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Modeling herbivore functional responses causing boom‐bust dynamics following predator removal

Native biodiversity is threatened by invasive species in many terrestrial and marine systems, and conservation managers have demonstrated successes by responding with eradication or control programs. Although invasive species are often the direct cause of threat to native species, ecosystems can rea...

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Detalles Bibliográficos
Autores principales: Haller‐Bull, Vanessa, Bode, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7920789/
https://www.ncbi.nlm.nih.gov/pubmed/33717449
http://dx.doi.org/10.1002/ece3.7185
Descripción
Sumario:Native biodiversity is threatened by invasive species in many terrestrial and marine systems, and conservation managers have demonstrated successes by responding with eradication or control programs. Although invasive species are often the direct cause of threat to native species, ecosystems can react in unexpected ways to their removal or reduction. Here, we use theoretical models to predict boom‐bust dynamics, where the removal of predatory or competitive pressure from a native herbivore results in oscillatory population dynamics (boom‐bust), which can endanger the native species’ population in the short term. We simulate control activities, applied to multiple theoretical three‐species Lotka‐Volterra ecosystem models consisting of vegetation, a native herbivore, and an invasive predator. Based on these communities, we then develop a predictive tool that—based on relative parameter values—predicts whether control efforts directed at the invasive predator will lead to herbivore release followed by a crash. Further, by investigating the different functional responses, we show that model structure, as well as model parameters, are important determinants of conservation outcomes. Finally, control strategies that can mitigate these negative consequences are identified. Managers working in similar data‐poor ecosystems can use the predictive tool to assess the probability that their system will exhibit boom‐bust dynamics, without knowing exact community parameter values.