Biology subjects

Ruffley, M.

Publications and source records attributed to Ruffley, M..

4 recordsLinked to original sources

bioRxiv2022

An image is worth a thousand species: combining neural networks, citizen science, and remote sensing to map biodiversity

Anthropogenic habitat destruction and climate change are altering the composition of plant communities worldwide1,2. However, traditional species distribution models cannot detect rapid, local plant species changes due to their low spatial and temporal resolution3,4, and remote sensing models can only identify changes in coarse vegetation categories5,6. Here we combine open-access remote sensing imagery, citizen science observations, and deep learning to create a multi-species prediction model at high spatial and temporal resolution. We train a novel deep convolutional neural network using [~]half a million observations within California to simultaneously predict the presence of over 2,000 plant species at meter-level resolution. This model--deepbiosphere--accurately performs many key biodiversity monitoring tasks, from fine-mapping geographic distributions of individual species and communities, to detecting rapid plant community changes in space and time. Deepbiosphere shifts the paradigm for species distribution modeling, providing a roadmap for inexpensive, automatic, and scalable detection of anthropogenic impacts on species worldwide.

ecology↗

bioRxiv2021

Quantifying the scale of genetic diversity extinction in the Anthropocene

More species than ever before are at risk of extinction due to anthropogenic habitat loss and climate change. But even species that are not threatened have seen reductions in their populations and geographic ranges, likely impacting their genetic diversity. Although preserving genetic diversity is key to maintaining adaptability of species, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. By bridging theories of biodiversity and population genetics, we introduce a mathematical framework to understand the loss of naturally occurring DNA mutations within decreasing habitat within a species. Analysing genome-wide variation data of 10,095 geo-referenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a power law with geographic area (the mutations-area relationship), which can predict genetic diversity loss in spatial computer simulations of local population extinctions. Given pre-21st century values of ecosystem transformations, we estimate that over 10% of genetic diversity may already be lost, surpassing the United Nations targets for genetic preservation. These estimated losses could rapidly accelerate with advancing climate change and habitat destruction, highlighting the need for forecasting tools that facilitate implementation of policies to protect genetic resources globally.

ecology↗

bioRxiv2021

Detecting the ecological footprint of selection

The structure of communities is influenced by many ecological and evolutionary processes, but the way this manifests in classic biodiversity patterns often remains unclear. Here, we aim to distinguish the ecological footprint of selection - through competition or environmental filtering - from that of neutral processes that are invariant to species identity. We build on existing Massive Eco-evolutionary Synthesis Simulations (MESS), which uses information from three biodiversity axes - species abundances, genetic diversity, and trait variation - to distinguish between mechanistic processes. In order to correctly detect and characterise competition, we add a new and more realistic form of competition that explicitly compares the traits of each pair of individuals. Our results are qualitatively different to those of previous work in which competition is based on the distance of each individuals trait to the community mean. We find that our new form of competition is easier to identify in empirical data compared to the alternatives. This is especially true when trait data are available and used in the inference procedure. Our findings hint that signatures in empirical data previously attributed to neutrality may in fact be the result of pairwise-acting selective forces. We conclude that gathering more different types of data, together with more advanced mechanistic models and inference as done here, could be the key to unravelling the mechanisms of community assembly.

ecology↗

bioRxiv2020

A unified model of species abundance, genetic diversity, and functional diversity revealsthe mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (MESS) with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of spatial scales.

evolutionary biology↗