Accounting for demographic uncertainty increases predictions for species coexistence: A case study with annual plants. (28th May 2022)

• Record Type: Journal Article
• Authors: Bowler, Catherine H.; Weiss‐Lehman, Christopher; Towers, Isaac R.; Mayfield, Margaret M.; Shoemaker, Lauren G.
• Editors: Enquist, Brian
• Is Part Of: Ecology letters. Volume 25:Number 7(2022)
• Page Start: 1618
• Page End: 1628
• Publication Date: 2022-05-28
• Subjects: Bayesian -- biological realism -- empirical data -- exotic -- native -- species interactions -- stochasticity -- York gum–jam woodlands; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.14011
• Languages: English
• ISSNs: 1461-023X

Abstract

Abstract: Natural systems contain more complexity than is accounted for in models of modern coexistence theory. Coexistence modelling often disregards variation arising from stochasticity in biological processes, heterogeneity among individuals and plasticity in trait values. However, these unaccounted‐for sources of uncertainty are likely to be ecologically important and have the potential to impact estimates of coexistence. We applied a Bayesian modelling framework to data from an annual plant community in Western Australia to propagate uncertainty in coexistence outcomes using the invasion criterion and ratio of niche to fitness differences. We found accounting for this uncertainty altered predictions of coexistence versus competitive exclusion for 3 out of 14 species pairs and yielded a probability of priority effects for an additional species pair. The propagation of uncertainty arising from sources of biological complexity improves our ability to predict coexistence more accurately in natural systems. Abstract : Coexistence modelling often disregards variation arising from stochasticity in biological processes, heterogeneity among individuals and plasticity in trait values. However, these unaccounted for sources of uncertainty are likely to be ecologically important and have the potential to impact estimates of coexistence. We applied a Bayesian modelling framework to data from an annual plant community in Western Australia to propagate uncertainty in coexistence outcomes and found that it widened the window for coexistence for 3 out of 14 species pairs.