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.

Sequestration of precious and pollutant metals in biomass of cultured water hyacinth ( Eichhornia crassipes )

Abstract: The aim of this study was to investigate the overall root/shoot allocation of metal contaminants, the amount of metal removal by absorption and adsorption within or on the external root surfaces, the dose-response of water hyacinth metal uptake, and phytotoxicity. This was examined in a single-metal tub trial, using arsenic (As), gold (Au), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), uranium (U), and zinc (Zn). Iron and Mn were also used in low-, medium-, and high-concentration treatments to test their dose effect on water hyacinth’s metal uptake. Water hyacinth was generally tolerant to metallotoxicity, except for Cu and Hg. Over 80 % of the total amount of metals removed was accumulated in the roots, of which 30–52 % was adsorbed onto the root surfaces. Furthermore, 73–98 % of the total metal assimilation by water hyacinth was located in the roots. The bioconcentration factor (BCF) of Cu, Hg, Au, and Zn exceeded the recommended index of 1000, which is used in selection of phytoremediating plants, but those of U, As, and Mn did not. Nevertheless, the BCF for Mn increased with the increase of Mn concentration in water. This suggests that the use of BCF index alone, without the consideration of plant biomass and metal concentration in water, is inadequate to determine the potential of plants for phytoremediation accurately. Thus, this study confirms that water hyacinth holds potential for a broad spectrum of phytoremediation roles. However, knowing whether these metals are adsorbed on or assimilated within the plant tissues as well as knowing their allocation between roots and shoots will inform decisions how to re-treat biomass for metal recovery, or the mode of biomass reduction for safe disposal after phytoremediation.

Changing distributions of larger ungulates in the Kruger National Park from ecological aerial survey data

Abstract: Documenting current species distribution patterns and their association with habitat types is important as a basis for assessing future range shifts in response to climate change or other influences. We used the adaptive local convex hull ( a -LoCoH) method to map distribution ranges of 12 ungulate species within the Kruger National Park (KNP) based on locations recorded during aerial surveys (1980–1993). We used log-linear models to identify changes in regional distribution patterns and chi-square tests to determine shifts in habitat occupation over this period. We compared observed patterns with earlier, more subjectively derived distribution maps for these species. Zebra, wildebeest and giraffe distributions shifted towards the far northern section of the KNP, whilst buffalo and kudu showed proportional declines in the north. Sable antelope distribution contracted most in the north, whilst tsessebe, eland and roan antelope distributions showed no shifts. Warthog and waterbuck contracted in the central and northern regions, respectively. The distribution of impala did not change. Compared with earlier distributions, impala, zebra, buffalo, warthog and waterbuck had become less strongly concentrated along rivers. Wildebeest, zebra, sable antelope and tsessebe had become less prevalent in localities west of the central region. Concerning habitat occupation, the majority of grazers showed a concentration on basaltic substrates, whilst sable antelope favoured mopane-dominated woodland and sour bushveld on granite. Buffalo showed no strong preference for any habitats and waterbuck were concentrated along rivers. Although widespread, impala were absent from sections of mopane shrubveld and sandveld. Kudu and giraffe were widespread through most habitats, but with a lesser prevalence in northern mopane-dominated habitats. Documented distribution shifts appeared to be related to the completion of the western boundary fence and widened provision of surface water within the park. Conservation implications: The objectively recorded distribution patterns provide a foundation for assessing future changes in distribution that may take place in response to climatic shifts or other influences.

Extracting Structural Vegetation Components From Small-Footprint Waveform Lidar for Biomass Estimation in Savanna Ecosystems

Abstract: Measurement of vegetation biomass accumulation is critical for ecosystem assessment and monitoring, but doing so typically involves extensive field data collection that yields relatively crude structural outputs, e.g., plot- or site-level metrics. This study assessed the utility of airborne light detection and ranging (lidar) waveform features to explain structural and biomass variation in a savanna ecosystem across a land-use gradient. The ability of aboveground waveform lidar features to model field-based woody and herbaceous biomass measurements was evaluated statistically by regression models using forward variable selection. Waveform features explained 76% of the variation in woody biomass in a regulated communal land use area (RMSE = 29.0 kg). The waveform features were also correlated to herbaceous measurements in the same land-use area, with increased correlations at higher biomass levels. These results indicate that small-footprint waveform lidar data potentially can be used as a single modality to describe heterogeneous woody cover in a savanna environment; however, further research is warranted during the full growing season to fully evaluate its performance.

Evidence for climate-induced range shift in Brachystegia (miombo) woodland.

Abstract: Brachystegia spiciformis Benth. is the dominant component of miombo, the sub-tropical woodlands which cover 2.7 million km2 of south-central Africa and which is coincident with the largest regional centre of endemism in Africa. However, pollen records from the genus Brachystegia suggest that miombo has experienced rapid range retraction (~450 km) from its southernmost distributional limit over the past 6000 years. This abrupt biological response created an isolated (by ~200 km) and incomparable relict at the trailing population edge in northeast South Africa. These changes in miombo population dynamics may have been triggered by minor natural shifts in temperature and moisture regimes. If so, B. spiciformis is likely to be especially responsive to present and future anthropogenic climate change. This rare situation offers a unique opportunity to investigate climatic determinants of range shift at the trailing edge of a savannah species. A niche modelling approach was used to produce present-day and select future B. spiciformis woodland ecological niche models. In keeping with recent historical range shifts, further ecological niche retraction of between 30.6% and 47.3% of the continuous miombo woodland in Zimbabwe and southern Mozambique is predicted by 2050. Persistence of the existing relict under future climate change is plausible, but range expansion to fragmented refugia in northeast South Africa is unlikely. As Brachystegia woodland and associated biota form crucial socio-economic and biodiversity components of savannas in southern Africa, their predicted further range retraction is of concern.

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.