Biology subjects

Gillespie, L.

Publications and source records attributed to Gillespie, L..

4 recordsLinked to original sources

bioRxiv2026

Helotiales fungi as potential nutritional partners for non-mycorrhizal plants: a machine learning and experimental approach

BackgroundMost land plants depend on the ancestral arbuscular mycorrhizal (AM) symbiosis for phosphorus (P) acquisition. However, several plant lineages have independently lost this symbiosis, raising fundamental questions about how these non-mycorrhizal plants meet their nutritional requirements without this crucial partnership. ResultsComparative genomic analyses confirmed that Cyperaceae, Caryophyllaceae, and Brassicaceae lack genes essential for AM symbiosis, indicating that these lineages independently abandoned this association 90-122 million years ago. Field surveys of 42 wild populations across seven sites revealed that while non-mycorrhizal plants generally maintain shoot P levels comparable to those in AM neighbors, lower shoot P levels can be observed in low P soils. To identify fungal taxa potentially associated with P nutrition in non-mycorrhizal plants, we applied a machine-learning approach to predict plant P-accumulation from root microbiome composition. The model explained substantial variance in plant P-accumulation (57-69%), and identified 85 fungal taxa as key predictors of shoot P-accumulation, predominantly belonging to the Helotiales (28%) and Pleosporales (23%) orders. Experimental validation of two phylogenetically distant Helotiales lineages (Tetracladium maxilliforme OTU29 and Helotiales sp. OTU7), using isotopic tracing, demonstrated their capacity to enhance plant growth and transfer P (and N) to their native non-mycorrhizal hosts under P-limiting conditions. ConclusionsOur findings suggest that non-mycorrhizal plants engage in nutritional partnerships with diverse Helotiales lineages that could collectively contribute to their mineral nutrition. However, given the widespread distribution of these Helotiales fungi, including in roots of AM plants, they may play a broader role in plant nutrition, i.e. also in mycorrhizal hosts. This study provides proof of concept for a novel framework integrating machine-learning predictions with experimental validation to identify functionally important microbial partnerships in natural plant communities.

microbiology↗

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↗

bioRxiv2020

Saturation of influenza virus neutralization and antibody consumption can both lead to bistable growth kinetics

Influenza virus is a major human health threat. Neutralizing antibodies elicited through prior infection or vaccination play an irreplaceable role in protection from subsequent infection. The efficacy of antibody-dependent vaccines relies on both virus replication and neutralization, but their quantitative relationship was unknown. Here we use mathematical models to quantitatively investigate viral survivability determined by antibody concentration and inocula size. We performed focus reduction assays for 49 seasonal influenza A/H3N2 viruses circulating during 2017-2019 against influenza antisera raised in ferrets, and find that the antibody consumption rates of individual reactions were either small or large, and this was strongly positively correlated with virus saturation. Regardless of antibody consumption rate, virus-antibody interactions always lead to antibody-induced bistable viral kinetics. As a result, at a specific interval of antibody concentration, small viral inocula are eliminated but not large virus inocula, which is triggered by saturated virus neutralization or antibody consumption. Our finding highlights virus-antibody interaction with different antigenic properties, thereby explaining commonly observed influenza re-infection and enhancing vaccine efficiency.

microbiology↗