Life finds a way.
Scientists discover rare frog
species surviving against the odds.
Edited by Sam Suren | Published on 01/JUL/2026 10:36 A.M EST
Summary
The Mid-Atlantic Coast Leopard Frog is the last known frog species still occupying freshwater habitats in the New Jersey Meadowlands, a heavily urbanized landscape where freshwater habitat has become increasingly limited.
Because this rare frog is difficult to find by sight, researchers are using passive acoustic monitoring to detect its distinctive low-pitched, quacking call across restored and remaining wetland habitats.
The work shows why biodiversity monitoring matters: the frog’s voice can reveal where it survives, whether restoration is working, and what needs protection before this hidden species disappears unnoticed.
March 30, 2026 · 42 MINS
Episode 2: The Future of Real
with Jon Bruno - CEO, Rainforest Connection
Photos of Mike Turso
About the Specie
Michael Turso, a wildlife biologist at the Meadowlands Research and Restoration Institute (MRRI), inherited that gap. The institute's monitoring work — split between a biotic team tracking species and a separate abiotic team running GIS, water quality, and sediment elevation work — had spent years building out acoustic infrastructure for other purposes, including bat surveys, with hardware partly supplied by the U.S. Fish and Wildlife Service. Turso's contribution was narrower and more pointed: turn that same infrastructure on the leopard frog and find out, finally, whether the unconfirmed assumption was true.
The obstacle was noise, in the literal sense. The Meadowlands sit inside a wall of anthropogenic sound — traffic, industry, air corridors — thick enough that researchers genuinely doubted whether acoustic recording units could pick a frog call out of it. Turso's team ran passive acoustic monitoring across a network of sites and leaned on pattern-matching and random forest models to do what a human ear, worn down by hours of highway hiss, eventually cannot: separate one species' low, quacking call from everything else competing for the same frequency. The team also found that combining multiple acoustic indices rather than relying on any single one produced a clearer signal — useful in a landscape where almost every recording carries some trace of the city leaning on the wetland.
Photos of Mike Turso
Conservationist behind the discovery
Michael Turso, a wildlife biologist at the Meadowlands Research and Restoration Institute (MRRI), inherited that gap. The institute's monitoring work — split between a biotic team tracking species and a separate abiotic team running GIS, water quality, and sediment elevation work — had spent years building out acoustic infrastructure for other purposes, including bat surveys, with hardware partly supplied by the U.S. Fish and Wildlife Service. Turso's contribution was narrower and more pointed: turn that same infrastructure on the leopard frog and find out, finally, whether the unconfirmed assumption was true.
The obstacle was noise, in the literal sense. The Meadowlands sit inside a wall of anthropogenic sound — traffic, industry, air corridors — thick enough that researchers genuinely doubted whether acoustic recording units could pick a frog call out of it. Turso's team ran passive acoustic monitoring across a network of sites and leaned on pattern-matching and random forest models to do what a human ear, worn down by hours of highway hiss, eventually cannot: separate one species' low, quacking call from everything else competing for the same frequency. The team also found that combining multiple acoustic indices rather than relying on any single one produced a clearer signal — useful in a landscape where almost every recording carries some trace of the city leaning on the wetland.
Findings/Evidence
Michael Turso, a wildlife biologist at the Meadowlands Research and Restoration Institute (MRRI), inherited that gap. The institute's monitoring work — split between a biotic team tracking species and a separate abiotic team running GIS, water quality, and sediment elevation work — had spent years building out acoustic infrastructure for other purposes, including bat surveys, with hardware partly supplied by the U.S. Fish and Wildlife Service. Turso's contribution was narrower and more pointed: turn that same infrastructure on the leopard frog and find out, finally, whether the unconfirmed assumption was true.
The obstacle was noise, in the literal sense. The Meadowlands sit inside a wall of anthropogenic sound — traffic, industry, air corridors — thick enough that researchers genuinely doubted whether acoustic recording units could pick a frog call out of it. Turso's team ran passive acoustic monitoring across a network of sites and leaned on pattern-matching and random forest models to do what a human ear, worn down by hours of highway hiss, eventually cannot: separate one species' low, quacking call from everything else competing for the same frequency. The team also found that combining multiple acoustic indices rather than relying on any single one produced a clearer signal — useful in a landscape where almost every recording carries some trace of the city leaning on the wetland.
Impact
Michael Turso, a wildlife biologist at the Meadowlands Research and Restoration Institute (MRRI), inherited that gap. The institute's monitoring work — split between a biotic team tracking species and a separate abiotic team running GIS, water quality, and sediment elevation work — had spent years building out acoustic infrastructure for other purposes, including bat surveys, with hardware partly supplied by the U.S. Fish and Wildlife Service. Turso's contribution was narrower and more pointed: turn that same infrastructure on the leopard frog and find out, finally, whether the unconfirmed assumption was true.
The obstacle was noise, in the literal sense. The Meadowlands sit inside a wall of anthropogenic sound — traffic, industry, air corridors — thick enough that researchers genuinely doubted whether acoustic recording units could pick a frog call out of it. Turso's team ran passive acoustic monitoring across a network of sites and leaned on pattern-matching and random forest models to do what a human ear, worn down by hours of highway hiss, eventually cannot: separate one species' low, quacking call from everything else competing for the same frequency. The team also found that combining multiple acoustic indices rather than relying on any single one produced a clearer signal — useful in a landscape where almost every recording carries some trace of the city leaning on the wetland.
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