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.

Biomass Increases Go under Cover: Woody Vegetation Dynamics in South African Rangelands

Abstract: Woody biomass dynamics are an expression of ecosystem function, yet biomass estimates do not provide information on the spatial distribution of woody vegetation within the vertical vegetation subcanopy. We demonstrate the ability of airborne light detection and ranging (LiDAR) to measure aboveground biomass and subcanopy structure, as an explanatory tool to unravel vegetation dynamics in structurally heterogeneous landscapes. We sampled three communal rangelands in Bushbuckridge, South Africa, utilised by rural communities for fuelwood harvesting. Woody biomass estimates ranged between 9 Mg ha -1 on gabbro geology sites to 27 Mg ha -1 on granitic geology sites. Despite predictions of woodland depletion due to unsustainable fuelwood extraction in previous studies, biomass in all the communal rangelands increased between 2008 and 2012. Annual biomass productivity estimates (10–14% p.a.) were higher than previous estimates of 4% and likely a significant contributor to the previous underestimations of modelled biomass supply. We show that biomass increases are attributable to growth of vegetation <5 m in height, and that, in the high wood extraction rangeland, 79% of the changes in the vertical vegetation subcanopy are gains in the 1-3m height class. The higher the wood extraction pressure on the rangelands, the greater the biomass increases in the low height classes within the subcanopy, likely a strong resprouting response to intensive harvesting. Yet, fuelwood shortages are still occurring, as evidenced by the losses in the tall tree height class in the high extraction rangeland. Loss of large trees and gain in subcanopy shrubs could result in a structurally simple landscape with reduced functional capacity. This research demonstrates that intensive harvesting can, paradoxically, increase biomass and this has implications for the sustainability of

Distributional niche of relatively rare sable antelope in a South African savanna: habitat versus biotic relationships

Abstract: The geographic distribution of a species is governed by climatic conditions, topography, resources and habitat structure determining the fundamental niche, while the local distribution expressed via home range occupation may be compressed by biotic interactions with competitors and predators, restricting the realised niche. Biotic influences could be especially important for relatively rare species. We investigated how rainfall, geology, land type and abundance of other ungulate species serving as competitors or prey for predators contributed to the patchy distribution of sable antelope herds within Kruger National Park. Data were provided by annual aerial surveys of ungulate populations conducted between 1978 and 1988. Sable herds were more commonly present on granitic and sandstone substrates than on more fertile basalt. They occurred both in the moist south-west and dry north of the park. They were most abundant in sour bushveld and mopane savanna woodland, and mostly absent from knob thorn-marula parkland. The presence of sable was negatively associated with high concentrations of impala and wildebeest, less consistently related to the abundance of zebra, and positively associated with the occurrence of buffalo herds. Best supported models included the separate effects of the most abundant grazers along with land type. Interspecific relationships seemed more consistent with vulnerability to predation as the underlying mechanism restricting the distribution of sable herds than with competitive displacement. Sable favoured land types distinct from those where wildebeest, the most preferred prey of lions, and impala, numerically the most important resident prey species, were most abundant. Hence the risk of predation, associated with habitat conditions where abundant prey species are most concentrated, can exert an overriding influence on the distribution of rarer species in terms of their home range occupation.

Investigating potential determinants of the distribution limits of a savanna woody plant: Colophospermum mopane

Abstract: Questions Early bioclimatic models predict that climate change in southern African savannas will cause a huge southward and westward range shift of the savanna tree Colophospermum mopane (Kirk ex Benth.) Kirk ex J.Leon. C. mopane is an economically and ecologically important subtropical savanna tree that forms mono-dominant stands across 30% of southern African savannas. We investigate the validity of these initial range expansion predictions to answer the following questions: what are the regional-scale drivers of the distribution of C. mopane in southern African savannas; and what are the landscape-scale distribution patterns of this species? Location Central Lowveld, Kruger National Park, South Africa. Methods We investigate the validity of very early range expansion modelling predictions using a regional-scale, climate envelope niche model, and fine-scale field mapping of the current boundary, to understand which environmental variables may determine the distribution limit of this signature species. Results Our findings indicate that both non-climatic (dry season day length) and climatic (minimum temperatures) variables limit the regional distribution of C. mopane. At the landscape scale, the distribution of this species is restricted to the warmer parts of the landscape, suggesting minimum temperature appears to be the primary factor determining its landscape-scale distribution. Conclusions This study provides the first detailed model of environmental factors that may limit the regional distribution of C. mopane, and allows us to formulate testable hypotheses regarding the determinants of the range of a keystone species.

Humans and elephants as treefall drivers in African savannas

Abstract: Humans have played a major role in altering savanna structure and function, and growing land-use pressure will only increase their influence on woody cover. Yet humans are often overlooked as ecological components. Both humans and the African elephant, Loxodonta africana, alter woody vegetation in savannas through removal of large trees and activities that may increase shrub cover. Interactive effects of both humans and elephants with fire may also alter vegetation structure and composition. Here we capitalize on a macroscale experimental opportunity - brought about by the juxtaposition of an elephant-mediated landscape, human-utilized communal harvesting lands and a nature reserve fenced off from both humans and elephants - to investigate the influence of humans and elephants on height-specific treefall dynamics. We surveyed 6 812 ha using repeat, airborne high resolution Light Detection and Ranging (LiDAR) to track the fate of 453 685 tree canopies over two years. Human-mediated biennial treefall rates were 2-3.5 fold higher than the background treefall rate of 1.5% treefall ha-1 yr-2, while elephant-mediated treefall rates were 5 times higher at 7.6% treefall ha-1 yr-2 than the control site. Model predictors of treefall revealed that human or elephant presence was the most important variable, followed by the interaction between geology and fire frequency. Treefall patterns were spatially heterogeneous with elephant-driven treefall associated with geology and surface water, while human patterns were related to perceived ease of access to wood harvesting areas and settlement expansion. Our results show humans and elephants utilize all height-classes of woody vegetation, and that large tree shortages in a heavily utilized communal land has transferred treefall occurrence to shorter vegetation. Elephant- and human-dominated landscapes are tied to interactive effects that may hinder tree seedling survival which, combined with tree loss in the landscape, may compromise woodland sustainability. This article is protected by copyright. All rights reserved.

Conservation during times of change: correlations between birds, climate and people in South Africa

Abstract: Few studies have investigated the ability of national conservation networks to adapt to changes in underlying environmental drivers (such as precipitation) and their consequences for factors such as human density and species richness patterns. In this article, the South African avifauna is used as the basis for such analysis to ascertain the likely extent of current, and future, anthropogenic impacts on priority conservation areas. We show that human population pressure is high in or around most of these priority areas and is likely to increase, given the magnitude of post-climate change estimated from predicted changes in precipitation and relationships between species richness, human densities, and rainfall. Although additional conservation areas, such as the Important Bird Area (IBA) network, are likely to introduce valuable flexibility to conservation management, only limited options are available for such expansions, and the conservation value of these areas is likely to be compromised by changing climate. Ultimately, a more integrated conservation approach is needed for effective conservation policies. Such an approach should confer adequate protection on current reserves and emphasize sustainable utilization of non-reserve areas.

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.