Theory and methods in ecology and evolution

We are constantly improving our analytical tool box by developing and refining methods for data collection, handling and analysis in order to deepen our understanding of ecological dynamics in wildlife.

Project details
Duration: ongoing
Third-party funded: partly
Involved Department(s): Dept Ecological Dynamics, Dept Evolutionary Genetics
Leibniz-IZW Project Leader(s): Sarah Benhaiem, Stephanie Kramer-Schadt, Andreas Wilting (all: Dept Ecological Dynamics)
Leibniz-IZW Project Team: Jan Axtner, Tobias Kürschner, Cedric Scherer (all: Dept Ecological Dynamics), Alexandre Courtiol (Dept Evolutionary Genetics)
Consortium Partner(s): Helmholtz-Zentrum für Umweltforschung UFZ, Universität Potsdam, University of California Davis
Current Funding Organisation: various
Research Foci: Developing theories, methods, and tools


We have a strong interest and focus on developing computational models and tools to understand and better forecast wildlife responses to anthropogenic challenges, and to advance theory and concepts in wildlife research. To provide a solid basis we are developing tools for data collection, data management and final analysis. To this end we focus on methods and protocols for high-throughput methods such as automated camera-traps or environmental DNA (eDNA). We deploy a full range of approaches ranging from statistical and spatial models to mathematical and fully-fledged dynamic individual-based spatially-explicit stochastic simulations. We back up our empirical findings by developing and improving theoretical models. Our models and tools are made available to the scientific community through open repositories, such as the R packages NLMR, IsoriX (with Depts Evolutionary Genetics, Evolutionary Ecology) and camtrapR (with Dept Evolutionary Genetics). Our dynamic population models are hosted at CoMSES.

Selected Publications

Theory and concepts:

Bush A, Sollmann R, Wilting A, Bohmann K, Cole B, Balzter H, Martius C, Zlinsky A, Calvignac-Spencer S, Cobbold CA, Dawson TP, Emerson BC, Ferrirer S, Gilbert MTP, Herold M, Jones L, Leendertz FH, Matthews L, Millington JDA, Olson JR, Ovaskainen O, Raffaelli D, Reeve R, Rödel M-O, Rodgers TW, Snape S, Visseren-Hamakers I, Vogler AP, White PCL, Wooster MJ, Yu DW (2017): Connecting Earth observation to high-throughput biodiversity data. NAT ECOL EVOL 1, 176. doi:10.1038/s41559-017-0176.

Radchuk V, Kramer-Schadt S, Grimm V (2019): Transferability of mechanistic ecological models is about emergence. TRENDS ECOL EVOL 34, 487-488. doi:10.1016/j.tree.2019.01.010

Radchuk V, de Laender F, Sarmento Cabral J, Boulangeat I, Crawford M, Bohn F, de Raedt J, Scherer C, Svenning JC, Thonicke K, Schurr FM, Grimm V, Kramer-Schadt S (2019): The dimensionality of stability depends on disturbance type. ECOL LETT 22, 674-684. doi:10.1111/ele.13226

Scherer C, Radchuk V, Franz M, Thulke H, Lange M, Grimm V, Kramer-Schadt S (2020): Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. OIKOS 129, 651-667. doi:10.1111/oik.07002

Statistical advancements:

Benhaiem S, Marescot L, Hofer H, East ML, Lebreton J-D, Kramer-Schadt S, Gimenez O (2018): Robustness of eco-epidemiological capture-recapture parameter estimates to variation in infection state uncertainty. FRONT VET SCI 5, 197. doi:10.3389/fvets.2018.00197

Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri D, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013): The importance of correcting for sampling bias in MaxEnt species distribution models. DIVERS DISTRIB 19, 1366-1379. doi:10.1111/ddi.12096

Tools, methods, workflows:

Abrams JF, Axtner J, Bhagwat T, Mohamed A, Nguyen A, Niedballa J, Sollmann R, Tilker A, Wilting A (2018): Studying terrestrial mammals in tropical rainforests – A user’s guide for camera-trapping and environmental DNA.

Abrams JF, Hörig LA, Brozovic R, Axtner J, Crampton-Platt A, Mohamed A, Wong ST, Sollmann R, Yu DW, Wilting A (2019): Shifting up a gear with iDNA: From mammal detection events to standardised surveys. J APPL ECOL 56, 1637-1648. doi:10.1111/1365-2664.13411.

Abrams JF, Vashishtha A, Wong ST, Nguyen A, Mohamed A, Wieser S, Kuijper A, Wilting A, Mukhopadhyay A (2019): Habitat-Net: Segmentation of habitat images using deep learning. ECOL INFORM 51, 121-128. doi:10.1016/j.ecoinf.2019.01.009

Axtner J, Crampton-Platt A, Hörig LA, Mohamed A, Xu CCY, Yu DW, Wilting A (2019): An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies. GIGASCIENCE 8, giz025. doi:10.1093/gigascience/giz029.

Courtiol A, Rousset F, Rohwäder M-S, Soto DX, Lehnert LS, Voigt CC, Hobson KA, Wassenaar LI, Kramer-Schadt S (2019): Isoscape computation and inference of spatial origins with mixed models using the R package IsoriX. Tracking Animal Migration with Stable Isotopes (Eds. Hobson K & Wassenaar LI, Academic Press), Chapter 9, 207-236. doi:10.1016/B978-0-12-814723-8.00009-X.

Niedballa J, Sollmann R, Courtiol A, Wilting A (2016): camtrapR: An R package for efficient camera trap data management. METHODS ECOL EVOL 7, 1457-1462. doi:10.1111/2041-210X.12600.

Niedballa J, Wilting A, Sollmann R, Hofer H, Courtiol A (2019): Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. REMOTE SENS ECOL CONSERV 5, 272-285. doi:10.1002/rse2.107

Sciaini M, Fritsch M, Scherer C, Simpkins CE (2018): NLMR and landscape tools: An integrated environment for simulating and modifying neutral landscape models in R. METHODS ECOL EVOL 9, 2240-2248. doi:10.1111/2041-210X.13076