Reduce biodiversity loss.

Engineering Biology for Climate & Sustainability

Conservation of Ecosystems and Biodiversity Goal:

Current State-of-the-Art

Biodiversity is necessary to maintain ecosystems, supply chains, and the health and persistence of all species, including humans. Engineering biology can help to reduce biodiversity loss through less-invasive and more-sustainable monitoring and by ensuring that we can protect and support the resilience of keystone species and those that are threatened, particularly due to climate change and human activities.

Measurement technologies (i.e., multi-omics techniques) could be leveraged to rapidly catalog existing biodiversity in field environments with regard to genetic expression, metabolomics, and species composition. By documenting microbial species and their strain level diversity and identifying keystone functional guilds within a given ecosystem, we can support foundational microbiomes that are essential, designing and engineering networks, pathways, and heterogeneity to support at-risk ecosystems and keystone guilds. Genome monitoring and editing can also help to conserve biodiversity by tracking at-risk organisms, informing adaptation approaches or genetic rescue, and helping to limit or prevent poaching.¹

Breakthrough Capabilities & Milestones

Enable monitoring of ecosystem health with bio-sensors and -reporters.

Improve data collection and libraries of mitochondrial DNA for sensitive ecosystem members (i.e., threatened and keystone species).

Increase application of genetic marker surveillance.

Bottleneck/Challenge: Genetic data required for designing primers for microsatellite surveillance.³

Potential Solution: Increase genetic sequencing and library generation for organisms in at-risk ecosystems.

Potential Solution: Increase use of single-nucleotide polymorphism (SNP) surveillance.⁴

Use genetic fingerprinting and marker surveillance to develop species demographic and evolutionary data libraries for conservation management strategies.*

Expand activity-based monitoring (metafunctional genomics, metabolomics, stable tracer analyses) to track critical system function across diverse environments.

Develop integrated, continuous biosensors to track the effects of ecological forces (e.g., dispersal, drift, and selection) on community members in target biomes.

Track sourcing through supply chains by scaling-up biomolecular forensics (DNA, protein, and metabolic profiling) and genetic barcoding.

*Zimmerman, S. J., Aldridge, C. L., & Oyler-McCance, S. J. (2020). An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genomics, 21(1), 382. https://doi.org/10.1186/s12864-020-06783-9

Enable strengthening and protection of keystone and threatened species.

Build reference genome libraries for currently threatened species.*

Utilize genetic monitoring to determine best approaches for introducing and optimizing intrapopulation genetic variation.**

Design and build targeted gene-drives for introducing beneficial or adaptive traits into threatened species.

Bottleneck/Challenge: Current limitations in genetic data for targeting edit sites.

Potential Solution: Increase genetic sequencing and library generation for higher-order organisms, especially with regard to reproductive biology.⁵

Potential Solution: Improve understanding of homologous recombination and non-homologous end-joining in mammals.⁶

Engineer keystone and threatened species to be more adaptive or robust to climate change and anthropogenic impacts, such as through engineered microbiome robustness.

Enable introduction of genetic diversity into threatened species populations.

*Paez, S., Kraus, R. H. S., Shapiro, B., Gilbert, M. T. P., Jarvis, E. D., & VERTEBRATE GENOMES PROJECT CONSERVATION GROUP. (2022). Reference genomes for conservation. Science, 377(6604), 364–366. https://doi.org/10.1126/science.abm8127

**Kaczmarczyk, D. (2019). Techniques based on the polymorphism of microsatellite DNA as tools for conservation of endangered populations. Applied Ecology and Environmental Research, 17(2), 1599–1615. https://doi.org/10.15666/aeer/1702_15991615

Footnotes

Phelps, M. P., Seeb, L. W., & Seeb, J. E. (2020). Transforming ecology and conservation biology through genome editing. Conservation Biology, 34(1), 54–65. https://doi.org/10.1111/cobi.13292
Khan, H. A., Arif, I. A., Bahkali, A. H., Al Farhan, A. H., & Al Homaidan, A. A. (2008). Bayesian, maximum parsimony and UPGMA models for inferring the phylogenies of antelopes using mitochondrial markers. Evolutionary Bioinformatics Online, 4, 263–270. https://doi.org/10.4137/ebo.s934
Arif, I. A., Khan, H. A., Bahkali, A. H., Al Homaidan, A. A., Al Farhan, A. H., Al Sadoon, M., & Shobrak, M. (2011). DNA marker technology for wildlife conservation. Saudi Journal of Biological Sciences, 18(3), 219–225. https://doi.org/10.1016/j.sjbs.2011.03.002
Zimmerman, S. J., Aldridge, C. L., & Oyler-McCance, S. J. (2020). An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern. BMC Genomics, 21(1), 382. https://doi.org/10.1186/s12864-020-06783-9
Bier, E. (2022). Gene drives gaining speed. Nature Reviews Genetics, 23(1), 5–22. https://doi.org/10.1038/s41576-021-00386-0
Conklin, B. R. (2019). On the road to a gene drive in mammals. Nature, 566(7742), 43–45. https://doi.org/10.1038/d41586-019-00185-y