Associate Marine Scientist (Marine Population Genetics), South Carolina Dept. of Natural Resources, Charleston SC, USA

The Marine Resources Division for South Carolina Dept. of Natural Resources is hiring a permanent PhD level scientist to join a team investigating the genetics of natural marine populations of the SE USA. While experience with marine and/or freshwater species is desirable, we welcome anyone with skills in molecular labwork, bioinformatics, conservation and evolutionary genetics, or functional genomics to apply. This is an excellent position for someone looking to do impactful population biology research in a collaborative atmosphere, while still encouraging academic curiosity and connection.

OFFICIAL TITLE:    Associate Marine Scientist

LOCATION:        Charleston SC

SALARY:        $58,405

JOB DESCRIPTION: The successful candidate will join our population genetics team to contribute to our population genetic and molecular tool research projects on marine and freshwater fishes in areas of stock enhancement, fisheries management, conservation, and tool development.

Specific duties will include field and laboratory research, data collection and analysis, collection and data management, preparation of reports and presentations, authoring technical manuscripts, coordination and supervision of team personnel, proposal preparation, and budget management. Some projects will include work on multi-disciplinary teams.  

MINIMUM EDUCATION AND EXPERIENCE:

Applicants will be required to have an Ph.D. degree in biology, marine science, or population/evolutionary genetics and at least 5 years research experience following the award of their terminal degree.

Individuals should have demonstrated abilities to conduct population genetics research including experimental design, data collection and analyses, and report preparation.  Publication record and experience with microsatellites, automated sequencing systems, whole genome sequencing, and bioinformatics required; successful grant funding record preferred.

Applicants must also have knowledge of marine and/or freshwater fishes of the southeastern US and the capability to work independently.

Strong communication, quantitative, organizational, computer, writing and inter-personal skills required.

OTHER PREFERRED KNOWLEDGE, SKILLS AND ABILITIES:

Valid SC driver’s license and a copy of driving record are required.

Applicant must have dependable transportation and a satisfactory driving record.  A current CV is requested to supplement the required state application.

TO APPLY:  Submit a state application and letter of interest via the SC State Job Website (https://careers.sc.gov/).

Extension associate (Fisheries and aquaculture), Minnesota Sea Grant (MNSG), St. Paul or Duluth, MN, USA

Minnesota Sea Grant (MNSG) seeks a fisheries and aquaculture extension associate who will focus their research and outreach efforts on fisheries, aquaculture, and aquatic ecosystems in Minnesota.

The position is a one year, full-time position (renewable for up to three years) for a highly motivated individual with experience working in fisheries and/or aquaculture research. Outreach is also a critical responsibility of the position and the successful candidate will support multiple projects and programs within MNSG.

Link to job announcement: https://seagrant.umn.edu/articles/fisheries-aquaculture-extension-associate

To apply for this position: Go to https://hr.umn.edu/Jobs/Find-Job and search for job opening 353297.

Complete the online application and attach a 1) cover letter, 2) resume or curriculum vitae,

3) diversity statement and 4) contact information for three professional references.

Quick reference:

Job Title: Fisheries and Aquaculture Extension Associate Job Code: 9742R5 -Researcher 5

Location: St. Paul or Duluth, MN

Full/part -time: Full-time (40 hours per week) and annually renewable for three years Travel percentage: Up to 10% Application open date: January 6, 2023 Application close date: Review begins January 30, 2023. Applications continue until position is filled

Paleo-diatom records reveal ecological change not detected using traditional measures of lake eutrophication

Authors

Rose Gregersen, Jamie D. Howarth, Javier Atalah, John K. Pearman, Sean Waters, Xun Li, Marcus J. Vandergoes, Susanna A. Wood

Lakes provide crucial ecosystem services and harbour unique and rich biodiversity, yet despite decades of research and management focus, cultural eutrophication remains a predominant threat to their health. Our ability to manage lake eutrophication is restricted by the lack of long-term monitoring records. To circumvent this, we developed a bio-indicator approach for inferring trophic level from lake diatom communities and applied this to sediment cores from two lakes experiencing eutrophication stress. Diatom indicators strongly predicted observed trophic levels, and when applied to sediment cores, diatom predicted trophic level reconstructions were consistent with monitoring data and land-use histories. However, there were significant recent shifts in diatom communities not captured by the diatom-based index or monitoring data, suggesting that conventional trophic level indices obscure important ecological change. New approaches, such as the one in this study, are critical to detect early changes in water quality and prevent the decline of lake ecosystems worldwide.

Restoration effects of submerged macrophytes on methane production and oxidation potential of lake sediments

Authors

Jianglong Zhu, Yahua Li, Minghui Huang, Dong Xu, Yi Zhang, Qiaohong Zhou, Zhenbin Wu, Chuan Wang

The restoration of submerged macrophytes is an important step in lake ecosystem restoration, during which artificially assisted measures have been widely used for macrophyte recolonization. Compared with natural restoration, the impact of artificially assisted methods on methane (CH₄) production and oxidation of lake sediments remains unclear. Therefore, after the restoration of submerged macrophytes in some parts of West Lake (Hangzhou, China), sediment samples from West Lake were collected according to restoration methods and plant coverage. The CH₄ production potential, oxidation potential, and microbial community structure in the sediment were discussed through whole-lake sample analysis and resampling verification from typical lake areas. From the analysis of the whole lake, the average daily CH₄ production potential (ADP) of artificially restored lake areas (0.12 μg g⁻¹ d⁻¹) was significantly lower than that of the naturally restored lake areas (0.52 μg g⁻¹ d⁻¹). From the resampling analysis of typical lake areas, the ADP of naturally restored lake areas was 1.8 times that of artificially restored lake areas (P < 0.01). Although there was no significant difference in the CH₄ oxidation potential between the two restoration methods, the presence of submerged macrophytes significantly increased the abundance of the dominant methanotroph Methylocaldum in the sediment, and the rate of increase in the abundance of the dominant methanotroph Methylosinus was significantly higher in artificially assisted restoration than in natural restoration. This study revealed that the artificially assisted restoration of submerged macrophytes reduced the potential for CH₄ production and increased the abundance of dominant methanotrophs in the lake sediment, which would be beneficial for the reduction of CH₄ emissions during lake ecological restoration and environmental management.

2 X Post Docs (Wetland Restoration and Nutrient Cycling), Kent State University, Kent, OH, USA

The Department of Biological Sciences at Kent State University seeks TWO post-doctoral research associates to join a broad-scale data-intensive wetland research project. Come work in a collaborative environment and contribute to development of synthesis, new quantitative methods, and solutions-oriented environmental science. The project is funded by the Great Lakes Research Initiative and candidates will work with a treasure trove of data and people from the H2Ohio Wetland Monitoring Program to assess the impact of dozens of wetland restoration projects throughout on nutrient biogeochemistry and pollution.

For more information and how to apply, please visit: https://laurenkinsmancostello.weebly.com/opportunities.html

Postdoc (Coupled biogeochemical cycles), Michigan State University, East Lansing, MI, USA

The Watershed Biogeochemistry in the Anthropocene Lab (https://gersonlab.weebly.com/) at Michigan State University is recruiting a motivated, collaborative, and curious postdoc in coupled biogeochemical cycles to start in Fall 2023. We study how anthropogenic activities have altered the coupled cycling of nutrients and contaminants through watersheds, with a focus on mercury. We examine the fate, transport, and transformation of these elements within and between terrestrial and aquatic ecosystems, including their consequences for people and animals.

This research will involve a combination of fieldwork, lab analyses, and lab experiments. The postdoc will have opportunities to work both independently and collaboratively, to communicate results in peer-reviewed scientific articles and conferences presentations, and to share their work with relevant community members and leaders.

If interested, please review our lab website for more information and then contact Dr. Jacqueline Gerson at gersonja@msu.edu to arrange a zoom call. In your email, please include your CV and briefly address the following: 1) your research background and interests and 2) why you are interested in joining this lab.

Location: Michigan State University is located in East Lansing, Michigan, with Kellogg Biological Station ~1.5 hours away in Hickory Corners, Michigan. MSU is a community of ~50,000 undergraduate and graduate students. East Lansing offers an excellent quality of life, with a reasonable cost of living and access to many outdoor activities (hiking, paddling, biking, cross-country skiing). East Lansing is also ~1 hour from Detroit and Ann Arbor, and ~4 hours from Chicago. MSU is an equal opportunity employer. We encourage applications from women, persons of color, veterans, persons with disabilities and other individuals who can contribute to the intellectual diversity and cultural richness at Michigan State University. Michigan State University occupies the ancestral, traditional, and contemporary Lands of the AnishinaabegâÂEUR”Three Fires Confederacy of Ojibwe, Odawa, and Potawatomi peoples. The University resides on Land ceded in the 1819 Treaty of Saginaw.

Investigating the effects of anthropogenic stressors on lake biota using sedimentary DNA

Authors

Cécilia Barouillet, Marie-Eve Monchamp, Stefan Bertilsson, Katie Brasell, Isabelle Domaizon, Laura S. Epp, Anan Ibrahim, Hebah Mejbel, Ebuka Canisius Nwosu, John K. Pearman, Maïlys Picard, Georgia Thomson-Laing, Narumi Tsugeki, Jordan Von Eggers, Irene Gregory-Eaves, Frances Pick, Susanna A. Wood, Eric Capo

1. Analyses of sedimentary DNA (sedDNA) have increased exponentially over the last decade and hold great potential to study the effects of anthropogenic stressors on lake biota over time.
2. Herein, we synthesise the literature that has applied a sedDNA approach to track historical changes in lake biodiversity in response to anthropogenic impacts, with an emphasis on the past c. 200 years.
3. We identified the following research themes that are of particular relevance: (1) eutrophication and climate change as key drivers of limnetic communities; (2) increasing homogenisation of limnetic communities across large spatial scales; and (3) the dynamics and effects of invasive species as traced in lake sediment archives.
4. Altogether, this review highlights the potential of sedDNA to draw a more comprehensive picture of the response of lake biota to anthropogenic stressors, opening up new avenues in the field of paleoecology by unrevealing a hidden historical biodiversity, building new paleo-indicators, and reflecting either taxonomic or functional attributes.
5. Broadly, sedDNA analyses provide new perspectives that can inform ecosystem management, conservation, and restoration by offering an approach to measure ecological integrity and vulnerability, as well as ecosystem functioning.

LAGOS-US RESERVOIR: A database classifying conterminous U.S. lakes 4 ha and larger as natural lakes or reservoir lakes

Authors

Lauren K. Rodriguez, Sam M. Polus, Danielle I. Matuszak, Marcella R. Domka, Patrick J. Hanly, Qi Wang, Patricia A. Soranno, Kendra S. Cheruvelil

The LAGOS-US RESERVOIR data module classifies all 137,465 lakes ≥ 4 ha in the conterminous U.S. into three categories using a machine learning predictive model based on visual interpretation of lake outlines and a lake shape classification rule. Natural Lakes (NLs) are defined as naturally formed, lacking large, flow-altering structures; Reservoir Class A’s (RSVR_A) are defined as lakes likely human-made or human-altered by a large water control structure; and Reservoir Class B’s (RSVR_Bs) are lakes likely human-made but are not connected to streams and have a shape rare in NLs. We trained machine learning models on 12,162 manually classified lakes to predict assignment as an NL or RSVR, then further classified RSVRs based on NHD Fcodes, isolation, and angularity. Our classification indicates that > 46% of lakes ≥ 4 ha in the conterminous U.S. are reservoir lakes. These data can be easily combined with other LAGOS-US modules and U.S. national databases for the broad-scale study of reservoir lakes and NLs.

Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

Authors

Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman

With increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, the gradient boost regressor (GBR) and long short-term memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements and the others applying a two-step approach, first estimating lake nutrients that have limited observations and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training–testing year combinations.

A simple metric for predicting the timing of river phytoplankton blooms

Authors

Nicholas E. Bruns, James B. Heffernan, Matthew R. V. Ross, Martin Doyle

In rivers, phytoplankton populations are continuously exported by unidirectional, advective flow. Both transport and growth conditions determine periods of excess phytoplankton growth, or blooms, in a given reach. Phytoplankton abundance, however, has mainly been compared to the state of either growth or transport conditions alone rather than in tandem. Previous studies have not yielded consistent driver–response relationships, limiting our ability to predict the timing of riverine phytoplankton blooms based on environmental factors. Here, we derive a simple joint metric that combines the state of growth and transport conditions, specifically the ratio of temperature and discharge (T/Q ). We then compare the metric to biomass abundance data (daily, sensor-based chlorophyll a [chl a] data) from a mid-sized Great Plains river (the Kansas).  T/Q  was an excellent predictor of low to high biomass, outperforming either variable alone. However, it could not differentiate between very high biomass values, values well above the biomass threshold designating bloom conditions. Our findings of reduced performance at the highest values of T/Q indicated that T/Q  could predict the occurrence (timing) but not magnitude of phytoplankton blooms, and we used T/Q to correctly predict 71% of days when bloom conditions occurred. Analyzing chl a abundance with  T/Q  also revealed likely switching from transport and temperature to nutrient control.  T/Q  offers a simple tool for (1) predicting the timing of river phytoplankton blooms, (2) forecasting how river ecosystems will respond to surrounding environmental changes, and (3) determining which environmental factors shape phytoplankton blooms at specific locations along a river.