Description: Agricultural sensitivity mapping based on the best data available at the time (December 2016). Sensitivity mapping done in the context of large scale development of Wind and Solar PV energy.
The data used consist of the DAFF draft 2016 Land Capability dataset which categorises all land nationally into 15 different classes of agricultural land capability. The land capability dataset was supplemented by the DAFF 2013 &2015 Field Crop Boundaries dataset which delineates the boundaries of all cultivated land, based on satellite and aerial imagery. By combining these two datasets, four agricultural sensitivity classes is mapped over South Africa.
Definition Expression: N/A
Copyright Text: Base data: DAFF; 2013; 2015 & 2016; Sensitivity mapping: Department of Environmental Affairs; 2016
Description: Land capability sensitivity mapping based on the best data available at the time (December 2016). The data used consist of the DAFF draft 2016 Land Capability dataset which categorises all land nationally into 15 different classes of agricultural land capability. The 15 classes of agricultural land capability are reclassified into four sensitivity classes in accordance with a protocol agreed to between the Department of Environmental Affairs and the Department of Agriculture, Forestry and Fisheries.
Description: Field crop boundaries sensitivity mapping based on the best data available at the time (December 2016). The data used consist of the DAFF 2013 and 2015 Field Crop Boundary datasets which delineates the boundaries of all cultivated land, based on satellite and aerial imagery. The field crop boundaries were grouped into two sensitivity classes. Very high sensitivity corresponds to irrigated land, horticulture and viticulture. High sensitivity corresponds to all remaining cultivated areas.
Description: GENERAL NOTE
A habitat segment layer was used across multiple taxa to intersect points in the High sensitivity category. This layer was derived from remotely sensed 90m Landsat imagery. The imagery was used to create fine-scale habitat patches that delineated areas of similar vegetation type. This layer was used across multiple taxa as a basis for transforming point occurrence data into polygon layers by intersecting the two layers and retaining the selected habitat segments. MAMMALS (Class: Mammalia)
Sensitivity= High
The majority of the mammal data was extract from the Endangered Wildlife Trust’s Red List database (https://www.ewt.org.za/resources/resources-mammal-red-list/).
Species occurrence records were filtered to only include those recorded post-2002 and those which had accurate GPS coordinates. All occurrence records were filtered to remove any low quality data.
Following that, for each species, the associated GPS points were intersected with the habitat segment layer. The segments were then extracted and each was designated as High sensitivity.
Sensitivity= Medium
Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space. SDMs were developed for each species independently and paired all valid species occurrence points (including those collected prior to 2002) with remotely sensed environmental variables that represented land cover, habitat type, topography, soils, primary productivity and climate. The SDMs were run at the 30 arc-second spatial scale.
Several SDMs were produced for each species and various statistics such as the AUC measure were used to evaluate model performance allowing only high quality models to be retained for the remainder of the modelling procedure. Models with low quality were discarded. SDMs produce a probability surface representing relative habitat suitability across the predicted range of occurrence. This probability surface was converted to a binary (present/absent) surface using a threshold to most accurately incorporate true presences and true absences.
The binary vector surface was then filtered to only include habitat patches where a species can be regarded as present that were larger than ~1km2. REPTILES (Class: Reptilia)
Sensitivity= Very high
Taxa that qualify are those with a EOO of less than 10km2.
Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Reptile IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Reptile IUCN Red List assessment were used to delineate areas of Medium sensitivity for each species. AMPHIBIANS (Class: Amphibia)
Sensitivity=Very high
Taxa that qualify are those with a EOO of less than 10km2.
Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Amphibian IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Amphibian IUCN Red List assessment were used to delineate areas of Mediumsensitivity for each species. BIRDS (Class: Aves)
Sensitivity= Very high
NA for any species included in the environmental screening tool.
Sensitivity= High
Species distribution models (SDMs) and SABAP2 data (sabap2.adu.org.za) were combined to delineate the High sensitivity. The models were created by BirdLife South Africa.
SDMs were created using an ensemble modelling approach, namely the Biomod2 package in the R platform. The package makes use of multiple SDM algorithms and produces a number of model outputs from which to compare model performance and fit. The SDMtoolbox and R package BlockCV was used to control for spatial autocorrelation within the occurrence data used within SDM, as well as control for how data was split amongst model folds and runs. Environmental covariate layers used in SDM differed amongst species and/or guilds. An ecological trait-based assessment of species and guilds was conducted in order to select, collate and/or create ecologically meaningful variables for SDM frameworks. Broad groups of covariates used across all species included bioclimatic layers representing climate (e.g. annual rainfall, temperature range, etc.), topographical layers (e.g. slope, aspect, etc.), land cover and metric/s of habitat quality (remote sensing based).
In addition to scrutinising facets of model performance such as AUC and kappa coefficient (κ), we conducted an additional assessment of model validation. The assessment compared the modelled distribution of suitable habitat to independent sources (i.e. not used in the SDM) of known occurrence and distribution. If models did not conform to the known distribution, and/or failed to predict known areas of suitability with a reasonable accuracy, the model was rejected and further refined/rerun with varied covariates and/or occurrence data In addition, point locations were used to inform the SDM as well as for verification of the model. These point data were obtained through the mobile app BirdLasser as well as point data collected through tracking projects as well as academic and other studies. SABAP2 data for each species was downloaded from the SABAP2 website in geoJSON format and then converted into shapefile format. SDM data received in raster format. Raster then converted to a polygon shapefile using the appropriate tool in ArcMap. Shapefile then projected to determine size of each polygon and smaller patches deleted ( < 2 – 4 ha). The size of the patch to be deleted differs from species to species, for example smaller areas will be deleted for forest based species than species with large ranges. The Select by Location tool was then used to identify the areas in the SDM which ntersects with SABAP2 data. A small buffer was added to each pentad to include a wider area. Areas which do not overlap with pentads were excluded from the data layer (these can potentially added in tier three in the future and after further evaluation). The final sensitivity layers represents areas where the species was actually observed during SABAP2. Unsuitable habitat was excluded from the relative course area covered by one pentad by identify suitable habitat used by the species. Sensitivity= Medium
NA for any species currently included in the environmental screening tool.
BUTTERFLIES (Class: Insecta)
Sensitivity= Very high
Taxa that qualify are those with a EOO of less than 10km2.
Experts (Dr Silvia Kirkman and Dr Dave Edge) reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. TSP mapped occupied habitat based on data points and habitat descriptions for each selected taxa Expert Dr Dave Edge reviewed mapped distributions
Maps were corrected based on comments from expert
Sensitivity= High
Species occurrence data from the Butterfly Red Listing process were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space.
SDMs developed by Dr S Kirkman from her PhD thesis were used. These models were generated at a 5 arc minute resolution.
Description: GENERAL NOTE
⦁ A habitat segment layer was used across multiple taxa to intersect points in the High sensitivity category. This layer was derived from remotely sensed 90m Landsat imagery. The imagery was used to create fine-scale habitat patches that delineated areas of similar vegetation type. This layer was used across multiple taxa as a basis for transforming point occurrence data into polygon layers by intersecting the two layers and retaining the selected habitat segments. MAMMALS (Class: Mammalia)
Sensitivity= High
⦁ The majority of the mammal data was extract from the Endangered Wildlife Trust’s Red List database (https://www.ewt.org.za/resources/resources-mammal-red-list/).
⦁ Species occurrence records were filtered to only include those recorded post-2002 and those which had accurate GPS coordinates. All occurrence records were filtered to remove any low quality data.
⦁ Following that, for each species, the associated GPS points were intersected with the habitat segment layer. ⦁ The segments were then extracted and each was designated as High sensitivity.
Sensitivity= Medium
⦁ Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space. SDMs were developed for each species independently and paired all valid species occurrence points (including those collected prior to 2002) with remotely sensed environmental variables that represented land cover, habitat type, topography, soils, primary productivity and climate. The SDMs were run at the 30 arc-second spatial scale.
⦁ Several SDMs were produced for each species and various statistics such as the AUC measure were used to evaluate model performance allowing only high quality models to be retained for the remainder of the modelling procedure. Models with low quality were discarded. ⦁ SDMs produce a probability surface representing relative habitat suitability across the predicted range of occurrence. This probability surface was converted to a binary (present/absent) surface using a threshold to most accurately incorporate true presences and true absences.
⦁ The binary vector surface was then filtered to only include habitat patches where a species can be regarded as present that were larger than ~1km2. REPTILES (Class: Reptilia)
Sensitivity= Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Reptile IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Reptile IUCN Red List assessment were used to delineate areas of Medium sensitivity for each species. AMPHIBIANS (Class: Amphibia)
Sensitivity=Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Amphibian IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Amphibian IUCN Red List assessment were used to delineate areas of Mediumsensitivity for each species. BIRDS (Class: Aves)
Sensitivity= Very high
⦁ NA for any species included in the environmental screening tool.
Sensitivity= High
⦁ Species distribution models (SDMs) and SABAP2 data (sabap2.adu.org.za) were combined to delineate the High sensitivity. The models were created by BirdLife South Africa.
⦁ SDMs were created using an ensemble modelling approach, namely the Biomod2 package in the R platform. The package makes use of multiple SDM algorithms and produces a number of model outputs from which to compare model performance and fit. The SDMtoolbox and R package BlockCV was used to control for spatial autocorrelation within the occurrence data used within SDM, as well as control for how data was split amongst model folds and runs. ⦁ Environmental covariate layers used in SDM differed amongst species and/or guilds. An ecological trait-based assessment of species and guilds was conducted in order to select, collate and/or create ecologically meaningful variables for SDM frameworks. Broad groups of covariates used across all species included bioclimatic layers representing climate (e.g. annual rainfall, temperature range, etc.), topographical layers (e.g. slope, aspect, etc.), land cover and metric/s of habitat quality (remote sensing based).
⦁ In addition to scrutinising facets of model performance such as AUC and kappa coefficient (κ), we conducted an additional assessment of model validation. The assessment compared the modelled distribution of suitable habitat to independent sources (i.e. not used in the SDM) of known occurrence and distribution. If models did not conform to the known distribution, and/or failed to predict known areas of suitability with a reasonable accuracy, the model was rejected and further refined/rerun with varied covariates and/or occurrence data ⦁ In addition, point locations were used to inform the SDM as well as for verification of the model. These point data were obtained through the mobile app BirdLasser as well as point data collected through tracking projects as well as academic and other studies. ⦁ SABAP2 data for each species was downloaded from the SABAP2 website in geoJSON format and then converted into shapefile format. ⦁ SDM data received in raster format. Raster then converted to a polygon shapefile using the appropriate tool in ArcMap. Shapefile then projected to determine size of each polygon and smaller patches deleted ( < 2 – 4 ha). The size of the patch to be deleted differs from species to species, for example smaller areas will be deleted for forest based species than species with large ranges. ⦁ The Select by Location tool was then used to identify the areas in the SDM which ntersects with SABAP2 data. A small buffer was added to each pentad to include a wider area. Areas which do not overlap with pentads were excluded from the data layer (these can potentially added in tier three in the future and after further evaluation). ⦁ The final sensitivity layers represents areas where the species was actually observed during SABAP2. Unsuitable habitat was excluded from the relative course area covered by one pentad by identify suitable habitat used by the species. Sensitivity= Medium
⦁ NA for any species currently included in the environmental screening tool.
BUTTERFLIES (Class: Insecta)
Sensitivity= Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts (Dr Silvia Kirkman and Dr Dave Edge) reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ TSP mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Expert Dr Dave Edge reviewed mapped distributions
⦁ Maps were corrected based on comments from expert
Sensitivity= High
Species occurrence data from the Butterfly Red Listing process were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
⦁ Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space.
⦁ SDMs developed by Dr S Kirkman from her PhD thesis were used. These models were generated at a 5 arc minute resolution.
Description: GENERAL NOTE
⦁ A habitat segment layer was used across multiple taxa to intersect points in the High sensitivity category. This layer was derived from remotely sensed 90m Landsat imagery. The imagery was used to create fine-scale habitat patches that delineated areas of similar vegetation type. This layer was used across multiple taxa as a basis for transforming point occurrence data into polygon layers by intersecting the two layers and retaining the selected habitat segments. MAMMALS (Class: Mammalia)
Sensitivity= High
⦁ The majority of the mammal data was extract from the Endangered Wildlife Trust’s Red List database (https://www.ewt.org.za/resources/resources-mammal-red-list/).
⦁ Species occurrence records were filtered to only include those recorded post-2002 and those which had accurate GPS coordinates. All occurrence records were filtered to remove any low quality data.
⦁ Following that, for each species, the associated GPS points were intersected with the habitat segment layer. ⦁ The segments were then extracted and each was designated as High sensitivity.
Sensitivity= Medium
⦁ Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space. SDMs were developed for each species independently and paired all valid species occurrence points (including those collected prior to 2002) with remotely sensed environmental variables that represented land cover, habitat type, topography, soils, primary productivity and climate. The SDMs were run at the 30 arc-second spatial scale.
⦁ Several SDMs were produced for each species and various statistics such as the AUC measure were used to evaluate model performance allowing only high quality models to be retained for the remainder of the modelling procedure. Models with low quality were discarded. ⦁ SDMs produce a probability surface representing relative habitat suitability across the predicted range of occurrence. This probability surface was converted to a binary (present/absent) surface using a threshold to most accurately incorporate true presences and true absences.
⦁ The binary vector surface was then filtered to only include habitat patches where a species can be regarded as present that were larger than ~1km2. REPTILES (Class: Reptilia)
Sensitivity= Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Reptile IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Reptile IUCN Red List assessment were used to delineate areas of Medium sensitivity for each species. AMPHIBIANS (Class: Amphibia)
Sensitivity=Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ Experts mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Additional experts then reviewed mapped distributions and maps were corrected based on feedback received.
Sensitivity= High
Species occurrence data from the Amphibian IUCN Red List assessment were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
Species distribution maps compiled for the Amphibian IUCN Red List assessment were used to delineate areas of Mediumsensitivity for each species. BIRDS (Class: Aves)
Sensitivity= Very high
⦁ NA for any species included in the environmental screening tool.
Sensitivity= High
⦁ Species distribution models (SDMs) and SABAP2 data (sabap2.adu.org.za) were combined to delineate the High sensitivity. The models were created by BirdLife South Africa.
⦁ SDMs were created using an ensemble modelling approach, namely the Biomod2 package in the R platform. The package makes use of multiple SDM algorithms and produces a number of model outputs from which to compare model performance and fit. The SDMtoolbox and R package BlockCV was used to control for spatial autocorrelation within the occurrence data used within SDM, as well as control for how data was split amongst model folds and runs. ⦁ Environmental covariate layers used in SDM differed amongst species and/or guilds. An ecological trait-based assessment of species and guilds was conducted in order to select, collate and/or create ecologically meaningful variables for SDM frameworks. Broad groups of covariates used across all species included bioclimatic layers representing climate (e.g. annual rainfall, temperature range, etc.), topographical layers (e.g. slope, aspect, etc.), land cover and metric/s of habitat quality (remote sensing based).
⦁ In addition to scrutinising facets of model performance such as AUC and kappa coefficient (κ), we conducted an additional assessment of model validation. The assessment compared the modelled distribution of suitable habitat to independent sources (i.e. not used in the SDM) of known occurrence and distribution. If models did not conform to the known distribution, and/or failed to predict known areas of suitability with a reasonable accuracy, the model was rejected and further refined/rerun with varied covariates and/or occurrence data ⦁ In addition, point locations were used to inform the SDM as well as for verification of the model. These point data were obtained through the mobile app BirdLasser as well as point data collected through tracking projects as well as academic and other studies. ⦁ SABAP2 data for each species was downloaded from the SABAP2 website in geoJSON format and then converted into shapefile format. ⦁ SDM data received in raster format. Raster then converted to a polygon shapefile using the appropriate tool in ArcMap. Shapefile then projected to determine size of each polygon and smaller patches deleted ( < 2 – 4 ha). The size of the patch to be deleted differs from species to species, for example smaller areas will be deleted for forest based species than species with large ranges. ⦁ The Select by Location tool was then used to identify the areas in the SDM which ntersects with SABAP2 data. A small buffer was added to each pentad to include a wider area. Areas which do not overlap with pentads were excluded from the data layer (these can potentially added in tier three in the future and after further evaluation). ⦁ The final sensitivity layers represents areas where the species was actually observed during SABAP2. Unsuitable habitat was excluded from the relative course area covered by one pentad by identify suitable habitat used by the species. Sensitivity= Medium
⦁ NA for any species currently included in the environmental screening tool.
BUTTERFLIES (Class: Insecta)
Sensitivity= Very high
⦁ Taxa that qualify are those with a EOO of less than 10km2.
⦁ Experts (Dr Silvia Kirkman and Dr Dave Edge) reviewed the species list and added any missing species not selected with EOO calculation and to remove taxa that are Data Deficient. ⦁ TSP mapped occupied habitat based on data points and habitat descriptions for each selected taxa ⦁ Expert Dr Dave Edge reviewed mapped distributions
⦁ Maps were corrected based on comments from expert
Sensitivity= High
Species occurrence data from the Butterfly Red Listing process were used. Only data collected post-2002 were included. These data were then intersected with the habitat segment layer. All data were vetted by taxon experts.
Sensitivity= Medium
⦁ Areas delineated as Medium sensitivity were derived from a statistical method known as species distribution modelling. Species distribution models (SDMs) are empirical methods that relate species occurrence data to environmental predictor variables based on statistically derived response curves that best reflect the ecological requirements of the species. These relationships are then used to predict the potential distribution of a species in geographic space.
⦁ SDMs developed by Dr S Kirkman from her PhD thesis were used. These models were generated at a 5 arc minute resolution.
Description: The Aquatic Biodiversity Combined Dataset was sourced from SANBI (April 2023). The layer is a merge of the following layers:
Aquatic CBAs
National Wetland map 5
Freshwater Ecosystem Priority Areas
Estuarine Functional Zone
Rivers
Strategic Water Source Areas
Description: Maps of Critical Biodiversity Areas (CBA) and Ecological Support Areas (ESA), referred to as CBA Maps, are developed using systematic biodiversity planning principles (representation, persistence, connectivity, setting quantitative biodiversity targets and conflict avoidance). These maps identify important biodiversity priority areas within the respective administrative boundaries and in the country all the provinces have developed the maps of Critical Biodiversity Areas. The maps of Critical Biodiversity Areas identify both terrestrial and aquatic themes. However, not all provinces have identified and distinguish both terrestrial and aquatic CBA. The Western Cape, Eastern Cape, Mpumalanga, and North West provinces as well as Ekurhuleni municipality, have identified and developed the aquatic CBA Maps. The maps identify important freshwater biodiversity priority areas. They also identify CBA Map subcategories that distinguish features used (i.e., wetlands, rivers, FEPA sub catchment, etc.). During the merging of the provincial CBA Map to create a single wall-to-wall layer further analysis were performed. The analysis undertaken standardized CBA Map categories identified within the attribute table. This was due to inconsistency observed from various plans developed when it comes to reporting the information within the attribute table. The final layer developed represented was a wall-to-wall aquatic CBA Map that show all the CBA Map categories and subcategories as identified by the various provinces. The composite map of Critical Biodiversity Areas (CBA) and Ecological Support Areas (ESA) for aquatic theme. The map was created from merging all the provincial aquatic CBA Maps.
Description: This is the final National Wetland Map version 5 (NWM5) that was issued on 3 October 2019 with the launch of the National Biodiversity Assessment of 2018 (NBA 2018). This dataset forms part of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) and the report should be cited:Van Deventer, H., Smith-Adao, L., Mbona, N., Petersen, C., Skowno, A., Collins, N.B., Grenfell, M., Job, N., Lötter, M., Ollis, D., Scherman, P., Sieben, E. & Snaddon, K. 2018. South African National Biodiversity Assessment 2018: Technical Report. Volume 2a: South African Inventory of Inland Aquatic Ecosystems (SAIIAE). Version 3, final released on 3 October 2019. Council for Scientific and Industrial Research (CSIR) and South African National Biodiversity Institute (SANBI): Pretoria, South Africa. Report Number: CSIR report number CSIR/NRE/ECOS/IR/2018/0001/A; SANBI report number http://hdl.handle.net/20.500.12143/5847.If the Estuaries are used, please citeVan Niekerk, L., Adams, J.B., Lamberth, S.J., MacKay, F., Taljaard, S., Turpie, J.K., Weerts S. & Raimondo, D.C., 2019 (eds). South African National Biodiversity Assessment 2018: Technical Report. Volume 3: Estuarine Realm. CSIR report number CSIR/SPLA/EM/EXP/2019/0062/A. South African National Biodiversity Institute, Pretoria. Report Number: SANBI/NAT/NBA2018/2019/Vol3/A. http://hdl.handle.net/20.500.12143/6373.
Description: The layer codes for River Freshwater Ecosystem Priority Areas (FEPAs) and
associated sub-quaternary catchments, Fish Support Areas and associated subquaternary
catchments and Upstream Management Areas.
Note:
This GIS layer codes:
• River Freshwater Ecosystem Priority Areas (FEPAs) and associated
sub-quaternary catchments
• Fish Support Areas and associated sub-quaternary catchments
• Upstream Management Areas
FEPAs for wetlands and wetland clusters are provided in the wetland GIS
layers (NFEPA_Wetlands_30Jul11.shp and Wetcluster_30Jul11.shp).
Description: This is the most recent version of the Estuarine Functional Zone Map. The map was developed
as a collaborative effort and delineates the Estuarine Functional Zone for South Africa (Van
Niekerk et al. 2019).
In South Africa, the EFZ is defined as the area that not only encapsulates the estuary
waterbody, but also the supporting physical and biological processes necessary for estuarine
function and health. It includes all dynamic areas influenced by long-term estuarine
sedimentary processes, i.e. sediment stored or eroded during floods, changes in channel
configuration, aeolian transport processes, and/or changes due to coastal storms. It also
encompasses the multiple ecotones of floodplain and estuarine vegetation that contribute
detritus (food source) to the estuary and/or provide refuge during high flow events. The EFZ
captures the natural, historical estuarine extent and should not be confused with
setback/management lines that often exclude developed areas. The EFZ purpose is to identify
the ‘space’ in which estuarine physical and biological functions take place over long time scales
(>decades). Development in the EFZ is captured as an aspect of habitat degradation or decline
in overall estuary condition.
The upstream boundary of the estuaries was determined as the limits of tidal variation or
salinity penetration, whichever penetrates furthest. The estuary mouth was taken as the
downstream boundary of an estuary. The highly dynamic nature of this area presents a
significant challenge to accessing change in biodiversity and even managing estuaries. To
account for this, and to allow for a seamless integration with the Marine and Coastal Realm, the
concept of ‘Estuarine Shore’ was introduced to reflect the dynamic nature of the interface
between estuaries and the coast. Estuarine Shores refers to sand berms or bars that form in
front of estuaries. They vary substantially in size and shape over decadal scales and can be
completely absent during a flood or a near permanent feature during periods of low flow.
Estuarine Shore was defined as the area from the base of the foredune, or where this line
would be if dunes were present, to the back of the surf zone. The full extent of the Estuarine
Shore is encapsulated in the EFZs and not be considered separate from the functional unit. The
surfzone was included to reflect a continuum in estuarine-marine connectivity through estuarine
inputs to the surfzone, either as direct flow through an open mouth or in seepage through the
berm in a closed system.
Description: Strategic Water Source Areas (SWSAs) refer to the 10% of South Africa’s land area that provides a disproportionate 50% of the country’s water runoff. Understanding where these SWSAs are is crucial to planning and management of water resources, including the ecosystems that support water quality and quantity. These areas extend into Lesotho and eSwatini.
National SWSAs for surface water have been delineated in various forms over the past 15 years, with increasing precision in each iteration. In 2018, 22 SWSAs were identified based on a generalised 1.7 x 1.7 km resolution Mean Annual Runoff dataset, providing a widely accepted product that gained strong traction with government and non-government audiences, proving effective for building awareness and integrating SWSAs in a range of national policies and frameworks. However, the coarse resolution does not align well with the scales used for implementation at catchment and local levels. So, using best available information and the latest geostatistical approaches, South Africa’s SWSAs for surface water have now been delineated at a finer resolution of 90 x 90 m. The work, which concluded in 2021 resulted in two products (both explained in greater detail in Lotter & Le Maitre (2021)):
1. A downscaled mean annual precipitation surface: through consideration of several explanatory variables in the modelling process (such as elevation, latitude, distance from coast, topographical positional index, and Mean Annual Runoff), the “new” Mean Annual Precipitation surface layer was “interpolated” using a dataset of over 8000 rainfall stations and a Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Model (DEM) (available as a stand-alone product).
2. 2021 SWSA layer: This precipitation surface layer was used to delineate fine-scale SWSA boundaries, which were compared with the older 2018 SWSAs for surface water to maintain the 22 SWSAs with similar extent and location to those identified in the 2018 SWSAs.
Delineating SWSAs at a finer scale was done across South Africa, Lesotho and eSwatini because of the shared water catchments that feed into water supply systems in South Africa. Both above mentioned layers therefore extend across the three countries.
Description: This dataset contains the combined sensitivity of military areas of interest within the context of commercial scale renewable solar PV energy installations
Definition Expression: N/A
Copyright Text: CSIR; 2013. Department of Environmental Affairs; 2016
Description: This dataset contains the combined sensitivity of military areas of interest within the context of commercial scale renewable solar PV energy installations
Description: This dataset contains the combined sensitivity of military areas of interest within the context of commercial scale renewable solar PV energy installations
Description: The South African Protected Areas Database (SAPAD) contains spatial data for the conservation estate of South Africa. It includes spatial and attribute information for both formally protected areas and areas that have less formal protection. Data is collected by parcels which are aggregated to protected area level. Only outer boundaries are defined in this public release.SAPAD is updated on a continuous basis. It forms the basis for the Register of Protected Areas which is a legislative requirement under the National Environmental Management: Protected Areas Act, Act 57 of 2003.
Definition Expression: N/A
Copyright Text: Depratment of Environment Affairs, DEA
Description: The South African Protected Areas Database (SAPAD) contains spatial data for the conservation estate of South Africa. It includes spatial and attribute information for both formally protected areas and areas that have less formal protection. Data is collected by parcels which are aggregated to protected area level. Only outer boundaries are defined in this public release.SAPAD is updated on a continuous basis. It forms the basis for the Register of Protected Areas which is a legislative requirement under the National Environmental Management: Protected Areas Act, Act 57 of 2003.
Definition Expression: N/A
Copyright Text: Depratment of Environment Affairs, DEA
Name: Other PA Botanical Garden (1.5, 3 and 5 km buffer)
Display Field: SENSITIVIT
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The South African Protected Areas Database (SAPAD) contains spatial data for the conservation estate of South Africa. It includes spatial and attribute information for both formally protected areas and areas that have less formal protection. Data is collected by parcels which are aggregated to protected area level. Only outer boundaries are defined in this public release.SAPAD is updated on a continuous basis. It forms the basis for the Register of Protected Areas which is a legislative requirement under the National Environmental Management: Protected Areas Act, Act 57 of 2003.
Definition Expression: N/A
Copyright Text: Depratment of Environment Affairs, DEA
Description: Summary This dataset contains a combined set of known and predicted (modelled) distributions for threatened plant species in South Africa, as well as a selected group of currently non-threatened species that have distribution ranges smaller than 10 km². The intention of the dataset is to guide botanical specialists in terms of which species to specifically look for in a site assessment as per the requirements of Screening Tool protocols for Environmental Authorisation.
Description The dataset contains distribution maps for plant species in three different classes, as indicated in the attribute field SENSITIVIT.
SENSITIVIT = "Very high"
Maps of critical habitat for highly range-restricted species, here defined as any plant species with a known distribution range smaller than 10 km². These polygons were expert delineated based on the known extent of suitable habitat for these species at sites where they are known to occur. Loss of natural vegetation at these sites is therefore highly likely to result in species extinctions, or otherwise a significant increase in the risk of extinction of the species present.
SENSITIVIT = "High"
Known distributions for species with ranges larger than 10 km²were generated based on recent, precise, point occurrence records confirming the presence of a species at a site. The point occurrence records were generalized to small areas of similar spectral signatures, that are representing relatively uniform habitat patches where the species is known to be present. The point occurrence dataset is maintained by the Threatened Species Unit at the South African National Biodiversity Institute, and combines occurrence data from more than 40 unique data sources, including herbaria, national and provincial conservation agencies, and citizen science projects. Extreme care is taken to confirm the accuracy of records used to generate the maps, but the dataset may contain errors.
SENSITIVIT = "Medium"
Medium sensitivity is based on modelled distribution ranges, and indicates areas where suitable habitat for a species is present, but its presence at the site needs to be confirmed by field surveys. Distribution models are expert-driven suitable habitat models, and is derived by combining areas of suitable vegetation types and altitudes within the known ranges of species. Distribution maps are based on best available data for plant species, many of which are poorly known, and therefore the ability to generate accurate maps is constrained. This third level of sensitivity is a critically important consideration in a country with very high levels of biodiversity where most areas have not recently or never been thoroughly surveyed. Care was taken to limit predicted areas to no more than 10 km outside the known range of a species, so as to avoid excessive survey requirements.
SENSITIVIT = "Low"
There are large parts of South Africa where no plant species of conservation concern are expected to occur, and these areas are designated in the Low sensitivity category.
Description: Summary This dataset contains a combined set of known and predicted (modelled) distributions for threatened plant species in South Africa, as well as a selected group of currently non-threatened species that have distribution ranges smaller than 10 km². The intention of the dataset is to guide botanical specialists in terms of which species to specifically look for in a site assessment as per the requirements of Screening Tool protocols for Environmental Authorisation.
Description The dataset contains distribution maps for plant species in three different classes, as indicated in the attribute field SENSITIVIT.
SENSITIVIT = "Very high"
Maps of critical habitat for highly range-restricted species, here defined as any plant species with a known distribution range smaller than 10 km². These polygons were expert delineated based on the known extent of suitable habitat for these species at sites where they are known to occur. Loss of natural vegetation at these sites is therefore highly likely to result in species extinctions, or otherwise a significant increase in the risk of extinction of the species present.
SENSITIVIT = "High"
Known distributions for species with ranges larger than 10 km²were generated based on recent, precise, point occurrence records confirming the presence of a species at a site. The point occurrence records were generalized to small areas of similar spectral signatures, that are representing relatively uniform habitat patches where the species is known to be present. The point occurrence dataset is maintained by the Threatened Species Unit at the South African National Biodiversity Institute, and combines occurrence data from more than 40 unique data sources, including herbaria, national and provincial conservation agencies, and citizen science projects. Extreme care is taken to confirm the accuracy of records used to generate the maps, but the dataset may contain errors.
SENSITIVIT = "Medium"
Medium sensitivity is based on modelled distribution ranges, and indicates areas where suitable habitat for a species is present, but its presence at the site needs to be confirmed by field surveys. Distribution models are expert-driven suitable habitat models, and is derived by combining areas of suitable vegetation types and altitudes within the known ranges of species. Distribution maps are based on best available data for plant species, many of which are poorly known, and therefore the ability to generate accurate maps is constrained. This third level of sensitivity is a critically important consideration in a country with very high levels of biodiversity where most areas have not recently or never been thoroughly surveyed. Care was taken to limit predicted areas to no more than 10 km outside the known range of a species, so as to avoid excessive survey requirements.
SENSITIVIT = "Low"
There are large parts of South Africa where no plant species of conservation concern are expected to occur, and these areas are designated in the Low sensitivity category.
Description: The South African Protected Areas Database (SAPAD) contains spatial data for the conservation estate of South Africa. It includes spatial and attribute information for both formally protected areas and areas that have less formal protection. Data is collected by parcels which are aggregated to protected area level. Only outer boundaries are defined in this public release.SAPAD is updated on a continuous basis. It forms the basis for the Register of Protected Areas which is a legislative requirement under the National Environmental Management: Protected Areas Act, Act 57 of 2003.
Definition Expression: N/A
Copyright Text: Depratment of Environment Affairs, DEA
Description: The National Protected Area Expansion Strategy, first published in 2008 (NPAES 2008), presents a 20-year strategy for the expansion of protected areas in South Africa.Provision is made for the review and updating of the NPAES every 5 years. This document (NPAES 2016) represents the first full revision of the NPAES 2008, and the updated strategy for the next 5-years (2016 – 2020). Each new revision of the NPAES refers to a rolling 20-year period, so this revision sets out a future 20-year strategy.The updated NPAES 2016 now includes:New biodiversity data and newly declared protected areas as well as updated provincial conservation plans and provincial protected area expansion strategies (PAES), to improve the setting of targets and the identification of priority areas for meeting these targets.The goal of the NPAES is to achieve cost effective protected area expansion for improved ecosystem representation, ecological sustainability and resilience to climate change. It sets protected area targets, maps priority areas for protected area expansion, and makes recommendations on mechanisms to achieve this.A review of the performance of protected area institutions in protected area expansion for the first implementation phase of the NPAES (2008 – 2014).A description of the priority activities, with explicit performance targets, for the second implementation phase (2016 – 2020) of the NPAES.In order to maintain continuity of the NPAES over the 20 years of the strategy, the structure of this document has been maintained using similar formatting to the NPAES 2008. The document has similar sections, but the information has been revised and updated.
Description: Listed threatened ecosystems for South Africa, listed through NEM:BA 54(1). This list was gazetted in December 2011Ecosystem status consists of the following categories: critically endangered, endangered, vulnerable or least threatened. Ecosystem status was calculated based on the percentage of remaining vegetation area (i.e. not transformed by agriculture, mining, forestry plantations, roads and urban areas) and the biodiversity target set for each vegetation type. The ecosystem status of vegetation types which cannot longer meet its biodiversity target due to habitat transformation was set to “critically endangered” that means the percentage of remaining vegetation type is less than what is required to capture species diversity (biodiversity target). The ecosystem status of other vegetation types was set as follows:- if % of remaining area <60% of original area then status = endangered- if % of remaining area <80% of original area then status = vulnerable- if % of remaining area >80% of original area then status = least threatened.