Predicting and managing the recovery of tropical forests

Background: The majority of the Earth’s tropical forests have been logged at some point. Recovery of forests following logging is however poorly understood. To better manage forests in the future, we, therefore, need data that will allow us to predict how forests recover, as well as to understand what limits recovery.

It is increasingly recognised that a major limitation on recovery is the growth of epiphytic lianas: recent estimates suggest that these can comprise up to 20% of the biomass of formerly logged forests. They inhibit tree growth, prevent seedling recruitment and increase tree mortality. To facilitate forest recovery, large-scale liana cutting programmes have been initiated in many forests. However, the long-term effectiveness of these is poorly understood. In this project, we aim to develop data-driven modelling tools that allow us to predict long-term outcomes.

Timeliness: The work is timely given the increasing interest in both understanding the role of forests in carbon capture, as well as predicting what future forests will look like. Our project is novel in linking experimental manipulations to predictive models.

Objectives: The overall aim of the project is to collect data that will allow us to develop models that can be used to predict how forests will recover under different management regimes (e.g. removing epiphytes).

(1) Set up a series of experimental plots in which species composition and tree / seedling size are measured in experimental plots in which lianas are removed as well as controls in which they remain. We will use a protocol developed by RF and DE in 2016.
(2) Analyse the data to estimate carbon stocks, tree growth and survival, and how these change immediately following removal.
(3) Using the long-term information on forest management, we will analyse forest structure in areas cleared of lianas in the past 20 years. Information on past clearance is available from the INFAPRO project in Borneo; we will resurvey areas previously cleared.
(4) Develop models predicting forest structure in terms of size and composition.

Person specification: The project has a significant field component in tropical forests, so we are looking for someone who enjoys spending time in the field. You will be working as part of a team so communication skills are important. Academically we are looking for a background in ecology, conservation and ideally with some training in statistics (familiarity with R would be ideal). Modelling is part of the project objectives and training will be provided in this, as well as in advanced statistics and field techniques relevant to the project.

Suggested Reading:
Gilroy JJ, Woodcock P, Edwards FA, Wheeler CE, Baptiste BLG, Medina CA, Haugaasen T, Edwards DP (2014) Cheap carbon and biodiversity co-benefits from forest regeneration in a hotspot of endemism. Nature Climate Change 4: 503-507
Magnago LFS, Magrach A, Barlow J, Goncalves CE, Schaefer R, Laurance WF, Martins SV, Edwards DP. Fragment size and edge effects predict carbon stocks in trees and lianas in tropical forests. Functional Ecology in press
Magrach A, Senior R, Rogers A, Nurdin D, Benedick S, Laurance WF, Santamaria L, Edwards DP (2016) Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species. Proceedings of the Royal Society B 283: 20153008.