Structured Population Dynamics: An Introduction to Integral Modeling

Briggs, J. H.; Dabbs, Kathryn; Holm, Michael; Lubben, Joan; Rebarber, Richard; Tenhumberg, Brigitte; Riser-Espinoza, Daniel

doi:10.4169/002557010x521778

Cited by 16 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models, in contrast to traditional matrix models, allow the use of a continuous range of plant stages/attributes (Briggs et al . ) and, the generation of population projections (Merow et al . ) incorporating genotypes with different ontogenetic trajectories in plant defence.…”

Section: Scaling Up From Ontogenetic Trajectories Of Individuals To Pmentioning

confidence: 99%

Future directions in the ontogeny of plant defence: understanding the evolutionary causes and consequences

2017

View full text Add to dashboard Cite

Plant defence often varies by orders of magnitude as plants develop from the seedling to juvenile to mature and senescent stages. Ontogenetic trajectories can involve switches among defence traits, leading to complex shifting phenotypes across plant lifetimes. While considerable research has characterised ontogenetic trajectories for now hundreds of plant species, we still lack a clear understanding of the molecular, ecological and evolutionary factors driving these patterns. In this study, we identify several non-mutually exclusive factors that may have led to the evolution of ontogenetic trajectories in plant defence, including developmental constraints, resource allocation costs, multi-functionality of defence traits, and herbivore selection pressure. Evidence from recent physiological studies is highlighted to shed light on the underlying molecular mechanisms involved in the regulation and activation of these developmental changes. Overall, our goal is to promote new research avenues that would provide evidence for the factors that have promoted the evolution of this complex lifetime phenotype. Future research focusing on the questions and approaches identified here will advance the field and shed light on why defence traits shift so dramatically across plant ontogeny, a widespread but poorly understood ecological pattern.

show abstract

Section: Scaling Up From Ontogenetic Trajectories Of Individuals To Pmentioning

confidence: 99%

Future directions in the ontogeny of plant defence: understanding the evolutionary causes and consequences

2017

View full text Add to dashboard Cite

show abstract

“…. In a population projection matrix (PPM) model (Caswell, 2001) X 1 is a finite dimensional space R m (so n t is a population vector), and in an integral projection model (IPM) (Briggs et al 2010;Childs et al 2003Childs et al , 2004Ellner and Rees 2006;Rebarber et al 2012;Ramula et al 2009) X 1 is often the space L 1 [L , U ] of integrable functions on the interval of stages [L , U ] (so n t (x) is a function of a continuous variable x). In the absence of a seed bank, and with the assumption that only newly created seedlings compete with each other for space to establish, the plant population is governed by the abstract nonlinear population projection model n t+1 = An t + b f (α 1 h(c T n t )),…”

Section: Plant Populationmentioning

confidence: 99%

Global asymptotic stability of plant-seed bank models

2013

Self Cite

View full text Add to dashboard Cite

Many plant populations have persistent seed banks, which consist of viable seeds that remain dormant in the soil for many years. Seed banks are important for plant population dynamics because they buffer against environmental perturbations and reduce the probability of extinction. Viability of the seeds in the seed bank can depend on the seed's age, hence it is important to keep track of the age distribution of seeds in the seed bank. In this paper we construct a general density-dependent plant-seed bank model where the seed bank is age-structured. We consider density dependence in both seedling establishment and seed production, since previous work has highlighted that overcrowding can suppress both of these processes. Under certain assumptions on the density dependence, we prove that there is a globally stable equilibrium population vector which is independent of the initial state. We derive an analytical formula for the equilibrium population using methods from feedback control theory. We apply these results to a model for the plant species Cirsium palustre and its seed bank.

show abstract

“…IPMs have been considered to be analogous to IDEs (Briggs et al . ) or to be a type of IDE (Ghosh, Gelfand & Clark ). The key difference between the two approaches is in the form and interpretation of the kernel, as well as the interpretation of system dynamics.…”

Section: Extending Mhms: Potential Applications To Global Change Biologymentioning

confidence: 99%

Moving forward: insights and applications of moving‐habitat models for climate change ecology

et al. 2017

View full text Add to dashboard Cite

Summary Predicting and managing species’ responses to climate change is one of the most significant challenges of our time. Tools are needed to address problems associated with novel climatic conditions, biotic interactions and greater climate velocities. We present a spatially explicit moving‐habitat model (MHM) and demonstrate its versatility in tackling critical questions in climate change research, including dispersal in multiple spatial dimensions, population stage structure, interspecific interactions, asymmetric range shifts, Allee effects and the presence of infectious diseases. The model utilizes integrodifference equations to track changes in population density over time in a habitat that is moving. The model is quite flexible and can accommodate variation in demography, dispersal patterns, biotic interaction and stochasticity in the velocity of climate change. The methods provide a general mechanistic understanding of the underlying ecological processes that drive a system. Field data can be readily incorporated into the model to give insight into specific populations of interest and inform management decisions. Synthesis. Moving‐habitat models unite ecological theory, data‐centred modelling and conservation decision support under a single framework. Their ability to generate testable hypotheses, incorporate data and inform best management practices proves that these models provide a valuable framework for climate change biologists.

show abstract

Structured Population Dynamics: An Introduction to Integral Modeling

Cited by 16 publications

References 20 publications

Future directions in the ontogeny of plant defence: understanding the evolutionary causes and consequences

Future directions in the ontogeny of plant defence: understanding the evolutionary causes and consequences

Global asymptotic stability of plant-seed bank models

Moving forward: insights and applications of moving‐habitat models for climate change ecology

Contact Info

Product

Resources

About