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  4. Corresponding dataset for effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

Corresponding dataset for effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly

Dates

Publication Date
Start Date
2020-12-10
End Date
2021-12-21

Citation

Fuller, A., and Augustine, B., 2024, Corresponding dataset for effectiveness of canine-assisted surveillance and human searches for early detection of invasive spotted lanternfly: U.S. Geological Survey data release, https://doi.org/10.5066/P1744ZNW.

Summary

This study experimentally tested whether utilizing trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. This dataset includes the data from spotted lanternflys (SLF) surveys in 20 vineyards in Pennsylvania and New Jersey, USA using both human and trained detection dogs as observers. We used a multi-scale occupancy model to estimate detection probability of human observers and detection dogs as a function of SLF infestation level, weather, and habitat covariates. We modeled transect-level occupancy of SLF as a function of infestation level, habitat type, topographic position index, and distance to forests. The dataset includes six csv files with the data from the project, including [...]

Contacts

Point of Contact :
Benjamin C. Augustine
Originator :
Angela K Fuller, Benjamin C. Augustine
Metadata Contact :
Benjamin C. Augustine
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
USGS Mission Area :
Ecosystems
SDC Data Owner :
Cooperative Research Units

Attached Files

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FullerLanternfly2024ed4.xml
Original FGDC Metadata
View 		55.96 KB application/fgdc+xml
Vine Detection Data.csv 463.72 KB text/csv
Transect Covs.csv 69.29 KB text/csv
Site by Visit Human.csv 3.52 KB text/csv
Site by Transect.csv 5.94 KB text/csv
Forest Detection Data.csv 36.12 KB text/csv
Site by Visit by Transect Dog.csv 76.83 KB text/csv
Transects.cpg 5 Bytes text/plain
Transects.dbf 378.78 KB text/plain
Transects.prj 424 Bytes text/plain
Transects.shp 13.22 KB x-gis/x-shapefile
Transects.shx 3.85 KB x-gis/x-shapefile
lanternfly occupancy data.RData 14.52 KB application/x-gzip
1. Build occupancy dataset.R 17.26 KB text/x-rsrc
2a. Run Lanternfly Occupancy Landscape Covs FiniteMix with detection.R 9.83 KB text/x-rsrc
2b. Run search times.R 2.72 KB text/x-rsrc
3. Process Posterior.R 30.62 KB text/x-rsrc

Purpose

Spotted lanternfly (SLF), Lycorma delicatula, is a recently introduced invasive insect whose range in the USA has been expanding rapidly since it was first discovered in Pennsylvania in 2014. Feeding by this planthopper can cause severe impacts to agricultural production, particularly grapes (Vitis spp.). Human visual surveys are the most common search method employed for detection, but can be ineffective due to the insect’s cryptic egg masses and low density during early stages of infestation. Therefore, finding alternative SLF early detection methods has become a priority for agencies tasked with addressing SLF management. This study experimentally tested whether utilizing trained detector dogs could improve the probability of detecting SLF in both agricultural and forest settings. An appropriate use of the data is modeling SLF occurrence and testing dogs vs. humans as the best detection method.

Rights

This work is marked with Creative Commons Zero v1.0 Universal (https://creativecommons.org/publicdomain/zero/1.0/).

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  • Cooperative Fish and Wildlife Research Units
  • USGS Data Release Products

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Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P1744ZNW

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