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.

Simulating tick distributions over sub-Saharan Africa: the use of observed and simulated climate surfaces

Abstract: Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or highresolution model-derived climate data [Division of Atmospheric Research limited-area model (DARLAM)] for predictive species modelling, using tick distributions from subSaharan Africa. Location The analysis is restricted to sub-Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 · 60 km. Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions The use of data derived from high-resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km 2 for Africa). In addition, when exploring

Human-modified landscapes: patterns of fine-scale woody vegetation structure in communal savannah rangelands.

Abstract: Despite electrification, over 90% of rural households in certain areas of South Africa continue to depend on fuelwood, and this affects woody vegetation structure, with associated cascading effects on biodiversity within adjacent lands. To promote sustainable use, the interactions between anthropogenic and environmental factors affecting vegetation structure in savannahs need to be understood. Airborne light detection and ranging (LiDAR) data collected over 4758 ha were used to examine woody vegetation structure in five communal rangelands around 12 settlements in Bushbuckridge, a municipality in the Kruger to Canyons Biosphere Reserve (South Africa). The importance of underlying abiotic factors was evaluated by measuring size class distributions across catenas and using canonical correspondence analysis. Landscape position was significant in determining structure, indicating the importance of underlying biophysical factors. Differences in structure were settlement-specific, related to mean annual precipitation at one site, and human population density and intensity of use at the other four sites. Size class distributions of woody vegetation revealed human disturbance gradients around settlements. Intensity of use affected the amplitude, not the shape, of the size class distribution, suggesting the same height classes were being harvested across settlements, but amount harvested varied between settlements. Highly used rangelands result in a disappearance of disturbance gradients, leading to homogeneous patches of low woody cover around settlements with limited rehabilitation options. Reductions in disturbance gradients can serve as early warning indicators of woodland degradation, a useful tool in planning for conservation and sustainable development.

The impacts of changes in land use on woodlands in an equatorial african savanna

Abstract: Savanna landscapes are vitally important in providing both ecological and economic services that sustain local livelihoods and national economies, particularly for sub-Saharan African countries whose economies are mainly agrarian. Development prospects in savanna landscapes are however dependent on actions to avoid and to slow or reverse degradation and that are aided with a clear understanding of trends in land use/cover changes, their causes and implications for conservation. We analysed land use/cover changes based on three Landsat satellite images (1984, 1995 and 2000/2001) and the influence of human utilization on the changes in an equatorial African savanna, central Uganda, for the period 1984–2000/2001. The land cover classification and change analysis clearly identified the dominant land cover types, revealing a severe reduction in woodland cover with dense woodlands decreasing by 64%, over a 17-year period. Consequently, medium woodland, open woodland and cultivation/settlements areas cover increased by 31%, 3% and 80%, respectively. The cover change analysis results were corroborated with interview results that also attributed the woodland cover loss to increasing commercial charcoal production, expanding livestock grazing, subsistence crop cultivation and an insecure land use tenure system. Indeed, the major land use types in the savanna are charcoal production, shifting crop cultivation and livestock rearing. The decreasing woody vegetation cover threatens the savanna's ability to continue providing ecosystems services to support the livelihoods of people who mainly depend on natural resources and are vulnerable to the impacts of climate change. Copyright © 2014 John Wiley & Sons, Ltd.

How to halt the global decline of lands

Abstract: The assessment of land degradation and restoration by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services shows that land degradation across the globe is a wide and severe issue and is showing no signs of slowing down. This trend must be halted and reversed.

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.