Biology subjects

Fowler, M. S.

Publications and source records attributed to Fowler, M. S..

3 recordsLinked to original sources

bioRxiv2026

The modelling of community assembly during seagrass restoration

Successful seagrass restoration will provide habitat for a variety of species. Here, ecological community assembly in a newly planted seagrass meadow has been modelled mathematically using a combination of numerical integration and a permanence-based method, and using real data to parametrise the models. We have studied the transient dynamics of the system: how the ecological communities assemble and change over a 100-year period. Using a trophic structure and a range of species pool sizes, we investigated how much variability there was in community size for a given sized species pool, whether it is possible to use early monitoring to predict the final community size, and to what extent monitoring gives an indication of final vs transient species. For the majority of cases modelled, the community either reached or was headed towards an endpoint community which was uniquely determined by the species pool. However, for 1.4% of cases, no unique endpoint community could be calculated. The simulated communities began to assemble within the first ten years, but 13% had still not reached their endpoint community even after 100 years. In 62% of our models, no consumer species colonised in the first two years, suggesting that monitoring should certainly be continued beyond a two-year period. We counted how many of the species that were present at any observation point in the 100 years would also be present in the endpoint community, and found that this proportion generally decreased with increasing species pool size, to an average of 86% when the species pool had 49-56 consumer species. By monitoring the community over the first ten years, it is not possible to deduce what the final community will be; however a very small number of fauna species present over the first ten years might be used to predict very small endpoint communities.

ecology↗

bioRxiv2025

Expert-led priorities for a response diversity research agenda in ecology

Response diversity aims to capture and explain the variation in ecological responses to environmental change. Response diversity is expected to drive ecological stability since a wider variety of responses to one or more environmental factors should stabilise fluctuations of ecosystem functions. However, uptake of empirical response diversity research has been slow. Here we assess current thinking around response diversity by conducting a targeted expert survey of response diversity researchers. Our survey revealed that one barrier to a unified research agenda on response diversity is the lack of agreement among respondents on the definition of response diversity, and to which dimension(s) of ecological stability response diversity might relate. When asked to select the temporal, spatial, and biological scales at which response diversity may be most relevant for ecological stability, respondents chose a wide range of scales indicating differences in how experts view response diversitys stabilising effect. Respondents considered studies incorporating both biotic interactions and abiotic environmental responses to be especially challenging, as were those thinking about responses to multiple environmental changes simultaneously. Moreover, respondents thought inconsistencies in the definitions of, and methods for measuring, response diversity were a major challenge facing the field. Despite these barriers, the survey revealed a desire for globally coordinated research efforts on response diversity in the form of syntheses, workshops, and distributed experiments, but that a standardised response diversity metric across diverse use-cases could be too restrictive. Our findings suggest we can shift response diversity from a loose collection of conceptual studies and inconsistent empirical applications towards a quantitative and coordinated research programme mechanistically linking biodiversity and ecological stability. As such, we are launching the Response Diversity Network--a research community interested in the science and application of response diversity-- whose activities we hope will benefit both individual studies of response diversity and globally coordinated research efforts.

ecology↗

bioRxiv2017

A mechanistic perspective of ecological networks highlights the contribution of alternative interaction strategies

O_LISpecies traits mediate ecological interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits required to characterise observed networks, i.e. the minimum dimensionality. Existing methods for estimating minimum dimensionality often lack three features commonly associated with a higher numbers of traits: a mechanistic description of observed interactions, alternative interaction modes (e.g. different feeding strategies such as active vs sit-and-wait feeding), and trait-mediated forbidden links. Omitting these features can lead to underestimation of the trait numbers involved, and therefore, minimum dimensionality. Here, we develop a minimum mechanistic dimensionality measure, accounting for these three features. C_LIO_LIThe only input required by our method is the observed network of interaction outcomes. We then assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between the interacting species. E.g. if a predator feeds upon a prey species via a typical predation mode, in each step of the predation sequence the predators performance must be greater than the preys. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The minimum number of traits required for a feasible system of inequalities provides our dimensionality estimate. C_LIO_LIWe applied our method to 658 published empirical ecological networks including animal dominance, predator-prey, primary consumption, pollination, parasitism and seed dispersal networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), forbidden interactions as trait-mediated interaction outcomes (92%), or a mechanistic perspective (81%), compared to network dimensionality estimates missing these features. C_LIO_LIOur method can reduce the risk of omitting essential traits that are involved mechanistically, in different interaction modes, or in failure outcomes. More accurate estimates will allow us to parameterise models to generate theoretical networks with a more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics. C_LI

ecology↗