Ecosystem Ecology and Evolutionary Biology, a New Frontier for Experiments and Models

Pastor, John

doi:10.1007/s10021-016-0069-9

Cited by 13 publications

(16 citation statements)

References 27 publications

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“…When considering heuristic models (in which much detail has been abstracted and subsumed into generic descriptions of processes) it is appropriate to use simple heuristic representations of average population fitness [2,16,8,32]. Many of these approaches define the average population fitness to be part or all of the population growth function f i .…”

Section: Average Population Fitnessmentioning

confidence: 99%

An eco-evolutionary system with naturally bounded traits

Cropp

Norbury

2019

Theor Ecol

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We consider eco-evolutionary processes that include natural bounds on adaptive trait distributions. We implement ecological axioms, that a population grows if it is replete with resources, and doesn't grow if it has none. These axioms produce natural bounds on the trait means that suggest that the assumption of gamma-distributed traits, where the trait variance is a function of the trait mean, is more appropriate than the usual assumption of normally distributed traits, where the trait variance is independent of the trait mean. We use a Lotka-Volterra model to simulate two plant populations, whose trait means evolve according to an evolutionary model, to simulate populations adapting during invasions. The results of our model simulations using gammadistributed traits suggest that adapting populations may endure bottlenecks by increasing their fitness and recovering from near extinction to stably coexist. The inclusion of eco-evolutionary processes into ecosystem models generates K * theory which predicts the long term states of populations that make it through evolutionary bottlenecks. Otherwise, the final states of populations that do not make it through bottlenecks are predicted by the non-evolutionary R * theory. Keywords eco-evolutionary modelling • K * theory • gamma-distribution • trait variance • fitness • coexistence • competition • invasion

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Section: Average Population Fitnessmentioning

confidence: 99%

An eco-evolutionary system with naturally bounded traits

Cropp

Norbury

2019

Theor Ecol

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“…2012, or scan the titles of any volume of Theoretical Ecology). For-understanding models are less well developed in ecosystem biogeochemistry (but see Ågren and Bosatta 1996), arguably resulting in a less well established theoretical foundation (see Menge andothers 2008 andPastor 2016 for approaches that merge evolutionary ecology and biogeochemistry). Conversely, fornumbers models have made enormous strides in biogeochemistry, especially in relation to global-carbon budgets (e.g., Thornton and others 2009).…”

Section: Discussionmentioning

confidence: 99%

Modeling for Understanding v. Modeling for Numbers

Rastetter

2016

Ecosystems

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Abstract. I draw a distinction between Modeling for Numbers, which aims to address how much, when, and where questions, and Modeling for Understanding, which aims to address how and why questions. For-numbers models are often empirical, which can be more accurate than their mechanistic analogues as long as they are well calibrated and predictions are made within the domain of the calibration data. To extrapolate beyond the domain of available system-level data, for-numbers models should be mechanistic, relying on the ability to calibrate to the system components even if it is not possible to calibrate to the system itself. However, development of a mechanistic model that is reliable depends on an adequate understanding of the system. This understanding is best advanced using a for-understanding modeling approach. To address how and why questions, for-understanding models have to be mechanistic. The best of these forunderstanding models are focused on specific questions, stripped of extraneous detail, and elegantly simple. Once the mechanisms are well understood, one can then decide if the benefits of incorporating the mechanism in a for-numbers model is worth the added complexity and the uncertainty associated with estimating the additional model parameters.Introduction. I draw a distinction between two types of modeling that actually represent extremes on a continuum. The first I call Modeling for Numbers. The questions addressed using these models can be summarized as: How much, where, and when? For example, how much carbon will be sequestered or released, by which parts of the biosphere, on what time course over the next 100 years (e.g., Cramer and others 2001)? The use of these models is clearly important; they address pressing environmental issues and attract a large amount of research money and effort. The second type of modeling I call Modeling for Understanding. The questions addressed with these models can be summarized as: How and why? For example, why can there be only one species per limiting factor (Levin 1970)? These for-understanding questions are more qualitative than the for-numbers questions. The emphasis of modeling for understanding is to understand underlying mechanisms, often by stripping away extraneous detail and thereby sacrificing quantitative accuracy. Modeling for understanding is at least as important as modeling for numbers (Ågren and Bosatta 1990), although the application to pressing ecological issues might be less direct.

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“…Within ecosystems, this can create spatial variation of soil organisms and processes (Bardgett and Wardle 2010). Similar variation can also be created by intraspecific genetic variation, however, and this variation is increasingly recognized as an important driver of the structure and dynamics of plant associated communities and ecosystem functioning (Pastor 2017;Whitham et al 2006;Whitham et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Intrapopulation genotypic variation in leaf litter chemistry does not control microbial abundance and litter mass loss in silver birch, Betula pendula

et al. 2018

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Background and aims Differences among plant genotypes can influence ecosystem functioning such as the rate of litter decomposition. Little is known, however, of the strength of genotypic links between litter quality, microbial abundance and litter decomposition within plant populations, or the likelihood that these processes are driven by natural selection. Methods We used 19 Betula pendula genotypes randomly selected from a local population in southeastern Finland to establish a long-term, 35-month litter decomposition trial on forest ground. We analysed the effect of litter quality (N, phenolics and triterpenoids) of senescent leaves and decomposed litter on microbial abundance and litter mass loss. Results We found that while litter quality and mass loss both had significant genotypic variation, the genotypic variation among silver birch trees in the quantity of bacterial and fungal DNA was marginal. In addition, although the quantity of bacterial DNA at individual tree level was negatively associated with most secondary metabolites of litter and positively with litter N, litter chemistry was not genotypically linked to litter mass loss. Conclusions Contrary to our expectations, these results suggest that natural selection may have limited influence on overall microbial DNA and litter decomposition rate in B. pendula populations by reworking the genetically controlled foliage chemistry of these populations.

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Ecosystem Ecology and Evolutionary Biology, a New Frontier for Experiments and Models

Cited by 13 publications

References 27 publications

An eco-evolutionary system with naturally bounded traits

An eco-evolutionary system with naturally bounded traits

Modeling for Understanding v. Modeling for Numbers

Intrapopulation genotypic variation in leaf litter chemistry does not control microbial abundance and litter mass loss in silver birch, Betula pendula

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