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      "individual_name": "Adam Wilson",
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  "doi": "10.15493/SAEON.FYNBOS.10000001",
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  "doi": "10.15493/SAEON.FYNBOS.10000001",
            
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  "lineage_statement": "A climate-aided Bayesian kriging approach is 
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  "lineage_statement": "A climate-aided Bayesian kriging approach is 
            
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used to interpolate 20 years of daily meteorological observations 
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used to interpolate 20 years of daily meteorological observations 
            
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(maximum and minimum temperature and precipitation) to a 1 arc-minute 
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(maximum and minimum temperature and precipitation) to a 1 arc-minute 
            
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grid for the Cape Floristic Region of South Africa. Independent 
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grid for the Cape Floristic Region of South Africa. Independent 
            
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validation data revealed overall predictive performance of the 
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validation data revealed overall predictive performance of the 
            
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interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum 
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interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum 
            
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temperature, minimum temperature, and precipitation, respectively. A 
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temperature, minimum temperature, and precipitation, respectively. A 
            
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suite of ecologically-relevant climate metrics that include the 
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suite of ecologically-relevant climate metrics that include the 
            
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uncertainty introduced by the interpolation were then generated. By 
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uncertainty introduced by the interpolation were then generated. By 
            
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providing the high resolution climate metric surfaces and 
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providing the high resolution climate metric surfaces and 
            
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uncertainties, this work facilitates richer and more robust predictive 
48
uncertainties, this work facilitates richer and more robust predictive 
            
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modeling in ecology and bio- geography. These data can be incorporated 
49
modeling in ecology and bio- geography. These data can be incorporated 
            
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into ecological models to propagate the uncertainties through to the 
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into ecological models to propagate the uncertainties through to the 
            
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final predictions",
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final predictions",
            
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  "notes": "Conservation of biodiversity demands comprehension of 
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  "notes": "Conservation of biodiversity demands comprehension of 
            
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evolutionary and ecological patterns and processes that occur over 
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evolutionary and ecological patterns and processes that occur over 
            
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vast spatial and temporal scales. A central goal of ecology is to 
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vast spatial and temporal scales. A central goal of ecology is to 
            
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understand the climatic factors that control ecological processes and 
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understand the climatic factors that control ecological processes and 
            
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this has become even more important in the face of climate change. 
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this has become even more important in the face of climate change. 
            
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Especially at global scales, there can be enormous uncertainty in 
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Especially at global scales, there can be enormous uncertainty in 
            
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underlying environmental data used to explain ecological processes, 
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underlying environmental data used to explain ecological processes, 
            
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but that uncertainty is rarely quantified or incorporated into 
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but that uncertainty is rarely quantified or incorporated into 
            
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ecological models. In this study a climate-aided Bayesian kriging 
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ecological models. In this study a climate-aided Bayesian kriging 
            
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approach is used to interpolate 20 years of daily meteorological 
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approach is used to interpolate 20 years of daily meteorological 
            
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observations (maximum and minimum temperature and precipitation) to a 
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observations (maximum and minimum temperature and precipitation) to a 
            
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1 arc-minute grid for the Cape Floristic Region of South Africa. 
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1 arc-minute grid for the Cape Floristic Region of South Africa. 
            
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Independent validation data revealed overall predictive performance of 
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Independent validation data revealed overall predictive performance of 
            
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the interpolation to have R2 values of 0.90, 0.85, and 0.59 for 
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the interpolation to have R2 values of 0.90, 0.85, and 0.59 for 
            
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maximum temperature, minimum temperature, and precipitation, 
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maximum temperature, minimum temperature, and precipitation, 
            
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respectively. A suite of ecologically-relevant climate metrics that 
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respectively. A suite of ecologically-relevant climate metrics that 
            
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include the uncertainty introduced by the interpolation were then 
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include the uncertainty introduced by the interpolation were then 
            
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generated. By providing the high resolution climate metric surfaces 
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generated. By providing the high resolution climate metric surfaces 
            
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and uncertainties, this work facilitates richer and more robust 
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and uncertainties, this work facilitates richer and more robust 
            
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predictive modeling in ecology and bio- geography. These data can be 
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predictive modeling in ecology and bio- geography. These data can be 
            
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incorporated into ecological models to propagate the uncertainties 
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incorporated into ecological models to propagate the uncertainties 
            
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through to the final predictions.",
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"interpolated_observed_weather_data_for_the_cape_floristic_region",
            
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