Enhancing the predictive ability of species distribution models: stressor interactions, life cycle, and depicting degradation and recovery
Species distribution models (SDMs) are widely-used tools to understand and predict the distribution of species in space and time, inferring the likelihood of species occurrence by their relationship to the environment. They are used to describe the response to potential changes under future environmental conditions. While the use of SDMs in riverine environments increased in the recent past, methodological advancements are required to substantially improve their predictive ability.
This project aims to advance previous SDM modelling approaches by the involvement i) of environmental predictor (stressor) interactions, ii) life cycle related habitat use, and iii) asymmetric recovery patterns. We will first set up basic SDMs for selected species of diatoms, invertebrates, and fish, i.e. organism groups central to RESIST. Using these SDMs, we will test if considering the interaction of environmental variables and stressors as predictor variables will affect modelling results and to what extent. We further aim to develop an SDM framework covering different life stages of selected invertebrate species, e.g. considering the use of both instream and terrestrial habitats. We hypothesise that such revised models will enhance the predictive capability and accuracy of SDMs.
We will further investigate, if building SDMs for different time periods, i.e. periods reflecting stressor increase and recovery, has significant effects on model output and quality. This analysis will test the Asymmetric Response Concept of RESIST. Overall, the new model framework will allow a better representation of a species’ fundamental niche. The ultimate aim is the integration of these three advancements (multiple stressors, life stages, and asymmetry) into ‘one SDM’, to consider critical timing and importance of environmental conditions during different life stages and during phases of degradation and recovery. Our work is closely linked to A14 by exchanging temporal and spatially explicit environmental variables. We will produce habitat suitability maps relevant to A16 for dispersal modelling, to A17 for identifying suitable locations of establishing communities, and for determining focal points to hook up process-based models of A19. The project is fundamental to forthcoming phases of RESIST by providing habitat suitability maps at the catchment and reach scale in the Emscher/Boye and Kinzig catchments with an increased predictive ability of SDMs, and eventually to allow for improved projections of change.
Graciela Medina-Madariaga (University of Berlin)
Enhancing the predictive ability of species distribution models: stressor interactions, life cycle and depicting degradation and recovery
Where can we find them? This is the question Species Distribution Models (SDMs) try to solve. SDMs are among the most used models used in environmental sciences, expressing information about the probability to find a species at certain location and time. They are considered valuable for reintroduction and conservation of species programmes. However, their application in freshwater environments has been limited due to the complexity of such ecosystems. In addition, there is a need to enhance the predictive ability of such models to improve management strategies in a changing world.
This project focuses in the investigation of new approaches to enhance the predictive ability of SDMs. Three aspects have been identified as crucial: (i) investigating the effect of the interaction occurring among multiple stressors in the distribution of species, (ii) analysing the spatial distribution of species with complex life-cycles at different life stages, and finally (iii) to consider trajectories of environments subject to different status and stressors including recovery.
First Supervisor: Prof. Dr. Sonja Jähnig (Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Ecosystem Research)
Second Supervisor: PD Dr. Christian Feld (University of Duisburg-Essen, Aquatic Ecology)
Mentor: Dr. Alain Maasri (Niedersächsische Landesbetrieb für Wasserwirtschaft, Küsten- und Naturschutz (NLWKN))