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Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii

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AbstractAim  Eleutherodactylus coqui (commonly known as the coqui) is a frog species native to Puerto Rico and non‐native in Hawaii. Despite its ecological and economic impacts, its potential range in Hawaii is unknown, making control and management efforts difficult. Here, we predicted the distribution potential of the coqui on the island of Hawaii.Location  Puerto Rico and Hawaii.Methods  We predicted its potential distribution in Hawaii using five biophysical variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) as predictors, presence/absence data collected from Puerto Rico and Hawaii and three classification methods – Classification Trees (CT), Random Forests (RF) and Support Vector Machines (SVM).Results  Models developed separately using data from the native range and the invaded range predicted potential coqui habitats in Hawaii with high performance. Across the three classification methods, mean area under the ROC curve (AUC) was 0.75 for models trained using the native range data and 0.88 for models trained using the invaded range data. We achieved the highest AUC value of 0.90 using RF for models trained with invaded range data.Main conclusions  Our results showed that the potential distribution of coquis on the island of Hawaii is much larger than its current distribution, with RF predicting up to 49% of the island as suitable coqui habitat. Predictions also show that most areas with an elevation between 0 and 2000 m are suitable coqui habitats, whereas the cool and dry high elevation areas beyond 2000 m elevation are unsuitable. Results show that MODIS‐derived biophysical variables are capable of characterizing coqui habitats in Hawaii.
Title: Predicting the distribution potential of an invasive frog using remotely sensed data in Hawaii
Description:
AbstractAim  Eleutherodactylus coqui (commonly known as the coqui) is a frog species native to Puerto Rico and non‐native in Hawaii.
Despite its ecological and economic impacts, its potential range in Hawaii is unknown, making control and management efforts difficult.
Here, we predicted the distribution potential of the coqui on the island of Hawaii.
Location  Puerto Rico and Hawaii.
Methods  We predicted its potential distribution in Hawaii using five biophysical variables derived from Moderate Resolution Imaging Spectroradiometer (MODIS) as predictors, presence/absence data collected from Puerto Rico and Hawaii and three classification methods – Classification Trees (CT), Random Forests (RF) and Support Vector Machines (SVM).
Results  Models developed separately using data from the native range and the invaded range predicted potential coqui habitats in Hawaii with high performance.
Across the three classification methods, mean area under the ROC curve (AUC) was 0.
75 for models trained using the native range data and 0.
88 for models trained using the invaded range data.
We achieved the highest AUC value of 0.
90 using RF for models trained with invaded range data.
Main conclusions  Our results showed that the potential distribution of coquis on the island of Hawaii is much larger than its current distribution, with RF predicting up to 49% of the island as suitable coqui habitat.
Predictions also show that most areas with an elevation between 0 and 2000 m are suitable coqui habitats, whereas the cool and dry high elevation areas beyond 2000 m elevation are unsuitable.
Results show that MODIS‐derived biophysical variables are capable of characterizing coqui habitats in Hawaii.

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