Phragmites Adaptive Management Framework (PAMF) participant and model data (2017-2021)
Non-native <em>Phragmites australis</em> is one of the most aggressive plant species invading North America and is managed using a suite of conventional approaches (e.g., herbicide, cutting/crushing, flooding, burning). These management actions are resource intensive and differ in effectiveness, largely because there are uncertainties about how the plant responds to treatment given site-specific conditions and variations in how managers implement control efforts. Moreover, managers often disagree on what outcomes can be expected from <em>Phragmites</em> control efforts. In addition, it is difficult to coordinate management efforts across the landscape and to disseminate what we can learn from every action taken across the broad scale of site characteristics and invasion states. To address these challenges, the Great Lakes <em>Phragmites</em> Collaborative (www.greatlakesphragmites.net) developed an adaptive management strategy called the <em>Phragmites</em> Adaptive Management Framework (PAMF). <br><br>
PAMF is an adaptive management and collective learning program that anyone managing Phragmites can join. Participants from around the Great Lakes basin submit <em>Phragmites</em> monitoring and management data to bolster the PAMF predictive model, which uses participant data to continually 'learn' more about which management techniques are working to reduce <em>Phragmites</em> infestations and which ones are not. In turn, the PAMF model predicts optimal management guidance for each site being managed based on the most up-to-date information collected from all the participants. This process repeats annually, reducing uncertainty with additional data collected. By undergoing this collective learning process, PAMF can determine which management techniques are efficient and effective for controlling <em>Phragmites</em> quicker than if managers were working alone. The goal of PAMF is to determine best management practices for <em>Phragmites</em> in the Great Lakes.<br><br>
This release contains anonymized participant <em>Phragmites</em> monitoring and management data submitted to the PAMF Web Hub, inputs used to run the PAMF Model (https://doi.org/10.5066/P9YNJS47), and data generated by the PAMF Model (e.g., management guidance) through June 30, 2021, at which time management of PAMF transitioned from the U.S. Geological Survey to the Great Lakes Commission. Important: this data release serves as an archive, but the data has been updated over time. For the newest version of all available PAMF data, see https://doi.org/10.5066/P9RKEY74 (coming soon).
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Kurt P Kowalski",
"@type": "vcard:Contact",
"hasEmail": "mailto:kkowalski@usgs.gov"
}
|
| description | Non-native <em>Phragmites australis</em> is one of the most aggressive plant species invading North America and is managed using a suite of conventional approaches (e.g., herbicide, cutting/crushing, flooding, burning). These management actions are resource intensive and differ in effectiveness, largely because there are uncertainties about how the plant responds to treatment given site-specific conditions and variations in how managers implement control efforts. Moreover, managers often disagree on what outcomes can be expected from <em>Phragmites</em> control efforts. In addition, it is difficult to coordinate management efforts across the landscape and to disseminate what we can learn from every action taken across the broad scale of site characteristics and invasion states. To address these challenges, the Great Lakes <em>Phragmites</em> Collaborative (www.greatlakesphragmites.net) developed an adaptive management strategy called the <em>Phragmites</em> Adaptive Management Framework (PAMF). <br><br> PAMF is an adaptive management and collective learning program that anyone managing Phragmites can join. Participants from around the Great Lakes basin submit <em>Phragmites</em> monitoring and management data to bolster the PAMF predictive model, which uses participant data to continually 'learn' more about which management techniques are working to reduce <em>Phragmites</em> infestations and which ones are not. In turn, the PAMF model predicts optimal management guidance for each site being managed based on the most up-to-date information collected from all the participants. This process repeats annually, reducing uncertainty with additional data collected. By undergoing this collective learning process, PAMF can determine which management techniques are efficient and effective for controlling <em>Phragmites</em> quicker than if managers were working alone. The goal of PAMF is to determine best management practices for <em>Phragmites</em> in the Great Lakes.<br><br> This release contains anonymized participant <em>Phragmites</em> monitoring and management data submitted to the PAMF Web Hub, inputs used to run the PAMF Model (https://doi.org/10.5066/P9YNJS47), and data generated by the PAMF Model (e.g., management guidance) through June 30, 2021, at which time management of PAMF transitioned from the U.S. Geological Survey to the Great Lakes Commission. Important: this data release serves as an archive, but the data has been updated over time. For the newest version of all available PAMF data, see https://doi.org/10.5066/P9RKEY74 (coming soon). |
| distribution |
[
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"title": "Digital Data",
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|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_62e82160d34e749ac04cba33 |
| keyword |
[
"Great Lakes",
"Illinois",
"Indiana",
"Invasive Species",
"Michigan",
"Minnesota",
"New York",
"Ohio",
"Ontario",
"Pennsylvania",
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"natural resource management",
"plants",
"plants (organisms)",
"state and transition modeling",
"statistical modeling",
"vascular plants"
]
|
| modified | 2026-03-06T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -97.4707, 36.8093, -73.0371, 57.0885 |
| theme |
[
"geospatial"
]
|
| title | Phragmites Adaptive Management Framework (PAMF) participant and model data (2017-2021) |