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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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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 
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uncertainties, this work facilitates richer and more robust predictive 
            
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modeling in ecology and bio- geography. These data can be incorporated 
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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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  "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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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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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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      "state": "active",
            
207
      "vocabulary_id": null
            
208
    },
            
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    {
            
210
      "display_name": "climatology",
            
211
      "id": "68f2471a-c336-4fc9-afd2-d252d6c41b7c",
            
212
      "name": "climatology",
            
213
      "state": "active",
            
214
      "vocabulary_id": null
            
215
    },
            
216
    {
            
217
      "display_name": "ecology",
            
218
      "id": "d8a5a11d-c929-4410-a2b3-65aa6b9d8263",
            
219
      "name": "ecology",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
            
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    {
            
224
      "display_name": "environmental science",
            
225
      "id": "30cb3d17-47d2-409e-9026-2c95191db59e",
            
226
      "name": "environmental science",
            
198
      "state": "active",
227
      "state": "active",
            
199
      "vocabulary_id": null
228
      "vocabulary_id": null
            
200
    }
229
    }
            
201
  ],
230
  ],
            
202
  "title": "Interpolated observed weather data for the Cape Floristic 
231
  "title": "Interpolated observed weather data for the Cape Floristic 
            
203
Region",
232
Region",
            
204
  "topic_and_saeoss_themes": [
233
  "topic_and_saeoss_themes": [
            
205
    {
234
    {
            
206
      "iso_topic_category": "climatologyMeteorologyAtmosphere"
235
      "iso_topic_category": "climatologyMeteorologyAtmosphere"
            
207
    }
236
    }
            
208
  ],
237
  ],
            
209
  "type": "dataset",
238
  "type": "dataset",
            
210
  "url": null,
239
  "url": null,
            
211
  "version": null
240
  "version": null
            
212
}
241
}