The path North followed to her current place in the scientific high beams derives from a lifelong connection to the Bay. She grew up on the shores of the Severn River, "catching yellow perch and then not catching yellow perch" when major fish kills occurred in the Bay during her elementary school years. She attended ecology camps in the summer run by the Chesapeake Bay Foundation and interned at the National Aquarium in Baltimore. Her father, a physician, taught her to fish. Her mother, an artist, taught her to identify marsh plants.

As a college student at Swarthmore, North studied comparative religion, with some biology classes along the way. She wanted to learn about differences and commonality in the human experience. As it turned out, studying religion prepared her well for studying science. Both, she says, offer a framework, a structure for understanding the world.

After college, North started down a path of long-time interest. She took a job in Annapolis with the Chesapeake Bay Office of the National Oceanic and Atmospheric Administration. She went on to work for the Environmental Protection Agency Chesapeake Bay Program in Solomons and to pursue a master's degree in environmental policy at Johns Hopkins, becoming deeply aware of the need for good science to support sound resource management.

Her interest in applied research next led her to pursue a Ph.D. with UMCES fisheries biologist Ed Houde. At the Chesapeake Biological Laboratory, she focused on physical oceanography, studying how the Bay's complex water circulation affects the distribution of fish larvae in the Bay. She spent long hours on research vessels and peering through a microscope, honing her knowledge of the Bay's intricate biology. As she went on with her research, North realized that mathematical modeling would provide a valuable tool to help link her observations in the physical and biological domains, to "visualize the world" in a way that would be useful to fisheries managers and other decision makers.

North became fluent in the language of modeling through a post-doctoral fellowship with UMCES researcher Raleigh Hood, a biological oceanographer at Horn Point Laboratory who uses mathematical modeling to study algae and primary production in ecosystems around the world. She later accepted a faculty position at the Horn Point Laboratory — a rare occasion to remain at the institution that trained her. For North, this job was the ideal chance to still further strengthen her link to the Bay, an opportunity to continue crabbing and fishing on the Choptank with her husband Tim, a research vessel engineer who used to tong for oysters in the fishery's more prosperous days.

Through her research program, North tries to link the Bay's physical environment to its biological resources, combining modeling, field, and lab-based approaches to studies of blue crabs, underwater grasses, and oysters. Models, she knows, provide just one tool of many, an attempt to visualize the complex network of relationships in the Bay, making the real world easier to understand. But the right tool must match a specific problem, North is careful to point out. "If we had only one tool for every project," she says, "there wouldn't be Home Depot."

A few more keystrokes from North's slender fingers and a new screen pops up: a blank graph stares back, waiting for her to execute a subsection of code that accounts for the different larval behavior of the two species. As native larvae mature, they tend to cluster above the salt barrier (halocline) that cleaves the Bay in two layers: a buoyant, less salty layer of river water flowing seaward and a layer of dense, saltier ocean water flowing upriver. But non-native C. ariakensis larvae stay low and hover within one meter of the bottom, according to new experiments by oyster researchers Joan Manuel, Roger Newell and Vic Kennedy, also at Horn Point Laboratory.

North believes that these differences in behavior could strongly affect which parts of the Bay these two species will populate. For example, if non-native oyster larvae hang close to the bottom, they may ride bottom ocean currents up the estuary. If larvae hover in the surface water as do the native oyster, they might go down estuary, she explains. For her model to accurately simulate a virtual larval journey, she must capture these differences in behavior.

When North starts the model clock running, the two virtual oyster species behave as she expects. The simulated native oyster larvae float up above the salt barrier, five meters off the Bay's bottom. The non-native oyster larvae stay low. Day 1. . .Day 2. The model seems to be working well. Day 14. . . . The larvae are now biologically competent to settle.

On Day 15, however, North encounters a problem. All of the larvae of both species freeze in place. She recognizes this as an error, perhaps a bug in her computer code, perhaps a problem with the boundary conditions that keep particles from jumping out of the virtual water onto land. At this stage, the simulated larvae should have had at least another seven days to swim around looking for suitable habitat. She knows that she'll diagnose the problem, but needs to find it fast. She wants to present this portion of her model results at an upcoming scientific meeting.

With another series of rapid keystrokes, North calls up the screen that masterminds her model, filled with code that to the untrained eye might as well be hieroglyphics. Leaning forward, she scans the language intently, proofreading and editing in an attempt to pinpoint the source of the problem.

In many ways, North's work as a modeler is much like that of a writer. She writes in the language of mathematics, but the actual syntax is the computer code Fortran. She weaves together themes with a complicated architecture of concepts to recount a classic epic journey, a coming-of-age tale of sorts. Her model uses mathematics to reflect the story of an oyster's search for a place to start life. Not just one oyster protagonist but a cast of hundreds of thousands of each of the two species.

As complicated as the plot of a complex computer model can become, the larval transport model developed by North and her colleagues dramatically simplifies ecological reality. For example, the model cannot track more than 100,000 oyster larvae in one run, while in the real world, a single oyster in the Bay can release millions of eggs, explains North. The challenge of modeling is to represent reality as accurately as possible while dealing with necessary limitations of available data and computer power, she says. The model must be strategically simplified to maintain realism yet complete simulations within a reasonable time frame, she continues. "If we don't simplify, it will be 2050 before we have an answer."

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