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.

Scale-dependent selection of greenness by African elephants in the Kruger-private reserve transboundary region, South Africa

Abstract: Foraging behaviour and habitat selection occur as hierarchical processes. Understanding the factors that govern foraging and habitat selection thus requires investigation of those processes over the scales at which they occur. We investigated patterns of habitat use by African elephants (Loxodonta africana) in relation to vegetation greenness to investigate the scale at which that landscape attribute was most closely related to distribution of elephant locations. We analysed Global Positioning System radio-collar locations for 15 individuals, using the Normalized Difference Vegetation Index as a representation of vegetation greenness in a Geographic Information Systems framework. We compared the importance of vegetation greenness at three spatial scales: the total home range, the seasonal home range and the 16-day home range. During the wet season, seasonal home ranges for both sexes were associated with intermediate greenness within the total home range; there was no evidence of selection based on greenness at finer scales. During the dry season, the strongest associations were within the 16-day home range: individual locations for males tended to be in areas of intermediate greenness, and those for females were in areas of intermediate and high greenness. Our findings suggest that the role of vegetation greenness varies with the scale of analysis, likely reflecting the hierarchical processes involved in habitat selection by elephants.

Hyperspectral reflectance features of water hyacinth growing under feeding stresses of Neochetina spp. and different heavy metal pollutants

Abstract: Spectral signatures of water hyacinth grown with biocontrol agents Neochetina eichornia and N. bruchi and various heavy metal pollutants were collected at the plant canopy level using a hand-held spectrometer to detect the biocontrol agent and heavy metal-induced plant stresses and the interaction between the two stressors. Water hyacinth was grown in 65l tubs, each with a single element from one of: As 1 mg l−1, Au 1 mg l−1, Cu 2 mg l−1, Fe 0.5, 2 and 4 mg l−1, Hg 1 mg l−1, Mn 0.5, 2, and 4 mg l−1, U 1 mg l−1, and Zn 4 mg l−1, with the exception of the control treatment. Three weeks after the metal treatments, the weevils were added to each tub, including those of the control treatment. Spectral measurements were taken before and after the addition of the weevils. Several spectral indicators of plant stress including red edge normalized difference vegetation index RE-NDVI, modified red edge NDVI mNDVI705, modified simple ratio mSR, photochemical reflectance index PRI, and red edge position REP calculated using first derivative and linear extrapolation and water band index WBI were used to identify the plant stresses of water hyacinth. The spectral indicators of both metal and weevil plant stressors were correlated with the leaf chlorophyll content from the SPAD-502 readings at the end of the experiment. Correlations of mNDVI705 with SPAD-502 readings were the highest followed by the indicators of REP. Cu-, Hg-, and Zn-treated plants showed significantly lower chlorophyll contents compared with the control treatment. A similar trend with four additional treatments As, Fe-M, Mn-L, and Mn-H was seen after the release of the weevils, indicating plant stress due to feeding by the biocontrol agent. However, adult and larval feeding was significantly reduced by Cu, Hg, and Zn elements, of which Cu was the most stressful. These results indicate that hyperspectral remote sensing has potential as a tool to determine the health status of water hyacinth from a remote location, to inform management interventions in control of the weed. However, its usage at a larger scale requires further studies.

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.