Biology subjects

Eklöf, A.

Publications and source records attributed to Eklöf, A..

4 recordsLinked to original sources

bioRxiv2026

Maximum trophic level predicts food webs susceptibility to coextinctions

As ecosystems face a growing number of threats, coextinctions (resultant extinctions following a primary extinction) are expected to proliferate. However, less is known about the conditions under which coextinctions could outpace primary extinctions. Because coextinctions often occur through lost species interactions, we posit that aspects of food web structure and complexity can help predict differences in vulnerability to coextinction across ecosystems. To test this, we leverage Bayesian network models to assess the extent to which variation in ecosystem vulnerability to coextinction varies with food web structure. We find that food webs with high maximum trophic level are most vulnerable to coextinction, and that maximum trophic level is a better predictor than other aspects of food web structure, such as species richness or trophic connectance. Extending this approach, we also find that maximum trophic level uncovers the relative vulnerability of ecosystem services to species coextinction across 12 empirical food webs.

ecology↗

bioRxiv2020

A dynamical perspective to community detection in ecological networks

O_LIAnalysing how species interact in modules is a fundamental problem in network ecology. Theory shows that a modular network structure can reveal underlying dynamic ecological and evolutionary processes, influence dynamics that operate on the network and affect the stability of the ecological system. C_LIO_LIAlthough many ecological networks describe flows, such as biomass flows in food webs or disease trans-mission, most modularity analyses have ignored network flows, which can hinder our understanding of the interplay between structure and dynamics. C_LIO_LIHere we present Infomap, an established method based on network flows to the field of ecological networks. Infomap is a flexible tool that can identify modules in virtually any type of ecological network and is particularly useful for directed, weighted and multilayer networks. We illustrate how Infomap works on all these network types. We also provide a fully documented repository with additional ecological examples. Finally, to help researchers analyse their networks with Infomap, we introduce the open source R package infomapecology. C_LIO_LIAnalysing flow-based modularity is useful across ecology and transcends to other biological and non-biological disciplines. A dynamic approach for detecting modular structure has strong potential to provide new insights into the organisation of ecological networks. C_LI

ecology↗

bioRxiv2020

The importance of species interactions in spatially explicit eco-evolutionary community dynamics under climate change

Eco-evolutionary dynamics are essential in shaping the biological response of communities to ongoing climate change. Here we develop a spatially explicit eco-evolutionary framework which integrates evolution, dispersal, and species interactions within and between trophic levels. This allows us to analyze how these processes interact to shape species- and community-level dynamics under climate change. Additionally, we incorporate the heretofore unexplored feature that species interactions themselves might change due to increasing temperatures and affect the impact of climate change on ecological communities. The new modeling framework captures previously reported ecological responses to climate change, and also reveals two new key results. First, interactions between trophic levels as well as temperature-dependent competition within a trophic level mitigate the negative impact of climate change on global biodiversity, emphasizing the importance of understanding biotic interactions in shaping climate change impact. Second, using a trait-based perspective, we found a strong negative relationship between the within-community variation in preferred temperatures and the capacity to respond to climate change. Communities resulting from different ecological interaction structures form distinct clusters along this relationship, but varying species abilities to disperse and adapt to new temperatures leave it unaffected.

ecology↗

bioRxiv2018

A quantitative framework for investigating the reliability of network construction

Descriptions of ecological networks typically assume that the same interspecific interactions occur each time a community is observed. This contrasts with the known stochasticity of ecological communities: community composition, species abundances, and link structure all vary in space and time. Moreover, finite sampling generates variation in the set of interactions actually observed. Here we develop the conceptual and analytical tools needed to capture uncertainty in the estimation of pairwise interactions. To define the problem, we identify the different contributions to the uncertainty of an interaction and its implications for the estimation of network properties. We then outline a framework to quantify the uncertainty around each interaction. We illustrate this framework using the most extensively sampled network to date. We found significant uncertainty in estimates for the probability of most pairwise interactions which we could, however, limit with informative priors. Through these efforts, we demonstrate the utility of our approach and the importance of acknowledging the uncertainty inherent in network studies. Most importantly, we stress that networks are best thought of as systems constructed from random variables, the stochastic nature of which must be acknowledged for an accurate representation. Doing so will fundamentally change networks analyses and yield greater realism.

ecology↗