A major challenge in ecology is the need for a better theoretical framework for understanding how species assemblages (ecological communities) arise, why some are species-rich and others species-poor, and why some species are present or dominant whereas others are not.
Current community assembly theory is largely based on static models. However, ecological dynamics (e.g. ecological drift, competition, immigration), or evolutionary dynamics (e.g. genetic drift, natural selection, speciation) generate continual changes in the constituents of communities and the sources from which they are assembled. The dynamical models that do exist do not take the community perspective or do not readily allow inferences from data. Moreover, there is often a mismatch between models and data.
We attempt to solve these problems simultaneously by developing a fully stochastic, dynamical and data-friendly theory of community assembly, and testing and informing this theory with model-oriented experiments and field studies of both macro-organisms and micro-organisms. The theory should contain models of speciation, extinction, immigration and demographic change that vary in spatial, phylogenetic and biotic complexity, and which I design for confrontation with data by providing each model’s likelihood given the data.
We conduct evolutionary experiments on the mite Tetranychus urticae and the bacterium Escherichia coli, which are ideal model organisms due to their short generation times. The experiments will provide insight into how diversity affects diversification, a great unknown in current macro-evolutionary theory. Apart from these highly controlled experiments, we apply the theory to naturally occurring micro-landsnails in South-East Asia, and micro-organisms in geothermal pools in New Zealand. Their small size, endemism and spatially limited, discrete habitat create a miniature world that facilitates sampling and confrontation with models. We provide software tools for scientists and conservationists to assess the processes underlying natural communities and predict their future composition and diversity.
From left to right: Cyrus, Mallon, Francisco Richter, Hanno Hildenbrandt, Liang Xu, Shu Xie, Annabel Belliard, Giovanni Laudanno, Raphael Scherrer, Theo Pannetier, Rampal Etienne, Leonel Herrera-Alsina, Elodie Wilwert, Megan Korte, Gerrit Potkamp, Pedro Neves, Richel Bilderbeek, Hylke Kortenbosch, Kasper Hendriks, Karen Bisschop. Missing: Tiphaine Bailly.
Group Member of the Month - November 2019: Lucas Porto
- None at the moment
- Xiaoguang Du (September 2008 – 2015)
- Saleta Perez-Vila (February 2007 – 2012), jointly supervised with B. Wertheim (RUG)
Former PhD students
- Paul van Els (January 2016 - December 2018, PhD degree on 18 May 2018, PhD thesis)
- Thijs Janzen (September 2010 – November 2014, PhD degree on 27 March 2015, PhD thesis)
- Ellen van Velzen (January 2009 – October 2014, PhD degree on 2 March 2015, PhD thesis)
- Francisco Encinas-Viso (October 2008 – March 2013, PhD degree on 14 June 2013, PhD thesis)
- Xubing Liu (May 2011 – March 2013, PhD degree on 14 June 2013, PhD thesis)
- Cyrus Mallon (March 2015 - June 2020)
- Thijs Janzen (January 2019 - June 2020)
- Josselin Cornuault (August 2015 - March 2017)
- Alex Pigot (September 2013 – September 2016)
- secsse package for R. Simultaneously infers state-dependent diversification across two or more states of a single or multiple traits while accounting for the role of a possible concealed trait.
- SADISA package for R. Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model.
- DAISIE package for R. Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction.
- DAMOCLES package for R. Simulation and likelihood methods for a dynamical community assembly that accounts for phylogenetic history.
- DDD package for R. Calculates the likelihood of diversity-dependent diversification models for a given data set of branching times of a phylogenetic tree.
- PBD package for R. Calculates the likelihood of the lineage-based protracted speciation model for a given data set of branching times of a phylogenetic tree.
- GUILDS package for R (Author: T. Janzen). Implementation of sampling formulas for the unified neutral model of biodiversity and biogeography, with or without guild structure.
- nLTT package for R (Author: T. Janzen). Provides functions to calculate the normalised Lineage-Through- Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.
- STEPCAM package for R (Author T. Janzen). Collection of model estimation, and model plotting functions related to the STEPCAM family of community assembly models. STEPCAM is a STEPwise Community Assembly Model that infers the relative contribution of Dispersal Assembly, Habitat Filtering and Limiting Similarity from a dataset consisting of the combination of trait and abundance data.
- Public Seelye lecture A simple view on biological complexity, New Zealand, November 2013
- Predicting the past from the present. Hungary, August 2012