Abstract
Reptile habitats are described using various indices. The definitions of such indices are crucial, as they are applied to habitat modelling for numerous species on local to continental scales. We examined the Leaf Area Index (LAI) for its value as a tool for determining reptile habitat. During measurements carried out in spring and summer months between 2011 and 2013, LAI values were assessed and surveys were conducted on reptile fauna at 11 survey sites in the Solska Forest and Roztocze National Parks areas in Eastern Poland. In total, six Squamata reptiles occurring in Poland were found. We determined that LAI can be utilized as a reptile habitat index, with reptile species associated with LAI seasonal variability as well as LAI range. Moreover, we found that the higher the LAI median value, the greater the variety of reptile species. These findings are useful for development of spatial models of habitats based on LAI as they point to the importance of its seasonal variation.
Highlights
Reptile habitats are described using a variety of indices which can be classified as climatic, topographic and biological
In this research we focused on the analysis of reptile habitats as evaluated by Leaf Area Index (LAI) index
The Leaf Area Index (LAI) determines the abundance of leaves at the given measurement site, which influences the amount of solar radiation reaching it and may thereby influence the presence of various species of reptiles
Summary
Reptile habitats are described using a variety of indices which can be classified as climatic, topographic and biological. From among a number of animal habitat modelling methods, Leyequien [1] pointed out the important role of remote sensing and geostatistics By means of these methods, Rodríguez et al [2] found that the presence of reptiles was strongly associated with the energy available in the environment, which could be measured by potential evapotranspiration. Models of reptile habitat suitability were obtained using a number of environmental variables in a hydrological model [3] and extensive analysis of LiDAR (light detection and ranging) data [4]. Another approach used biological remote sensing indices to model herpetofauna [5] and mammal [6] habitats.
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