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.

Habitat associations and conservation status of an endemic forest dwarf chameleon (Bradypodion sp.) from South Africa

Abstract: We quantified habitat associations and evaluated the conservation status of a recently identified, undescribed species of dwarf chameleon, Bradypodion sp. nov. Dhlinza, endemic to scarp forest remnants in KwaZulu-Natal Province, South Africa. At the microhabitat scale the Dhlinza dwarf chameleon was found more often in forest gaps and near paths than highly disturbed edges or forest interior. Chameleon presence was not explained by forest physiognomic variables such as vine cover, shrub and tree density, or canopy cover. Presence near gaps may be better explained by the combined effects of the thermal microenvironment and food availability. The species is moderately common where it occurs, with estimated densities of 4.7, 8.7 and 29.7 individuals per ha within forest interior, edges and gaps respectively. At the landscape scale, the chameleon occurs only in three remnant forests: the Dhlinza, Entumeni and Ongoye Forests. The species' extent of occurrence was estimated to be 88 km2 and its area of occupancy 49 km2. Based on the small area of remaining suitable habitat, this species meets the requirements for categorization as Endangered according to IUCN Red List criteria.

Connecting the dots between laser waveforms and herbaceous biomass for assessment of land degradation using small-footprint waveform LiDAR data

Abstract: Measurement and management of vegetation biomass accumulation in ecosystems typically involves extensive field data collection, which can be expensive and time consuming, while leaving the user with relatively crude inputs to intricate biomass models. Light detection and ranging (LiDAR) remote sensing, which provides extensive height measurements of terrain and vegetation, has become an effective alternative to characterize vegetation structure. In this paper, we report on ongoing efforts at developing signal processing approaches to model herbaceous biomass using a new generation of airborne laser scanners, namely full-waveform LiDAR systems. Structural and statistic-based feature metrics are directly derived from LiDAR waveforms at the pixel level and related to plot-level field data. Initial results reveal a definite correlation between the LiDAR waveform and herbaceous biomass. Ongoing research focuses on the links between fractional cover estimated from imaging spectroscopy and woody biomass.

Animal taxa contrast in their scale-dependent responses to land use change in rural Africa

Abstract: S1 Table. List of bat species, families and foraging groups recorded from manual identifications of a random subset of four sites (two nights each) per village, and the codes given to species-groups defined for subsequent automated identification with minimal overlap in call parameters using scans and filters in Analook v. 4.1t, 2015 (Titley Electronics, www. hoarybat.com). Single asterisk denotes species which were identified very rarely using manual identification but not detected from automated scans. Double asterisk denotes one species which was not manually detected in the sub-sampled sites but detected unequivocally with the automated scans.

Thresholds of change in a multi-use conservation landscape of South Africa: historical land-cover, future transformation and consequences for environmental decision-making

Abstract: As multi-use conservation landscapes, biosphere reserves (BRs) exemplify the landscape mosaic approach to environmental decision-making. In this study, time-series remotely-sensed data (1993–2006–2012) were used to monitor vegetation transformation in the Kruger to Canyons Biosphere Reserve (K2C) of South Africa, updating previous land-cover research. We identified changes in spatial extent, rate and intensity of land-cover change and extrapolated observed trends to 2018. The increased rate of change in the recent observation period (2.3 vs. 5.7%) was driven by more intensive gains in impacted vegetation and settlement since 2006 (>210 km2 and >120 km2), with resultant transformation of intact habitat undermining regional connectivity. By 2012, intact vegetation had suffered losses of 6.3% (>350 km2) since 2006 and >14% (>750 km2) since 1993. A further 9.5% loss of intact habitat may represent a critical threshold, establishing K2C above the 50% threshold of landscape transformation, whereafter a rapid decline in landscape resilience is likely. Given the BR's spatial zonation, such a loss across the full extent of K2C is unlikely, at least in the short-term (i.e., by 2018). Yet, based on past trends of transformation in the unprotected transition zone, anticipating such losses in the longer term, is not unfounded (i.e., 2024).

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.