Modelling biological invasions: species traits, species interactions, and habitat heterogeneity

Cannas, Sergio A.; Marco, Diana E.; Páez, Sergio A.

doi:10.1016/s0025-5564(02)00213-4

Cited by 53 publications

(44 citation statements)

References 24 publications

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“…3) (15, 16). In other words, there is a nonreproductive leading front that delays spread advancement (28). In addition, first-order dynamics in invader spread are congruent with the enemy-release hypothesis (29,30), where feedback structure does not involve predators or competitors that regulate invasion spread (14,(31)(32)(33), given that this kind of feedback produces higher-order dynamics (e.g., cycles; see refs.…”

Section: R-functions) (Right) Unregulated Processes Such As Exponementioning

confidence: 65%

Spread dynamics of invasive species

Arim

Abades

Neill

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

211

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Species invasions are a principal component of global change, causing large losses in biodiversity as well as economic damage. Invasion theory attempts to understand and predict invasion success and patterns of spread. However, there is no consensus regarding which species or community attributes enhance invader success or explain spread dynamics. Experimental and theoretical studies suggest that regulation of spread dynamics is possible; however, the conditions for its existence have not yet been empirically demonstrated. If invasion spread is a regulated process, the structure that accounts for this regulation will be a main determinant of invasion dynamics. Here we explore the existence of regulation underlying changes in the rate of new site colonization. We employ concepts and analytical tools from the study of abundance dynamics and show that spread dynamics are, in fact, regulated processes and that the regulation structure is notably consistent among invasions occurring in widely different contexts. We base our conclusions on the analysis of the spread dynamics of 30 species invasions, including birds, amphibians, fish, invertebrates, plants, and a virus, all of which exhibited similar regulation structures. In contrast to current beliefs that species invasions are idiosyncratic phenomena, here we provide evidence that general patterns do indeed exist.conservation ͉ invasion ͉ population dynamics ͉ range expansion ͉ regulation

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Section: R-functions) (Right) Unregulated Processes Such As Exponementioning

confidence: 65%

Spread dynamics of invasive species

Arim

Abades

Neill

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

211

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“…The plant population dynamics can be described by a model of plant spread previously published (Cannas et al, 2003). Briefly, if δ p is the carrying capacity of the field where the plants grow, the density population per unit field area is p p (t)=δ p .…”

Section: Model Development and Biological Backgroundmentioning

confidence: 99%

An experimental and modelling exploration of the host-sanction hypothesis in legume–rhizobia mutualism

Marco

Carbajal

Cannas

et al. 2009

Journal of Theoretical Biology

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“…The model evolves in discrete time steps, with the cell states updated synchronously. The discrete and dynamic nature of CA models allows broad applications in a variety of natural phenomena and human activities, such as urban planning (Li et al, 2014), resource management (Bone and Dragićević, 2010), forest fire spread prediction (Alexandridis et al, 2008;Yassemi et al, 2008), plant invasion (Cannas et al, 2003), insect propagation (Bone et al, 2006;Pérez and Dragićević, 2011), animal migration patterns (Bennett and Tang, 2006), and landscape ecology (Parker et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Huang

et al. 2017

Ecological Modelling

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Huabing, "Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance" (2017 Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses; however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work.

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Modelling biological invasions: species traits, species interactions, and habitat heterogeneity

Cited by 53 publications

References 24 publications

Spread dynamics of invasive species

Spread dynamics of invasive species

An experimental and modelling exploration of the host-sanction hypothesis in legume–rhizobia mutualism

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

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