Evolution of complex flowering strategies: an age– and size–structured integral projection model

Childs, Dylan Z.; Rees, Mark; Rose, Karen; Grubb, P. J.; Ellner, Stephen P.

doi:10.1098/rspb.2003.2399

Cited by 87 publications

(131 citation statements)

References 44 publications

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“…Most studies of reproduction in modular organisms emphasize the age or size dependence of reproduction (Law 1983, Lacey 1986, Klinkhamer et al 1992, Kapela and Lasker 1999, Childs et al 2003, Burd et al 2006. Our results are a clear departure from these studies because the reproductive characteristics of W. subtorquata are not determined by colony size or age.…”

Section: Discussioncontrasting

confidence: 90%

Does size predict demographic fate? Modular demography and constraints on growth determine response to decreases in size

Hart

Keough

2009

Ecology

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Abstract. The modular construction of many plants and animals defies conventional approaches to the study of life histories and population dynamics. An important complication of modular construction is that individuals can rapidly decrease in size when some modules are removed or die or when an individual fragments. Most attempts to describe life histories and population dynamics of modular organisms classify individuals according to their size. This approach relies on the fundamental assumption that fragmentation and module loss have no consequences for an individual apart from a simple decrease in size. Here we experimentally test this assumption.Using a modular marine invertebrate, the encrusting bryozoan Watersipora subtorquata, as a model species, we manipulated colony size and then assessed performance against three potential explanatory models based on size, age, and damage. In a second experiment we disrupted the internal modular demography of colonies to determine whether the performance of a fragment is influenced by the type of modules that remain. Finally, we investigated how constraints on growth in modular organisms uniquely influence growth after module loss. We found that single-state variables such as size or age do not describe performance in our species. Internal constraints substantially reduce growth after a decrease in size, and the age of modules that remain determines the timing of reproductive onset and fecundity. A knowledge of the size history of individuals, including any decreases in size, is necessary to accurately describe life histories and population dynamics in this modular organism. Our results have major consequences for established methods for modeling the demography of modular organisms.

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Section: Discussioncontrasting

confidence: 90%

Does size predict demographic fate? Modular demography and constraints on growth determine response to decreases in size

Hart

Keough

2009

Ecology

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“…The vast majority of IPMs have been constructed phenomenologically, using statistical descriptions of observational data. Several authors have shown how fixed and random effects incorporated into these statistical functions can be formulated within IPMs (Childs et al 2003;Rees and Ellner 2009;Coulson 2012), but additional statistical estimation is required to parameterize the evolutionarily explicit IPMs we have developed. Fitness functions in evolutionarily explicit IPMs can be parameterized using standard general, generalized, and additive regression methods that are routinely used to parameterize phenomenological IPMs (Rees and Ellner 2009).…”

Section: Parameterizing and Analyzing Evolutionarily Explicit Ipmsmentioning

confidence: 99%

“…In these cases, standard nonevolutionarily explicit IPMs have accurately captured observed dynamics (Childs et al 2003;Ozgul et al 2010;Merow et al 2014).…”

Section: When Should Evolutionarily Explicit Ipms Be Used To Predict mentioning

confidence: 99%

“…Numerous quantities of interest to ecologists and evolutionary biologists describing life-history, population dynamic, and phenotypic traits can be calculated from IPMs (Childs et al 2003;Ellner and Rees 2006;Coulson et al 2010Coulson et al , 2011Rees et al 2014;Steiner et al 2014Steiner et al , 2012Vindenes and Langangen 2015). They consequently offer great potential to study ecological and evolutionary responses to environmental change (Coulson et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Modeling Adaptive and Nonadaptive Responses of Populations to Environmental Change

Coulson

Kendall

Barthold

et al. 2017

The American Naturalist

113

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. abstract: Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both populationlevel plastic and evolutionary responses, with plastic responses being either adaptive or nonadaptive. We develop an approach that links quantitative genetic theory with data-driven structured models to allow prediction of population responses to environmental change via plasticity and adaptive evolution. After introducing general new theory, we construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses and that the rate of adaptive evolution to a new environment depends on whether plastic responses are adaptive or nonadaptive. Parameterization of the models we develop requires information on genetic and phenotypic variation and demography that will not always be available, meaning that simpler models will often be required to predict responses to environmental change. We consequently develop a method to examine whether the full machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change or whether simpler nonevolutionary models that are now widely constructed may be sufficient.

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“…This technique is rapidly gaining in popularity because of its obviously utility (Godfray and Rees 2002, Metcalf et al 2003, Rose et al 2005, Williams and Crone 2006, Kuss et al 2008, Zuidema et al 2010. Reanalysis of data previously used in matrix models provides additional ecological insights (e.g., Easterling et al 2000) and has greatly improved our understanding of how complex local demographic processes, and the associated individual variation, affect population growth and the evolution of life history strategies (Rees and Rose 2002, Childs et al 2003, Ellner and Rees 2006.…”

Section: Introductionmentioning

confidence: 99%

Importance of individual and environmental variation for invasive species spread: a spatial integral projection model

Jongejans

Shea

Skarpaas

et al. 2011

Ecology

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Abstract. Plant survival, growth, and flowering are size dependent in many plant populations but also vary among individuals of the same size. This individual variation, along with variation in dispersal caused by differences in, e.g., seed release height, seed characteristics, and wind speed, is a key determinant of the spread rate of species through homogeneous landscapes. Here we develop spatial integral projection models (SIPMs) that include both demography and dispersal with continuous state variables. The advantage of this novel approach over discrete-stage spread models is that the effect of variation in plant size and size-dependent vital rates can be studied at much higher resolution. Comparing NeubertCaswell matrix models to SIPMs allowed us to assess the importance of including individual variation in the models. As a test case we parameterized a SIPM with previously published data on the invasive monocarpic thistle Carduus nutans in New Zealand. Spread rate (c*) estimates were 34% lower than for standard spatial matrix models and stabilized with as few as seven evenly distributed size classes. The SIPM allowed us to calculate spread rate elasticities over the range of plant sizes, showing the size range of seedlings that contributed most to c* through their survival, growth and reproduction. The annual transitions of these seedlings were also the most important ones for local population growth (k). However, seedlings that reproduced within a year contributed relatively more to c* than to k. In contrast, plants that grow over several years to reach a large size and produce many more seeds, contributed relatively more to k than to c*. We show that matrix models pick up some of these details, while other details disappear within wide size classes. Our results show that SIPMs integrate various sources of variation much better than discrete-stage matrix models. Simpler, heuristic models, however, remain very valuable in studies where the main goal is to investigate the general impact of a life history stage on population dynamics. We conclude with a discussion of future extensions of SIPMs, including incorporation of continuous time and environmental drivers.

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Evolution of complex flowering strategies: an age– and size–structured integral projection model

Cited by 87 publications

References 44 publications

Does size predict demographic fate? Modular demography and constraints on growth determine response to decreases in size

Does size predict demographic fate? Modular demography and constraints on growth determine response to decreases in size

Modeling Adaptive and Nonadaptive Responses of Populations to Environmental Change

Importance of individual and environmental variation for invasive species spread: a spatial integral projection model

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