Data provided by National Oceanic and Atmospheric Administration
Maxent software (v3.3.3k) for habitat species modeling was used to predict the potential nesting distribution of sea turtles. Climate data were used to develop nine independent predictor variables that are biologically meaningful to successful sea turtle reproduction: mean diurnal range in temperature, isothermality (the mean diurnal temperature range/the annual temperature range), maximum temperature of the warmest month, annual range in temperature, precipitation seasonality (coefficient of variation), precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The Maxent model used these climate variables along with existing observed georeferenced nesting beach locations from the SWOT and WIDECAST databases and randomly selected background points from globally distributed coastlines. Species-specific GIS maps were generated that show the likelihood of nesting occurring within each grid cell, scaled from 0 (no nesting) to 1.0 (a very high probability of nesting) with values near 0.5 representing the typical probability of nesting at locations where the species is known to occur.
About this Dataset
Updated: 2025-04-21
Metadata Last Updated:
2025-04-04T12:26:52.967Z
Date Created:
N/A
Data Provided by:
Dataset Owner:
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| Title | Predicted Global Sea Turtle Nesting Habitats GIS |
|---|---|
| Description | Maxent software (v3.3.3k) for habitat species modeling was used to predict the potential nesting distribution of sea turtles. Climate data were used to develop nine independent predictor variables that are biologically meaningful to successful sea turtle reproduction: mean diurnal range in temperature, isothermality (the mean diurnal temperature range/the annual temperature range), maximum temperature of the warmest month, annual range in temperature, precipitation seasonality (coefficient of variation), precipitation of the wettest quarter, precipitation of the driest quarter, precipitation of the warmest quarter, and precipitation of the coldest quarter. The Maxent model used these climate variables along with existing observed georeferenced nesting beach locations from the SWOT and WIDECAST databases and randomly selected background points from globally distributed coastlines. Species-specific GIS maps were generated that show the likelihood of nesting occurring within each grid cell, scaled from 0 (no nesting) to 1.0 (a very high probability of nesting) with values near 0.5 representing the typical probability of nesting at locations where the species is known to occur. |
| Modified | 2025-04-04T12:26:52.967Z |
| Publisher Name | N/A |
| Contact | N/A |
| Keywords | Caretta caretta , Chelonia mydas , Dermochelys coriacea , Eretmochelys imbricata , Lepidochelys kempii , Lepidochelys olivacea , Natator depressa , Loggerhead Turtle , Green Turtle , Leatherback Turtle , Hawksbill Turtle , Kemps Ridley Turtle , Olive Ridley Turtle , Flatback Turtle , Sea turtle , Climate , Ecological niche modeling , Nesting habitat , Gulf of Mexico , Global , Caribbean , Indopacific , Mediterranean |
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