arXiv subjects

Tim Coulson

Publications and source records attributed to Tim Coulson.

3 recordsLinked to original sources

arXiv2026

Linking individual bioenergetics to ecosystem dynamics with integral projection models

Linking individual-level processes, such as survival and reproduction, to ecosystem dynamics is challenging due to interactions among populations and species. These interactions depend on organism traits, such as age, body size, and mass, and are influenced by fluctuations in population structure. Integral projection models (IPMs) have advanced our understanding of dynamics arising from population structure in continuous traits. Yet, these models have only been applied to relatively simple systems, featuring one or two species and spanning a fraction of the trophic levels observed in natural food webs. We extend these models in four ways. First, we provide a general multi-species IPM where population structure can be included in primary producers and primary and secondary consumers. Second, we link individual-level processes to ecosystem functioning by mapping fluxes of biomass between species to fluxes within individuals, using bioenergetic principles for allocation to maintenance, reproduction, or growth. Third, we introduce a nutrient recycling loop through which decomposers break down organic matter into nutrients that enable plant growth. Finally, we provide an efficient fitting algorithm to calibrate the model with time series of population sizes. We showcase our approach by parameterising and fitting the model to counts of elk, bison, wolves, and cougars in northern Yellowstone National Park to study the impact of predator extirpation and recovery. Our model predicts that bison decrease following predator removal and recover following predator recovery, suggesting a positive contribution of wolves and cougars to the increase of bison in northern Yellowstone. It also reproduces indirect effects of predators on woody deciduous vegetation regeneration. Our approach is suitable for a broad range of ecosystems, with applications for population management and ecological forecasting.

q-bio.PE↗

arXiv2022

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data

1. Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions nonparametrically from time series data. NODEs, however, are slow to fit, and inferred interactions have not been truthed. 2. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks, and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate, and rotifer population, as well as in the hare and lynx system. 3. Our BNGM approach allows us to cut down the fitting time of NODE systems to only a few seconds. The method provides accurate estimates of ecological interactions in the artificial system, as linear and nonlinear true interactions are estimated with minimal error. In the real systems, dynamics are driven by a mixture of linear and nonlinear ecological interactions, of which only the strongest are consistent across replicates. 4. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting, cross-validation, and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.

q-bio.PE↗

arXiv2015

Quantitative Genetics Meets Integral Projection Models: Unification of Widely Used Methods from Ecology and Evolution

1) Micro-evolutionary predictions are complicated by ecological feedbacks like density dependence, while ecological predictions can be complicated by evolutionary change. A widely used approach in micro-evolution, quantitative genetics, struggles to incorporate ecological processes into predictive models, while structured population modelling, a tool widely used in ecology, rarely incorporates evolution explicitly. 2) In this paper we develop a flexible, general framework that links quantitative genetics and structured population models. We use the quantitative genetic approach to write down the phenotype as an additive map. We then construct integral projection models for each component of the phenotype. The dynamics of the distribution of the phenotype are generated by combining distributions of each of its components. Population projection models can be formulated on per generation or on shorter time steps. 3) We introduce the framework before developing example models with parameters chosen to exhibit specific dynamics. These models reveal (i) how evolution of a phenotype can cause populations to move from one dynamical regime to another (e.g. from stationarity to cycles), (ii) how additive genetic variances and covariances (the G matrix) are expected to evolve over multiple generations, (iii) how changing heritability with age can maintain additive genetic variation in the face of selection and (iii) life history, population dynamics, phenotypic characters and parameters in ecological models will change as adaptation occurs. 4) Our approach unifies population ecology and evolutionary biology providing a framework allowing a very wide range of questions to be addressed. The next step is to apply the approach to a variety of laboratory and field systems. Once this is done we will have a much deeper understanding of eco-evolutionary dynamics and feedbacks.

q-bio.PE↗