C2 Remote sensing as surrogate for phylodiversity and functional processes along land use and elevation gradients.

Project staff:


Prof. Dr. Jörg Bendix
Prof. Dr. Nina Farwig
Prof. Dr. Roland Brandl
Mrs Christine Wallis
Dr. Yvonne Tiede

Abstract:

The proposed project aims at investigating how changes in land use and elevation affects the functional and phylodiversity of trees and birds and how this translates into the associated processes in particular herbivory and predation. As it is time consuming to quantify these measures of biodiversity and processes we need a simple indicator system for routine monitoring across large areas. New developments in remote sensing provide promising information for predicting biodiversity as well as ecosystem processes. Spectral diversity derived from remote sensing is for example positively linked to biochemical diversity of trees. In addition, the vegetation reacts on subtle changes due to herbivory by detectable changes in netto primary production and leaf pigment status. Therefore, we expect that we can predict variables describing the status of biodiversity as well as certain processes by measures of spectral and structural diversity derived from remote sensing. This would provide the ground to develop a simple and integrative indicator for ecosystem services. Such an indicator system based on remote sensing would be an important step towards an integrative monitoring of the status of biodiversity, ecosystem functioning and finally ecosystem services that can be used across large areas and even in areas with rough terrains.



Description:

Introduction
Increasing impact of land use and climate change demand for monitoring of basic and functional indicators. However, ground-based monitoring programs are time-consuming and costly. Remote sensing technologies offer the possibilities to develop integrative indicators over large spatial areas and at high resolutions. Therefore, we expect that we can predict variables describing the status of biodiversity and processes by measures of spectral and structural diversity derived from remote sensing.

 

Study Design
For ground-truthing we will investigate 5 elevations in the mountain rain forest with 3 replicates in undisturbed and disturbed forests each. The overall plot size will be 90m x 90m partly included in the selected 100m x 100m core plots of the program.

 

WP1 Trees
We will assess species diversity, phylodiversity and functional diversity of trees on all joint 30 plots (50% sampled by J. Homeier). For functional diversity measures, we will take advantage of the database on traits (e.g. specific leaf area, leaf lamina toughness) developed by J. Homeier. Both, the species diversity and additionally sequence data from chloroplast DNA regions will feed the phylogenetic analyses.

 

WP2 Predators
We will determine species richness, phylodiversity and functional diversity of birds on all 30 plots. Functional traits (e.g. degree of insectivory, body mass) will be obtained from the literature or measured using museum material. Sufficient phylogenetic information is available to estimate a phylogeny with branch length of the recorded species.

 

WP3 Ecosystem Processes
We will quantify herbivory by leaf area loss of understory plants on the core 20m x 20m plots while predation will be recorded using experimentally exposed plasticine insect larvae.

 

WP4 & 5 Analysis & Synthesis
Phylodiversity will be calculated based on the phylogenetic trees as well as by using various null models for community assembly. Functional diversity will be split into three independent components (functional richness, evenness and divergence) to achieve a more detailed analysis of the mechanisms linking environmental variables to species and functional diversity. Analyses will proceed along two lines:
(1) We will use GLMs and structural equation modeling to test for effects of land use and elevation on our measures of diversity and processes.
(2) We will develop transfer functions relating the status of biodiversity and processes to remotely sensed indicators by using machine learning programs as well as partial least square regression (together with C6).




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