When do we have the power to detect biological interactions in spatial point patterns?

03/05/2018
by   T. Rajala, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/18/2021

Quantifying the overall effect of biotic interactions on species communities along environmental gradients

Separating environmental effects from those of biotic interactions on sp...
research
03/09/2021

Covariate-informed latent interaction models: Addressing geographic taxonomic bias in predicting bird-plant interactions

Climate change and reductions in natural habitats necessitate that we be...
research
07/15/2020

Testing biodiversity using inhomogeneous summary statistics

McGill's theory of biodiversity is based upon three axioms: individuals ...
research
07/13/2021

A Deep Generative Artificial Intelligence system to decipher species coexistence patterns

1. Deciphering coexistence patterns is a current challenge to understand...
research
05/30/2023

Species interactions reproduce abundance correlation patterns in microbial communities

During the last decades macroecology has identified broad-scale patterns...
research
04/03/2023

Computational Validation of a Mathematical Model of Stable Multi-Species Communities in a Hawk Dove Game

We revisit the original hawk-dove game with slight modifications to payo...
research
08/26/2019

Machine learning algorithms to infer trait matching and predict species interactions in ecological networks

Ecologists have long suspected that species are more likely to interact ...

Please sign up or login with your details

Forgot password? Click here to reset