Details of Award
NERC Reference : NE/X011518/1
CryptoBioVision: Applying Computer Vision for Cryptic Species Discovery
Grant Award
- Principal Investigator:
- Dr P Fenberg, University of Southampton, Sch of Ocean and Earth Science
- Grant held at:
- University of Southampton, Sch of Ocean and Earth Science
- Science Area:
- Marine
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Biodiversity
- Science Topics:
- Conservation Ecology
- DNA barcoding
- Museum collections
- Species richness
- Systematics & Taxonomy
- Population Genetics/Evolution
- Image & Vision Computing
- Bioinformatics
- Abstract:
- Most of the biodiversity on the planet remains undescribed, unstudied, and unseen. This is sometimes referred to as the "Linnean shortfall", named after the great Swedish naturalist Carl Linneaus. A solid understanding of modern species diversity, their relationships to each other, how they live and where, is necessary to confidently study and answer many of the most basic questions in ecology and evolution, such as: how and why did modern biodiversity evolve? But perhaps more urgently, the Linnean shortfall fundamentally impedes our understanding of the magnitude of the global biodiversity crisis, and ultimately, how to respond to it. One of the biggest reasons for the shortfall is because many closely related species look very similar or indistinguishable to the eyes of scientists, so called "cryptic species". Thus, to help close the shortfall, we need to develop new methods to accelerate the discovery and aid the descriptions of cryptic species. In our proposal, we aim to show that recent advances in Computer Vision (hereafter CV) technology can help provide a solution. CV is a rapidly developing field in which computers are trained to recognise, extract and measure information from digital images. While CV is poised to become an essential tool for eco-evolution and taxonomy research, there are currently no studies showing that CV can be used for cryptic species discovery and description. Our overarching aim for this proposal is to bring together emerging technologies (CV) with traditional and established methods (fieldwork, DNA barcoding, taxonomy) to enhance and accelerate biodiversity discovery and aid descriptions of cryptic species from multiple habitat types. Our pilot research shows that we can train our models to match individual specimens to the correct species using digital images. We also show that CV can identify the morphological features that distinguish between pairs of cryptic species. These novel and innovative methods will provide scientists the means to accelerate the discovery and descriptions of cryptic species and to better study their ecology and evolution. We develop three proof-of-concept studies that apply CV to digital images of field collected and natural history museum specimens. We focus on animal groups with high cryptic diversity from coastal, deep-sea, and terrestrial habitats - spanning temperate, subtropical, and tropical regions. In the first study, we propose field trips to collect specimens of coastal molluscs from a region of high cryptic diversity, the Baja peninsula in Mexico (where the PI has a strong research background). We will apply our CV models to distinguish between cryptic species of limpets in the genus Lottia. Pilot results are promising and reveal that the outer margins of the shells are useful distinguishing features. In the second study, we will apply our CV models to >600 specimens of deep-sea amphipods in the genus Eurythenes, which has high cryptic diversity. Pilot results show that our CV models identify >90% of specimens to the correct species and highlights dorsal regions as distinctive areas of interest. In our third study, we work with our NMH partners to develop CV models for identifying cryptic species within the digital pinned insect collection, which is the largest and most diverse collection of its kind (millions of specimens from all over the globe). In this era of global change, we need to develop ground-breaking and innovative approaches to help close the "Linnean shortfall" if we are to: (i) fully grasp the magnitude of the biodiversity crisis and (ii) do something about it. Collectively, our studies aim to be the first to showcase the powerful utility of integrating CV with traditional methods for addressing the Linnean shortfall. Our future goal is to develop an online platform (CryptoBioVision) that will allow researchers from across the globe to apply our CV models to their study groups.
- NERC Reference:
- NE/X011518/1
- Grant Stage:
- Awaiting Completion
- Scheme:
- Standard Grant FEC
- Grant Status:
- Active
- Programme:
- Exploring the frontiers
This grant award has a total value of £80,594
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £23,000 | £12,809 | £17,280 | £3,080 | £9,192 | £4,411 | £10,823 |
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