Progress to Date Field work: 66% of sites have grids established and tree data recorded.

1. Data Collection Experimental Macroecology: Effects of Temperature on Biodiversity S. T. Hammond1, J. W. Voordeckers2, J. H. Brown1, B. J. Enquist3, Z. He2, M. Kaspari4, R. B. Waide5, J. Zhou2 1 Department of Biology, University of New Mexico; 2Institute for Environmental Genomics, University of Oklahoma; 3 Department of Ecology and Evolutionary Biology, University of Arizona;4Department of Zoology, University of Oklahoma; 5 Long Term Ecological Research Network Data Archive Other Labs • Overview of Design & Procedures • We use the interplay between theoretical models and collected data to understand the changes in taxonomic, functional, and genetic diversity in two gradients: variation in temperature across geographic space, and over the course of succession in decomposing leaves. The general project design has three key elements: • 1) Geographic-scale comparisons across six forest sites along a thermal gradient. -- Six LTER (or equivalent) sites along a latitudinal/elevational gradient of increasing temperature: Niwot, Andrews, Harvard, Coweeta, Luquillo, Barro Colorado Island (BCI) (Table 1). • 2) Documentation of successional dynamics in decomposing leaves. --Field sampling and laboratory mesocosm experiments to monitor changes in standardized resources (packs of leaves of selected • 3) Identify the phylogenetic, taxonomic and functional diversity among three groups: trees, litter invertebrate and soil microbes(Figure 2): Data Analysis & Inferential Statistics This study is building unprecedented datasets, overlaying the diversity of trees, litter invertebrates, and bacteria, across geographic space and successional time. Hypotheses like H2 require comparisons across such different kinds of organisms that they pose challenges to traditional taxonomic and ecological traditions (Ricotta 2005), although results of recent empirical studies highlight the contributions of such comparative studies (Bryant et al. 2008, Donoso et al. 2010). While we cannot go into great detail here, our philosophy will be to use simple statistical analyses to compare diversity and species turnover within and across our focal taxa. When more complicated transformations are called for, we will search for models that are robust across a variety of statistical models (Levins 1968). For example, when comparing taxonomic diversity we will compare simple richness (number of OTUs) among our standardized samples with measures that take account of differences in relative and absolute abundance. Broader Impacts We believe that this project – its shortcomings as well as its successes – will be useful in developing a biodiversity component for the planned National Environmental Observatory Network (NEON). We are field testing a comprehensive design for documenting patterns of diversity in different kinds of organisms across diverse spatial and temporal scales, understanding the underlying mechanisms, and predicting impacts of global climate change and other natural and human-caused perturbations. We are also testing ways of organizing a collaborative research team, incorporating field stations, managing and inter-relating large quantities of data, interfacing theory and data, and making comparisons across multiple systems. Introduction The acknowledgement of biodiversity gradients, such as the latitudinal gradient, pre-date modern ecology, however the cause(s) of this pervasive pattern remain elusive and controversial. The patterns of diversity--latitude and elevation on land and latitude and depth in aquatic environments--are strongly related to environmental temperature (e.g., Currie 1991, Tittensoret al. 2010). By focusing on temperature, we aim to better understand its role in generating and maintaining biodiversity and to more accurately forecast the impacts of global warming by developing a robust theoretical framework. Empirical Analysis Theory and Simulation Figure 1. Six sampling sites, colour-coded to indicate relative temperature gradients (see Table 1) Progress to Date Field work: 66% of sites have grids established and tree data recorded. Theory: Computer simulations using are being run testing various factors contributing to species diversity. Inter-group communication: Wiki setup, monthly Skype meetings for PIs, inter-institutional visits, and ESA presentations. Data management: Draft plan in place and hire planned. • Trees: identifying taxonomic species, and characterizing their functional diversity by leaf (including morphology, chemistry and allocation traits), stem and reproductive traits. • Litter invertebrates: quantifying abundance of 27 groups of invertebrates (down to species/morphospecies) and their functional trophic role via stable isotopes. • Soil microbes: characterizing species of archaea, bacteria, fungi and protista by high-throughput sequencing and functionally by micro-arrays that assay for specific enzymes and biochemical processes (GeoChip: He et al. 2010). Literature cited Currie, D. J. 1991. Energy and large-scale patterns of animal-and plant-species richness. Am. Nat.137:27-49. Bryant, J. A., C. Lamanna, H. Morlon, A. J. Kerkhoff, B. J. Enquist, and J. L. Green. 2008. Microbes on mountainsides: contrasting elevational patterns of bacterial and plant diversity. Proceedings of the National Academy of Sciences 105:11505-11511. Donoso, D., M. Johnston, and M. Kaspari. 2010. Trees as templates for tropical litter arthropod diversity. Oecologia164:201-211. He, Z., Y. Deng, J. D. Van Nostrand, Q. Tu, M. Xu, C. L. Hemme, X. Li, L. Wu, T. J. Gentry, and Y. Yin. 2010. GeoChip 3.0 as a high-throughput tool for analyzing microbial community composition, structure and functional activity. The ISME Journal 4:1167-1179. Levins, R. 1968. Evolution in changing environments. Princeton University Press, Princeton, New Jersey. Tittensor, D. P., C. Mora, W. Jetz, H. K. Lotze, D. Ricard, E. V. Berghe, and B. Worm. 2010. Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098-1101. Working Hypotheses The models we develop will make predictions that can be tested empirically. Below, we present some of our working hypotheses with the caveats that 1) we do not expect our sampling and experiments to support all of the hypotheses and 2) failure of our data to support a particular hypothesis will be especially informative. Figure 3. Data collection scheme. Idealized flow chart showing the interplay between data gathered from plots, its availability to the wider community, analysis and refinement of collection schemes based on both empirical analysis and refinements to theories. • H1: Taxonomic and functional diversity and relevant ecological rate processes are temperature-dependent. • H2: Processes that determine taxonomic and functional diversity scale with body size. • H3: Temperature dependence of decomposition varies with chemical composition of the substrate. • H4: Diversity beget diversity, and more diversity at higher temperatures. • H5: Niches are conservative. • H6: Taxonomic, genetic and functional diversity are correlated and similarly temperature dependent. Figure 2. Data collection scheme. Each site will be a 1km2 plot (A) of relatively homogeneous forest. Within this plot, five 50m2plots (B) will be used to identify all individuals with a DBH of 1.0cm or greater. The 50m2 plot is further subdivided into 1m2 plots(C) of which 15 will have litter and the top 1cm of soil sampled to recover litter invertebrates. From each of these 1m2 plots, nine core soil samples (2x15cm) will be recovered an pooled to sample microbial communities. For further information Please contact James Brown jhbrown@unm.edu or Robert Waiderwaide@lternet.edu.