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Neil Brummitt

Publications and source records attributed to Neil Brummitt.

2 recordsLinked to original sources

Dynamic virtual ecosystems as a tool for detecting large-scale responses of biodiversity to environmental and land-use change

Ecosystems are governed by dynamic processes such as competition for resources, reproduction and dispersal. These shape their biodiversity and how the system responds to change. Current approaches to modelling ecosystems, especially plants, focus on either describing fine-scale processes for individual species or broad-scale patterns for limited groups of plant functional types. Digitisation of herbarium and other plant records has unlocked a wealth of information that can be used to drive models of plant communities and make predictions for their future under different scenarios of climate change. The advent of increased computational capacity and fast, high level programming languages allows for simulation of such landscapes at unprecedented scales. Here, we demonstrate a tool for Ecosystem Simulation through Integrated Species Trait-Environment Modelling (EcoSISTEM), which models plant species across multiple ecosystem sizes, from patches and small islands to regions and entire continents. These simulated ecosystems support the ability to generate many different types of habitat, as well as reproducing different disturbance scenarios such as climate change, habitat loss and invasion. EcoSISTEM also reproduces examples of real-world species distributions by integrating plant occurrence records and global climate reconstructions to simulate plant species throughout the continent of Africa for the past century. EcoSISTEM allows us to flexibly explore the dynamics of tens of thousands of species interacting across a continent. The code parallelises efficiently across multiple nodes on high performance computing platforms, and has been scaled up to run on over 1000 cores. It allows us to study the impact of changes to climate, resources and habitat and investigate real-life mechanisms surrounding climate change and biodiversity loss.

q-bio.QM

How to partition diversity

Diversity measurement underpins the study of biological systems, but measures used vary across disciplines. Despite their common use and broad utility, no unified framework has emerged for measuring, comparing and partitioning diversity. The introduction of information theory into diversity measurement has laid the foundations, but the framework is incomplete without the ability to partition diversity, which is central to fundamental questions across the life sciences: How do we prioritise communities for conservation? How do we identify reservoirs and sources of pathogenic organisms? How do we measure ecological disturbance arising from climate change? The lack of a common framework means that diversity measures from different fields have conflicting fundamental properties, allowing conclusions reached to depend on the measure chosen. This conflict is unnecessary and unhelpful. A mathematically consistent framework would transform disparate fields by delivering scientific insights in a common language. It would also allow the transfer of theoretical and practical developments between fields. We meet this need, providing a versatile unified framework for partitioning biological diversity. It encompasses any kind of similarity between individuals, from functional to genetic, allowing comparisons between qualitatively different kinds of diversity. Where existing partitioning measures aggregate information across the whole population, our approach permits the direct comparison of subcommunities, allowing us to pinpoint distinct, diverse or representative subcommunities and investigate population substructure. The framework is provided as a ready-to-use R package to easily test our approach.

q-bio.QM