Complex systems, like the internet, the brain, or ecological communities, can be represented as networks of interconnected websites, neurons, or species. As networks grow and change, special properties emerge that can tell us about system stability and function. Ecological interaction networks can reveal how communities are assembled, how species coexist, and resilience to disturbance. I study networks of mutualistic interactions between plants and their pollinators and between figs and seed dispersing vertebrates.
Habitat loss can reduce the number of species in a community. But habitat loss can also indirectly change the way the remaining species interact in a network, which may exacerbate the negative effects of habitat loss (Spiesman & Inouye 2013).
In adaptive networks, species shuffle their interaction partners to increase net resource gain. Pollinator species adaptively partition resources among themselves in order to reduce competition for floral resources. This results in highly modular networks (Spiesman & Gratton 2016). In addition to allowing more species to coexist, emergent modularity may also benefit communities by limiting cascading disturbances through the network, e.g., contagious disease.
A network approach is also useful for understanding how direct interactions between two species can have indirect consequences for others. For example, being attacked by a predator may ruin your day. But if that same predator also attacks your competitor, suddenly having the predator around is not so bad. Indirect effects can range from positive to negative and can have important effects on biodiversity. I’m using models to examine how multiple interaction types (mutualism, competition, predation) can simultaneously influence coexistence, community dynamics, and network structure (e.g., Spiesman & Inouye 2015).