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Biology subjects

Groom, B.

Publications and source records attributed to Groom, B..

2 recordsLinked to original sources

Advancing global DNA-referencing of bushmeat in African tropical forests: a tool for identifying trade hotspots and dynamics in western and central Africa

The bushmeat trade in Africa is a largely unregulated activity that drives the unsustainable exploitation of wild terrestrial vertebrates--an issue often coined the bushmeat crisis. We build on a four-gene mitochondrial DNA-typing approach to develop an unprecedented reference database framework for effectively tracing the bushmeat trade in tropical Africa. Our dataset comprises over 2,500 samples collected over a 13-yr period across 10 African countries and two European airports. Relying on an expert analytical pipeline and [~]8,700 nucleotide sequences, we identified 96% of samples to the species-level. In contrast, we estimated that conventional two-gene approaches would have yielded 18-26% erroneous or inconclusive taxonomic assignments. DNA-typing refined > 50% of field identifications, with refinement reaching 92-95% for highly processed carcasses seized in Europe. Leveraging expanded taxonomic representation, we empirically refined the genetic species thresholds applied to bushmeat and provide a reproducible pipeline for using our expert reference database from NCBI. Overall, we identified 133 species--mostly mammals--with one-third listed as of conservation concern, and uncovered cryptic diversity evidence within several taxa. Our results also demonstrated the value of community ecology indexes for large-scale monitoring of the bushmeat trade. While national markets generally mirrored regional biodiversity patterns across tropical Africa, Benin emerged as a notable outlier and a key wildlife trade hotspot. We advocate for the integration of community ecology frameworks into surveillance efforts to guide more effective, regionally tailored mitigation strategies. Continued, standardized sampling is essential to broaden taxonomic coverage and enhance detection of cryptic biodiversity in genetic bushmeat monitoring.

genetics↗

Using artificial intelligence to optimize ecological restoration for climate and biodiversity

The restoration of degraded ecosystems is critical for mitigating climate change and reversing biodiversity loss. Depending on the primary objective - such as maximizing carbon sequestration or protecting threatened species - and within the boundaries of budget constraints, different spatial priorities have been identified at global and regional scales. Funding mechanisms to support such work comprise public sources, philanthropy, and the private sector, including the sales of carbon and biodiversity credits. However, effectively exploring tradeoffs among restoration objectives and estimating the price of biodiversity and carbon credits to design financially viable projects remain challenging. Here we harness the power of artificial intelligence in our software CAPTAIN, which we further develop to identify spatial priorities for ecological restoration that maximize multiple objectives at once and to allow a robust evaluation of biodiversity and climate outcomes. We find through a series of simulations that even low-to-moderate consideration of biodiversity in restoration projects leads to the selection of restored areas that disproportionately improve the conservation of threatened species, while resulting in a relatively smaller total amount of carbon captured. We propose a data-driven valuation of biodiversity credits in relation to carbon credits, enabling the design of a bundled financial model that could support restoration efforts even in areas previously excluded for economic reasons. Applying our methodology to plant diversity in the Atlantic Forest of eastern South America, one of the most biodiverse and threatened ecosystems globally, we demonstrate its practical utility in guiding real-world restoration and quantifying the essential trade-offs between climate and nature outcomes. Our study provides a robust, scalable methodological pathway to optimize the outcomes of restoration efforts for climate and nature.

bioinformatics↗