You can’t control what you can’t find: Detecting invasive species while they’re still scarce
By Jake Walsh
Most of the 10,000 ships lost to the bottom of the Great Lakes in wrecks over the past 400 years are still lost — hidden somewhere in 6 quadrillion gallons of water. Finding anything in a lake is a lesson in humility, so life as a freshwater biologist is always humbling. If we can’t account for huge steel freighters, imagine the challenge of finding a single tiny organism.
But it is crucial to detect invasive species as early as possible. Aquatic invasive species cause billions of dollars in economic damages, and regulators base multimillion-dollar management decisions on scientists’ and managers’ ability to detect them. It is much more cost-effective to invest in prevention measures than to react after a species becomes established. And low-density populations are easier to manage than species that have taken over an ecosystem.
But since funding, gear and time are limited, scientists often can only sample for invasives over small fractions of vulnerable areas. Compounding the challenge, our target species tend to lurk at low densities — that is, they are rare in most places.
I have spent eight years studying the spiny water flea, an invasive zooplankton, in Wisconsin. In a recent study, I worked with colleagues to develop a theoretical framework that uses math and computer modeling to improve detection of invasive species at low densities. Our model provides a simple rule of thumb for designing surveillance programs with no information other than an estimate of expected population densities. In other words, if managers have a ballpark understanding of how many individuals are in a system, our models can provide some basic information about how much effort they need to invest in sampling to detect the species reliably.
Rules of thumb for detecting invasive species: Since invasive species often lurk at low densities, missing invasive populations is more likely the rule than the exception, even in well-monitored ecosystems.
Detecting invasive species is the first step of any management strategy, and early detection is challenging but critical for effectively managing harmful invaders, such as Asian carp and zebra mussels. Failing to detect spiny water flea has been a key stumbling block in managing its spread across the Midwest. Similar dynamics are occurring with other invasive species, including medflies in California and Didymo algae, also known as “rock snot,” which is causing blooms in rivers across America.
We wanted to see whether there were ways to make detection more effective. To do this, we used theoretical models that explore detection at low densities to provide simple rules of thumb that aim to improve the process.
At low densities, detecting a small invasive organism in a large area can be nearly impossible without extraordinary effort. Even if there were one spiny water flea for every cubic meter of water in Lake Mendota, catching one in a net would be like finding a sesame seed in roughly 250 gallons of water.
However, managers can dramatically improve detection rates by targeting their sampling to areas or time periods when the target species is likely to be present at higher densities. Humans do this naturally when we have the necessary information. For example, I don’t search grocery stores randomly for blueberries – I look in the produce section, mainly in late summer when blueberries are in season in Wisconsin.
Detection is key
Invasive species cause enormous ecological and economic harm. As just one example, invasive insects do some $13 billion in damage yearly to crops in the United States. Our rules of thumb can help scientists and managers work smarter. Ultimately, though, the United States needs to invest much more in effective and comprehensive invasive species prevention efforts to prevent future ecological and economic harm by invasive species.