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|Bwindi Impenetrable National Park (321 km²)
|Bwindi Impenetrable National Park (321 km²)
|Reconnaissance walk
|Reconnaissance walk
|Spatial mapping
|
|McNeilage et al. 2001
|McNeilage et al. 2001
|Sweep method, with high density of reconnaissance trails covering the forest.
|Sweep method, with high density of reconnaissance trails covering the forest.
|Bwindi Impenetrable National Park (321 km²)
|Bwindi Impenetrable National Park (321 km²)
|Reconnaissance walk
|Reconnaissance walk
|Spatial mapping
|
|McNeilage et al. 2006
|McNeilage et al. 2006
|Sweep method, with high density of reconnaissance trails covering the forest.
|Sweep method, with high density of reconnaissance trails covering the forest.
|Bwindi-Sarambwe ecosystem
|Bwindi-Sarambwe ecosystem
|Reconnaissance walk
|Reconnaissance walk
|Spatial mapping
|
|Hickey et al. 2018
|Hickey et al. 2018
|Sweep method, with high density of reconnaissance trails covering the forest.
|Sweep method, with high density of reconnaissance trails covering the forest.
= Exposure to climate change impacts =
= Exposure to climate change impacts =
As part of a study on the exposure of African great ape sites to climate change impacts, Kiribou et al. (2024) extracted climate data and data on projected extreme climate impact events for the site. Climatological characteristics were derived from observation-based climate data provided by the Inter-Sectoral Impact Model Intercomparison Project ([www.isimip.org ISIMIP]). Parameters were calculated as the average across each 30-year period.
As part of a study on the exposure of African great ape sites to climate change impacts, Kiribou et al. (2024) extracted climate data and data on projected extreme climate impact events for the site. Climatological characteristics were derived from observation-based climate data provided by the Inter-Sectoral Impact Model Intercomparison Project ([ISIMIP www.isimip.org]). Parameters were calculated as the average across each 30-year period.
For future projections, two Representative Concentration Pathways (RCP) were used. RCP 2.6 is a scenario with strong mitigation measures in which global temperatures would likely rise below 2°C. RCP 6.0 is a scenario with medium emissions in which global temperatures would likely rise up to 3°C by 2100. For the number of days with heavy precipitation events, the 98th percentile of all precipitation days (>1mm/d) was calculated for the 1979-2013 reference period as a threshold for a heavy precipitation event. Then, for each year, the number of days above that threshold was derived. The figures on temperature and precipitation anomaly show the deviation from the mean temperature and mean precipitation for the 1979-2013 reference period. The estimated exposure to future extreme climate impact events (crop failure, drought, river flood, wildfire, tropical cyclone, and heatwave) is based on a published dataset by Lange et al. 2020 derived from ISIMIP2b data. The same global climate models and RCPs as described above were used. Within each 30-year period, the number of years with an extreme event and the average proportion of the site affected were calculated (Kiribou et al. 2024).
For future projections, two Representative Concentration Pathways (RCP) were used. RCP 2.6 is a scenario with strong mitigation measures in which global temperatures would likely rise below 2°C. RCP 6.0 is a scenario with medium emissions in which global temperatures would likely rise up to 3°C by 2100. For the number of days with heavy precipitation events, the 98th percentile of all precipitation days (>1mm/d) was calculated for the 1979-2013 reference period as a threshold for a heavy precipitation event. Then, for each year, the number of days above that threshold was derived. The figures on temperature and precipitation anomaly show the deviation from the mean temperature and mean precipitation for the 1979-2013 reference period. The estimated exposure to future extreme climate impact events (crop failure, drought, river flood, wildfire, tropical cyclone, and heatwave) is based on a published dataset by Lange et al. 2020 derived from ISIMIP2b data. The same global climate models and RCPs as described above were used. Within each 30-year period, the number of years with an extreme event and the average proportion of the site affected were calculated (Kiribou et al. 2024).