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East Africa hosts a striking diversity of terrestrial ecosystems, which vary both in space and time due to complex regional topography and a dynamic climate. The structure and functioning of these ecosystems under this environmental setting can be studied with dynamic vegetation models (DVMs) in a spatially explicit way. Yet, regional applications of DVMs to East Africa are rare and a comprehensive validation of such applications is missing. Here, we simulated the present-day and mid-Holocene vegetation of East Africa with the DVM, LPJ-GUESS and we conducted an exhaustive comparison of model outputs with maps of potential modern vegetation distribution, and with pollen records of local change through time. Overall, the model was able to reproduce the observed spatial patterns of East African vegetation. To see whether running the model at higher spatial resolutions (10′ × 10′) contribute to resolve the vegetation distribution better and have a better comparison scale with the observational data (i.e. pollen data), we run the model with coarser spatial resolution (0.5° × 0.5°) for the present-day as well. Both the area- and point-wise comparison showed that a higher spatial resolution allows to better describe spatial vegetation changes induced by the complex topography of East Africa. Our analysis of the difference between modelled mid-Holocene and modern-day vegetation showed that whether a biome shifts to another is best explained by both the amount of change in precipitation it experiences and the amount of precipitation it received originally. We also confirmed that tropical forest biomes were more sensitive to a decrease in precipitation compared to woodland and savanna biomes and that Holocene vegetation changes in East Africa were driven not only by changes in annual precipitation but also by changes in its seasonality.
Low-dimensional trade-offs fail to explain richness and structure in species-rich plant communities
(2011)
Mathematical models and ecological theory suggest that low-dimensional life history trade-offs (i.e. negative correlation between two life history traits such as competition vs. colonisation) may potentially explain the maintenance of species diversity and community structure. In the absence of trade-offs, we would expect communities to be dominated by 'super-types' characterised by mainly positive trait expressions. However, it has proven difficult to find strong empirical evidence for such trade-offs in species-rich communities. We developed a spatially explicit, rule-based and individual-based stochastic model to explore the importance of low-dimensional trade-offs. This model simulates the community dynamics of 288 virtual plant functional types (PFTs), each of which is described by seven life history traits. We consider trait combinations that fit into the trade-off concept, as well as super-types with little or no energy constraints or resource limitations, and weak PFTs, which do not exploit resources efficiently. The model is parameterised using data from a fire-prone, species-rich Mediterranean-type shrubland in southwestern Australia. We performed an exclusion experiment, where we sequentially removed the strongest PFT in the simulation and studied the remaining communities. We analysed the impact of traits on performance of PFTs in the exclusion experiment with standard and boosted regression trees. Regression tree analysis of the simulation results showed that the trade-off concept is necessary for PFT viability in the case of weak trait expression combinations such as low seed production or small seeds. However, species richness and diversity can be high despite the presence of super-types. Furthermore, the exclusion of super-types does not necessarily lead to a large increase in PFT richness and diversity. We conclude that low-dimensional trade-offs do not provide explanations for multi-species co-existence contrary to the prediction of many conceptual models.
We analyzed relative sensitivities of small- and medium-sized carnivores to livestock husbandry (stocking rates and predator control) in Kalahari, South Africa, rangelands at a regional scale. We monitored small carnivores using track counts on 22 Kalahari farms across a land-use gradient ranging from low to high stocking rates and also interviewed each farm manager to identify farmers" perception of small carnivores as potential predators for livestock. We recorded 12 species of small- and medium-sized carnivores across 22 Kalahari farms. Stocking rate was the most important driving variable for local carnivore abundance. Abundance of all species was lowest on farms where stocking rate was high. Most farm managers perceived medium-sized carnivores, in particular, African wildcat (Felis silvestris lybica), black-backed jackal (Canis mesomelas), and caracal (Caracal caracal), as potential predators of livestock. Multiple regression analysis shows that black-backed jackal, African wildcat, and caracal were negatively affected by predator control measures, whereas bat-eared fox (Otocyon megalotis), cape fox (Vulpes chama), and small-spotted genet (Genetta genetta) were positively affected. Our results show a need for expanding research and conservation activities toward small- and medium-sized carnivores in southern African savannah rangelands. We, therefore, suggest developing a monitoring program combining passive tracking with indigenous knowledge of local Khoisan Bushmen to monitor carnivore populations, and we recommend additional predator removal experiments that manipulate predator densities.