Neighbor density‐dependent facilitation promotes coexistence and internal oscillation
The ability of species to form diverse communities is not fully understood. Species are known to interact in various ways with their neighborhood. Despite this, common phenomenological models of species coexistence assume that per capita interactions are constant and competitive, even as the environ...
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| Published in: | Ecological monographs Vol. 95; no. 4 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.11.2025
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| ISSN: | 0012-9615, 1557-7015 |
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| Abstract | The ability of species to form diverse communities is not fully understood. Species are known to interact in various ways with their neighborhood. Despite this, common phenomenological models of species coexistence assume that per capita interactions are constant and competitive, even as the environment changes. In this study, we investigate how neighbor density‐dependent variation in the strength and sign of species interactions changes species and community dynamics. We demonstrate that incorporating these sources of variation significantly improves predictions of ecological dynamics compared to the outcomes of typical models, which hold interaction strengths constant. We compared the performance of models based on different functions of neighbor density and identity in describing population trajectories (i.e., persistence over time) and community dynamics (i.e., temporal stability, synchrony, and degree of oscillation) in simulated two‐species communities and a real, diverse annual plant system. In our simulated communities, we observed the highest level of coexistence between species pairs when species interactions varied from competitive to facilitative, depending on neighbor density (i.e., following a sigmoid function). Introducing within‐guild facilitation through a nonlinear bounded function allowed populations, both simulated and empirical, to avoid extinction or runaway growth. In fact, nonlinear bounded functions (i.e., exponential and sigmoid functions) accurately predicted population trends over time within the range of abundances observed over the last 10 years. With the sigmoid function, the simulated communities of two species exhibited a higher probability of synchrony and oscillation compared to other functional forms. These simulated communities did not always show temporal stability, even when they were predicted to coexist. Overall, varying species interactions lead to realistic ecological trajectories and community dynamics when bounded by asymptotes based on neighbor density. These findings are crucial for advancing our understanding of how diverse communities are sustained and for applying ecological theory to real‐world studies. |
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| AbstractList | The ability of species to form diverse communities is not fully understood. Species are known to interact in various ways with their neighborhood. Despite this, common phenomenological models of species coexistence assume that per capita interactions are constant and competitive, even as the environment changes. In this study, we investigate how neighbor density‐dependent variation in the strength and sign of species interactions changes species and community dynamics. We demonstrate that incorporating these sources of variation significantly improves predictions of ecological dynamics compared to the outcomes of typical models, which hold interaction strengths constant. We compared the performance of models based on different functions of neighbor density and identity in describing population trajectories (i.e., persistence over time) and community dynamics (i.e., temporal stability, synchrony, and degree of oscillation) in simulated two‐species communities and a real, diverse annual plant system. In our simulated communities, we observed the highest level of coexistence between species pairs when species interactions varied from competitive to facilitative, depending on neighbor density (i.e., following a sigmoid function). Introducing within‐guild facilitation through a nonlinear bounded function allowed populations, both simulated and empirical, to avoid extinction or runaway growth. In fact, nonlinear bounded functions (i.e., exponential and sigmoid functions) accurately predicted population trends over time within the range of abundances observed over the last 10 years. With the sigmoid function, the simulated communities of two species exhibited a higher probability of synchrony and oscillation compared to other functional forms. These simulated communities did not always show temporal stability, even when they were predicted to coexist. Overall, varying species interactions lead to realistic ecological trajectories and community dynamics when bounded by asymptotes based on neighbor density. These findings are crucial for advancing our understanding of how diverse communities are sustained and for applying ecological theory to real‐world studies. |
| Author | Mayfield, Margaret Godoy, Oscar Vesk, Peter Buche, Lisa Hallett, Lauren M. Shoemaker, Lauren G. |
| Author_xml | – sequence: 1 givenname: Lisa orcidid: 0000-0001-8004-2670 surname: Buche fullname: Buche, Lisa organization: School of Biosciences University of Melbourne Melbourne Victoria Australia – sequence: 2 givenname: Lauren G. orcidid: 0000-0002-4465-8432 surname: Shoemaker fullname: Shoemaker, Lauren G. organization: Department of Botany University of Wyoming Laramie Wyoming USA – sequence: 3 givenname: Peter orcidid: 0000-0003-2008-7062 surname: Vesk fullname: Vesk, Peter organization: School of Agriculture, Food and Ecosystem Sciences University of Melbourne Melbourne Victoria Australia – sequence: 4 givenname: Lauren M. orcidid: 0000-0002-0718-0257 surname: Hallett fullname: Hallett, Lauren M. organization: Biology Department and Environmental Studies Program University of Oregon Eugene Oregon USA – sequence: 5 givenname: Oscar orcidid: 0000-0003-4988-6626 surname: Godoy fullname: Godoy, Oscar organization: Estación Biológica de Doñana (EBD‐CSIC) Sevilla Spain – sequence: 6 givenname: Margaret orcidid: 0000-0002-5101-6542 surname: Mayfield fullname: Mayfield, Margaret organization: School of Biosciences University of Melbourne Melbourne Victoria Australia |
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