Assessing risks to marine ecosystems with the IUCN Red List of Ecosystems

You can read this article Open Source until the 21st of October 2018 here:

The IUCN Red List of Ecosystems has made some great strides over the last few years, with over 1,500 ecosystems assessed in 90 countries. However, the application of the protocol to offshore marine ecosystems has been lacking. To date, only 10% of ecosystems listed on the global Red List belong to the marine realm, and these are mostly coral reefs and coastal ecosystems. Yet current evidence shows that offshore marine ecosystems are at considerable risk from regime shifts, mostly due to fishing and environmental change.

It can be difficult to assess risks to marine ecosystems because unlike most terrestrial ecosystems, we cannot simply map their distributions and call it a risk assessment (oh no! a car park has replaced my favourite forest!). We need to look at the functioning of the ecosystem – the different species that inhabit it and how they relate to each other in the food web.

Time series data also tend to be less available for marine ecosystems – yes, we have catch data but most often, these data do not truly represent underlying trends in the ecosystem. Long time-series data are particularly important for the ocean due to the shifting baseline syndrome (i.e. a generational skewing in what is perceived as ‘normal’). Marine ecosystems have been modified for hundreds of years, with little trace of what actually happened.

I had many challenges to tackle to apply the IUCN Red List of Ecosystems to an offshore marine ecosystem. In 2016, I formed a collaboration with Lynne Shannon and Kate Watermeyer at the University of Cape Town in South Africa (Kate is now a postdoc with us at Deakin). I had read about the southern Benguela – the large ecosystem lining the west and southern coasts of South Africa – and found the case study fascinating.

First, the southern Benguela is only one of two similar ecosystems around the African coast. The northern Benguela, located in Namibia, was a very similar upwelling ecosystem until it underwent a regime shift in the 1970s, most likely due to the combined effects of environmental change and fishing. Despite more conservative fisheries management in Namibia, the regime shift has not been reversed to date.

The diagram below illustrates some of the changes that occurred in the northern Benguela in the 1970s. First, populations of forage fish (sardines and anchovies) decreased dramatically, leading to a decrease in their predators such as seabirds and Cape fur seals. The forage fish were replaced by pelagic gobies and jellyfish, which aren’t so much to the taste of the predators.

Changes in the northern Benguela before and after collapse. Figure from Roux et al. (2013). Jellyfication of marine ecosystems as a likely consequence of overfishing small pelagic fishes: lessons from the Benguela. Bulletin of Marine Science, 89(1), 249-284.

Knowing what happened to the northern Benguela was key to defining collapse for the southern Benguela. Luckily, Kate had created food web models of both the northern and southern Benguela during her Master’s degree. The models went far back in time to the 1600s and combined with the extensive knowledge of the current functioning of the southern Benguela formed by Lynne and her group, we had a strong basis for a risk assessment.

We pulled everything together: the results from the food web models from 1900 to the present, the time series from surveys, and the catch data. But we were still missing these precious collapse thresholds that enable us to say that an indicator (and thereby an ecosystem) has gone past the state of collapse.

To derive the collapse thresholds, we did something that hadn’t been done yet in ecosystem Red Listing: structured expert elicitation. Up to know, collapse thresholds in indicators were often derived from one expert or one study to conduct a Red List assessment (e.g. a coral cover <1% indicates that a coral reef is collapsed). But this wasn’t going to be good enough for an ecosystem as complex as the southern Benguela, with more than 20 indicators to be evaluated.

We presented our experts with our definition of what collapse would look like in the southern Benguela (e.g. loss of different functional groups) and asked them to derive collapse thresholds based on data for the southern Benguela and the northern Benguela (pre- and post-collapse). Using the “Investigate, Discuss, Estimate, and Aggregate” method recently published by Hemming et al. (2018), we obtained collapse thresholds and uncertainty bounds for each indicator.

Here are some of the main results from our study, looking at indicator change over different timeframes. First, we can see that the percent decline toward collapse (i.e. relative severity) for different indicators tends to be worse over historical timeframes (i.e. 1900 to 1960 or 1900 to 2015). In the last 50 years, many trophic groups have increased or shown signs of recovery. But in the bottom right corner, we can see that seabird biomass has decreased by 66-86% over different time frames (the error bars represent uncertainty in our collapse thresholds).

Red boxes represent the Critically Endangered category; orange boxes the Endangered category; and yellow boxes the Vulnerable category. MTL: mean trophic level.


Despite good trends in many indicators, many of the seabird species in the southern Benguela have decreased dramatically due to threats in both in the ocean (e.g. lack of food) and on land (e.g. threats to nesting areas). Seabirds are recognized as good indicators of marine ecosystem health globally as they closely respond to food availability. Our study suggests that focusing on sensitive predator species may be useful for Red List of Ecosystems assessments for marine ecosystems.

The African penguin is also listed as Endangered on the IUCN Red List of Threatened Species, indicating some congruence between the results of the two Red lists.

Overall, we listed the southern Benguela as Endangered in both 2015 and 1960 (we did a retrospective assessment based on our historical data). However, this over-arching assessment hides subtle changes in different indicators and ecosystem functioning: many trophic groups are now healthier than in the 1960s and fishing has generally reduced, but the dismal state of seabirds skews the assessment towards a threatened category.

Our study shows that it is possible to assess risks to marine ecosystems: we need a strong definition of collapse, which can be based on other similar systems; good time series data, from either models or surveys; and ecosystem experts that can bring all this knowledge together into a sound, repeatable risk assessment.

You can read this article Open Source until the 21st of October 2018 here:

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