EGU2020-5174, updated on 12 Jun 2020
https://doi.org/10.5194/egusphere-egu2020-5174
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Potential of LiDAR for species richness prediction at Mount Kilimanjaro

Alice Ziegler1 and the Research Group at the Kilimanjaro*
Alice Ziegler and the Research Group at the Kilimanjaro
  • 1Philipps-Universität Marburg, FB 19 Geographie, Umweltinformatik, Marburg, Germany (alice.ziegler@geo.uni-marburg.de)
  • *A full list of authors appears at the end of the abstract

To mitigate the negative effects of biodiversity loss, monitoring of species and functional diversity is an important prerequisite for focused management plans. However, sampling of biodiversity during field campaigns is labor- and cost-intensive. Therefore, researchers often use proxies extracted from three-dimensional and high-resolution airborne LiDAR (Light Detection and Ranging) data of the vegetation for predicting biodiversity measures (e.g. species richness or diversity).

This study aims at (i) assessing the suitability of LiDAR observations to map species richness across 17 taxonomic groups and four trophic levels at Mount Kilimanjaro and (ii) differentiating the predictive power of LiDAR-derived structural information from what is already explained by elevation, thereby comparing the prediction potential across taxa and trophic levels.

The field data for this study were collected across 59 plots along an elevation gradient of about 4000 meters at the southern slopes of Mount Kilimanjaro using established methods to sample the selected groups of organisms. The prediction is accomplished with three consecutive steps: (1) Species richness of each taxon is estimated using Partial Least Square Regression (PLSR) with only elevation and its square as independent variables. (2) The residuals of this model are then predicted using the LiDAR-derived variables and PLSR. (3) This third model is subsequently compared to a model that uses the same LiDAR-derived variables and PLSR to predict species richness directly rather than its residuals. This procedure allows to analyze the impact of elevation versus structure on each taxon. Furthermore, the standardized study design allows to compare the predictability of species richness across the selected groups of organisms.

Results of this study show that most taxa can be best predicted by elevation, even though in most cases the structural models perform almost equally. As expected, results of the model performances of trophic levels indicate, that herbivores are influenced more by structure than decomposers and generalists.

Research Group at the Kilimanjaro:

Alice Ziegler, Insa Otte, Roland Brandl, Marcell K. Peters, Tim Appelhans, Christina Behler, Alice Classen, Florian Detsch, Jürgen Deckert, Connal D. Eardley, Andreas Ensslin, Stefan W. Ferger, Sara B. Frederiksen, Friederike Gebert, Michael Haas, Maria Helbig-Bonitz, Andreas Hemp, Claudia Hemp, Victor Kakengi, William J. Kindeketa, Antonia Mayr, Hanna Meyer, Ephraim Mwangomo, Christine Ngereza, Marion Renner, Juliane Röder, Gemma Rutten, David Schellenberger Costa, Matthias Schleuning, Maximilian G.R. Vollstädt, Ralph S. Peters, Axel Ssymank, Joseph Tardanico, Stephan Wöllauer, Giulia Zancolli, Jie Zhang, Ingolf Steffan-Dewenter, Markus Fischer, Katrin Böhning-Gaese, Elisabeth K.V. Kalko, Michael Kleyer, Matthias Schleuning, Marco Tschapka, Thomas Nauss

How to cite: Ziegler, A. and the Research Group at the Kilimanjaro: Potential of LiDAR for species richness prediction at Mount Kilimanjaro , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5174, https://doi.org/10.5194/egusphere-egu2020-5174, 2020

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