Species interactions: estimating per-individual interaction strength and covariates before simplifying data into per-species ecological networks
Article first published online: 5 OCT 2012
© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 1, pages 1–8, January 2013
How to Cite
Wells, K., O'Hara, R. B. (2013), Species interactions: estimating per-individual interaction strength and covariates before simplifying data into per-species ecological networks. Methods in Ecology and Evolution, 4: 1–8. doi: 10.1111/j.2041-210x.2012.00249.x
- Issue published online: 24 JAN 2013
- Article first published online: 5 OCT 2012
- Manuscript Accepted: 29 AUG 2012
- Manuscript Received: 3 JUN 2012
- ecological fallacy;
- ecological networks;
- biotic interactions;
- hierarchical models;
- random graphs;
- species specialization
- Ecological network models based on aggregated data from species interactions are widely used to make inferences about species specialization, functionality and extinction risk. While increasing number of network data are available and are used in comparative studies, data quality and uncertainty have received little attention. Moreover, key individual-level information such as the proportion of individuals not involved in interactions and underlying processes driving interactions are ignored by aggregated data analysis.
- We suggest an individual-level hierarchical interaction model as a more flexible approach to considering uncertainty, sampling effort and conditions under which interactions take place and from which network attributes can be derived. We performed a simulation exercise to compare inference under different sample sizes and from aggregated data matrices to those from our individual-level model.
- Formalizing the process of network formation in an individual-level model made clear that per-species interaction frequencies are not independent of sample size and population pools and also ignore important information given by the proportion of non-interacting individuals. Hierarchical linear models are a possible solution to infer community-level attributes of network formation and allow various kinds of comprehensive model extensions to capture variation of per-individual interactions in space and time that shape upper level organization.
- Individual-level hierarchical models provide the link between individual behaviour and interactions under variable environmental conditions and can be summarized into networks in a conceptually neat way. Such models may not only help to account for various sources of variation but also conceptualize aspects overlooked in aggregated data. In particular, the quantification of per-individual interactions under different sampling scenarios emphasizes that per-species interaction frequencies at the species level are not necessarily a surrogate of species abundance in natural systems under investigation.