This story was originally published by Knowable Magazine.
In 2003, Floridians collected 23 Burmese pythons from Everglades
National Park. The huge snake, they knew, didn’t belong there — it
is native to Southeast Asia, though popular as a pet in the US. In
2005, the park found another 95. The following year, with pythons
devouring native wildlife at a worrying rate, the South Florida
Water Management District asked the US Fish and Wildlife Service to
ban the invasive species from trade. The agency took until 2012 to
do so, and that was probably too late.
“By the time they decided they were going to try and ban the
sale, it was well established and spreading,” says Reuben Keller,
an ecologist at Loyola University Chicago. Researchers think the
animal is now a permanent Everglades resident. And the python is
just one of 4,300 invasive species in the US that together cause
more than $120 billion in damage every
The most cost-effective way to deal with those destructive
species is to keep them from getting established in the first
place, researchers have found — which means that ecologists and
wildlife officials need to be able to predict, early on, which
foreign organisms have invasive potential. It’s a skill they have
been building in recent decades. Some of their newer prediction
tools offer quick estimates of risk, allowing managers to focus
their time and effort on species most likely to pose problems in
the future. Other approaches deploy machine learning to recognize
threats that other methods might miss, especially in cases where
data are limited. Both strategies could help make preemptive bans
more common — if the US could muster the political will to use them
and act on them.
Block that pet!
Proactive bans are particularly useful for species that people
want to introduce as pets, ornamental plants or game for hunting or
fishing. Since 1900, the Fish and Wildlife Service has had the
power to ban a species from importation, under legislation called
the Lacey Act, if its potential environmental impact outweighs its
value in trade. But to make this call, ecologists need to know
ahead of time how much harm a species might do if introduced.
Until recently, the standard approach has been to dive deep into
an organism’s natural history. Before banning Burmese pythons and
other constrictors, for example, reptile experts had to learn all
they could about each species — the kind of habitat it needs, what
it eats and if and where it has become invasive before — then
compile the results into reports so thick they “bordered on being
books,” Keller says. Such a process takes months or years for each
species, Keller and his colleagues point out in the 2016 Annual Review of Environment and
Resources, which is one reason the constrictor ban came
too late to prevent serious environmental harm.
Matters improved somewhat after 2010, when the Fish and Wildlife
Service introduced a new invasivity prediction technique that was
faster and less data-hungry. Called the Ecological Risk Screening
Summaries, it catalogs the impacts of past introductions of a
species and runs a climate matching model that scans for other
environments in the US that the species could find homey. In less
than two days, it labels a species with a high, low or uncertain
risk of invasivity and highlights the regions where it might thrive
Kate Wyman-Grothem, who manages the program, says the speed
sometimes allows her department to rapidly offer wildlife officials
a quick assessment, pro or con, about a foreign plant or animal
they’re worried about. But more often — about 80 percent of the
time — species wind up in the “uncertain” category, because
biologists can’t find enough published data to make a call.
Keller and other researchers thought they saw a way ahead.
Around the same time that Fish and Wildlife produced its new tool,
they began designing different prediction techniques that cope
better with limited data. These methods use machine learning to
analyze species that have and haven’t become invasive in the past
and so identify key traits that determine invasivity. From that,
researchers can predict which newcomers might follow the same path.
The database “has now come to the size where we can at least say
with more confidence what’s likely to happen,” says Julian Olden, a
conservation ecologist at the University of Washington.
Olden and his colleagues used this kind of machine learning to
assess the potential invasivity of hundreds of
fish species that might one day reach the Great Lakes as bait,
food or escaped aquarium stock. They began by collecting data on 18
traits, such as salinity tolerance, number of offspring and
breeding frequency, for 24 introduced species with a documented
history in the ecosystem. Half were considered invasive to those
lakes, while the other half had never become a problem. By
contrasting the traits of the invasive species with traits of the
innocuous ones, their machine-learning algorithm learned to
correctly categorize each species more than three-quarters of the
Then the scientists turned to 787 species of potentially
invasive fish and used their algorithm to predict which group —
invasive or benign — each species was likely to fall into. The team
identified four strong threats to the Great Lakes: European
catfish, blue catfish, striped bass and ide. All four, according to
their model, are almost certainly going to become invasive if
introduced to those waters, and all are likely to cause
considerable damage, experienced ecologists think.
Difficulty doing the math
But the mathematics underlying machine learning — the advanced
calculations that turn data points into perceived risk — are hard
to understand. “In some cases, you can’t really explain what’s
going on with these tools,” says Keller, who worked with Olden on
the project. Explaining the complex algorithms can pose a challenge
when researchers try to persuade regulators, who may not be
familiar with this type of mathematical model, to use them as a
tool in decision-making.
Instead, for now these predictions play a background role in
managing invasive species. The Nature Conservancy, for example,
used them to help state and provincial officials in the Great Lakes
region draft a list of the “least wanted” invasive
species in 2013, says Lindsay Chadderton, aquatic invasive species
director for the organization’s Great Lakes Program. The list
included species such as killer shrimp and various water plants.
Other US agencies, such as the Department of Agriculture’s Animal
and Plant Health Inspection Service, use similarly intricate models
to track where existing invasive species might roam next, as they
did in 2015 when evaluating where feral pigs were likely to
spread. Intricate models of scenarios can even highlight which
habitats are, if not invasion-friendly, potentially good enough to keep a species
alive, as recent research found with Asian carp in the Great
Other nations have begun using a variety of predictive
techniques to screen potential invaders before they can be
considered for import. Decades ago, Australia and New Zealand made
invasive species risk assessments integral to ecosystem protection.
The nations have published a list of pets that are allowed for
import, and any species not on the list is banned. Similarly, any
ornamental plant not already on the approved list must be assessed
for invasive potential ahead of time.
But the US still uses the Lacey Act just as it was written in
1900, which puts the burden of proof on those who seek to ban a
species from trade. That requires a huge amount of evidence, a lot
of time and more transparency than machine learning affords — with
the result that proposed bans take an average of more than four years to implement.
For a thriving invasive creature like the Burmese python, which
eats virtually any animal it comes across and can lay 100 eggs at
one go, that is plenty of time to get a foothold in a new
“It’s glacial,” says Keller, “when you compare it to the speed
at which these organisms can be released and then spread.” But
until the US takes a more proactive, precautionary stance on
imports of foreign species, predictive efforts are unlikely to be
effective and bans will be too little, too late.