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.

Rural Outmigration, Natural Capital, and Livelihoods in South Africa.

Abstract: Rural households across the globe engage in both migration and natural resource use as components of livelihood strategies designed to meet household needs. Yet, migration scholars have only recently begun to regularly integrate environmental factors into empirical modelling efforts. To examine the migration-environment association in rural South Africa, we use vegetation measures derived from satellite imagery combined with detailed demographic data from over 9000 households at the Agincourt Health and Demographic Surveillance Site. Results reveal that household-level temporary migration is associated with higher levels of local natural capital, although no such association exists for permanent migration. Further, more advantaged households exhibit a stronger association between migration-environment, in-line with the 'environmental capital' hypothesis, suggesting that natural resource availability can facilitate household income diversification. We argue that a focus on migration's environmental aspects is especially timely in the contemporary era of climate change and that natural capital availability and variability represent critical pieces of the empirical migration puzzle, especially regarding cyclical livelihood migration.

Unsustainable fuelwood extraction from South African savannas.

Abstract: Wood and charcoal supply the majority of sub-Saharan Africa’s rural energy needs. The long-term supply of fuelwood is in jeopardy given high consumption rates. Using airborne light detection and ranging (LiDAR), we mapped and investigated savanna aboveground biomass across contrasting land uses, ranging from densely populated communal areas to highly protected areas in the Lowveld savannas of South Africa. We combined the LiDAR observations with socio-economic data, biomass production rates and fuelwood consumption rates in a supply‐demand model to predict future fuelwood availability. LiDAR-based biomass maps revealed disturbance gradients around settlements up to 1.5 km, corresponding to the maximum distance walked to collect fuelwood. At current levels of fuelwood consumption (67% of households use fuelwood exclusively, with a 2% annual reduction), we calculate that biomass in the study area will be exhausted within thirteen years. We also show that it will require a 15% annual reduction in consumption for eight years to a level of 20% of households using fuelwood before the reduction in biomass appears to stabilize to sustainable levels. The severity of dwindling fuelwood reserves in African savannas underscores the importance of providing affordable energy for rural economic development.

Impacts of Climate Change on Health and Wellbeing in South Africa

Abstract: Given its associated burden of disease, climate change in South Africa could be reframed as predominately a health issue, one necessitating an urgent health-sector response The growing impact of climate change has major implications for South Africa, especially for the numerous vulnerable groups in the country We systematically reviewed the literature by searching PubMed and Web of Science Of the 820 papers screened, 34 were identified that assessed the impacts of climate change on health in the country Most papers covered effects of heat on health or on infectious diseases (20/34; 59%) We found that extreme weather events are the most noticeable effects to date, especially droughts in the Western Cape, but rises in vector-borne diseases are gaining prominence Climate aberration is also linked in myriad ways with outbreaks of food and waterborne diseases, and possibly with the recent Listeria epidemic The potential impacts of climate change on mental health may compound the multiple social stressors that already beset the populace Climate change heightens the pre-existing vulnerabilities of women, fishing communities, rural subsistence farmers and those living in informal settlements Further gender disparities, eco-migration and social disruptions may undermine the prevention—but also treatment—of HIV Our findings suggest that focused research and effective use of surveillance data are required to monitor climate change’s impacts; traditional strengths of the country’s health sector The health sector, hitherto a fringe player, should assume a greater leadership role in promoting policies that protect the public’s health, address inequities and advance the country’s commitments to climate change accords

Geographic sampling bias in the South African Frog Atlas Project: implications for conservation planning

Abstract: Quality conservation planning requires quality input data. However, the broad scale sampling strategies typically employed to obtain primary species distribution data are prone to geographic bias in the form of errors of omission. This study provides a quantitative measure of sampling bias to inform accuracy assessment of conservation plans based on the South African Frog Atlas Project. Significantly higher sampling intensity near to cities and roads is likely to result in overstated conservation priority and heightened conservation conflicts in urban areas. Particularly well sampled protected areas will also erroneously appear to contribute highly to amphibian biodiversity targets. Conversely, targeted sampling in the arid northwest and along mountain ranges is needed to ensure that these under-sampled regions are not excluded from conservation plans. The South African Frog Atlas Project offers a reasonably accurate picture of the broad scale west-to-east increase in amphibian richness and abundance, but geographic bias may limit its applicability for fine scale conservation planning. The Global Amphibian Assessment species distribution data offered a less biased alternative, but only at the cost of inflated commission error.

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.