Species distribution models are useful tools for predicting the potential distribution of a species based on environmental variables. The environmental variables used in these models may vary depending on the type of species and the spatial and temporal scale being considered. However, some of the most common environmental variables used in species distribution models include the following:
- Temperature: Temperature is one of the most important environmental variables for species distribution models, as it influences the physiology, growth, and reproduction of species.
- Precipitation: Precipitation is also an important environmental variable, as it influences the availability of water and therefore the survival and growth of species.
- Altitude: Altitude can influence species distribution due to variation in temperature, atmospheric pressure, and humidity.
- Soil type: Soil type can influence the availability of nutrients and water, which can affect species distribution.
- Vegetation type: Vegetation type can be an indicator of environmental conditions and resource availability for species.
- Topography: Topography can influence species distribution due to variation in solar exposure, orientation, and slope of the terrain.
- Sunlight: The amount of sunlight can influence photosynthesis and growth of species, and therefore their distribution.
- Relative humidity: Relative humidity can influence plant transpiration and the availability of water for species.
These are just some of the environmental variables commonly used in species distribution models. The selection of appropriate environmental variables will depend on the species and specific context being studied.
Download sources
There are several sources where environmental variable data can be downloaded for use in species distribution models. Some of the most common sources include:
- WorldClim: WorldClim is a global climate database that provides precipitation and temperature data for different spatial and temporal resolutions. These data can be used to model the climate conditions of a particular region and predict species distribution.
- GBIF: The Global Biodiversity Information Facility (GBIF) is a platform that collects and shares biodiversity data from around the world. The platform includes species distribution data, as well as environmental and geospatial data that can be used in species distribution models.
- DIVA-GIS: DIVA-GIS is an open-source software program that provides global environmental variable data, such as climate, topography, and vegetation, that can be used in species distribution models.
- GeoCAT: The Geospatial Data Analysis Consortium for Conservation (GeoCAT) is a platform that provides geospatial and environmental data for biodiversity conservation. The platform includes environmental variable data such as climate, topography, and vegetation.
- Climond: Climond is a climate data portal that provides climate variable data, such as precipitation and temperature, for different spatial and temporal resolutions around the world.
It is important to note that the availability and quality of data may vary depending on the geographic region and the type of species being considered. Additionally, it is important to verify the usage conditions and licenses before using the data.
Other things that are important to consider
There are several other things that are important to consider when using environmental variables for species distribution models:
- Spatial resolution: It is important to consider the spatial resolution of the environmental variable data. Spatial resolution refers to the level of detail or precision in the geographic information provided. Spatial resolution can affect the accuracy of species distribution models.
- Time scales: Environmental variables can vary on different time scales, from daily variability to seasonal or long-term changes. It is important to choose appropriate time scales for the species and research question being considered.
- Correlation: Environmental variables are often correlated with each other, meaning that they may be highly related and may influence species distribution in similar ways. It is important to consider correlation when selecting environmental variables for a species distribution model.
- Accessibility: The availability of environmental variable data can vary depending on the geographic region, and data accessibility may be limited in some areas. It is important to research and verify the availability of environmental variable data before starting a species distribution model.
- Extreme values: It is important to consider extreme values of environmental variables, such as maximum and minimum temperatures, which may influence species distribution at the limits of their range.
- Uncertainty: Environmental variables may have uncertainties associated with their measurement and sampling. It is important to consider uncertainty when interpreting the results of a species distribution model.
Considering these factors can help to improve the accuracy and validity of species distribution models based on environmental variables.
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