spatiodiversity people

Explaining the complexity of tropical forests: Towards a Unified Spatial Theory of Biodiversity

ERC advanced grant 2009-2015

Summary

The general objective of the SPATIODIVERSITY project is to advance in a persistent challenge in contemporary ecology; to understand the relative importance of processes and factors that govern the composition and dynamics of species-rich communities such as tropical forests. Advances in this issue have important implications for efforts to protect terrestrial biodiversity from climate and land use change. To contribute to this question we adopted a radically different approach than previous attempts and focused on the rich source of information on spatial patterns contained in the 25-50 ha fully-mapped forest mega-plots of the Center for Tropical Forest Science. Our innovative idea is to combine the spatial data of the forest mega-plots with recent spatial statistics, last generation dynamic vegetation models, and huge computational power. This information extraction involves three tasks:

  1. Spatial pattern analysis.
    The goal is to quantify the manifold spatial patterns at fully mapped vegetation plots with special attention to an assessment of the degree of non-random spatial structures in the uni-, bi-, and multivariate species interactions. This involved developments of novel statistical methods of spatial point pattern analysis, especially for multivariate analysis to enable quantification of the complex spatial structures in species, functional and phylogenetic diversity.
  2. Forest simulation models
    The goal is to develop a spectrum of individual-based vegetation dynamic simulation models, ranging from simple neutral models (were all species are functionally equivalent) to complex process based models of the FORMIND family. This involved development of spatially-explicit extensions of neutral models and of the FORMIND models.
  3. Model selection based on spatial patterns
    Finally, the spatial patterns generated by the vegetation simulation models developed in task 2 were systematically compared with the patterns quantified in task 1 to identify the most parsimonious model(s) that account simultaneously for all observed patterns. This involved development of novel techniques of inverse parameterization and model selection (statistical inference) of stochastic simulation models.

Explaining the complexity of forest ecosystems

    Surprisingly, although most processes which are thought to contribute to species coexistence have a strong spatial component, the rich source of information on spatial patterns has not been used yet. For example, tropical forests comprise hundreds of tree species at different stages which show complex spatial structures. Quantification of these manifold spatial structures delivers the decisive information which has been missed by previous approaches and allowed us to advance significantly beyond the state-of-the-art in the question on the relative importance of processes and factors that govern the structure and dynamics of tropical forest ecosystems.

schematics From Hartig et al. (2014)

We explored and simulated the fate and the interactions of every tree in a 50ha plot, and fitting a range of models with different complexity to the spatial data reveals the mechanisms and processes needed to re-create fundamental structural properties of the forests. This approach of “strong inference” demands that a valid model should re-create several fundamental structural properties of the forests simultaneously and allows for a rigorous and systematic confrontation of alternative models with data (Grimm et al. 2005, Wiegand 2003). The underlying premise is that coexistence mechanisms should leave a spatial signature that can be detected by comparing patterns in large data sets of individual tree locations in species rich forest with that predicted by spatially-explicit and dynamics forest simulation models.

Such an ambitious project could only be undertaken by a team that combines unique expertise in a variety of     disciplines such as field ecology, spatial statistics, computer simulation modeling, and statistical inference.

Spatial pattern analysis

Special methodological highlights of the spatial analysis were:

  • Wiegand et al. (2009)
    provided novel methods for analyzing univariate patterns by means of complex cluster point process models with two critical scales of clustering. This allows for a detailed quantification of the aggregation properties of species patterns which can be linked to species properties.
  • Wiegand et al. (2012), Wang et al. (2016)
    synthesized interspecific associations of several forest communities. We found evidence that “stochastic dilution effects” due to increasing species richness overpower signals of species associations. This hypothesis can explain why species placement in species-rich communities approximates independence. As following up we organized in 2016 two sDiv WORKSHOPS sNICHE to develop a stochastic coexistence theory for species rich communities.
  • Wiegand, Hubbel and He (2013)
    present a method and software for pattern reconstruction that solves longstanding null models problems, especially for testing independence in bivariate patterns (e.g., Getzin and Wiegand 2014) and for assembling null communities in multivariate patterns (Punchi-Manage et al. 2015, Wang et al. 2015, 2016). Pattern reconstruction allows generating stochastic replicate patterns that closely approximate the several summary functions of the observed pattern. This unconventional, but computer intensive, methodology is a big step forward and is extensively applied in the analyses of the mega-plots.
  • Shen et al. (2013)
    introduced a new methodology for analyzing spatial phylogenetic (or functional) structures in fully mapped plots, based on phylogenetic beta diversity and randomization of the dissimilarity matrix.
  • Fedriani et al. (2015)
    presented a new methodology based on mark correlation functions that is able to directly detect density dependent effects on plant fitness (e.g., growth rate, number of flowers). Density dependence is one of the key processes operating in tropical forests.
  • Taubert et al. (2015)
    used principles of stochastic geometry to explain a key quantity of forest structure: the tree diameter distribution. We found that the tree diameter distributions of tropical forests emerge accurately from a surprisingly simple set of principles of stochastic sphere packing combined with site-specific tree allometries, random placement of trees, competition for space, and mortality.
  • Velázquez et al. (early view)
    is an extensive review of studies in ecology and related disciplines that use techniques of spatial point pattern analysis. We assessed the use of different key elements of a point pattern analysis, and summarized new directions and methods (such as multivariate analyses) developed under the ERC project that allow current key questions in ecology to be effectively addressed.
  • Wiegand and Moloney (2014)
    the Handbook of spatial point pattern analysis in Ecology summarizes the methodology of spatial pattern analysis developed in the project.

Statistical inference for stochastic simulation models

The project requires rigorous and systematic confrontation of alternative models with data (i.e., the spatial non-spatial patterns) by means of “strong inference”. This approach was proposed in Wiegand et al. (2003, 2004) and in a highly cited review paper that appeared in Science (Grimm et al. 2005). Alternative forest simulation models that fail to reproduce important patterns are rejected, and additional patterns with more falsifying power can be used to contrast successful alternatives. The technical problem here is to fit the complex stochastic simulation models to the multiple pattern data sets to find out if parameter combinations exist that simultaneously match the multiple observed patterns. We accomplished pioneering work in this field by developing methods based on summary statistics that bypass the impossible task of deriving likelihood functions for complex and mechanism rich forest simulation models:

  • Hartig et al (2011)
    is an important Ecology Letters review paper on statistical inference for stochastic simulation models that describes and compares novel methods that allow fitting stochastic simulation models to spatial data.
  • Hartig et al. (2014)
    tests these techniques, based on a parametric likelihood approximation placed in a conventional MCMC, and shows that the method performs well in retrieving known parameter values from virtual field data generated by the forest simulation model FORMIND.
  • Lehmann and Huth (2015)
    present a refined software that explored alternative optimization methods which are quicker than MCMS approaches.
  • May et al. (2015, 2016)
    applied these methods for parameterization of the (extended) neutral models to fit the data of the BCI and Sinaraja forests. The fitting procedures worked very well and were feasible for the several hundreds of species found at these plots. These studies also confirmed the central hypothesis of the SPATIODIVERSITY project that the data on spatial patterns indeed contain information to identify underlying processes.

Simple models can explain spatial structures in tropical forests

Stephen Hubbell showed in his seminal book (Hubbell 2001) and three subsequent papers (Volkov et al. 2003; 2005, 2007) that simple (non-spatial) dynamic models, resting on the assumption that species are functionally identical, can successfully predict typical biodiversity pattern in tropical forest (e.g. the relative abundance of different species within a community). He coined for this model type the term “neutral theory”. Hubbell’s neutral model has been a “quantum leap” in the explanation of non-spatial biodiversity pattern in tropical forest and has strongly stimulated the development of theory and mathematical modeling in Ecology.

Our results on spatial-explicit extension of the classical neutral models indicate a second a “quantum leap” in the explanation of fundamental, spatially-explicit structural properties of tropical forests. To our big surprise, we found that much simpler models than anticipated do already provide a correct characterization of the complex spatial structure and several additional structural properties of hyperdiverse BCI and Sinharaja forests (May et al. 2015, 2016). This result has important consequences for the theoretical foundation of ecology.

References

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  • Wang, X, T. Wiegand, N. G. Swenson, A. Wolf, R. Howe, and Z. Hao. 2015. Mechanisms underlying local functional and phylogenetic beta diversity in two temperate forests. Ecology 96: 1062–1073
  • 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. 2014. A complex network of interactions controls coexistence and relative abundances in Patagonian grass–shrub steppes. Journal of Ecology 102: 776 - 788
  • Fedriani, J.M., and T. Wiegand. 2014. Hierarchical mechanisms of spatially contagious seed dispersal in complex seed-disperser networks. Ecology 95:514–526
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  • Martínez, I., F. Glonzález-Taboada, T. Wiegand, and J. R. Obeso. 2013. Spatiotemporal patterns of seedling-adult associations in a temperate forest community. Forest Ecology and Management 296: 74-80.
  • Mundo, I.A, T. Wiegand, R. Kanagaraj and T. Kitzberger. 2013. Spatial analysis of ignition point patterns and the probability of fire occurrence in the western area of Neuquén province, Argentina. Journal of Environmental Management 123: 77-87.
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  • Sutherland, W. J., Freckleton, R.P., Godfray, H.C.J., Beissinger, S.R., Benton, T., Cameron, D.D., Carmel, Y., Coomes, D.A., Coulson, T., Emmerson, M.C., Hails, R.S., Hays, G.C., Hodgson, D. J., Hutchings, M. J., Johnson, D., Jones, J. P.G., Keeling, M.J., Kokko, H., Kunin, W.E., Lambin, X., Lewis, O.T., Malhi, Y., Mieszkowska, N., Milner-Gulland, E.J., Norris, K., Phillimore, A.B., Purves, D.W., Reid, J.M., Reuman, D.C., Thompson, K., Travis, J.M.J., Turnbull, L.A., Wardle, D.A. and T. Wiegand. 2013. Identification of 100 fundamental ecological questions. Journal of Ecology 101: 58–67.
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  • Wiegand, T., J. Raventós, E. Mujica, E. González, and A. Bonet. 2013. Spatio-temporal analysis of the effects of hurricane Ivan on two contrasting epiphytic orchid species in Guanahacabibes, Cuba. Biotropica 45: 441–449
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  • Castilla, A.R., T. Wiegand, C. Alonso, and C.M. Herrera. 2012. Disturbance-dependent spatial distribution of sexes in a gynodioecious understory shrub. Basic and Applied Ecology 13: 405–413.
  • 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
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  • Lan, G., S. Getzin, T. Wiegand, H. Zhu, and M. Cao. 2012. Spatial distribution and interspecific associations of the canopy species in a tropical seasonal rain forest of China PlosOne 7(9): e46074.
  • Marion, G., G.J. McInerny, J. Pagel, S. Catterall, A.R. Cook, F. Hartig and R.B. O’Hara. 2012. Parameter and uncertainty estimation for process-oriented population and distribution models: data, statistics and the niche. Journal of Biogeography 39: 2225–2239
  • Martínez, I., F. González-Taboada, T. Wiegand, J. J. Camarero, and E. Gutiérrez. 2012. Dispersal limitation and spatial scale affect model based projections of Pinus uncinata response to climate change in the Pyrenees Global Change Biology 18: 1714–1724
  • Queenborough, S.A., M.R. Metz, T. Wiegand, and R. Valencia. 2012. Palms, peccaries and perturbations: widespread effects of small-scale disturbance in tropical forests. BMC Ecology 12:3.
  • Raventós, J., T. Wiegand, F. T. Maestre, and M. De Luis. 2012. A resprouter herb reduces negative density-dependent effects among neighboring seeders after fire. Acta Oecologica 38: 17-23.
  • Rayburn A. P. and T. Wiegand. 2012. Individual Species-Area Relationships and spatial patterns of species diversity in a Great Basin, semi-arid shrubland. Ecography 35:341-347
  • Rodríguez-Pérez, J., T. Wiegand, and A. Traveset. 2012. Adult proximity and frugivore activity structure plant populations – spatial patterns after the disperser’s loss. Functional Ecology 26:1221–1229
  • Schurr, F.M., J. Pagel, J.S. Cabral, J. Groeneveld, O. Bykova, R.B. O’ Hara, F. Hartig, W.D. Kissling, H.P. Linder, G.F. Midgley, B. Schröder, A. Singer, and N.E Zimmermann. 2012. How to understand species’ niches and range dynamics: a demographic research agenda for biogeography. Journal of Biogeography: 39: 2146–2162.
  • Wiegand, T., A. Huth, S. Getzin, X. Wang, Z. Hao, S. Gunatilleke, and N. Gunatilleke. 2012 Testing the independent species arrangement assertion made by theories of stochastic geometry of biodiversity. Proceedings B 279: 3312-3320
  • Getzin, S., M. Worbes, T. Wiegand and K. Wiegand. 2011. Size dominance regulates tree spacing more than competition within height classes in tropical Cameroon. Journal of Tropical Ecology 27: 93-102
  • 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
  • Kanagaraj, R. T. Wiegand, L. Comita, and A. Huth.2011 Tropical tree species assemblages in topographic habitats change in time and with life stage. Journal of Ecology 99:1441-1452
  • Martínez, I., T. Wiegand, J. J. Camarero, E. Batllori, and E. Gutiérrez. 2011. Elucidating demographic processes underlying tree line patterns: a novel approach to model selection for individual-based models using Bayesian methods and MCMC. American Naturalist 177: E136-E152
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  • Wang, X., T. Wiegand, A. Wolf, R. Howe, S. Davis, and Z. Hao. 2011. Spatial patterns of tree species richness in two temperate forests. Journal of Ecology 99:1382-1393
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  • 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
  • Raventós, J., T. Wiegand, and M. De Luis. 2010. Evidence for the spatial segregation hypothesis: a test with nine-year survivorship data in a Mediterranean fire-prone shrubland show that interspecific and density-dependent spatial interactions dominate. Ecology. 91:2110-2120
  • Wang, X., T. Wiegand, Z. Hao, B. Li, J. Ye, and J. Zhang. 2010. Species associations in an old-growth temperate forest in north-eastern China. Journal of Ecology 98: 674–686
  • Dislich C., Günter S., Homeier J., Schröder B. and Huth A. 2009. Simulating forest dynamics of a tropical montane forest in South Ecuador. Erdkunde 63: 347-364
  • Rueger, N., Huth, A., Hubbel, S., and R. Condit. 2009. Response of recruitment to light availability across a tropical lowland rainforest community. Journal of Ecology 97:1360-1368.
  • Wiegand, T, A. Huth., and I. Martínez. 2009. Recruitment in tropical tree species: revealing complex spatial patterns. The American Naturalist 174: E106 - E140