2017 ESA Annual Meeting (August 6 -- 11)

COS 147-6 - Linking plant functional traits and ecosystem functioning at the continental scale

Thursday, August 10, 2017: 3:20 PM
B118-119, Oregon Convention Center

ABSTRACT WITHDRAWN

Franziska Schrodt1, Milan Chytrý2, Fabian Gans3, Borja Jimenéz-Alfaro4, Jens Kattge3, Ingolf Kuehn5, Mario Liebergesell6, Sophia Ratcliffe7, Erik Welk8 and Miguel Mahecha3, (1)School of Geography, University of Nottingham, Nottingham, United Kingdom, (2)Department of Botany and Zoology, Masaryk University, Brno, Czech Republic, (3)Max Planck Institute for Biogeochemistry, Jena, Germany, (4)University of Halle, Germany, (5)Martin-Luther University Halle-Wittenberg, Germany, (6)University of Leipzig, Leipzig, Germany, (7)Special Botany and Functional Biodiversity, University Leipzig, Leipzig, Germany, (8)Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
Franziska Schrodt, University of Nottingham; Milan Chytrý, Masaryk University; Fabian Gans, Max Planck Institute for Biogeochemistry; Borja Jimenéz-Alfaro, University of Halle; Jens Kattge, Max Planck Institute for Biogeochemistry; Ingolf Kuehn, Martin-Luther University Halle-Wittenberg; Mario Liebergesell, University of Leipzig; Sophia Ratcliffe, University Leipzig; Erik Welk, Martin Luther University Halle-Wittenberg; Miguel Mahecha, Max Planck Institute for Biogeochemistry

Background/Question/Methods

Classical biogeography supports the notion that functional diversity (FD) is tightly linked to ecosystem functioning (EF). However, limited availability of large-scale data and unclear guidelines on the measurement of FD have resulted in many unanswered questions. Amongst them are the generality of spatial FD patterns, relative effects of FD on EF and to what degree FD influences ecosystem’s resilience to climatic change. Here, we capitalize on an extensive database of plant functional traits (TRY) in tandem with species distribution and satellite remote sensing data to derive a consistent continental scale map of FD to answer some of these questions.

We extract distribution data of 168 common woody species from eight distribution databases and link them to 13 foliar and woody traits from the TRY database. We apply two commonly used spatial extrapolation methods to integrate information from all distribution databases, namely map overlay, ensemble species distribution models (SDM) which we then validate against a null model of random species distributions. By calculating nine different FD indices, we explore the overall variability of FD resulting from differences in distribution databases and extrapolation methods to explore how spatial patterns of FD correlate with remotely sensed ecosystem functional properties in European eco-regions.

Results/Conclusions

Species distribution and functional diversity maps constructed using the ensemble of species distribution models are significantly different from the null model of random distribution with models not showing consistent differences in accuracy. However, despite of covering the same spatial extent and having a focus on the most common woody plants, species overlap between the distribution databases is surprisingly small with database containing up to four unique species. Spatial overlap is equally small. For example, in the case of Abies alba, a common conifer, the eight databases only agree to 3% about its distribution.

As a consequence of these sources of error, different combinations of data and models used to calculate functional diversity indices for the European continent result in a wide range of projected functional trait space and in some cases inverse relationships between FD and EF. We conclude that the accuracy of functional diversity maps depends to a large degree purely on the choice of distribution dataset which increases the potential to substantially alter conclusions drawn from extrapolated PFT data.

We discuss the implications of our findings in the face of increasing need for continuous trait data to improve predictions of ecosystems responses to a changing world.