Using local ecological knowledge to build mutualistic networks in hyper-diverse and logistically challenging ecosystems

1. Collecting interaction data to build frugivory or seed dispersal networks is logistically challenging in ecosystems that have very high plant and animal diversity and/or where fieldwork is difficult or dangerous. Consequently, the majority of available networks are from ecosystems with low species diversity or they represent a sub-set of the community. 

2. Here, we propose an approach using local ecological knowledge (LEK) of indigenous communities to build interaction databases and weighted networks that would otherwise be difficult to achieve with direct observations. Indigenous communities live and work in many hyper-diverse ecosystems and the people within these communities often have detailed knowledge of ecological processes. 

3. Working in a Sundaland biodiversity hotspot – Royal Belum State Park, Peninsular Malaysia – we used field data, visually-oriented interviews with indigenous people (Orang Asli, in the Jahai and Temiar ethnic subgroups), and published records to collate interactions, and their frequency of occurrence of animal fruit consumption and seed dispersal. 

4. We documented 2060 fruit consumption and 1330 seed dispersal interactions among 164 plant species and 34 animal taxa, the latter representing groups of closely related species or individual species. The majority of the interactions (97%) were identified by the LEK interviews, with the additional methods (field data and published records) used to support and marginally expand the interview data. The metrics for the networks we built reflect those of networks structured by biological mechanisms, supporting the validity of our novel method. 

5. Local ecological knowledge is highly relevant for building detailed databases for mutualistic interactions in hyper-diverse and/or challenging ecosystems. Such ecosystems are among the most vulnerable on earth, harbouring ecological interactions that are often poorly documented at a community-level. We show how LEK can broaden our knowledge of such sensitive ecosystems, but our approach is useful for any ecosystem where people retain rich local ecological knowledge.

Methods
The method is fully described in Ong et al. (2021), "Building ecological networks with local ecological knowledge in hyper-diverse and logistically challenging ecosystems". Further details are provided in the online supplementary materials.