By Jeff Hollister

Based on the title above, you probably think I don’t know what I am talking about. I mean really, what do sparrows, lakes, and nutrients have in common? In this case, a lot. So much so, an inter-agency team of EPA researchers in Narragansett RI, and a colleague from the U.S. Geological Survey (USGS) in New Hampshire have been working together to better understand how these three seemingly disparate concepts can be linked together. Some of the results of this work are outlined in a recent publication in the Open Access journal, PLos One

The sparrow I am referring to isn’t small and feathered, it is a model developed and refined by the USGS. Since the late 1990’s, USGS has been developing SPARROW models which have been widely used to understand and predict the total amount of nutrients (among other materials) that streams are exposed to over the long-term. This is known as “nutrient load.” The models are important because they provide a picture over a very large extent of where nutrients might be relatively high.

However, when it comes to lakes, SPARROW doesn’t directly provide the information we need. For our research on lakes, we need reasonable estimates of the quantity of nutrients in a given volume of water (i.e., nitrogen and phosphorus concentration), not long term nutrient load for the year. This is important, because the higher the nutrient concentrations at any given time, the greater the chance of triggering algal blooms—and more blooms mean a greater probability of toxins released by algae reaching unhealthy levels.

In order to better estimate the nutrient concentrations, we needed to use the SPARROW model for total load, but also account for the differences between load and concentration. Our solution: combining field data, data on lake volume and the SPARROW Model.

In our paper “Estimating Summer Nutrient Concentrations in Northeastern Lakes from SPARROW Load Predictions and Modeled Lake Depth and Volume,” recently published in PLoS One, we describe how we combined modeling information from SPARROW, summertime nutrient concentrations collected during EPA’s 2007 National Lakes Assessment, and estimated lake volume (see this and this for more).

The end result of this effort is better predictions, by an average of 18.7% and 19.0% for nitrogen and phosphorus, respectively.

What is the meaning of this in terms of our environment, and importantly, the potential human health impacts? If we are able to better predict concentrations of nutrients it will hopefully also improve our ability to know where and when we might expect to see harmful algal blooms, specifically harmful cyanobacterial algal blooms. Cyanobacteria have been associated with many human health issues, from gastro-intestinal problems, to skin rash, and even a hypothesized association with Lou Gehrig’s Disease (for example, see this). So, in short, better predictions of nutrients, will, in the long run, improve our understanding of cyanobacteria and hopefully reduce the public’s exposure to a potential threat to health.

About the author: Jeff Hollister, a co-author on the study outlined in this blog post, is a research ecologist with an interest in landscape ecology, Geographic Information Systems (GIS), the statistical language R, and open science. The focus of Jeff’s work is to develop computational and statistics tools to help with the cyanobacteria groups research efforts. Jeff is also an outspoken advocate for open science and open access among his colleagues.