A pressing challenge for ecologists is predicting how human-driven environmental changes will affect the complex pattern of interactions among species in a community. Weighted networks are an important tool for studying changes in interspecific interactions because they record interaction frequencies in addition to presence or absence at a field site. Here we show that changes in weighted host-parasitoid networks are, in principle, predictable. Our approach combines field data with mathematical models that separate changes in relative species abundance from changes in interaction preferences (which describe how interaction frequencies deviate from random encounters).
Results/Conclusions
The models with the best predictive ability compared to data requirement are those that capture systematic changes in interaction preferences between different habitat types. These changes likely reflect altered host selectivity by parasitoids in habitat types with minimal forest coverage. This new modelling approach represents a simple yet powerful way of scaling up existing data to predict weighted network structure across multiple field sites of a given habitat type, and inform the amount and type of additional data that should be collected in novel environments to improve predictions. Our results suggest a viable approach for predicting the consequences of rapid environmental change for the structure of complex ecological networks, even in the absence of detailed, system-specific empirical data.