Barend F.N. Erasmus

Bio: Barend F.N. Erasmus is an academic researcher from University of the Witwatersrand. The author has contributed to research in topics: Climate change & Vegetation. The author has an hindex of 29, co-authored 93 publications receiving 9608 citations. Previous affiliations of Barend F.N. Erasmus include University of York & University of Pretoria.

Initiatives to enhance African Environmental Science

Abstract: START has made a significant contribution to capacity development all around the world but particularly so in Africa. They developed a model which lead to the holistic development of the person as well as maintaining an excellent quality of scientifc outputs. This model has been adopted and developed further by three other programmes which are discussed in this paper. The three programmes are 1) Building the Next Generation of African Scholars, funded by the Carnegie Foundation, 2) The Climate Leadership Programme funded by the BMZ Ministry in Germany through GIZ and 3) The Post-graduate Curriculum Initiative for Global Change funded by the Open Society Foundation.

Scholarship on urban Africa’s water crisis narratives: the state of the art

Abstract: Water crises present a global water governance challenge. To date, scholarship has tended to focus on technological and policy-based solutions, while ignoring the influence of narratives on public buy-in during such crises. Africa is expected to become hotter and drier in future, while its cities experience high levels of informal population growth and inequality. These factors combine to make African cities particularly vulnerable to times of water stress. The aim in this paper is to investigate the state of the ‘art’ on narratives framing domestic water use in African cities during periods of acute water stress and ‘crises’, using a systematic literature review of peer-reviewed academic journal articles. The findings revealed a small population of recently published papers that engage critically with state-generated narratives framing the crisis, limited to case studies on Cape Town and Windhoek. We recommend, however, a greater critical engagement with the anti-establishment narratives that can flourish during periods of acute water stress, and tend to be inflammatory and divisive in nature.

Observing vegetation dynamics at medium spatial scale: Lessons from Africa and Asia

Abstract: In this paper, we drew on experience from current Earth Observation (EO) research in Africa and Asia based on the 250m MODIS vegetation index time series covering large spatial extents. In the first example in the southern Africa, we derived a key set of phenometrics (overall greenness, peak and timing of annual greenness) and analyzed trends in these parameters. In the second example in the eastern Africa, we mapped human-induced vegetation productivity decline. The climate-induced changes in vegetation were largely disentangled from the anthropogenic-driven changes by using rainfall data from radar observations. In the last example, we revealed vegetation dynamic patterns across Central Asia by analyzing trends in overall greenness. The paper concluded on the possibilities and constraints of the available data and methods used to analyze vegetation dynamics at medium spatial resolution in relation to evolving issues in Africa and Asia.

Novel methods improve prediction of species' distributions from occurrence data

Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Extinction risk from climate change

Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.

Predicting species distribution: offering more than simple habitat models.

Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Abstract: Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use "default settings", tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presence-absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce "hinge features" that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore "background sampling" strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) "target-group" background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.