Biology subjects

Orr, J. A.

Publications and source records attributed to Orr, J. A..

4 recordsLinked to original sources

bioRxiv2026

Beyond Environmental Matching: Maternal Effects Depend on Offspring Environmental Complexity and Fitness Traits

The consequences of maternal effects for offspring fitness can depend on the environmental conditions offspring encounter and the fitness traits considered. We used a transgenerational experiment in Daphnia magna to test how maternal exposure to warming and elevated salinity influenced offspring survival, reproduction and body size across matching, mismatching and multiple-stressor environments. Maternal exposure to salinity enhanced offspring survival under matching salinity, and benefits extended to mismatching environments. In contrast, maternal exposure to warming provided no survival benefit under matching warming and reduced survival in mismatching environments, while accelerating reproduction across contexts. Multiple-stressor conditions revealed pronounced maternal effects, while contrasting responses across survival, reproduction and body size showed that benefits to one fitness trait did not extend to others. Our findings demonstrate that environmental matching alone is insufficient to predict maternal effects, whose consequences depend on offspring environmental complexity and the fitness trait considered.

ecology↗

bioRxiv2026

Shifting resource limitation explains multiphasic patterns of density dependence

Several recent studies have presented sublinear density dependence as a universal phenomenon across species, driven by factors unrelated to resource limitation (e.g. predation or non-resource based inhibition). Through a combination of bacterial growth experiments and mathematical modelling, we instead show that regimes of sublinear density dependence readily emerge under sequential shifts in resource limitation, without the need to invoke other processes. We nevertheless predict that superlinear density dependence, driven by standard resource limitation, will still predominate at both high and low densities.

ecology↗

bioRxiv2022

Stressor interactions at the community level: explaining qualitative mismatches between observations

Perturbations such as climate change, invasive species and pollution, impact the functioning and diversity of ecosystems. But because there is no unique way to measure functioning and diversity, this leads to a ubiquitous and overwhelming variability in community-level responses, that is often seen as a barrier to prediction in ecology. Here, we show that this variability can instead provide insights into hidden features of a communitys functions and responses to perturbations. By first analysing a dataset of global change experiments in microbial soil systems we show that variability of functional and diversity responses to a given perturbation is not random: aggregate properties that are thought to be mechanistically similar tend to respond similarly. We then formalise this intuitive observation to demonstrate that the variability of community-level responses to perturbations is not only predictable, but that it can also be used to access hidden and useful information about population-level responses to perturbations (i.e., response diversity and scaling by species biomass). Our theory offers a baseline expectation for the variability of community-level responses to perturbations and helps to explain the complexity of ecological responses to global change. Significance StatementMeasures of biodiversity and ecosystem functioning show highly variable responses to a given perturbation. This variability is traditionally thought of as reflecting our inability to predict ecological responses to global change. Our work, however, finds that variability of community-level responses is itself predictable and can even be used to gain insights about how species respond to perturbations and collectively contribute to ecosystem functions.

ecology↗

bioRxiv2020

Why scaling up uncertain predictions to higher levels of organisation will underestimate change

AO_SCPLOWBSTRACTC_SCPLOWUncertainty is an irreducible part of predictive science, causing us to over- or underestimate the magnitude of change that a system of interest will face. In a reductionist approach, we may use predictions at the level of individual system components (e.g. species biomass), and combine them to generate predictions for system-level properties (e.g. ecosystem function). Here we show that this process of scaling up uncertain predictions to higher levels of organization has a surprising consequence: it will systematically underestimate the magnitude of system-level change, an effect whose significance grows with the systems dimensionality. This stems from a geometrical observation: in high dimensions there are more ways to be more different, than ways to be more similar. This general remark applies to any complex system. Here we will focus on ecosystems thus, on ecosystem-level predictions generated from the combination of predictions at the species-level. In this setting, the ecosystems dimensionality is a measure of its diversity. We explain why dimensional effects do not play out when predicting change of a single linear aggregate property (e.g. total biomass), yet are revealed when predicting change of non-linear properties (e.g. absolute biomass change, stability or diversity), and when several properties are considered at once to describe the ecosystem, as in multi-functional ecology. Our findings highlight and describe the counter-intuitive effects of scaling up uncertain predictions, effects that will occur in any field of science where a reductionist approach is used to generate predictions.

ecology↗