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Completing the long-standing puzzle of biodiversity: How ecology can help us identify missing pieces

Completing the long-standing puzzle of biodiversity: How ecology can help us identify missing pieces
Geographical distribution of species richness and removal of predators. (A) Spatial distribution of species richness according to the original IUCN range maps of all 32 mammal species of the Serengeti food web. (B) Proportion of mammal species remaining in each local network (i.e., each pixel) after removing all species without a path to a primary producer. (C) Proportion of mammal species remaining in each local network as a function of the number of species given by the original IUCN range maps. Credit: PeerJ (2023). DOI: 10.7717/peerj.14620

Biodiversity databases are not "talking" to each other: species occurrences records and maps from different data sources do not match, and only some of them agree with data that inform how species interact with each other.

A comparison between the data obtained from the International Union for the Conservation of Nature (IUCN), the Global Biodiversity Information Facility (GBIF) and a dataset of food web interactions (the ones comprising predation and herbivory) from the Serengeti ecosystem (East Africa) revealed many areas of "mismatch" that could indicate a lack of data for nine predators and their prey.

For some predator mammals, nearly 100% of their range maps do not overlap with those of their prey, which leads us to question if the predators can really be found in these areas where there is no prey. This is especially true for those considered specialized predators in the Serengeti food web (such as the serval—a wild cat—and the black-backed jackal—a dog-like carnivoran).

For the golden jackal, this mismatch is probably caused by inconsistent taxonomic information between datasets: its scientific identity has been heavily debated in the literature, and it is possible that the databases are not catching up with the updates.

Species occurrence data are massively used by ecologists to understand and predict the distribution of biodiversity. These analyses inform conservation policies, actions to fight , public health guidelines and much more. This is only possible because these data are very often shared under a license that allows anyone to use them and are archived properly in databases such as IUCN and GBIF.

But these data have their flaws: GBIF data are known for being biased (as a result of historically biased scientific activity), IUCN is known for misestimating the distribution of rare species, and there are very few large-scale interaction data available.

This study was based on a very simple rationale: if a predator can't feed, it is very unlikely it will remain where it was found. We should expect that a range map of a predator would superpose almost perfectly with that of its prey items. If it does not, the reasons could be that we misestimate either the predator or the prey distribution, or because we lack information about species' diet.

It was with this initial idea that Gracielle Higino and others got together and started analyzing available data from IUCN and using the Serengeti food web to assess relationships between species.

"We are all interested in macroecology of interactions and species distribution modeling, and we think these things should be studied together. But we know that in order to integrate these two things, our available datasets need to talk," says Higino.

To assess if range maps and ecological interaction data were "talking" to each other, the authors divided the map of the African continent in grids of approximately 50km2 and created local food webs based on the published regional Serengeti food web and IUCN range maps. With that, they could trace a connection between a top predator and an herbivore within each grid. Whenever such connection was not possible, that grid cell was considered mismatched and the presence of a top or the lack of information on its prey was questioned.

This method can also be used to map priority sampling locations for interaction and occurrence data. This would contribute to the monitoring of biodiversity in face of climate change and and to another promising venue for ecological data: the prediction of species' diets.

Geographical (such as range maps) and ecological (such as food webs) data mismatch because of biases and incentives to are a way to dissolve that. The researchers believe that more access to data is important to mitigate error propagation in ecological models that can be caused by biased occurrence maps and incomplete interaction networks.

"Open science is a very core practice for all of us. We think open access to data and information is extremely important, and we want it to be done the right way. It is imperative that open are consistent and redundant across databases, which we didn't quite see in our study," says Higino

As we lose ecological interactions at least as fast as we lose due to , to data becomes crucial to help inform public policies in conservation and public health.

The paper is published in the journal PeerJ.

More information: Gracielle T. Higino et al, Mismatch between IUCN range maps and species interactions data illustrated using the Serengeti food web, PeerJ (2023). DOI: 10.7717/peerj.14620

Journal information: PeerJ

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Citation: Completing the long-standing puzzle of biodiversity: How ecology can help us identify missing pieces (2023, February 10) retrieved 17 June 2024 from
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