A common framework for identifying linkage rules across different types of interactions

Abstract

Summary1. Species interactions, ranging from antagonisms to mutualisms, form the architecture of biodiversity and determine ecosystem functioning. Understanding the rules responsible for who interacts with whom, as well as the functional consequences of these interspecific interactions, is central to predict community dynamics and stability. 2. Species traits sensu lato may affect different ecological processes by determining species interactions through a two-step process. First, ecological and life-history traits govern species distributions and abundance, and hence determine species co-occurrence and the potential for species to interact. Secondly, morphological or physiological traits between co-occurring potential interaction partners should match for the realization of an interaction. Here, we review recent advances on predicting interactions from species co-occurrence and develop a probabilistic model for inferring trait matching. 3. The models proposed here integrate both neutral and trait-matching constraints, while using only information about known interactions, thereby overcoming problems originating from undersampling of rare interactions (i.e. missing links). They can easily accommodate qualitative or quantitative data and can incorporate trait variation within species, such as values that vary along developmental stages or environmental gradients. 4. We use three case studies to show that the proposed models can detect strong trait matching (e.g. predator-prey system), relaxed trait matching (e.g. herbivore-plant system) and barrier trait matching (e.g. plant-pollinator systems). 5. Only by elucidating which species traits are important in each process (i.e. in determining interaction establishment and frequency), we can advance in explaining how species interact and the consequences of these interactions for ecosystem functioning.

Trophic level, successional age and trait matching determine specialization of deadwood-based interaction networks of saproxylic beetles

“…Finally, we used a recently developed framework by Bartomeus et al [23] that enables prediction of the probability of interactions between plants and animals (or other interaction partners) based upon their abundance and functional traits (implemented in R package traitmatch). A major strength of this framework is its ability to partition these probabilities into 'neutral' and 'niche' components, which can be compared using log-likelihoods.…”

“…For deadwood, the amounts of three different chemical compounds (acid-soluble lignin, Klason lignin and organic extractives) were determined as important resource traits, which are expected to shape interactions with the decomposer community. We were particularly interested in whether network characteristics were determined by niche-based matching of resource traits and beetle traits or by the relative abundance of co-occurring beetle species [23].…”

The use of DNA barcodes in food web construction—terrestrial and aquatic ecologists unite!

“…In other environments, the structuring force seems to vary with the specific type of interaction examined (Petchey et al 2008;Dunne et al 2013). Indeed, multiple recent studies stress the need for identifying common rules dictating who interacts with whom within large communities (Gravel et al 2013;Morales-Castilla et al 2015;Bartomeus et al 2016). Interestingly, when food webs from different environments are collated in joint analyses of, e.g., latitudinal trends, partly different patterns appear in different environments, again suggesting slightly different drivers in different systems (Schleuning et al 2012;Morris et al 2014;Cirtwill et al 2015).…”

The meaning of functional trait composition of food webs for ecosystem functioning

“…A trait-matching function describes the probability of observing this interaction, given the traits of the two species [66]:…”

“…The niche model was fitted directly with the assumption that body size is the main niche axis and that the predator-prey body size relationship represents the optimal niche of a species [85]. This method was successfully extended to other types of interactions, such as plant-herbivores where traits such as leaf dry matter content and incisive strength are good predictors of interactions [66]. Crea et al [86] considered multinomial regression to model interaction probability based on plant and pollinator traits and abundance.…”