This thesis contains seven chapters that deals with the intergration of ecosystem services into conservation planning in South Africa. It starts with a review on the inclusion of ecosystem services in conservation plans, through the mapping of key ecosystem services in South Africa, to the tradeoffs and cost of safeguarding ecosystem services and biodiversity in some parts of South Africa.

CAR mission Skukuza measured bidirectional reflection functions over different natural surfaces and ecosystems in southern Africa. The measurements were conducted to characterize surface anisotropy in support of the CAR SAFARI mission and to validate products from NASA’s Earth Observing System satellites.

Polygon features, representing Red List of Ecosystems (RLE) for terrestrial realm for South Africa. This dataset contains the current remaining natural extent (circa 2018) of each of the 458 ecosystem types assessed. This means that those portions of ecosystems that have been lost to anthropogenic activities such as mining or croplands are excluded and only the remnants are part of the dataset. A separate dataset (RLE_Terr_2021_June2021_ddw.shp) is also available and contains the historical / potential extent of each ecosystem type. This RLE is a revision of the “List of terrestrial ecosystems that threatened or in need of protection” published in the government gazette in December 2011. The revision is based on the best available data and used the IUCN RLE risk assessment framework version 1.1 (Bland et al. 2017). Ecosystems are categorised into one of four classes representing their risk of collapse; in descending order of risk: Critically Endangered, Endangered, Vulnerable, Least Concern. The national vegetation map, 2018 version (Mucina and Rutherford 2006; Dayaram et al., 2019) provided the ecosystem units of assessment for the RLE (Vegetation Unit / Type level). Refer to the website for more detail on the assessments and methods used http://ecosystemstatus.sanbi.org.za

The 2018 South African Inventory of Inland Aquatic Ecosystems (SAIIAE) geodatabase is a collection of data layers pertaining to ecosystem types and pressures for both rivers and inland wetlands. These data layers were developed and used for the 2018 National Biodiversity Assessment (NBA 2018).

This is South African Weather Service (SAWS) data from the Integrated Ecosystem Programme: Southern Benguela on Algoa Voyage 220, 18 - 25 November 2015. The Integrated Ecosystem Programme: Southern Benguela is a multi-disciplinary, multi-institutional platform to undertake relevant science in the Southern Benguela; also functioning as a platform for collaboration and learning. All projects aim to develop an ecosystem indicator that can be used to effectively monitor and understand the Southern Benguela i.e physical, chemical, planktonic, microbial, seabird and benthic ecosystem indicators, used for ecosystem-based management.

Arundo donax is an invasive species that mostly impacts on sensitive riparian ecosystems. In California, USA, Arundo worsens flood effects, outcompetes and displaces indigenous plant and animal species, creates fire hazards, and has the potential to alter soil nutrient status. Arundo is also invasive in South Africa, though less is known about its ecology, biology, and impacts. Since California and the Western Cape of South Africa have similar Mediterranean-type climates, we could assume that the impacts of Arundo on ecosystems in California are likely to be similar in the Western Cape, and that control methods used could be extrapolated for use in South Africa.

Invasive alien grasses are a group of increasing importance which respond to global changes and alter native plant community structure and ecosystem processes. To date there have been few studies that assess the ecological drivers of emerging alien grass invasions and their impacts on arid and semi-arid ecosystems of South Africa. This thesis is focused on the early stages of an invasion process, using fountain grass, Pennisetum setaceum as a model system to understand ecological factors promoting its current spread in South Africa. This is an invasive alien perennial C4 grass from North Africa and the Middle East. The study also investigates the interaction between its invasion and ongoing climate change, and its impact on native ecosystems. P. setaceum spreads along the edges of roads on the outskirts of most towns, and is common on mine dumps in many areas throughout South Africa. Occasionally, it escapes into natural vegetation along drainage lines or after fires. This grass can be a costly problem for agriculture and biodiversity conservation as it is unpalatable and increases fire risk. In this study I assessed how P. setaceum overcomes different invasion barriers in South Africa as an emerging invader. First, I determined the current distribution and habitat preferences of this grass in arid and semi-arid parts of South Africa. I show that the grass performs better on road-river interchanges, although it is found in other parts of the landscape. I suggest that these interchanges should be targeted for management and control of this species. Second, I assessed environmental resources and habitat conditions that promote its invasive potential, and found that nutrient addition and extra soil moisture promoted performance of established grass seedlings. I suggest that management and control should focus on areas with high nutrients and extra water, as these areas facilitate growth and reproduction. Third, I then determined the influence of habitat and competition on its performance along a rainfall seasonality gradient. I demonstrated that established P. setaceum seedlings do not perform well under competition from native vegetation, an aspect which was similar across this gradient. From this finding, I suggest maintaining a high indigenous cover along road verges. Fourth, I assessed differences in reproductive output and potential along this rainfall seasonality gradient, and found no differences between populations at different localities. This suggests high local adaptation of this grass especially in areas of low resources where it persists until favourable conditions return. Predicting future distribution of this grass is important for its management. Fifth, I therefore determined its potential future distribution based on current distribution and spread dynamics using climate-matching and dynamic probabilistic spread models. This predicted most biomes to be at increasing risk of invasion by this grass, except the succulent karoo, in 2050 and 2100. I found that disturbance is a major factor promoting its invasion into semi-natural areas away from roadsides. I finally determined the response of an arid ecosystem to P. setaceum invasion and fire promotion. I show that this ecosystem function and structure will be adversely altered by P. setaceum invasion. My work has emphasised the need for early detection and rapid response of emerging invasive grass species in South Africa, and made recommendations for future research.

The U.S. Geological Survey (USGS) modeled the distribution of terrestrial ecosystems for the contiguous United States using a standardized, deductive approach to associate unique physical environments with ecological systems characterized in NatureServe's Ecological Systems of the United States classification (Comer et al., 2003). This approach was first developed for South America (Sayre et al., 2008) and is now being implemented globally (Sayre et al., 2007).

Unique physical environments were delineated from a massive biophysical stratification of the nation into the major structural components of ecosystems: biogeographic regions (Cress et al., 2008c), land surface forms (Cress et al., 2008a), surficial lithology (Cress et al., 2008d), and topographic moisture potential (Cress et al., 2008b). Each of these structural components was mapped for the contiguous United States and then spatially combined to produce ecosystem structural footprints which represented unique abiotic (physical) environments. Among 49,168 unique structural footprint classes, 13,482 classes which met a minimum pixel count threshold (20,000 pixels) were aggregated into 419 NatureServe ecosystems through semi-automated labeling process using rule set formulations for attribution of each ecosystem.

NBA 2018 Rivers assessment layer. This data set is part of the first version of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) 2018 released in July 2018. A second update of the SAIIAE 2018 was issued with the launch of the NBA 2018, and includes the condition, Ecosystem Threat Status (ETS) and Ecosystem Protection Level (EPL) information for the rivers. The river lines data set is associated with the National Wetland Map 5 (NWM5) issued with the SAIIAE version 1 and 2. Dr Lindie Smith-Adao generated this rivers data set by 2018/06/29.

This is South African Weather Service (SAWS) data from the St Helena Bay Monitoring Line September 2015 cruise (now under the Integrated Ecosystem Programme: Southern Benguela). The St Helena Bay Monitoring Line was initiated as a BENEFIT-driven project on "shipboard monitoring" which linked with similar lines run in Namibia and Angola. The aims are to obtain seasonal and interannual information on the hydrology and productivity of the area. Data on harmful algal blooms, low oxygen water and intrusions of Agulhas Bank water along the west coast will also be collected. A long-term, multi-decadel time-series (from 1951 onward) of information already exists for this important region and has continued in the form of the IEP:SB to detect long-term changes in the hydrology and the plankton, which are important for the detection of regime shifts.

In South Africa, anthropogenic pressures such as water over-abstraction, invasive species impacts, land-use change, pollution, and climate change have caused widespread deterioration of the health of river ecosystems. This comes at great cost to both people and biodiversity, with freshwater fishes ranked as the country’s most threatened species group. Effective conservation and management of South Africa’s freshwater ecosystems requires access to reliable and comprehensive biodiversity data. Despite the existence of a wealth of freshwater biodiversity data, access to these data has been limited. The Freshwater Biodiversity Information System (FBIS) was built to address this knowledge gap by developing an intuitive, accessible and reliable platform for freshwater biodiversity data in South Africa. The FBIS hosts high quality, high accuracy biodiversity data that are freely available to a wide range of stakeholders, including researchers, conservation practitioners and policymakers. We describe how the system is being used to provide freshwater fish data to a national conservation decision-support tool—The Department of Forestry, Fisheries, and the Environment (DFFE) National Environmental Screening Tool (NEST). The NEST uses empirical and modelled biodiversity data to guide Environmental Impact Assessment Practitioners in conducting environmental assessments of proposed developments. Occurrence records for 34 threatened freshwater fishes occurring in South Africa were extracted from the FBIS and verified by taxon specialists, resulting in 6 660 records being used to generate modelled and empirical national distribution (or sensitivity) layers. This represents the first inclusion of freshwater biodiversity data in the NEST, and future iterations of the tool will incorporate additional freshwater taxa. This case study demonstrates how the FBIS fills a pivotal role in the data-to-decision pipeline through supporting data-driven conservation and management decisions at a national level.

The distribution, structure and function of mesic savanna grasslands are strongly driven by fire regimes, grazing by large herbivores, and their interactions. This research addresses a general question about our understanding of savanna grasslands globally: Is our knowledge of fire and grazing sufficiently general to enable us to make accurate predictions of how these ecosystems will respond to changes in these drivers over time? Some evidence suggests that fire and grazing influence savanna grassland structure and function differently in South Africa (SA) compared to North America (NA). These differences have been attributed to the contingent factors of greater biome age, longer evolutionary history with fire and grazing, reduced soil fertility, and greater diversity of plants and large herbivores in SA. An alternative hypothesis is that differences in methods and approaches used to study these systems have led to differing perspectives on the role of these drivers. If the impacts of shared ecosystem drivers truly differ between NA and SA, this calls into question the generality of our understanding of these ecosystems and our ability to forecast how changes in key drivers will affect savanna grasslands globally. Since 2006, an explicitly comparative research program has been conducted to determine the degree of convergence in ecosystem (productivity, N and C cycling) and plant community (composition, diversity, dynamics) responses to fire and grazing in SA and NA.Thus far, initial support has been found for convergence at the ecosystem level and divergence at the community level in response to alterations in both fire regimes and grazing. However, there have also been two unexpected findings (1) the ways in which fire and grazing interact differed between NA and SA, and (2) the rate of change in communities when grazers were removed was much greater in NA than in SA. These unexpected findings raise a number of important new questions: (Q1) Will exclusion of grazing eventually affect community structure and composition across all fire regimes in SA? (Q2) Will these effects differ from those observed in NA? (Q3) What are the determinants of the different rates of community change? (Q4) How will these determinants influence future trajectories of change? (Q5) Will the different rates and trajectories of community change be mirrored by responses in ecosystem function over time? This project is based on a large herbivore exclusion study established within the context of long-term (25-50+ yr) experimental manipulations of fire frequency at the Konza Prairie Biological Station (KPBS) in NA and the Kruger National Park (KNP) in SA. The suite of core studies and measurements include plant community composition, ANPP, and herbivore abundance and distribution at both study sites to answer these research questions.

These data include total anchovy and sardine biomass west and southeast of Cape Agulhas (sampled in November), and anchovy and sardine recruitment west of Cape Infanta (sampled in May). Seabird variables included % of the diet comprised of anchovy, % of the diet comprised of sardine, breeding success, and survival.

Benguela Current African Penguin - These data were collected at two seabird colonies: Dassen Island (-33.4205 lat, 18.0872 lon) and Robben Island (-33.8067, 18.371 long), South Africa.

Benguela Current Cape Gannett - These data were collected at two seabird colonies: Lamberts Bay (-32.0896 lat, 18.3026 lon) and Malgas Island (-33.0526, 17.9254 long), South Africa.

Thanks to the high diversity of ecosystems and habitats, South Africa harbours tremendous diversity of insects. The Kruger National Park, due to its position close to the border between two biogeographic regions and high heterogeneity of environmental conditions, represents an insufficiently studied hotspot of lepidopteran diversity. During our ecological research in the Kruger National Park, we collected abundant moth material, including several interesting faunistic records reported in this study.We reported 13 species of moths which had not yet been recorded in South Africa. In many cases, our records represented an important extension of the species’ known distribution, including two species (Ozarba gaedei and O. persinua) whose distribution ranges extended into the Zambezian biogeographic region. Such findings confirmed the poor regional knowledge of lepidopteran diversity.

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

This dataset contains the digitized treatments in Plazi based on the original journal article Hesse, A. J. (1974): A new South African representative of the South West African genus Namibimydas Hesse (Diptera: Mydaidae), with some ecological notes on the habits of the species. Annals of the South African Museum 66 (2): 25-34, DOI: 10.5281/zenodo.4002986, URL: https://www.biodiversitylibrary.org/page/40911005

The Africa portion of the Biogeographical Provinces of the World, which shows ecological or "Life Zones" within eight biogeographical realms of the world, was digitized from source documents by UNEP/GRID-Geneva as part of the UNEP/FAO Africa Database in 1986. This classification of biogeographical zones was proposed by Dr. Miklos Udvardy in his paper for IUCN and UNESCO in 1975, with intended use as a unified system for biogeographical and conservation purposes. Biogeographical realms are defined as continental or subcontinental-sized areas having unifying features of geography and fauna/flora/vegetation, and correspond to the terms "kingdom" for the florist and "region" for the faunist. Biogeographical provinces are defined as ecosystematic or biotic subdivisions of the realms (floral "regions" and faunal "provinces").

Biogeographical realms were established by Udvardy on the basis of geographic and historic elements, utilizing ground-breaking work as appears on this topic in the published literature. Udvardy's paper makes reference to at least three preceding reports on this topic, and also includes an extensive bibliography of five pages. There are 8 biogeographical realms recognized by Udvardy in Paper #18: the Palearctic, the Nearctic, the Afrotropical, the Indomalayan, the Oceanian, the Australian, the Antarctic and the Neotropical.

The Udvardy Biogeographical Provinces of Africa (Afrotropical "Life Zones") data set covers the entire African continent at a spatial resolution of two minutes (120 seconds) of latitude/longitude, or approximately 3.7 kilometers. The data file consists of 2191 rows (lines, records) by 2161 columns (elements, pixels, samples). Its origin (upper-left or northwest corner) is 38 degrees, 0 minutes and 45 seconds North latitude (38d 00' 45" N), and -20 degrees, 1 minute and 15 seconds West longitude (-20d 01' 15" W); it extends to -35 degrees, 1 minute and 15 seconds South latitude (-35d 01' 15" S), and 52 degrees, 0 minutes and 45 seconds East longitude (52d 00' 45" E) at the terminal point (lower-right or southeast corner). The data file comprises 4.74 Megabytes.

The proper reference for this data set is "Udvardy, Miklos D. F. 1975. A Classification of the Biogeographical Provinces of the World. IUCN Occasional Paper No. 18, prepared as a contribution to UNESCO's Man and the Biosphere (MAB) Program, Project No. 8. International Union for the Conservation of Nature and Natural Resources, Morges (now Gland), Switzerland, 49 pages." A source citation should include IUCN, as digitized by UNEP/GRID in 1986.

GRID Class Udvardy No. Biogeographical Province

 1      3.1.1    Guinean Rain Forest
 2      3.2.1    Congo Rain Forest
 3      3.3.1    Malagasy Rain Forest
 4      3.4.4    West African Woodland/savanna
 5      3.5.4    East African Woodland/savanna
 6      3.6.4    Congo Woodland/savanna
 7      3.7.4    Miombo Woodland/savanna
 8      3.8.4    South African Woodland/savanna
 9      3.9.4    Malagasy Woodland/savanna
10      3.10.4    Malagasy Thorn Forest
11      3.11.6    Cape Sclerophyll
12      3.12.7    Western Sahel
13      3.13.7    Eastern Sahel
14      3.14.7    Somalian
15      3.15.7    Namib
16      3.16.7    Kalahari
17      3.17.7    Karroo
18      3.18.12   Ethiopian Highlands
19      3.19.12   Guinean Highlands *
20      3.20.12   Central African Highlands
21      3.21.12   East African Highlands
22      3.22.12   South African Highlands
--      3.23.13   Ascension and St. Helena Islands @
24      3.24.13   Comores Islands and Aldabra +
--      3.25.13   Mascarene Islands @
26      3.26.14   Lake Rudolf (Turkana)
27      3.27.14   Lake Victoria (Ukerewe)
28      3.28.14   Lake Tanganyika
29      3.29.14   Lake Malawi (Nyasa)

(The final three classes are from the Palearctic Realm:) 30 2.18.7 Sahara desert 31 2.17.7 Mediterranean Sclerophyll 32 2.28.11 Atlas Steppe

• - Includes island of Bioko (part of Equatorial Guinea).
• - GRID version includes the country of Sao Tome & Principe, the Madeira Islands and most of the Canary Islands in this class. @ - GRID version does not include these island groups.

Agro-Ecological Zones (AEZ) for Africa South of the Sahara (SSA) were developed based on the methodology developed by FAO and IIASA. The dataset includes three classification schemes: 5, 8, and 16 classes, referred to as the AEZ5, AEZ8, and AEZ16, respectively.

This data set contains five data files in text format (.txt). Three files contain biomass dynamics data for a broad-leaved savanna located in the 800-hectare Nylsvley study site 200 km north of Johannesburg, South Africa. One net primary productivity (NPP) file contains monthly above-ground biomass data from harvests made between mid-October 1974 and mid-September 1977. A second NPP file contains three-year mean monthly values for above-ground, standing dead, and litter biomass. The third NPP file contains monthly below-ground living and dead biomass data from excavations made from August 1988 to November 1989.

Climate data are provided in the other two files. One file contains air temperature data measured at the study site (1975-1983). The other file contains rainfall data measured at a nearby farmhouse (1917-1995). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published.

Plant trait data from North American and South African grassland communities to provide new insights on community assembly processes.