A Planetary Inventory of Life – a New Synthesis Built on Big Data Combined with Novel Statistical Methods (LIFEPLAN)
Main funder
Funder's project number: 856506
Funds granted by main funder (€)
- 500 000,00
Funding program
Project timetable
Project start date: 01/01/2021
Project end date: 31/03/2026
Summary
Abstract. Biodiversity underlies ecosystem functioning. To achieve the basis for a sustainable management of natural resources under current environmental change, we thus need a unified theory of the forces structuring vast sets of ecosystems and taxonomical groups on Earth. For the first time, such a synthesis is now within reach, based on a recent revolution in sampling methodology, globally relevant ecological data, and advances in statistical methods for linking immense data to community ecological theory. In LIFEPLAN, we bring together the key expertise needed to generate and interpret Big Ecological Data for a global synthesis of biotic patterning across our planet: world leaders in community ecology, methods for automated species recognition, and Bayesian statistics for immense data. Our objectives are to generate a new understanding of biodiversity patterns and dynamics by developing fundamentally new methods for big data statistics. To this end, we will generate fully standardized, global big data on a range of species groups, thus allowing quantification of variation in ecological communities at spatial scales covering six orders of magnitude (from 10-1 km to 104 km), across tens of thousands of species. The resulting data motivate the development of transformative big data statistics, in particular highly scalable algorithms for spatio-temporal data, as well as methods for automated species identification from DNA, audio and image samples. As a key deliverable, we will develop global joint species distribution models describing the spatio-temporal structure of life on Earth. Working together will allow each of us to tackle what we regard as the ultimate challenges in our own fields, while simultaneously collaborating around solutions changing the face of modern biodiversity science. Now is the time for this project, as the big data and big methods emerging today coincide with a great need for global understanding of biodiversity structure and dynamics.
Principal Investigator
Primary responsible unit
Related publications and other outputs
- Natural deadwood hosts more diverse pioneering wood‐inhabiting fungal communities than restored deadwood (2024) Saine, Sonja; et al.; A1; OA
- Novel community data in ecology : properties and prospects (2024) Hartig, Florian; et al.; A2; OA
- Recommendations for quantitative uncertainty consideration in ecology and evolution (2024) Simmonds, Emily G.; et al.; A2; OA
- Spatiotemporal variation in the negative effect of neighbourhood crowding on stem growth (2024) Zhang, Hong‐Tu; et al.; A1; OA
- A case study on joint species distribution modelling with bird atlas data : Revealing limits to species' niches (2023) Seoane, Javier; et al.; A1; OA
- A Successful Crowdsourcing Approach for Bird Sound Classification (2023) Lehikoinen, Petteri; et al.; A1; OA
- Covariate-informed latent interaction models : Addressing geographic & taxonomic bias in predicting bird-plant interactions (2023) Papadogeorgou, Georgia; et al.; A1; OA
- Dung beetle community patterns in Western Europe : responses of Scarabaeinae to landscape and environmental filtering (2023) Leandro, Camila; et al.; A1; OA
- Evaluating the predictive performance of presence–absence models : Why can the same model appear excellent or poor? (2023) Abrego, Nerea; et al.; A1; OA
- Habitat area and local habitat conditions outweigh fragmentation effects on insect communities in vineyards (2023) Bosco, Laura; et al.; A1; OA
