Beta-diversity patterns and multi-trophic interactions in an enriched oil palm plantation (Sumatra, Indonesia)

Oil-palm plantation expansion accounts as one of the leading causes of direct and indirect habitat damage and landscape fragmentation in the tropical rainforest. Taking measures to offset the resulting adverse effects that include the loss of biodiversity require to fill some gaps in knowledge regarding ecosystem functioning and the ecosystem response to ecological restoration strategies. My study, as part of the EFForTS-BEE collaborative research, will focus on continue evaluating the effects of species richness in oil palm plantations at a landscape level.

The objective of my study is to assess beta-diversity patterns and the underlying biotic and abiotic drives in the enriched oil-palm landscape. My research questions are 1. What is the pattern of beta-diversity in the landscape? 2. What are the main drivers of beta-diversity? And 3. How do multi-trophic interactions influence diversity distribution?

The data used in this study consist of the presence and abundance of trees and shrubs, herbs, birds, insects, soil fauna, soil fungi, and soil bacteria for the 56 plots (including four control plots), collected between October 2016 and May 2018. First, I will quantify beta-diversity with the abundance data of each fo the groups. For this, I will follow Baselga’s method of separating total dissimilarity into turnover and nestedness components with the idea of determining diversity patterns. For this, I will use the R package betapart (Baselga & Orme, 2012). I will support the results with Legendre’s approach of partitioning beta-diversity into Local Contribution of Beta Diversity LCBD and Species Contribution of Beta Diversity SCBD. Secondly, I will assess the drivers underlying beta-diversity, including multi-trophic groups, abiotic conditions, and experimental conditions on beta-diversity patterns, with the Graphical lasso method (Ohlmann et al., 2018), to represent the partial correlations among the mentioned factors.

This master thesis is supervised by Nathaly Guerrero Ramírez (B06) and Clara Zemp (B11).