The methods we use to measure biodiversity also vary for different types of organisms. They're usually not consistent across countries, locations, or different habitats.

But obviously biodiversity is interlinked. So it's really important that we can not only collect information, but also bring all of it together in an accessible form to start to understand nature slightly better.

Markus Müller: Can you explain to us what you are doing with e-DNA?

Current methods of ocean monitoring rely heavily on visual observation. So if you are talking about whales, you have to physically try to find those populations and note down from a vessel what species you’re seeing.

But there will be lots of really important data in remote locations that are difficult and expensive to gather. New technologies like environmental DNA (e-DNA) may make the process easier. With e-DNA, you just need to take a water sample – and then analyse this water sample for the DNA of multiple species, giving you information on the whole tree of life in that part of the ocean.

Markus Müller: Apart from relative ease of collection, what are the advantages?

Clara Johnston: In terms of species identification, we have done multiple trials and tests of measuring e-DNA alongside conventional methods. And quite consistently, we get more species identification from e-DNA (offshore and onshore) than traditional methods.

So it gets us one step closer to a fuller picture. From one sample we can look at everything from bacterial communities in the water all the way through to whales. And by taking samples at different depths, you can survey the whole three dimensions of the ocean.

You still require vessels to do offshore sampling. But one benefit of e-DNA is that you don’t need specialised personnel to collect these samples. So we work a lot with local communities and fishing groups to do the sample collection, and involve them in acting on the results. 

Markus Müller: How can we interpret and use the data?

Clara Johnston: This is where we can start to integrate developments like AI and machine learning techniques to the data. 

You can also interpret the results using different metrics to allow for more robust resource management and decision-making. An example of a basic, yet informative metric is species richness - the number of species found within a sample. But other metrics can provide many more insights as well. Evolutionary diversity tells you how many ecological niches are being fulfilled by species. Habitat-specific metrics will be important as well, for example, using invertebrate communities found within sediment as an indication of pollution levels or using the data to assess integrity of food chains. 

Ideally, you’ll also want to be looking at this over time (and adjusting for ongoing fluctuations, for example in fish stocks). You may want to survey for different marine communities based on the season and water temperatures.

Markus Müller: When you have your data, where does it go?

Clara Johnston: Often to universities and research institutes. But our client base is quite diverse. We started off doing a lot of work with NGOs and conservation organisations. But many commercial organisations have to collect environmental data when working offshore (for example offshore wind), so we work with them too.

We’ve seen great use of the data where local community groups have taken samples themselves, and the information has led to things like the IUCN (International Union for Conservation of Nature) reassessing an area which turns out to contain sensitive species.

Markus Müller: We often tend to over-simplify to try and understand things. Does this allow us to better embrace the complexity of nature?

Clara Johnston: Utilizing new technologies can give you a much broader approach, even if you do have a very focused objective initially. For example, we had a project in South Central America where the surveyor’s only target species was pygmy hippo. But there were close to 150 different other vertebrates also found within those samples at no extra effort or cost. And by being able to record smaller species – which have higher reproduction rates, so react faster to changing environments – we can achieve a far better understanding of the functioning and adjustment of ecosystems.

Markus Müller: This for me is what it’s all about. We don’t want to create a simplified model of the world: we want to accept the complexity of the world, so we can make good decisions. Metrics can be simple but they need to encapsulate the complexity of ecosystems and be applicable at scale. I go back to where we started: the urgent need to sustain and manage natural value chains. And you can’t really manage what you can’t measure. Let’s hope for continuing advances here.