Biology subjects

Brännström, A.

Publications and source records attributed to Brännström, A..

7 recordsLinked to original sources

bioRxiv2026

Contrasting relationships between evolved consumer diversity and productivity for essential vs. substitutable resources

Any mechanistic biodiversity-ecosystem functioning theory must explain how biodiversity in natural communities arises, which requires an evolutionary metacommunity perspective. Using a model of adaptive evolution of competitive metacommunities in landscapes with spatially heterogeneous supply of two resources, we studied the evolution of resource-uptake specialization as a function of resource type (substitutable/essential) and trade-off shape between resource-uptake affinities (generalist-/specialist-favouring). We identified "critical essentiality" as a key phenomenon separating parameter regions with fundamentally different biodiversity--ecosystem function relationships. Specifically, while greater spatial heterogeneity in resource supply increases biodiversity, this comes at the cost of reduced productivity when resources are essential. Moreover, when resources are essential, both generalist- and specialist-favoring trade-offs select for phenotypes that prevent efficient resource partitioning, leading to low resource use efficiency and consumer biomass - despite often high species richness. In contrast, when resources are substitutable, a specialist-favoring affinity trade-off selects for phenotypes that partition resources efficiently, promoting a metacommunity of only two species characterized by high functional diversity, resource use efficiency, and biomass. Overall, our analyses thus suggest that conditions promoting the evolution of diverse consumer communities often do not engender higher functional richness, consumer biomass, or resource use efficiency.

ecology↗

bioRxiv2025

On the influence of stratification and lake size on pelagic and benthic algal dynamics: A modelling study using 'Lake2D'

This study investigates the influence of mixed layer depth and lake size on the dynamics of pelagic and benthic algae in stratified lakes using a two-dimensional, spatially explicit modeling approach. To this end, we modified the process-based, two-dimensional model Lake2D to account for stratification in lakes by simulating a shallow layer with fast turbulent mixing, and a deep layer with slow turbulent mixing, separated by a sharp boundary. Using this modified version of Lake2D we comprehensively explored pelagic and benthic algal dynamics across a wide range of ecological conditions by varying lake mean depth, total nutrient content, lake size, and mixing depth in a full-factorial manner. Our results reveal distinct regimes of whole-lake algal dynamics: In shallow, nutrient-poor lakes, pelagic algae are nutrient-limited and sparse, while benthic algae thrive and constitute the majority of the total biomass, with no notable influence of mixed layer depth on algal dynamics. As lakes become deeper or more nutrient-rich, pelagic biomass increases and can become the dominant constituent of total biomass. For deep lakes, the model shows a characteristic L-shaped pattern in algal biomass for varying lake size and mixing depth. Pelagic algae exhibit low biomass in small lakes with a deep mixed layer, and high biomass otherwise. Benthic algae show the opposite pattern, exhibiting high biomass in small, deep lakes with a deep mixed layer, and significantly lower biomass otherwise. The study also uncovers a novel phenomenon, a benthic deep chlorophyll maximum, where the benthic algal biomass peaks at an intermediate depth along the lake bottom. This phenomenon arises in lakes of moderate depth and nutrient content, where a shallow mixed layer enables opposing vertical gradients of light and nutrients. These findings highlight the role of mixed layer depth and horizontal transport in structuring the spatial distribution and overall composition of algal biomass in lakes, underscoring the importance of vertical mixing and lake size for understanding lake primary production.

ecology↗

bioRxiv2025

Physical drivers of benthic and pelagic algal biomass dynamics in lakes: a conceptual exploration with 'Lake2D'

Size, depth and basin shape are important factors controlling the physics, chemistry and, ultimately, the productivity of lakes. To our knowledge, a comprehensive theoretical investigation of the physical determinants of lake primary production from a conceptual, process-based perspective has not been performed. To address this knowledge gap, we developed and analyzed Lake2D, a process-based, reaction-advection-diffusion model that adopts a 2-dimensional modeling approach by reducing lake bathymetry to its hypsographic depth distribution under the simplifying assumption of radial symmetry of the lake basin. In simpler terms, the model assumes the lake basin is perfectly circular and uses a cross-section to represent its depth and shape. We used Lake2D to explore and analyze the dynamics of pelagic and benthic algae on a whole-lake scale in response to six environmental drivers (lake area and depth, horizontal and vertical turbulent mixing, water transparency and nutrient status), which we varied over ranges that are representative of the vast majority of the worlds lakes. Numerical analyses reveal three distinct patterns across environmental parameter space. (1) Benthic algae dominate total biomass in shallow, clear lakes with low nutrient content. In these lakes, low horizontal and vertical mixing is beneficial to benthic but detrimental to pelagic algae, which experience high sinking losses, thus liberating nutrients and minimizing shading of benthic algae. (2) Increasing mean lake depth, abiotic turbidity and/or nutrient content strongly benefits pelagic algae, which increasingly shade out benthic algae and dominate total biomass. In these lakes, increased mixing affects both algal types positively due to increased nutrient transport to shallow, well-lit areas. (3) Finally, at high lake mean depth and/or abiotic turbidity, a large fraction of the lake volume is aphotic. In such lakes, high horizontal and vertical mixing is detrimental to pelagic algae but beneficial to benthic algae, because pelagic algae are mixed to aphotic depths, liberating nutrients and minimizing shading. Moving from shallow, clear lakes via deeper and/or nutrient-rich lakes to very deep and/or turbid lakes, benthic and pelagic algae therefore show opposite gradual biomass shifts in response to vertical and horizontal mixing. Specifically, benthic biomass is highest at low and lowest at high overall mixing in shallow, nutrient-poor lakes, but shows the opposite trend in all other lakes. Conversely, pelagic biomass is highest at low and lowest at high overall mixing in very deep and/or turbid lakes, but shows the opposite trend in all other lakes.

ecology↗

bioRxiv2023

libpspm: A feature-rich numerical package for solving physiologically structured population models

For a vast majority of organisms, life-history processes depend on their physiological state, such as body size, as well as on their environment. Size-structured population models, or more generally, physiologically structured population models (PSPMs), have emerged as powerful tools for modelling the population dynamics of organisms, as they account for the dependences of growth, mortality, and fecundity rates on an organisms physiological state and capture feedbacks between a populations structure and its environment, including all types of density regulation. However, despite their widespread appeal across biological disciplines, few numerical packages exist for solving PSPMs in an accessible and computationally efficient way. The main reason for this is that PSPMs typically involve solving partial differential equations (PDEs), and no single numerical method works universally best, or even at all, for all PDEs. Here, we present libpspm, a general-purpose numerical library for solving user-defined PSPMs. libpspm provides eight different methods for solving the PDEs underlying PSPMs, including four semi-implicit solvers that can be used for solving stiff problems. Users can choose the desired method without changing the code specifying the PSPM. libpspm allows for predicting the dynamics of multiple physiologically structured or unstructured species, each of which can have its own distinct set of physiological states and demographic functions. By separating model definition from model solution, libpspm can make PSPM-based modelling accessible to non-specialists and thus promote the widespread adoption of PSPMs.

ecology↗

bioRxiv2023

Competition for light can drive adverse species-composition shifts in the Amazon Forest under elevated CO2

The resilience of biodiverse forests to climate change depends on an interplay of adaptive processes operating at multiple temporal and organizational scales. These include short-term acclimation of physiological processes like photosynthesis and respiration, mid-term changes in forest structure due to competition, and long-term changes in community composition arising from competitive exclusion and genetic trait evolution. To investigate the roles of diversity and adaptation for forest resilience, we present Plant-FATE, a parsimonious eco-evolutionary vegetation model. Tested with data from a hyperdiverse Amazonian terra-firme forest, our model accurately predicts multiple emergent ecosystem properties characterizing forest structure and function. Under elevated CO2 conditions, we predict an increase in productivity, leaf area, and aboveground biomass, with the magnitude of this increase declining in nutrient-deprived soils if trees allocate more carbon to the rhizosphere to overcome nutrient limitation. Furthermore, increased aboveground productivity leads to greater competition for light and drives a shift in community composition towards fast-growing but short-lived species characterized by lower wood densities. Such a transition reduces the carbon residence time of woody biomass, dampening carbon-sink strength and potentially rendering the Amazon Forest more vulnerable to future climatic extreme events.

ecology↗

bioRxiv2022

Minor variations in multicellular life cycles have major effects on adaptation

Multicellularity has evolved several independent times over the past hundreds of millions of years and given rise to a wide diversity of complex life. Recent studies have found that large differences in the fundamental structure of early multicellular life cycles can affect fitness and influence multicellular adaptation. Yet, there is an underlying assumption that at some scale or categorization multicellular life cycles are similar in terms of their adaptive potential. Here, we consider this possibility by exploring adaptation in a class of simple multicellular life cycles of filamentous organisms that only differ in one respect, how many daughter filaments are produced. We use mathematical models and evolutionary simulations to show that despite the similarities, qualitatively different mutations fix. In particular, we find that mutations with a tradeoff between cell growth and group survival, i.e. "selfish" or "altruistic" traits, spread differently. Specifically, altruistic mutations more readily spread in life cycles that produce few daughters while in life cycles producing many daughters either type of mutation can spread depending on the environment. Our results show that subtle changes in multicellular life cycles can fundamentally alter adaptation. Author summaryEarly forms of multicellular organisms exhibit a wide range of life cycles. Though studies have explored how the structure of a life cycle determines the fitness of early multicellular organisms, far less is known about how it affects their adaptation. Studies that do investigate adaptation typically focus on large scale differences between life cycles, implicitly assuming that at some scale life cycles are similar in terms of their adaptation. In this study we consider this assumption by analyzing adaptation in a class of early multicellular life cycles where the only difference between them is the number of offspring they produce. We use mathematical models and evolutionary simulations to compute the fate of mutations that are either altruistic or selfish, depending on their effects on single cells and the groups to which they belong. We find that despite the similarity between life cycles they can adapt very differently. In particular, life cycles that produce few offspring consistently adapt via altruistic traits, while life cycles that produce many offspring adapt via either altruistic or selfish traits depending on the environment. Ultimately, we find that small scale differences in multicellular life cycles can have large effects on adaptation.

evolutionary biology↗

bioRxiv2018

Emergence of social inequality in a spatial-ecological public goods game

Spatial ecological public goods, such as forests, grasslands, and fish stocks risk being overexploited by selfish consumers, a phenomenon called \"the tragedy of commons\". The spatial and ecological dimensions introduce new features absent in non spatio-ecological contexts, such as consumer mobility, incomplete information availability, and rapid evolution by social learning. It is unclear how these different processes interact to influence the harvesting and dispersal strategies of consumers. To answer these questions, we develop and analyze an individual-based, spatially-structured evolutionary model with explicit resource dynamics. We find that, 1) When harvesting efficiency is low, consumers evolve a sedentary harvesting strategy, with which resources are harvested sustainably, but harvesting rates remain far below their maximum sustainable value. 2) As harvesting efficiency increases, consumers adopt a mobile consume-and-disperse strategy, which is sustainable, equitable, and allows for maximum sustainable yield. 3) Further increase in harvesting efficiency leads to large-scale overexploitation. 4) If costs of dispersal are significant, increased harvesting efficiency also leads to social inequality between frugal sedentary consumers and overexploitative mobile consumers. Whereas overexploitation can occur without social inequality, social inequality always leads to overexploitation. Thus, we identify four conditions, which are characteristic (and as such positive) features of modern societies resulting from technological progress, but also risk promoting social inequality and unsustainable resource use: high harvesting efficiency, moderately low costs of dispersal, high consumer density, and consumers tendency to rapidly adopt new strategies. We also show that access to global information, which is also a feature of modern societies, may help mitigate these risks.

evolutionary biology↗