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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.


Papers
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Journal ArticleDOI
08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
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.

7,089 citations

Journal ArticleDOI
TL;DR: This article applied a multivariate climate envelope approach and evaluated model performance using the most comprehensive bird data set, finding that 17% of species expanded their ranges, 78% displayed range contraction, 3% showed no response and 2% became locally extinct.
Abstract: The responsiveness of South African fauna to climate change events is poorly documented and not routinely incorporated into regional conservation planning. We model the likely range alterations of a representative suite of 179 animal species to climate change brought about by the doubling of CO2 concentrations. This scenario is expected to cause a mean temperature increase of 2 °C. We applied a multivariate climate envelope approach and evaluated model performance using the most comprehensive bird data set. The results were encouraging, although model performance was inconsistent in the eastern coastal area of the country. The levels of climate change induced impacts on species ranges varied from little impact to local extinction. Some 17% of species expanded their ranges, 78% displayed range contraction (4–98%), 3% showed no response and 2% became locally extinct. The majority of range shifts (41%) were in an easterly direction, reflecting the east–west aridity gradient across the country. Species losses were highest in the west. Substantially smaller westward shifts were present in some eastern species. This may reflect a response to the strong altitudinal gradient in this region, or may be a model artifact. Species range change (composite measure reflecting range contraction and displacement) identified selected species that could act as climate change indicator taxa. Red-data and vulnerable species showed similar responses but were more likely to display range change (58% vs. 43% for all species). Predictions suggest that the flagship, Kruger National Park conservation area may loose up to 66% of the species included in this analysis. This highlights the extent of the predicted range shifts, and indicates why conflicts between conservation and other land uses are likely to escalate under conditions of climate change.

360 citations

Journal ArticleDOI
TL;DR: DST-NRF Centre of Excellence for Invasion Biology, the University of Pretoria, EU FP6 MACIS species targeted project (Minimisation of and Adaptation to Climate change: Impacts on biodiversity, contract No. 044399) and EU FP 6 ECOCHANGE integrated project (Challenges in assessing and forecasting biodiversity and ecosystem changes in Europe).
Abstract: DST-NRF Centre of Excellence for Invasion Biology, the University of Pretoria, EU FP6 MACIS species targeted project (Minimisation of and Adaptation to Climate change: Impacts on biodiversity, contract No.: 044399) and EU FP6 ECOCHANGE integrated project (Challenges in assessing and forecasting biodiversity and ecosystem changes in Europe).

223 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the utility of the Carnegie Airborne Observatory (CAO) hyperspectral data, and WorldView-2 and Quickbird multispectral spectral data and a combined spectral+tree height dataset (derived from the CAO LiDAR system) for mapping seven common savanna tree species or genera in the Sabi Sands Reserve and communal lands adjacent to Kruger National Park, South Africa.

209 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.

13,120 citations

Journal ArticleDOI
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
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.

7,589 citations

Journal ArticleDOI
08 Jan 2004-Nature
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
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.

7,089 citations

Journal ArticleDOI
TL;DR: An overview of recent advances in species distribution models, and new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales are suggested.
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.

5,620 citations

Journal ArticleDOI
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
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.

5,314 citations