Phycology Section of the Patrick Center for Environmental Research
The Phycology Section of the Patrick Center for Environmental Research (PCER) studies the ecology and taxonomy of freshwater algae, particularly diatoms, to better understand the distributions of algal taxa throughout the U.S., and the environmental factors influencing both the occurrence of taxa over large scales and the composition of assemblages at individual sites. Historically, the Phycology Section applied knowledge of algal ecology to the assessment of a wide range of environmental issues, but primarily those related to water quality of rivers and streams.
Because there are so many algal taxa, because they live in a wide variety of habitats, and because they have specific ecological requirements, algae are excellent ecological indicators, particularly of water quality. Diatoms especially have a long history of use in ecological assessment. Dr. Ruth Patrick, founder of the Patrick Center, has performed research on use of diatoms for biomonitoring since the 1940s.
Some of the largest projects the Phycology Section worked on include the USGS National Water Quality Assessment Program (NAWQA), US EPA’s lake and stream National Aquatic Resource Surveys (NARS), and NSF’s National Ecological Observatory Network (NEON). In addition to sample analysis, the Phycology Section used algal data to assess ecological conditions, quantify ecological characteristics of taxa, create and evaluate metrics, and help develop nutrient criteria.
The Phycology Section's largest project was a cooperative agreement with the USGS NAWQA program to analyze algal samples from throughout the U.S. From 1995 until 2012, the Phycology Section and its subcontractors analyzed over 8,000 samples. Results were distributed to NAWQA biologists to help them assess ecological conditions. They also analyzed national distributions of taxa and used NAWQA's environmental data to quantify ecological characteristics of taxa and to develop new indicator metrics.
Regionally, the Phycology Section worked to help develop algal indicators of nutrient conditions in New Jersey streams for the NJ Department of Environmental Protection. Some of this involved use of the Biological Condition Gradient approach to develop nutrient criteria possibilities.
To help disseminate information from our studies, the Phycology Section developed its own website. The data accessible from that site included publications, lists of taxa names, an Algae Image database, the Diatom Paleolimnology Data Cooperative (DPDC) database, and autecological data sets.
Facilities
soft algae images The Phycology Section works with high quality facilities and equipment. Some are shared with other PCER Sections, which also provide valuable services (e.g., water chemistry analysis). They have a wide variety of field equipment for taking algal samples (water samplers, sediment coring equipment, boats), a separate laboratory for preparing algal samples and making diatom slides, a microwave apparatus for digesting diatom samples, six high quality research microscopes equipped with digital cameras, local access to scanning electron microscopes, and an extensive reference collection of taxonomic and ecological literature - both in the laboratory and the Academy Library. Algal analysts work near the ANSP Diatom Herbarium (largest in North America) and consult regularly with its curator, Dr. Marina Potapova. The section developed the North American Diatom Ecological Database (NADED), which contains count and other data on over 25,000 samples.
Capabilities
- Field collection of periphyton and phytoplankton samples
- Deployment and collection of diatometers
- Coring of lake and wetland sediments
- Analysis of periphyton and phytoplankton samples; identification and enumeration
- Analysis of diatoms in sediment cores; environmental reconstructions
- Digital imaging of algae specimens; light microscope and SEM
- Description of new diatom taxa
- Multivariate analysis of species and environmental data
- Calculation and development of metrics and other water quality indicators
- Calibration of inference models for inferring environmental characteristics
- Water quality assessment based on algal data (e.g., nutrient issues)