Eco-evolutionary optimality as a means to improve vegetation and land-surface models

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generate parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration, and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental chang

A tree’s quest for light—optimal height and diameter growth under a shading canopy

For trees in forests, striving for light is matter of life and death, either by growing taller toward brighter conditions or by expanding the crown to capture more of the available light. Here, we present a mechanistic model for the development path of stem height and crown size, accounting for light capture and growth, as well as mortality risk. We determine the optimal growth path among all possible trajectories using dynamic programming. The optimal growth path follows a sequence of distinct phases: (i) initial crown size expansion, (ii) stem height growth toward the canopy, (iii) final expansion of the crown in the canopy and (iv) seed production without further increase in size. The transition points between these phases can be optimized by maximizing fitness, defined as expected lifetime reproductive production. The results imply that to reach the canopy in an optimal way, trees must consider the full profile of expected increasing light levels toward the canopy. A shortsighted maximization of growth based on initial light conditions can result in arrested height growth, preventing the tree from reaching the canopy. The previous result can explain canopy stratification, and why canopy species often get stuck at a certain size under a shading canopy. The model explains why trees with lower wood density have a larger diameter at a given tree height and grow taller than trees with higher wood density. The model can be used to implement plasticity in height versus diameter growth in individual-based vegetation and forestry models.Originally included in thesis in manuscript form.</p

The network as an asset : reflections on an industrial case

Godkänd; 2004; Bibliografisk uppgift: Network: Theory, Research & Application Ph.D. Course 2004; 20120223 (andbra

A simulation-based approach to a near optimal thinning strategy : allowing for individual harvesting times for individual trees

As various methods for precision inventories, such as LiDAR, are becoming increasingly common in forestry, individual-tree level planning is becoming more viable. Here, we present a method for finding the optimal thinning times for individual trees from an economic perspective. The method utilizes an individual tree-based forest growth model that has been fitted to Norway spruce (Picea abies (L.) Karst.) stands in northern Sweden. We find that the optimal management strategy is to thin from above, i.e. harvesting trees that are larger than average. We compare our optimal strategy with a conventional management strategy and find that it results in approximately 20% higher land expectation value. Furthermore, we find that increasing the discount rate will, for the optimal strategy, reduce the final harvest age and increase the basal area reduction. Decreasing the cost to initiate a thinning (e.g., machinery-related transportation costs) increases the number of thinnings and delays the first thinning.Originally included in thesis in manuscript form </p

Sharing the Burdens of Climate Mitigation and Adaptation : Incorporating Fairness Perspectives into Policy Optimization Models

Mitigation of, and adaptation to, climate change can be addressed only through the collective action of multiple agents. The engagement of involved agents critically depends on their perception that the burdens and benefits of collective action are distributed fairly. Integrated Assessment Models (IAMs), which inform climate policies, focus on the minimization of costs and the maximization of overall utility, but they rarely pay sufficient attention to how costs and benefits are distributed among agents. Consequently, some agents may perceive the resultant model-based policy recommendations as unfair. In this paper, we propose how to adjust the objectives optimized within IAMs so as to derive policy recommendations that can plausibly be presented to agents as fair. We review approaches to aggregating the utilities of multiple agents into fairness-relevant social rankings of outcomes, analyze features of these rankings, and associate with them collections of properties that a model’s objective function must have to operationalize each of these rankings within the model. Moreover, for each considered ranking, we propose a selection of specific objective functions that can conveniently be used for generating this ranking in a model. Maximizing these objective functions within existing IAMs allows exploring and identifying climate polices to which multiple agents may be willing to commit

Mechanisms driving plant functional trait variation in a tropical forest

Plant functional trait variation in tropical forests results from taxonomic differences in phylogeny and associated genetic differences, as well as, phenotypic plastic responses to the environment. Accounting for the underlying mechanisms driving plant functional trait variation is important for understanding the potential rate of change of ecosystems since trait acclimation via phenotypic plasticity is very fast compared to shifts in community composition and genetic adaptation. We here applied a statistical technique to decompose the relative roles of phenotypic plasticity, genetic adaptation, and phylogenetic constraints. We examined typically obtained plant functional traits, such as wood density, plant height, specific leaf area, leaf area, leaf thickness, leaf dry mass content, leaf nitrogen content, and leaf phosphorus content. We assumed that genetic differences in plant functional traits between species and genotypes increase with environmental heterogeneity and geographic distance, whereas trait variation due to plastic acclimation to the local environment is independent of spatial distance between sampling sites. Results suggest that most of the observed trait variation could not be explained by the measured environmental variables, thus indicating a limited potential to predict individual plant traits from commonly assessed parameters. However, we found a difference in the response of plant functional traits, such that leaf traits varied in response to canopy-light regime and nutrient availability, whereas wood traits were related to topoedaphic factors and water availability. Our analysis furthermore revealed differences in the functional response of coexisting neotropical tree species, which suggests that endemic species with conservative ecological strategies might be especially prone to competitive exclusion under projected climate change

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change

Organizing principles for vegetation dynamics

ISSN:2055-026XISSN:2055-027