Main research areas

Main Research

Assembly and dynamics of plant communities

In the early 1990’s I started my Postdoc work with analyzing the complex, event-driven dynamics of semiarid plant communities (e.g., Wiegand et al. 1995, 1998a). To this end I developed methods of bottom-up simulation modeling, extending earlier work on cellular automata. My models were able to describe the spatial and temporal dynamics of several interacting species. Nowadays, these models are frequently used in ecology. Understanding the assembly and dynamics of plant communities remains one of my main research themes; it was the core question of my ERC advanced grant where we mostly analyzed tropical forest communities (e.g., Kazmierczak et al. 2014; May et al. 2015, 2016), and my lab applies these techniques to other systems such as semiarid plant communities (Cipriotti et al. 2012, 2014, 2016) and treelines (Wiegand et al. 2006; Martinez et al. 2011).

animal populations

Spatiotemporal dynamics of animal populations

In the early 2000’s I became interested in reconciling the perspectives of landscape ecology and population modeling to better understand the spatiotemporal dynamics of endangered animal populations such as those of brown bears (Wiegand et al. 1998a, 1999, 2008), lynx (Schadt et al. 2002, Kramer-Schadt et al. 2005; Revilla and Wiegand 2008), koala (Rhodes et al. 2006), woodpecker (Bruggeman et al. 2010, 2014), tiger (Kanagaraj et al. 2011, 2013), jaguar (De Angelo et al. 2013, ), albatross (Louzao et al. 2011, 2013, 2014), tortoise (Anadón et al. 2012) or lizards (Rodríguez-Pérez et al. 2012a,b), especially under habitat loss and fragmentation. An important element of my approach to spatially explicit population modeling is to define in a first step the landscape as perceived by the species of interest. This is accomplished with statistical methods of species distribution modeling where the presence/absence/abundance of the species is statistically related to environmental variables that are often based on remote sensing data.


Methods of model-data integration

During my work on spatially-explicit simulation models and endangered species it became obvious that existing modeling strategies were insufficient to face the two main challenges of bottom-up modeling: complexity and uncertainty. I also realized that in most cases only a small fraction of the available data have been used to address fundamental questions related to the emergence, consequences, and conservation of global biodiversity. To advance our ability of model-data integration I further developed the inverse pattern-oriented modeling strategy that allows using multiple observed patterns typically encoded in large and complex data sets. This method allows for statistical inference with dynamic and stochastic simulation models that incorporate the required real world ecological complexity. In other fields these methods are termed Approximate Bayesian Computation (ABC) and are extensions of statistical methods of model fitting and model selection that allow testing competing hypotheses, inclusion of data from different sources and hierarchical levels, and observer bias (i.e., a virtual ecologist). Key publications are Wiegand et al. (1998a, 2003, 2004), Grimm et al. (2005), Hartig et al. (2011, 2014), Martínez et al. (2011), Cipriotti et al. 2012, and May et al. (2015, 2016).

point pattern

Spatial point pattern analysis

To analyze the spatially-explicit outputs of my simulation models of plant communities (Wiegand et al. 1998b), I became interested in spatial pattern analysis. To this end I developed the software Programita that is based on many years of teaching experience and collaborative research with field ecologist and especially designed to meet the “real world” requirements of ecologists. My work on spatial point pattern analysis and the software Programita has initiated in ecology a new and productive line of thinking in the analysis of spatial data. In 2004 I summarized the state-of-the-art in point pattern analysis in Ecology in a Mini review in Oikos (Wiegand and Moloney 2004) and 10 years later we published a textbook on point pattern analysis in ecology ( Wiegand and Moloney 2014 ). I have applied spatial point pattern analysis in my own research in numerous studies to precisely describe spatial patterns in ecological communities for hypothesizing underlying processes or for testing ecological hypotheses. Recently I became interested in marrying spatial point pattern analysis with phylogenetic and trait analysis (Shen et al. 2013, Wang et al. 2016). Detailed spatial pattern analysis of forest communities was one of the three columns of my ERC advanced grand .


  • Anadón, J.D., Wiegand, T. and A. Giménez.2012. Hypothesis testing with individual-based movement models reveals sex-biased effects of landscape fragmentation on movement patterns of a terrestrial tortoise. Ecosphere 3: 64.
  • Bruggeman, D. J., T. Wiegand, and N. Fernández. 2010. The relative effects of habitat loss and fragmentation on population genetic structure. Molecular Ecology 19: 3679–3691
  • Bruggeman, D.J., T. Wiegand, J. R. Walters, F. Gonzalez Taboada , K. Convery. 2014. Contrasting the ability of data to make inferences regarding dispersal for the Red-cockaded woodpecker (Picoides borealis). Landscape Ecology 29: 639-653
  • Cipriotti, P.A., M.R.Aguiar, T, Wiegand, and J. M. Paruelo. 2012. Understanding the long term spatial dynamics of semiarid grass shrub steppes through inverse parameter selection for simulation models. Oikos 121: 848- 861
  • Cipriotti, P.A., M.R.Aguiar, T. Wiegand, and J. M. Paruelo. 2014. A complex network of interactions controls coexistence and relative abundances in Patagonian grass–shrub steppes. Journal of Ecology 102: 776 - 788
  • Cipriotti, P.A., T. Wiegand, S., Pütz, N.J., Bartoloni, and J.M. Paruelo. 2016. Non-parametric upscaling of stochastic simulation models using transition matrices. Methods in Ecology and Evolution 7: 313-322.
  • De Angelo, C., A. Paviolo, T. Wiegand, R. Kanagaraj and M. S. Di Bitetti. 2013. A management landscape for jaguars in the Upper Paraná Atlantic Forest. Biological Conservation 159: 422–433
  • Grimm, V., E. Revilla, U. Berger, F. Jeltsch, W. Mooij, S. F. Railsback, H. Thulke, J. Weiner, T. Wiegand, and D. L. DeAngelis. 2005 Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987-991.
  • Hartig, F. J. Calabrese, B. Reineking, T. Wiegand, and A. Huth. 2011. Statistical inference for stochastic simulations models - theory and application. Ecology Letters 14:816-827
  • Hartig, F., C. Dislich, T. Wiegand, and A. Huth. 2014. Approximate Bayesian parameterization of a complex tropical forest model. Biogeosciences 11: 1261–1272
  • Kanagaraj, R., T. Wiegand, S. Kramer-Schadt, M. Anwar, and S.P. Goyal. 2011. Assessing habitat suitability for tiger in the fragmented Terai Arc Landscape of India and Nepal. Ecography 34: 970-981
  • Kanagaraj, R. T. Wiegand, S. Kramer-Schadt, S.P. Goyal. 2013 Using individual-based movement models to assess inter-patch connectivity for large carnivores in fragmented landscapes. Biological Conservation 167: 298–309
  • Kazmierczak, M., T. Wiegand, and A. Huth. 2014. A neutral vs. non-neutral parametrizations of a physiological forest gap model. Ecological Modelling 288: 94–102
  • Kramer-Schadt, S., E Revilla, and T. Wiegand. 2005. Lynx reintroductions in fragmented landscapes of Germany: projects with future or misunderstood wildlife conservation? Biological Conservation 125: 169-182.
  • Louzao, M. D. Pinaud, C. Peron, K. Delord, T. Wiegand, and H. Weimerskirch. 2011. Conserving pelagic habitats: seascape modelling of an oceanic top predator. Journal of Applied Ecology 48: 121–132.
  • Louzao, M., T. Wiegand, F. Bartumeus and H. Weimerskirch. 2014. Coupling instantaneous energy-budget models and behavioural mode analysis to estimate optimal foraging strategy: an example with wandering albatrosses. Movement Ecology 2: 8
  • Louzao, M; O. Aumont, T. Hothorn, T. Wiegand, and H. Weimerskirch. 2013. Foraging in a changing environment: habitat shifts of an oceanic predator over the last half century. Ecography 36: 57-67
  • Martínez, I., T. Wiegand, F. Glez. Taboada, and J. R. Obeso. 2010. Spatial associations among tree species in a temperate forest community in North-western Spain. Forest Ecology and Management 260: 456-465
  • May, F., A. Huth, and T. Wiegand. 2015. Moving beyond abundance distributions – neutral theory and spatial patterns in a tropical forest. Proceedings R. Soc. B 282: 20141657
  • May, F., T. Wiegand, S. Lehmann, and A. Huth. 2016. Do abundance distributions and species aggregation correctly predict macroecological biodiversity patterns in tropical forests? Global Ecology and Biogeography 25: 575-585
  • Revilla, E., and T. Wiegand. 2008. Individual movement behavior, matrix heterogeneity and the dynamics of spatially structured populations. PNAS 105:19120-19125.
  • Rhodes, J. R., T. Wiegand, C.A. McAlpine, H.P. Possingham, D. Lunney, J. Callaghan, and M. Bowen. 2006. Species Distribution Models and Conservation Planning in Semi-urban Landscapes. A Case Study for the Koala. Conservation Biology 20: 449-459.
  • Rodríguez-Pérez, J., T. Wiegand, and A. Traveset. 2012a. Adult proximity and frugivore activity structure plant populations – spatial patterns after the disperser’s loss. Functional Ecology 26:1221–1229
  • Rodríguez-Pérez, J., T. Wiegand, and L. Santamaria. 2012b. Frugivore behavior determines plant distribution: a spatially-explicit analysis of a plant-disperser interaction. Ecography 35: 113-123.
  • Schadt, S., E. Revilla, T. Wiegand, F. Knauer, P. Kaczensky, U. Breitenmoser, L. Bufka, J. Cerveny, P. Koubek, T. Huber, C. Stanisa, and L. Trepl 2002. Assessing the suitability of central European landscapes for the reintroduction of Eurasian lynx. Journal of Applied Ecology 39:189-203.
  • Shen, G., T. Wiegand, X. Mi, and F. He. 2013. Quantifying spatial phylogenetic structures of fully mapped plant communities. Methods in Ecology and Evolution. 4: 1132-1141.
    Wang, X., T. Wiegand, N.J.B. Kraft, N.G. Swenson, S.J. Davies, Z. Hao, R. Howe, Y. Lin, K. Ma, X. Mi, S-H
  • Su, I-F Sun, and A Wolf. 2016. Stochastic dilution effects weaken deterministic effects of niche-based processes on the spatial distribution of large trees in species rich forests. Ecology 97: 347-360
  • Wiegand, T., and K. A. Moloney 2004. Rings, circles and null-models for point pattern analysis in ecology. Oikos 104: 209-229.
  • Wiegand, T., S. J. Milton, C. Wissel. 1995. A simulation model for a shrub ecosystem in the semiarid Karoo, South Africa. Ecology 76:2205-2211.
  • Wiegand, T., J. Naves, T. Stephan, A. Fernandez. 1998a. Assessing the risk of extinction for the brown bear (Ursus arctos) in the Cordillera Cantabrica, Spain. Ecological Monographs 68:539-571.
  • Wiegand, T., K. Moloney, S. J. Milton. 1998b. Population dynamics, disturbance, and pattern evolution: identifying the fundamental scales of organization in a model ecosystem. American Naturalist 152:321-337.
  • Wiegand, T., K. Moloney, J. Naves, F. Knauer. 1999. Finding the missing link between landscape structure and population dynamics: a spatially explicit perspective. American Naturalist 154:605-627.
  • Wiegand, T., Kissling, W.D., Cipriotti, P.A., and Aguiar, M.R. 2006. Extending point pattern analysis to objects of finite size and irregular shape. Journal of Ecology 94: 825-837.
  • Wiegand, T., J. Naves and M. Garbulsky, and N. Fernández. 2008. Animal habitat quality and ecosystem functioning: exploring seasonal patterns using NDVI. Ecological Monographs 78: 87-103.
  • Wiegand, T., F. Jeltsch, I. Hanski, and V. Grimm. 2003. Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and application. Oikos 100: 209-222.
  • Wiegand, T., E. Revilla, and F. Knauer. 2004. Dealing with uncertainty in spatially explicit population models. Biodiversity and Conservation 13:53-78.