The nations of the world have set themselves a target of reducing the rate of biodiversity loss by 2010. Here, we propose a biodiversity intactness index (BII) for assessing progress towards this ...target that is simple and practicalbut sensitive to important factors that influence biodiversity statusand which satisfies the criteria for policy relevance set by the Convention on Biological Diversity. Application of the BII is demonstrated on a large region (4 x10(6) km2) of southern Africa. The BII score in the year 2000 is about 84%: in other words, averaged across all plant and vertebrate species in the region, populations have declined to 84% of their presumed pre-modern levels. The taxonomic group with the greatest loss is mammals, at 71% of pre-modern levels, and the ecosystem type with the greatest loss is grassland, with 74% of its former populations remaining. During the 1990s, a population decline of 0.8% is estimated to have occurred.
Essential Biodiversity Variables Pereira, H. M.; Ferrier, S.; Walters, M. ...
Science,
01/2013, Volume:
339, Issue:
6117
Journal Article
Peer reviewed
Open access
Reducing the rate of biodiversity loss and averting dangerous biodiversity change are international goals, reasserted by the Aichi Targets for 2020 by Parties to the United Nations (UN) Convention on ...Biological Diversity (CBD) after failure to meet the 2010 target (1, 2). However, there is no global, harmonized observation system for delivering regular, timely data on biodiversity change (3). With the first plenary meeting of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) soon under way, partners from the Group on Earth Observations Biodiversity Observation Network (GEO BON) (4) are developing-and seeking consensus around-Essential Biodiversity Variables (EBVs) that could form the basis of monitoring programs worldwide.
This paper demonstrates a simulation approach for testing the sensitivity of linear and non-parametric trend analysis methods applied to remotely sensed vegetation index data for the detection of ...land degradation. The intensity, rate and timing of reductions in seasonally-summed NDVI are systematically varied on sample data to simulate land degradation, after which the trend analysis was applied and its sensitivity evaluated. The study was based on a widely-used, 1km2 AVHRR data set for a test area in southern Africa. The trends were the most negative and significant when the degradation was introduced rapidly (over a period of 2–5years) and in the middle of a 16-year time series. The seasonally-summed NDVI needs to be reduced by 30–40% before a significant negative linear slope or Kendall's correlation coefficient was apparent, given an underlying positive trend caused by rainfall. The seasonally-summed data were reordered to remove this underlying positive trend, before simulating degradation again. With no underlying positive trend present, degradation of 20% resulted in significant negative trends. Since areas widely agreed to be degraded show only 10–20% reductions compared to non-degraded areas, this raises doubts over the ability of trend analyses to detect degradation in a timely way in the presence of underling environmental trends. Residual Trends Analysis (RESTREND) was applied in an attempt to correct for variability and trends in rainfall. However, a simulated degradation intensity ≥20% caused the otherwise strong relationship between NDVI and rainfall to break down, making the RESTREND an unreliable indicator of land degradation. The results of such analyses will vary between different environments and need to be tested for sample areas across regions. Although the paper does not claim to solve the challenge of detecting land degradation amidst rainfall variability, it introduces a method of assessing the sensitivity of land degradation monitoring using remote sensing data.
► An alternative to “expert validation” of desertification trend maps is demonstrated. ► Land degradation was simulated in NDVI time series to test trend analysis methods. ► Intensity, start and rate of NDVI reductions were varied to test effects on trends. ► Large reductions in NDVI were required for significant trends to develop. ► The RESTREND method had a limited ability to correct for rainfall variability.
This paper, developed under the framework of the RECCAP initiative, aims at providing improved estimates of the carbon and GHG (CO2, CH4 and N2O) balance of continental Africa. The various components ...and processes of the African carbon and GHG budget are considered, existing data reviewed, and new data from different methodologies (inventories, ecosystem flux measurements, models, and atmospheric inversions) presented. Uncertainties are quantified and current gaps and weaknesses in knowledge and monitoring systems described in order to guide future requirements. The majority of results agree that Africa is a small sink of carbon on an annual scale, with an average value of −0.61 ± 0.58 Pg C yr−1. Nevertheless, the emissions of CH4 and N2O may turn Africa into a net source of radiative forcing in CO2 equivalent terms. At sub-regional level, there is significant spatial variability in both sources and sinks, due to the diversity of biomes represented and differences in the degree of anthropic impacts. Southern Africa is the main source region; while central Africa, with its evergreen tropical forests, is the main sink. Emissions from land-use change in Africa are significant (around 0.32 ± 0.05 Pg C yr−1), even higher than the fossil fuel emissions: this is a unique feature among all the continents. There could be significant carbon losses from forest land even without deforestation, resulting from the impact of selective logging. Fires play a significant role in the African carbon cycle, with 1.03 ± 0.22 Pg C yr−1 of carbon emissions, and 90% originating in savannas and dry woodlands. A large portion of the wild fire emissions are compensated by CO2 uptake during the growing season, but an uncertain fraction of the emission from wood harvested for domestic use is not. Most of these fluxes have large interannual variability, on the order of ±0.5 Pg C yr−1 in standard deviation, accounting for around 25% of the year-to-year variation in the global carbon budget. Despite the high uncertainty, the estimates provided in this paper show the important role that Africa plays in the global carbon cycle, both in terms of absolute contribution, and as a key source of interannual variability.
Climate Change Scholes, Bob; Scholes, Mary; Lucas, Mike
04/2018
eBook
Climate change affects us all, but it can be a confusing business. In this book, three scientists with several decades of experience in assessing the potential effects of climate change for the ...southern African region share their insights. Complex issues are dealt with in plain language, without oversimplification and with attention to accuracy. The material is up-to-date as is possible in such a fast-developing field.
Climate Change: Briefings from Southern Africa takes the form of 55 'frequently-asked' questions', each with a brief and clear reply. It is illustrated with colour diagrams and photographs, and examples are tailored to the regional context. The authors' introduction provides an overview of current national and international policies aimed at regulating climate change. The content is divided into four sections, which take the reader through the science of how climate system works; the projected impacts in southern Africa during the twenty-first century; what this means for the South African economy and society; and what can be done to avoid harm. The briefings can be read alone or in sequence.
The year 2015 is regarded as a watershed for global climate change action if a global average temperature rise of more than two degrees abbove the pre-Industrial level is to be avoided. This book provides compelling evidence that the impact on agriculture, fisheries, water resources, human health, plants and animals as well as sea levels will be dangerous. However, the book ends on a positive note by offering advice on how the world can avoid such bleak outcomes, while allowing a good life for all.
The volume is aimed at interested non-scientists, including business people, decision-makers, ordinary citizens and students
MOD17A2 provides operational gross primary production (GPP) data globally at 1km spatial resolution and 8-day temporal resolution. MOD17A2 estimates GPP according to the light use efficiency (LUE) ...concept assuming a fixed maximum rate of carbon assimilation per unit photosynthetically active radiation absorbed by the vegetation (εmax). Minimum temperature and vapor pressure deficit derived from meteorological data down-regulate εmax and constrain carbon assimilation. This data is useful for regional to global studies of the terrestrial carbon budget, climate change and natural resources. In this study we evaluated the MOD17A2 product and its driver data by using in situ measurements of meteorology and eddy covariance GPP for 12 African sites. MOD17A2 agreed well with eddy covariance GPP for wet sites. Overall, seasonality was well captured but MOD17A2 GPP was underestimated for the dry sites located in the Sahel region. Replacing the meteorological driver data derived from coarse resolution reanalysis data with tower measurements reduced MOD17A2 GPP uncertainties, however, the underestimations at the dry sites persisted. Inferred εmax calculated from tower data was higher than the εmax prescribed in MOD17A2. This, in addition to uncertainties in fraction of absorbed photosynthetically active radiation (FAPAR) explains some of the underestimations. The results suggest that improved quality of driver data, but primarily a readjustment of the parameters in the biome parameter look-up table (BPLUT) may be needed to better estimate GPP for African ecosystems in MOD17A2.
► We evaluate the MODIS GPP product for eddy covariance sites in Africa. ► Seasonality was well captured but MODIS underestimates GPP at dry sites. ► Modification of εmax and improved interpolation for FAPAR can improve MODIS GPP.
Question: Can satellite time series be used to identify tree and grass green-up dates in a semi-arid savanna system, and are there predictable environmental cues for green-up for each life form? ...Location: Acacia nigrescens/Combretum apiculatum savanna, Kruger National Park, South Africa (25° S, 31° E). Methods: Remotely-sensed data from the MODIS sensor were used to provide a five year record of greenness (NDVI) between 2000 and 2005. The seasonal and inter-annual patterns of leaf display of trees and grasses were described, using additional ecological information to separate the greening signal of each life form from the satellite time series. Linking this data to daily meteorological and soil moisture data allowed the cues responsible for leaf flush in trees and grasses to be identified and a predictive model of savanna leaf-out was developed. This was tested on a 22-year NDVI dataset from the Advanced Very High Resolution Radiometer. A day length cue for tree green-up predicted 86% of the green-ups with an accuracy better than one month. A soil moisture and day length cue for grass green-up predicted 73% of the green-ups with an accuracy better than a month, and 82% within 45 days. This accuracy could be improved if the temporal resolution of the satellite data was shortened from the current two weeks. Conclusions: The data show that at a landscape scale savanna trees have a less variable phenological cycle (within and between years) than grasses. Realistic biophysical models of savanna systems need to take this into account. Using climatic data to predict these dynamics is a feasible approach.
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