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research.qmd
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research.qmd
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## Experience
| | | | |
| ----------- | ------------------ | ------------------------------------------------------ | -------------- |
| 2023 - present | Research Associate | Bigelow Laboratory for Ocean Sciences | East Boothbay, ME |
| 2023 - present | Research Associate | Colby College | Waterville, ME |
| 2020 - 2023 | Research Technician | Bigelow Laboratory for Ocean Sciences | East Boothbay, ME |
| 2019 | Research Associate | [Commercial Fisheries Research Foundation](http://www.cfrfoundation.org/) | Saunderstown, RI |
| 2018 - 2019 | Research Fellow | [Richard H Goodwin Environmental Research Fellowship](https://www.conncoll.edu/goodwin-fellowship/) | New London, CT |
| Summer 2018 | Intern | NOAA Southwest Fisheries Science Center | La Jolla, CA |
| Summer 2017 | Student Researcher | Connecticut College Summer Research in the Sciences | New London, CT |
| Summer 2016 | Student Researcher | Connecticut College Summer Research in the Sciences | New London, CT |
| Summer 2014 | Volunteer | NOAA Southwest Fisheries Science Center | La Jolla, CA |
## Research Interests
* Fisheries Biology
* Highly Migratory Species
* Research Software Development
* Ocean Forecasting
* Knowledge Co-production
## Professional Affiliations
[Coastal and Esturaine Research Federation](https://www.cerf.science/)
[Association for the Sciences of Limnology and Oceanography](https://www.aslo.org/)
[Ecological Forecasting Initiative](https://ecoforecast.org/)
## Selected Projects
### Maine Shellfish Biotoxin Forecasting
Annual occurrence of paralytic shellfish poison (PSP) across the coast of Maine poses a challenge for fishery managers who implement regional harvesting closures to protect human health. Similarly, shellfish growers and harvesters also must make decisions to operate their businesses in the face of these closures. Two process-based predictive models exist that deliver seasonal and weekly *Alexandrium catenella* bloom potential forecasts. A more recently developed machine-learning model predicts the probabilistic risk of PSP accumulation in shellfish at a site-specific, weekly timescale. The latter model was developed with shellfish industry members and managers, to produce the most usable forecast possible. Through two seasons of delivering predictions in an experimental mode, the forecast has achieved high accuracy and received positive feedback from its users. Both the process-based and machine learning models provide important, but different insights for their users. While the process-based models capture a suite of environmental conditions that may lead to blooms of *A. catenella*, high cell concentrations cannot always predict spikes in toxicity. On the other hand, the machine learning model excels at predicting toxicity at a finer (weekly) timescale, but loses skill with longer lead times (> two weeks). Combining the two model types is being explored.
### Blue Mussel Spat Settlement in Coastal Maine
*Mytilus edulis* (blue mussel) seed availability is the biggest bottleneck in the Maine mussel aquaculture industry. *Perna canaliculus* spat settlement was demonstrated to be predicted with high accuracy 1 month ahead using historical spat collection records and 1 month of prior environmental covariates in New Zealand (Atalah, 2019). Originally suggested as a research idea from a mussel grower in Maine, a mussel spat settlement forecast would aid the industry in the annual process of collecting new mussels to grow. We conducted a survey of available mussel seed colleciton efforts in Maine, and attempted to look for relationships with environmental covariates.
### Assessing Marine Protected Area Success with OBIS Data
The Ocean Biodiversity Information System (OBIS) provides a global repository of marine species occurrence records. Using boundaries of marine protected areas (MPAs) around the world, species records were queried from OBIS for each MPA and used to calculate a biodiversity metric over 20 year time increments. Some of the guiding questions were: Is biodiversity in an MPA related to its size, age or distance from shore? The project team was formed during OceanHackWeek 2021 and continued to meet regularly for a year following the event. The analysis and tools developed for the project are located in this [repo](https://github.com/BigelowLab/mpatools).
### *Seriola dorsalis* Aquaculture Genomics
As a candidate species for aquaculture in Southern California, genomic resources are necessary to accelerate the development of Seriola dorsalis aquaculture. With knowledge of genetic basis behind locally adapted traits between wild populations of important aquaculture species, individuals that possess these valuable phenotypes can be used for selective breeding in aquaculture. In this study, a population genomic tool, Bayescan version 2.1, was implemented on a dataset of 76,974 loci of SNPs found within the genome of S. dorsalis, in order to search for loci being selected for based on allele frequencies between the three populations. No loci were found to have a probability of being naturally selected for higher than 11.042%, which does not provide any evidence of locally adapted, population specific traits that could be exploited through selective breeding in aquaculture. Based on these results, any fish taken from the region surrounding the three sample locations would possess similar traits, and the three populations can be managed as one.
### Fish Stock Genetic Diversity
Many current fishery stock assessment methods strongly rely on the amount of fish harvest reported at the dock by fishermen. We seek a method for fish stock assessment that is based on transcriptome measures. In this study we were interested in the correlation between transcriptome level diversity and changes in the phenotype expression ability of commercially targeted fish. By analyzing the complexity of miRNA/RNAi 7mer binding sites in the 3’UTR regions, inferences are made as to the accessible repertoire of phenotypes for the organism. If fewer phenotypes are available, for use in response to environmental change, or for use in extending habitable niche, such as by ‘schooling’, then significant loss of fishery stock may result. Preliminary results indicate Gadus Morhua (Atlantic Cod) has undergone such a loss in transcript regulatory complexity, which appears to be associated with the collapse of the Cod fishery in the Gulf of Maine.
## Awards and Grants
Bigelow Laboratory for Ocean Sciences - Spencer Fund ($23,426) January 2023
Project title: “Feasibility study: a blue mussel spat settlement forecast for Maine aquaculture”
Computer Science Award for Excellence in Research, Connecticut College April 2019
Richard H. Goodwin Environmental Research Fellowship, Connecticut College ($12,000) May 2018
Summer Science Research, Connecticut College ($4,000) May 2017
Summer Science Research, Connecticut College ($4,000) May 2016
Community Service Award, The Webb Schools May 2015