Researchers at the University of Córdoba have demonstrated that using massive, unstructured biodiversity databases from citizen observations can effectively predict species interactions and future ecosystem distributions under climate change, marking a significant advance in ecological modeling.
A team of researchers at the University of Córdoba has successfully demonstrated that massive biodiversity databases — comprising citizen-recorded observations of flora — can significantly enhance the accuracy of joint species distribution models, even when individual observations are used. This breakthrough in ecological modeling has major implications for predicting future changes in ecosystems under the influence of climate change.
“In principle, these databases shouldn’t be used to calibrate community models because they feature individual observations that do not take into account the relationship between species, but we wondered whether, by having billions of records, they could work, getting the models to give us predictions considering the relationships between species,” Diego Nieto Lugilde, a professor in the Department of Botany, Ecology and Plant Physiology at the University of Córdoba, said in a news release.
This research is critical in understanding how new climate conditions will affect species distribution, particularly for endangered species like the Spanish fir (Abies pinsapo). Accurate predictions could inform and improve conservation efforts, such as those by the Junta de Andalucía, which has protection plans for this species.
Historically, predicting species distributions has relied on models incorporating only environmental variables like climate and soil type. The new approach from the University of Córdoba includes interspecies relationships, offering a more holistic view of potential future scenarios. By moving from an individualistic to a community-based ecological model, scientists can generate more accurate predictions, improving our understanding of how different species will interact under changing climate conditions.
Nieto Lugilde and fellow researcher Daniel Romera-Romera tested the viability of these unstructured databases collected via platforms like GBIF and iNaturalist. The researchers conducted an experiment using an artificial dataset to simulate real-world conditions.
“We came up with a study area, the distribution of 10 different species, and simulated different levels of coverage of each one’s actual distribution. In one scenario, 10% of all the places where the species is found were sampled; in others 25%, 50%, 90% and 100%,” Nieto Lugilde added.
The study unveiled that even with incomplete data (50% to 75% species coverage), the models could predict interspecies interactions and species distributions accurately.
This finding is pivotal because it implies that widely available but unstructured data can be harnessed for complex ecological predictions, providing a valuable tool where comprehensive structured data is unavailable.
“The results are encouraging, as the model is able to calculate interactions even if you don’t have 100% of the recorded data on the species,” concluded the researchers.
To confirm the accuracy of these models, the researchers used a method evaluating the integrity of real data at the pixel level. This involved a case study of forest trees in Europe, assessing sampling completeness by comparing the number of observations per pixel with the total number of species observed.
Published in the journal Ecography, this study signifies a leap forward in ecological research by validating the use of large-scale, opportunistic biodiversity data collections for community modeling. As the impacts of climate change intensify, such advancements are essential for effective ecological forecasting and conservation strategies.