This elegant idea was first proposed by Abrams (doi.org/10.1086/603627), and we applied it with E. coli and chemostats. We manipulated dilution rates and measured equilibrium densities. Inverting the observed relationship (green) revealed strongly superlinear density-dependent growth (blue). 4/6

One reason why the shape of density dependence is still debated is that manipulating densities and measuring growth rates isn’t feasible in most systems. We therefore flipped the problem by manipulating growth rates and measuring densities in an approach we called “Growth-Density Inversion”. 3/6

Population growth slows down as density increases – but how does it slow? Do most declines in growth occur at low densities (sublinearity) or at high densities (superlinearity)? Theory suggests that superlinearity should be expected, but time-series analyses find evidence for sublinearity. 2/6

Using the brilliant dataset produced by Zhenghu Zhou and colleagues (doi.org/10.1038/s414...), we demonstrate how our framework can provide new insights into species’ contributions to functions and species' response to perturbations using only information about community-level responses.

We propose a framework, built on geometric arguments, that uses the variability of community-level responses to quantify: (i) the similarity of functions (ii) how many species contribute to functions (iii) the response diversity of communities (iv) the biomass scaling of perturbations

Measures of biodiversity and ecosystem functioning show variable responses to perturbations (white/red areas of heatmap). We show that this variability itself is predictable (modularity of heatmap) and can even be used to study species’ responses to perturbations and contributions to functions.

Just back from a great trip to the University of Canberra’s Institute of Applied Ecology for a seminar on multiple stressors. Loads of cool research going on there including some amazing breeding programs for critically endangered species

We also ask if there is an “ideal” multiple-stressor experiment by comparing different experimental designs based on how informative and feasible they are.

In our synthesis we explore whether or not the statistical interactions observed by these experiments actually reflect the stressor interactions that researchers are interested in, and we propose a framework (below) to help understand this.

The relative interest in different stressors has fluctuated over time and some combinations of stressors are far more likely to be studied than others. Temperature has become more popular in the past decade and has been paired with virtually all other types of stressors.

There’s been a lot of work done on stressor interactions – about four or five studies (from freshwater systems alone) are currently being published every week. These experiments have studied the effects of over 900 stressors (>80% of these being different chemicals).

I’m really excited to share our new synthesis of 2,396 (!) multiple-stressor experiments conducted in freshwater ecosystems that’s just been published in Ecology Letters: doi.org/10.1111/ele....

We used a hydrology and water quality model and UK Environment Agency datasets to study the interactive effects of improved wastewater treatment and climate change on the macroinvertebrate communities in the River Thames.

Here’s our new paper just published in Freshwater Biology where we used process-based models and large biomonitoring datasets to study multiple-stressor interactions at a landscape level: doi.org/10.1111/fwb....