Mathematisch-Naturwissenschaftliche Fakultät - Biodiversitätsmodellierung

Projects

<< Our group has moved to the University of Potsdam >>

 

 Overview:

2018-2023 Emmy Noether grant by the German Science Foundation DFG: BIOPIC
2018-2020 Interdisciplinary project funded by the Swiss Data Science Center: SPEEDMIND
2016-2018 Ambizione grant by the Swiss National Science Foundation SNF: SCODA

 

BIOPIC - Disentangling the effects of demography, dispersal and biotic interactions on population and community response to global change

This research is supported by an Emmy Noether grant by the German Science Foundation DFG (2018-2023; Grant no. ZU 361.1-1 to Damaris Zurell)

Summary

Understanding the interplay between different drivers of biodiversity change is vital for making robust predictions to novel environments. In BIOPIC, we will develop an integrated modelling framework able to disentangle the complex roles of demography, dispersal and biotic interactions in shaping species niches, and assess their effects on population and community response to global change. The framework and its single components will be validated using a mix of simulated and empirical data, and it will be operationalized for avian communities as test case. In particular, the project will focus on five key research objectives aimed at (1) improving our understanding how life history and environment shape dispersal, (2) improving our understanding how life history and demography shape species’ niches, (3) improving our understanding how biotic interactions shape species’ niches, (4) developing and operationalizing multi-species dynamic distribution models, and (5) developing new biodiversity scenarios for European birds. Overall, the proposed project will improve the scientific basis for model-based biodiversity assessments and increase reliability of biodiversity predictions for broad spatial scales by providing both theoretical and conceptual advancements and by defining practical requirements and guidelines for the development and application of biodiversity models.

 

SPEEDMIND - Improving species biodiversity analyses and citizen science feedback through mining data

Interdisciplinary project funded by the Swiss Data Science Center (ETH board, 2018-2020; awarded to Niklaus E. Zimmermann, and co-PIs Dirk N. Karger and Damaris Zurell).

Summary

In order to conserve and manage biodiversity, we need an improved understanding of essential biodiversity drivers and improved predictions of resulting biodiversity patterns in space and time. Here, we propose a novel approach based on data-mining and iterative machine learning to improve biodiversity models and to better exploit existing data as well as guide future data sampling efforts. Modern data-mining techniques are destined to improve traditional species distribution modelling. On the one hand, massive amounts of biodiversity data are becoming available through citizen science and technical advances in monitoring, with increasing data of species occurrence, morphological traits, evolutionary history, and environmental variables. On the other hand, these data are often incomplete in that clear sampling designs are missing and information is not equally accurate or complete for all species. These data gaps could be filled by modern machine learning algorithms that are able to find a way through the maze of uncertainties in these data, in which scientists so easily get lost.

 

SCODA - Scaling from individual interactions to community dynamics in avian assemblages

This research is supported by an Ambizione fellowship by the Swiss National Science Foundation (2016-2018; Grant no. PZ00P3_168136 to Damaris Zurell)

Summary

Quantitative biodiversity models are important tools for assessing potential species and community response to global environmental change. SCODA aims at testing and advancing the new approach of joint species distribution models (JSDMs). JSDMs simultaneously estimate the species-environment relationship of multiple species together with the residual correlation between species, which may carry information on interspecific interactions. The project combines empirical and theoretical analyses on avian communities to (a) test JSDMs ability to reliably quantify different interaction mechanisms, (b) evaluate the scale dependence of interspecific interactions and (c) their spatiotemporal variability, and (d) incorporate trait information into JSDMs as a more robust measure of potential interactions under novel environments. SCODA will advance our understanding of complex community dynamics and will define practical guidelines for making predictions under current and future environments.