Guy F. Midgley

Bio: Guy F. Midgley is an academic researcher from Stellenbosch University. The author has contributed to research in topics: Climate change & Biodiversity. The author has an hindex of 66, co-authored 217 publications receiving 30649 citations. Previous affiliations of Guy F. Midgley include University of Cape Town & International Union for Conservation of Nature and Natural Resources.

PDEMR Modelling of Protea Species in the Population Size of 1 to 10, in Cape Floristic Region from 1992 to 2002, South Africa

Abstract: Global warming and climate changes can lead to the movement of plant species as they find their original habitats are no longer suitable to their needs. It is often an urgent task to establish a mathematical model to catch up the trajectories of the endangered species to effectively manage environmental protection under the inevitable biodiversity changes taking place. However, as it often happens with the environmental data, within the study area, some areas are well sampled, while other areas are not sampled. Even the collected data are often just species presence or categorical data. This makes very difficult to a spatial analysis, and impossible to do a kriging prediction map. In this paper, we use the partial differential equation motivated regression (PDEMR) model, to model Protea species in the population size of 1 to 10, in the Cape Floristic Region, from 1992 to 2002, in South Africa.

Narrowing pathways to a sustainable future.

Abstract: At the United Nations Framework Convention on Climate Change (UNFCCC) Conference of the Parties in Copenhagen in 2009, the Alliance of Small Island States, supported by African countries, called for a temperature target of 1.5°C above the preindustrial level, as opposed to the more broadly accepted 2°C limit, as the basis for a global multilateral climate agreement. A subsequent UNFCCC-established review concluded that 2°C of warming cannot be considered safe and that less warming would be preferable ( 1 ). The Paris Agreement, adopted in December 2015, formally recognized this notion in its stated aims to hold the increase to well below 2°C and to pursue efforts to limit it to 1.5°C above preindustrial levels ( 2 ). On page 791 of this issue, Warren et al. ( 3 ) provide a detailed analysis of the avoided risk to species' geographic ranges if a 1.5°C rather than a 2°C target is attained.

BioMove - Improvement and Parameterization of a Hybrid Model fo the ASsessment of Climate Change impacts on the Vegetation of California

Abstract: There is substantial evidence that climate change is affecting ecosystems worldwide. California is no exception. With insights from historic climate change and subsequent species’ responses, scientists are developing refined tools to evaluate how species change may continue in the future and what impact this may have on biodiversity and conservation. Bioclimatic envelope modeling is one approach to modeling species distribution. However, it has many shortcomings by neglecting to account for individualistic species response or inter specific competition. Furthermore, bioclimatic envelope models do not account for species dispersal constraints or those imposed by disturbances such as land use change or fire. BioMove is a novel spatially explicit, dynamic species modeling approach developed to address these issues. It simulates a target species in a dynamic landscape, competing with a target species in competition with one or many PFTs. It combines various sub-models to integrate competition, dispersal and disturbance. It has important application potential for threatened species assessment, management coordination and decision support, invasive species modeling and other advanced climate change research.

The eco-evolutionary significance of rainfall constancy for facultative CAM photosynthesis.

Abstract: A flexible use of the crassulacean acid metabolism (CAM) has been hypothesised to represent an intermediate stage along a C3 to full CAM evolutionary continuum, when relative contributions of C3 vs CAM metabolism are co-determined by evolutionary history and prevailing environmental constraints. However, evidence for such eco-evolutionary interdependencies is lacking. We studied these interdependencies for the leaf-succulent genus Drosanthemum (Aizoaceae, Southern African Succulent Karoo) by testing for relationships between leaf δ13 C diagnostic for CAM dependence (i.e. contribution of C3 and CAM to net carbon gain), and climatic variables related to temperature and precipitation and their temporal variation. We further quantified the effects of shared phylogenetic ancestry on CAM dependence and its relation to climate. CAM dependence is predicted by rainfall and its temporal variation, with high predictive power of rainfall constancy (temporal entropy). The predictive power of rainfall seasonality and temperature-related variables was negligible. Evolutionary history of the tested clades significantly affected the relationship between rainfall constancy and CAM dependence. We argue that higher CAM dependence might provide an adaptive advantage in increasingly unpredictable rainfall environments when the anatomic exaptation (succulence) is already present. These observations might shed light on the evolution of full CAM.

Food Security: The Challenge of Feeding 9 Billion People

Abstract: Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here.

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.