Predicting the Impacts of Global Environmental Change on Ecological Networks
Lead Research Organisation:
University of Essex
Department Name: Life Sciences
Abstract
Global Environmental Change (GEC) is having profound effects on our natural environment, with declining biodiversity as a result of warming, acidification, and extreme climatic events such as heatwaves and droughts. Every species is part of a network of interactions that is integral to how ecosystems function and the loss of even one species can alter the population size of its consumers and resources, causing effects to ripple through the entire food web. The surviving species may also adapt by switching or expanding their diet in order to survive, resulting in complex reassembly of interactions. Thus, a deeper understanding of ecological network patterns will greatly enhance our ability to gauge how ecosystems will respond to GEC and help guide conservation efforts.
Existing ecological assessment of GEC primarily focuses on the population-level response of a few key species, or coarse network-level metrics such as connectance, but little is known about network organisation at finer scales. For example, sub-network structures can include a cohesive 'core' of closely interacting nodes and a loosely connected 'periphery'. These sub-structures have been observed in many different types of artificial network (e.g. telecommunications and social networks), and their significance for governing dynamics and stability is widely acknowledged. In ecology, sub-network structures have revealed important fine scale changes in food webs exposed to drought, but this line of research is largely unexplored. Hence, there is an urgent need for scientific advances to facilitate the coupling between network theory and ecosystem ecology, allowing us to better understand how our natural systems withstand and adapt to the effects of external stressors.
The overall aim of this project is to gauge and forecast the impact of GEC on ecological networks, and develop a more integrated modelling approach that can ultimately be expanded and adapted to cover all ecosystems. We will identify sub-network structures that provide resilience against GEC by profiling a database of 600 high-quality ecological networks from a wide range of climatic conditions across the globe, and conducting controlled experiments in semi-natural environments. Understanding how the sub-structural patterns have been altered in these networks will provide a new avenue for categorising the type and magnitude of ecosystem responses to GEC.
In addition, we are typically limited to information about the state of an ecosystem before and after a climatic disturbance, with little known about the intermediate stages. The order of biodiversity loss can affect the way in which the surviving species adjust their diet, due to the availability of resource species at different stages. Thus, we will use computational models to simulate realistic orders of biodiversity loss, validated and refined using our manipulative experiments, helping us to identify the rules and mechanisms that underpin ecosystem responses to GEC. These findings will provide vital information for protecting our natural resources and enable scientists to predict the future health of ecosystems more accurately.
Existing ecological assessment of GEC primarily focuses on the population-level response of a few key species, or coarse network-level metrics such as connectance, but little is known about network organisation at finer scales. For example, sub-network structures can include a cohesive 'core' of closely interacting nodes and a loosely connected 'periphery'. These sub-structures have been observed in many different types of artificial network (e.g. telecommunications and social networks), and their significance for governing dynamics and stability is widely acknowledged. In ecology, sub-network structures have revealed important fine scale changes in food webs exposed to drought, but this line of research is largely unexplored. Hence, there is an urgent need for scientific advances to facilitate the coupling between network theory and ecosystem ecology, allowing us to better understand how our natural systems withstand and adapt to the effects of external stressors.
The overall aim of this project is to gauge and forecast the impact of GEC on ecological networks, and develop a more integrated modelling approach that can ultimately be expanded and adapted to cover all ecosystems. We will identify sub-network structures that provide resilience against GEC by profiling a database of 600 high-quality ecological networks from a wide range of climatic conditions across the globe, and conducting controlled experiments in semi-natural environments. Understanding how the sub-structural patterns have been altered in these networks will provide a new avenue for categorising the type and magnitude of ecosystem responses to GEC.
In addition, we are typically limited to information about the state of an ecosystem before and after a climatic disturbance, with little known about the intermediate stages. The order of biodiversity loss can affect the way in which the surviving species adjust their diet, due to the availability of resource species at different stages. Thus, we will use computational models to simulate realistic orders of biodiversity loss, validated and refined using our manipulative experiments, helping us to identify the rules and mechanisms that underpin ecosystem responses to GEC. These findings will provide vital information for protecting our natural resources and enable scientists to predict the future health of ecosystems more accurately.
