Marie Vestergaard Henriksen

Research Scientist

(+47) 907 60 178
marie.henriksen@nibio.no

Place
Trondheim

Visiting address
Klæbuveien 153, bygg C 1.etasje, 7031 Trondheim

To document

Abstract

1. Ecological network structure is maintained by a generalist core of common species. However, rare species contribute substantially to both the species and functional diversity of networks. Capturing changes in species composition and interactions, measured as turnover, is central to understanding the contribution of rare and common species and their interactions. Due to a large contribution of rare interactions, the pairwise metrics used to quantify interaction turnover are, however, sensitive to compositional change in the interactions of, often rare, peripheral specialists rather than common generalists in the network. 2. Here we expand on pairwise interaction turnover using a multi-site metric that enables quantifying turnover in rare to common interactions (in terms of occurrence of interactions). The metric further separates this turnover into interaction turnover due to species turnover and interaction rewiring. 3. We demonstrate the application and value of this method using a host–parasitoid system sampled along gradients of environmental modification. 4. In the study system, both the type and amount of habitat needed to maintain interaction composition depended on the properties of the interactions considered, that is, from rare to common. The analyses further revealed the potential of host switching to prevent or delay species loss, and thereby buffer the system from perturbation. 5. Multi-site interaction turnover provides a comprehensive measure of network change that can, for example, detect ecological thresholds to habitat loss for rare to common interactions. Accurate description of turnover in common, in addition to rare, species and their interactions is particularly relevant for understanding how network structure and function can be maintained.

To document

Abstract

Understanding interactions between individual animals and their resources is fundamental to ecology. Agent-Based Models (ABMs) offer an opportunity to study how individuals move given the spatial distribution and characteristics of their resources. When contrasted with empirical individual-resource network data, ABMs can be a powerful method to detect the processes behind observed movement patterns, as they allow for a complete and quantitative analysis of the agent-to-environment relationships. Here we use the small-scale, within-patch movement of bumblebees (Bombus pascuorum) as a case study to demonstrate how ABMs can be combined with network statistics to provide a deeper understanding of the mechanisms behind the interactions between individuals and their resources. We build an ABM that explicitly simulates the influence of distance to the nearest flowering plant (allowing minimal energy expenditure and maximum time spent foraging), plant height and number of flower heads (as a proxy of food availability) on local foraging decisions of bumblebees. The relative importance of these three elements is determined using pattern-oriented modelling (POM), where we confront the network statistics (number of visited plants, number of interactions, nestedness and modularity) of a real B. pascuorum individual-resource network with the emergent patterns of our ABM. We also explore the model results using spatial analysis. The model is able to reproduce the observed network statistics. Despite the complex behaviour of bumblebees, our results show a surprisingly precise match between the structure of the simulated and empirical networks after adjusting a single model parameter controlling the importance of distance to the next plant visited. Our study illustrates the potential of combining field data, ABMs and individual-resource networks for evaluating small-scale, within-patch movement decisions to better understand animal movements in natural habitats. We discuss the benefits of our approach when compared to more classical statistical methods, and its ability to test various scenarios in a new or altered environment.