Predicting competitive outcomes in communities frequently involves inferences based on deterministic population models since these provide clear criteria for exclusion (e.g. R* rule) or long‐term coexistence (e.g. mutual invasibility). However, incorporating stochasticity into population‐ or community‐level processes into models is necessary if the goal is to explain variation in natural systems, which are inherently stochastic. Similarly, in systems with demographic or environmental stochasticity, weaker competitors have the potential to exclude superior competitors, contributing to what is known as ‘competitive indeterminacy’. The importance of such effects for natural communities is unknown, in part because it is difficult to demonstrate that multiple forms of stochasticity are present in these communities. Moreover, the effects of multiple forms of stochasticity on competitive outcomes are largely untested, even in theory. Here, we address these issues by examining the role of stochasticity in replicated communities of flour beetles ( By fitting models to experimental data, and simulating fit models to examine long‐ term behaviour, we found that both the duration of transient coexistence and the degree of competitive indeterminacy were sensitive to the forms of stochasticity included in our models. These findings suggest the current estimates of extinction risk, coexistence and time until competitive exclusion in communities may not be accurate when based on models that exclude relevant forms of stochasticity.
Unexpected population crashes are an important feature of natural systems, yet many observed crashes have not been explained. Two difficulties in explaining population crashes are their relative rarity and the multi‐causal nature of ecological systems. We approach this issue with experimental microcosms, with large numbers of replicates of red flour beetle populations (
- NSF-PAR ID:
- 10378371
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 25
- Issue:
- 12
- ISSN:
- 1461-023X
- Page Range / eLocation ID:
- p. 2573-2583
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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