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Savanna ecosystems are characterized by the co‐occurrence of trees and grasses. In this paper, we argue that the balance between trees and grasses is, to a large extent, determined by the indirect interactive effects of herbivory and fire. These effects are based on the positive feedback between fuel load (grass biomass) and fire intensity. An increase in the level of grazing leads to reduced fuel load, which makes fire less intense and, thus, less damaging to trees and, consequently, results in an increase in woody vegetation. The system then switches from a state with trees and grasses to a state with solely trees. Similarly, browsers may enhance the effect of fire on trees because they reduce woody biomass, thus indirectly stimulating grass growth. This consequent increase in fuel load results in more intense fire and increased decline of biomass. The system then switches from a state with solely trees to a state with trees and grasses. We maintain that the interaction between fire and herbivory provides a mechanistic explanation for observed discontinuous changes in woody and grass biomass. This is an alternative for the soil degradation mechanism, in which there is a positive feedback between the amount of grass biomass and the amount of water that infiltrates into the soil. The soil degradation mechanism predicts no discontinuous changes, such as bush encroachment, on sandy soils. Such changes, however, are frequently observed. Therefore, the interactive effects of fire and herbivory provide a more plausible explanation for the occurrence of discontinuous changes in savanna ecosystems.

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The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

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To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.

SUMMARYPalm oil (PO) is a very important commodity used as food, in pharmaceuticals, for cooking and as biodiesel: PO is a major contributor to the economies of many countries, especially Indonesia and Malaysia. Novel tropical regions are being explored increasingly to grow oil palm as current land decreases, whilst recent published modelling studies by the current authors for Malaysia and Indonesia indicate that the climate will become less suitable. Countries that grow the crop commercially include those in Latin America, Africa and Asia. How will climate change (CC) affect the ability to grow oil palm in these countries? Worldwide projections for apt climate were made using Climex software in the present paper and the global area with unsuitable climate was assessed to increase by 6%, whilst highly suitable climate (HSC) decreased by 22% by 2050. The suitability decreases are dramatic by 2100 suggesting regions totally unsuitable for growing OP, which are currently appropriate: the global area with unsuitable climate increased from 154 to 169 million km2 and HSC decreased from 17 to 4 million km2. This second assessment of Indonesia and Malaysia confirmed the original findings by the current authors of large decreases in suitability. Many parts of Latin America and Africa were dramatically decreased: reductions in HSC for Brazil, Columbia and Nigeria are projected to be 119 000, 35 and 1 from 5 000 000, 219 and 69 km2, respectively. However, increases in aptness were observed in 2050 for Paraguay and Madagascar (HSC increases were 90 and 41%, respectively), which were maintained until 2100 (95 and 45%, respectively). Lesser or transient increases were seen for a few other countries. Hot, dry and cold climate stresses upon oil palm for all regions are also provided. These results have negative implications for growing oil palm in countries as: (a) alternatives to Malaysia and Indonesia or (b) economic resources per se. The inability to grow oil palm may assist in amelioration of CC, although the situation is complex. Data suggest a moderate movement of apposite climate towards the poles as previously predicted.