Initial abundance and stochasticity influence competitive outcome in communities.
Tribolium
competition
extinction risk
priority effects
stochasticity
Journal
The Journal of animal ecology
ISSN: 1365-2656
Titre abrégé: J Anim Ecol
Pays: England
ID NLM: 0376574
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
30
12
2020
accepted:
18
03
2021
pubmed:
25
3
2021
medline:
31
7
2021
entrez:
24
3
2021
Statut:
ppublish
Résumé
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 (Tribolium sp.). To do so, we developed a set of two-species stochastic Ricker models incorporating four distinct forms of stochasticity: environmental stochasticity, demographic stochasticity, demographic heterogeneity and stochastic sex determination. 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.
Identifiants
pubmed: 33759453
doi: 10.1111/1365-2656.13485
doi:
Banques de données
figshare
['10.6084/m9.figshare.5593633']
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1691-1700Informations de copyright
© 2021 British Ecological Society.
Références
Adler, P. B., & Drake, J. M. (2008). Environmental variation, stochastic extinction, and competitive coexistence. The American Naturalist, 172, E186-E195. https://doi.org/10.1086/591678
Akçakaya, H. R. (1991). A method for simulating demographic stochasticity. Ecological Modelling, 54, 133-136. https://doi.org/10.1016/0304-3800(91)90103-8
Bullock, M., Legault, G., & Melbourne, B. A. (2020). Interspecific chemical competition between Tribolium castaneum and Tribolium confusum (Coleoptera: Tenebrionidae) reduces fecundity and hastens development time. Annals of the Entomological Society of America, 113(3), 216-222. https://doi.org/10.1093/aesa/saaa001
Chesson, P. L. (1986). Environmental variation and the coexistence of species. Community Ecology, 240, 54.
Costantino, R. F., & Desharnais, R. A. (1991). Dynamics and the Tribolium model. Population dynamics and the Tribolium model: Genetics and demography (pp. 214-237). Springer.
Dallas, T., Legault, G., Melbourne, B., & Hastings, A. (2018). Data and code to reproduce: Initial abundance and stochasticity influence competitive outcome in experimental communities. Figshare, https://doi.org/10.6084/m9.figshare.5593633.v1
Dallas, T., Melbourne, B., & Hastings, A. (2020). Community context and dispersal stochasticity drive variation in spatial spread. Journal of Animal Ecology, 89(11), 2657-2664. https://doi.org/10.1111/1365-2656.13331
Dawson, P. S. (1970). A further assessment of the role of founder effects in the outcome of Tribolium competition experiments. Proceedings of the National Academy of Sciences of the United States of America, 66, 1112-1118. https://doi.org/10.1073/pnas.66.4.1112
Desharnais, R. A., Costantino, R., Cushing, J., Henson, S. M., Dennis, B., & King, A. A. (2006). Experimental support of the scaling rule for demographic stochasticity. Ecology Letters, 9, 537-547. https://doi.org/10.1111/j.1461-0248.2006.00903.x
Desharnais, R. A., Edmunds, J., Costantino, R., & Henson, S. M. (2005). Species competition: Uncertainty on a double invariant loop. Journal of Difference Equations and Applications, 11, 311-325. https://doi.org/10.1080/10236190412331335391
Drake, J. M. (2005). Density-dependent demographic variation determines extinction rate of experimental populations. PLoS Biology, 3, e222. https://doi.org/10.1371/journal.pbio.0030222
Edmunds, J., Cushing, J. M., Costantino, R. F., Henson, S. M., Dennis, B., & Desharnais, R. (2003). Park's Tribolium competition experiments: A non-equilibrium species coexistence hypothesis. Journal of Animal Ecology, 72, 703-712. https://doi.org/10.1046/j.1365-2656.2003.00743.x
Ellner, S. P., Snyder, R. E., & Adler, P. B. (2016). How to quantify the temporal storage effect using simulations instead of math. Ecology Letters, 19, 1333-1342. https://doi.org/10.1111/ele.12672
Feller, W. (1939). The foundations of the volterravel theory of the fight for being in probability theoresthetic treatment. Acta Biotheoretica, 5, 11-40.
Goodnight, C. J., & Craig, D. M. (1996). The effect of coexistence on competitive outcome in Tribolium castaneum and Tribolium confusum. Evolution, 1241-1250.
Gravel, D., Guichard, F., & Hochberg, M. E. (2011). Species coexistence in a variable world. Ecology Letters, 14, 828-839. https://doi.org/10.1111/j.1461-0248.2011.01643.x
Hardin, G. (1960). The competitive exclusion principle. Science, 131, 1292-1297. https://doi.org/10.1126/science.131.3409.1292
Hart, S. P., Schreiber, S. J., & Levine, J. M. (2016). How variation between individuals affects species coexistence. Ecology Letters, 19(8), 825-838. https://doi.org/10.1111/ele.12618
Högnäs, G. (2012). On a stochastic Ricker competition model. International Conference on Difference Equations and Applications (pp. 135-144). Springer.
Hufbauer, R. A., Szűcs, M., Kasyon, E., Youngberg, C., Koontz, M. J., Richards, C., Tuff, T., & Melbourne, B. A. (2015). Three types of rescue can avert extinction in a changing environment. Proceedings of the National Academy of Sciences of the United States of America, 112, 10557-10562. https://doi.org/10.1073/pnas.1504732112
Kotaki, T., & Fujii, H. (1995). Crowding inhibits pupation in Tribolium freemani: Contact chemical and mechanical stimuli are involved. Entomologia Experimentalis et Applicata, 74, 145-149. https://doi.org/10.1111/j.1570-7458.1995.tb01886.x
Lande, R. (1993). Risks of population extinction from demographic and environmental stochasticity and random catastrophes. The American Naturalist, 142, 911-927. https://doi.org/10.1086/285580
Lande, R., Engen, S., & Saether, B. E. (2003). Stochastic population dynamics in ecology and conservation.
Lawson, C. R., Vindenes, Y., Bailey, L., & Pol, M. (2015). Environmental variation and population responses to global change. Ecology Letters, 18, 724-736. https://doi.org/10.1111/ele.12437
Legault, G., Bitters, M. E., Hastings, A., & Melbourne, B. A. (2020). Interspecific competition slows range expansion and shapes range boundaries. Proceedings of the National Academy of Sciences of the United States of America, 117, 26854-26860. https://doi.org/10.1073/pnas.2009701117
Legault, G., Fox, J. W., & Melbourne, B. A. (2019). Demographic stochasticity alters expected outcomes in experimental and simulated non-neutral communities. Oikos, 128, 1704-1715. https://doi.org/10.1111/oik.06028
Leslie, P. (1962). A stochastic model for two competing species of Tribolium and its application to some experimental data. Biometrika, 49, 1-25.
Leslie, P., Park, T., & Mertz, D. B. (1968). The effect of varying the initial numbers on the outcome of competition between two Tribolium species. Journal of Animal Ecology, 37(1), 9-23. https://doi.org/10.2307/2708
Luís, R., Elaydi, S., & Oliveira, H. (2011). Stability of a Ricker-type competition model and the competitive exclusion principle. Journal of Biological Dynamics, 5, 636-660. https://doi.org/10.1080/17513758.2011.581764
May, R. M. (1973). Stability in randomly fluctuating versus deterministic environments. The American Naturalist, 107, 621-650. https://doi.org/10.1086/282863
Melbourne, B. A., & Hastings, A. (2008). Extinction risk depends strongly on factors contributing to stochasticity. Nature, 454, 100-103. https://doi.org/10.1038/nature06922
Melbourne, B. A., & Hastings, A. (2009). Highly variable spread rates in replicated biological invasions: Fundamental limits to predictability. Science, 325, 1536-1539. https://doi.org/10.1126/science.1176138
Mertz, D. B., Cawthon, D., & Park, T. (1976). An experimental analysis of competitive indeterminacy in Tribolium. Proceedings of the National Academy of Sciences of the United States of America, 73, 1368-1372. https://doi.org/10.1073/pnas.73.4.1368
Mutshinda, C. M., O’Hara, R. B., & Woiwod, I. P. (2009). What drives community dynamics? Proceedings of the Royal Society B: Biological Sciences, 276(1669), 2923-2929. https://doi.org/10.1098/rspb.2009.0523
Neubert, M. G., & Caswell, H. (2000). Density-dependent vital rates and their population dynamic consequences. Journal of Mathematical Biology, 41, 103-121. https://doi.org/10.1007/s002850070001
Noonburg, E. G., Chen, A., Shima, J. S., & Swearer, S. E. (2015). Demographic heterogeneity and the dynamics of open populations. Ecology, 96, 1159-1165. https://doi.org/10.1890/14-1531.1
Okuyama, T. (2015). Demographic stochasticity alters the outcome of exploitation competition. Journal of Theoretical Biology, 365, 347-351. https://doi.org/10.1016/j.jtbi.2014.10.040
Orrock, J. L., & Watling, J. I. (2010). Local community size mediates ecological drift and competition in metacommunities. Proceedings of the Royal Society B: Biological Sciences, 277(1691), 2185-2191. https://doi.org/10.1098/rspb.2009.2344
Ovaskainen, O., & Meerson, B. (2010). Stochastic models of population extinction. Trends in Ecology & Evolution, 25, 643-652. https://doi.org/10.1016/j.tree.2010.07.009
Park, T. (1948). Experimental studies of interspecies competition. 1. Competition between populations of the flour beetles, Tribolium confusum Duval and Tribolium castaneum Herbst. Ecological Monographs, 18, 265-307.
Park, T. (1954). Experimental studies of interspecies competition II. Temperature, humidity, and competition in two species of Tribolium. Physiological Zoology, 27, 177-238. https://doi.org/10.1086/physzool.27.3.30152164
Park, T. (1957). Experimental studies of interspecies competition. III Relation of initial species proportion to competitive outcome in populations of Tribolium. Physiological Zoology, 30, 22-40. https://doi.org/10.1086/physzool.30.1.30166306
Park, T., & Frank, M. B. (1948). The fecundity and development of the flour beetles, Tribolium confusum and Tribolium castaneum, at three constant temperatures. Ecology, 29, 368-374. https://doi.org/10.2307/1930996
Pedruski, M. T., Fussmann, G. F., & Gonzalez, A. (2015). Predicting the outcome of competition when fitness inequality is variable. Royal Society Open Science, 2, 150274. https://doi.org/10.1098/rsos.150274
Petchey, O. L., Gonzalez, A., & Wilson, H. B. (1997). Effects on population persistence: the interaction between environmental noise colour, intraspecific competition and space. Proceedings of the Royal Society of London B. Biological Sciences, 264, 1841-1847.
Purvis, A., Gittleman, J. L., Cowlishaw, G., & Mace, G. M. (2000). Predicting extinction risk in declining species. Proceedings of the Royal Society of London. Series B: Biological Sciences, 267(1456), 1947-1952. https://doi.org/10.1098/rspb.2000.1234
R Core Team. (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
Sears, A. L., & Chesson, P. (2007). New methods for quantifying the spatial storage effect: An illustration with desert annuals. Ecology, 88, 2240-2247. https://doi.org/10.1890/06-0645.1
Simonis, J. L. (2012). Demographic stochasticity reduces the synchronizing effect of dispersal in predator-prey metapopulations. Ecology, 93, 1517-1524. https://doi.org/10.1890/11-0460.1
Vindenes, Y., Engen, S., & Saether, B. E. (2008). Individual heterogeneity in vital parameters and demographic stochasticity. The American Naturalist, 171, 455-467. https://doi.org/10.1086/528965
Weiss-Lehman, C., Hufbauer, R. A., & Melbourne, B. A. (2017). Rapid trait evolution drives increased speed and variance in experimental range expansions. Nature Communications, 8, 14303. https://doi.org/10.1038/ncomms14303