When do we have the power to detect biological interactions in spatial point patterns?
Determining the relative importance of environmental factors, biotic interactions and stochasticity in assembling and maintaining species-rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to leave a spatial signature in the form of positive or negative spatial correlations over distances relating to the spatial scale of interaction. Most studies using spatial point process tools have found relatively little evidence for interactions between pairs of species. More interactions tend to be detected in communities with fewer species. However, there is currently no understanding of how the power to detect spatial interactions may change with sample size, or the scale and intensity of interactions. We use a simple 2-species model where the scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of bivariate species independence. We show that the power to detect interactions is positively related to the abundances of the species tested, and the intensity and scale of interactions. Increasing imbalance in abundances has a negative effect on the power to detect interactions. At population sizes typically found in currently available datasets for species-rich plant communities we find only a very low power to detect interactions. Differences in power may explain the increased frequency of interactions in communities with fewer species. Furthermore, the community-wide frequency of detected interactions is very sensitive to a minimum abundance criterion for including species in the analyses.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Empowering individual trait prediction using interactions
One component of precision medicine is to construct prediction models with their predictive ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases have a polygen…
Dimensionality ReductionEpidemiologyPredictionImproved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias
Protein-protein interactions drive many biological processes, including the detection of phytopathogens by plants' R-Proteins and cell surface receptors. Many machine learning studies have attempted to predict protein-pr…
Automated Discovery of Pairwise Interactions from Unstructured Data
Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are low dimensional, hand crafted measuremen…
Active LearningPredicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. However, current biological networks are noisy, sparse…
Model SelectionInference of hyperedges and overlapping communities in hypergraphs
Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on stat…
Hyperedge Prediction