Estimating regional plant biodiversity with GIS modelling
Identifieur interne : 000B29 ( Istex/Corpus ); précédent : 000B28; suivant : 000B30Estimating regional plant biodiversity with GIS modelling
Auteurs : Louis Iverson ; Anantha PrasadSource :
- Diversity and Distributions [ 1366-9516 ] ; 1998-03.
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Abstract
In this study, we analysed a statewide species database together with a county‐level geographic information system (GIS) to build a model based on well‐surveyed areas to estimate species richness in less surveyed counties. The Illinois Plant Information Network (ILPIN), a species‐based database on all the vascular flora of Illinois, contains county distributions (totalling nearly 90,000) for each taxon and information on the taxonomy, ecology, biology, and ecodistribution. We compiled a statewide database with 112 variables on climate, landuse (current and historic), landscape pattern, soils and human population. We used a subset of this database to build a regression model for assessing native plant species richness for thirty‐three botanically well‐surveyed counties in Illinois. The best model was then used to predict the richness of the remaining sixty‐nine less botanically surveyed counties. The model involved GIS (Arc/Info) and statistics (S‐PLUS), including spatial statistics (S+SpatialStats). The resultant model had an R2 of 0.80 and used the following variables: percentage of the county in cropland, the percentage with soils somewhat limiting for agriculture, the percentage of urban land, and the average size of farms. Although this particular model is not transferable to other locations without validation, the methodology shown here should be useful in estimating species richness patterns across regions where botanical sampling is heterogeneous.
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DOI: 10.1046/j.1472-4642.1998.00007.x
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The Illinois Plant Information Network (ILPIN), a species‐based database on all the vascular flora of Illinois, contains county distributions (totalling nearly 90,000) for each taxon and information on the taxonomy, ecology, biology, and ecodistribution. We compiled a statewide database with 112 variables on climate, landuse (current and historic), landscape pattern, soils and human population. We used a subset of this database to build a regression model for assessing native plant species richness for thirty‐three botanically well‐surveyed counties in Illinois. The best model was then used to predict the richness of the remaining sixty‐nine less botanically surveyed counties. The model involved GIS (Arc/Info) and statistics (S‐PLUS), including spatial statistics (S+SpatialStats). The resultant model had an R2 of 0.80 and used the following variables: percentage of the county in cropland, the percentage with soils somewhat limiting for agriculture, the percentage of urban land, and the average size of farms. 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