Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Abstract

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.

Major challenges for correlational ecological niche model projections to future climate conditions

“…This idea has seen tests (in the present time) that have yielded both positive and negative results, and yet a genuinely cross‐taxon, comprehensive test across many types of interactions (including interactions that are not known a priori ) remains a gap in knowledge. Using patterns of co‐occurrence to detect interactions is an emerging temptation, but ignores the many reasons for which species may co‐occur or not, and therefore remains limited and assumption ridden …”

Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

“…Analyses of community turnover are usually performed with data represented in a table with rows corresponding to sites (or measurements) and columns to species. Some previous studies have considered how species distribution could be influenced by the joint effects of the abiotic and biotic environment (Stephens and Heau 2009, González-Salazar et al 2013, Cazelles et al 2015, Ovaskainen et al 2017), here we inverse the problem and describe how the distribution of biotic interactions is influenced by species distribution and the environment. Traditional approaches rely on measures of dissimilarity among communities, such as the Jaccard or Bray-Curtis indices.…”

Constructing Ecological Networks: A Tool to Infer Risk of Transmission and Dispersal of Leishmaniasis

“…In general, though, the number of potential species that are involved in the transmission of a disease will be more than the known ones (Acha and Szyfres, 2003; Reithinger et al., 2007; De Lima et al., 2008). In other words, the number of known reservoirs is usually much less than the number of potential reservoirs (Stephens et al., 2009). This is especially true of a neglected disease such as leishmaniasis.…”

“…However, what was not considered in that article is how this novel methodology can also be used to incorporate variable types other than point collection data. In this contribution, we show how the methodology proposed in the study of Stephens et al., 2009 to construct ecological networks can also be used to model other important variables. In particular, we use land cover to infer the role of landscape in determining the dynamics of disease transmission and dispersal and consequently identify geographical patterns and focal species for leishmaniasis transmission.…”

“…Thus, the logic of the present methodology is to construct ‘first‐order’ risk models for transmission and dispersal by assuming that in the absence of other information, competencies can be assumed to be equal. The fact that this approach can lead to predictive results is manifest in the study of Stephens et al. (2009), where the methodology correctly identified currently known reservoirs of Leishmania.…”