African elephants (Loxodonta africana) amplify browse heterogeneity in African savanna

There is a growing concern that the feeding habits of the African elephant, which include pushing over, uprooting and snapping trees, may have a negative impact on other herbivores. Browsed trees are known to respond by either increasing production (shoots and leaves) or defence (secondary compounds). It is not clear, however, what proportion of the browsed biomass can be made available at lower feeding heights after a tree is pushed over or snapped; thus, it is also unclear how the forage quality is affected. In a field survey in Kruger National Park, South Africa, 708 Mopane trees were measured over four elephant utilization categories: snapped trees, pushed-over trees, uprooted trees and control trees. The elephants' impact on the leaf biomass distribution was quantified, and the forage quality (Ca, P, K and Mg, N, digestibility and condensed tannin [CT] concentrations) were analyzed. Pushed-over and uprooted trees had the maximum leaf biomass at lower heights (2 m). In all three utilization categories, the minimum leaf biomass was seven times higher than it was for control trees at a height of below 1 m. Leaf nitrogen content increased in all three categories and was significantly higher in snapped trees. CT concentrations increased slightly in all trees that were utilized by elephants, especially on granitic soils in the dry season. The results provide the insight that elephants facilitate the redistribution and availability of browse and improve the quality, which may positively affect small browsing herbivores

Differentiation of plant age in grasses using remote sensing

Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems

Differentiation of plant age in grasses using remote sensing

Phenological or plant age classification across a landscape allows for examination of micro-topographical effects on plant growth, improvement in the accuracy of species discrimination, and will improve our understanding of the spatial variation in plant growth. In this paper six vegetation indices used in phenological studies (including the newly proposed PhIX index) were analysed for their ability to statistically differentiate grasses of different ages in the sequence of their development. Spectra of grasses of different ages were collected from a greenhouse study. These were used to determine if NDVI, NDWI, CAI, EVI, EVI2 and the newly proposed PhIX index could sequentially discriminate grasses of different ages, and subsequently classify grasses into their respective age category. The PhIX index was defined as: (An VNIR+ log(An SWIR2))/(An VNIR- log(An SWIR2)), where An VNIRand An SWIR2are the respective nor- malised areas under the continuum removed reflectance curve within the VNIR (500-800 nm) and SWIR2 (2000-2210 nm) regions. The PhIX index was found to produce the highest phenological classification accuracy (Overall Accuracy: 79%, and Kappa Accuracy: 75%) and similar to the NDVI, EVI and EVI2 indices it statistically sequentially separates out the developmental age classes. Discrimination between seedling and dormant age classes and the adult and flowering classes was problematic for most of the tested indices. Combining information from the visible near infrared (VNIR) and shortwave infrared region (SWIR) region into a single phenological index captures the phenological changes associated with plant pigments and the ligno-cellulose absorption feature, providing a robust method to discriminate the age classes of grasses. This work provides a valuable contribution into mapping spatial variation and monitoring plant growth across savanna and grassland ecosystems

Spatial distribution of lion kills determined by the water dependency of prey species

Predation risk from lions (Panthera leo) has been linked to habitat characteristics and availability and traits of prey. We separated the effects of vegetation density and the presence of drinking water by analyzing locations of lion kills in relation to rivers with dense vegetation, which offer good lion stalking opportunities, and artificial water points with low vegetation density. The spatial distribution of lion kills was studied at the Klaserie Private Nature Reserve, South Africa. The distance between 215 lion kills and the nearest water source was analyzed using generalized linear models. Lions selected medium-sized prey species. Lion kills were closer to rivers and to artificial water points than expected by random distribution of the kills. Water that attracted prey, and not the vegetation density in riverine areas, increased predation risk, with kills of buffalo (Syncerus caffer), kudu (Tragelaphus strepsiceros), and wildebeest (Connochaetes taurinus) as water-dependent prey species. Traits of prey species, including feeding type (food habits), digestion type (ruminant or nonruminant), or body size, did not explain locations of lion kills, and no seasonal patterns in lion kills were apparent. We argue that the cascading impact of lions on local mammal assemblages is spatially heterogeneou

Dry season mapping of savanna forage quality, using the hyperspectral Carnegie airborne observatory sensor

Forage quality within an African savanna depends upon limiting nutrients (nitrogen and phosphorus) and nutrients that constrain the intake rates (non-digestible fibre) of herbivores. These forage quality nutrients are particularly crucial in the dry season when concentrations of limiting nutrients decline and non-digestible fibres increase. Using artificial neural networks we test the ability of a new imaging spectrometer (CAO Alpha sensor), both alone and in combination with ancillary data, to map quantities of grass forage nutrients in the early dry season within an African savanna. Respectively 65%, 57% and 41%, of the variance in fibre, phosphorus and nitrogen concentrations were explained. We found that all grass forage nutrients show response to fire and soil. Principal component analysis, not only reduced image dimensionality, but was a useful method for removing cross-track illumination effects in the CAO imagery. To further improve the mapping of forage nutrients in the dry season we suggest that spectra within the shortwave infrared (SWIR) region, or additional relevant ancillary data, are required

Dry season mapping of savanna forage quality, using the hyperspectral Carnegie

Forage quality within an African savanna depends upon limiting nutrients (nitrogen and phosphorus) and nutrients that constrain the intake rates (non-digestible fibre) of herbivores. These forage quality nutrients are particularly crucial in the dry season when concentrations of limiting nutrients decline and non-digestible fibres increase. Using artificial neural networks we test the ability of a new imaging spectrometer (CAO Alpha sensor), both alone and in combination with ancillary data, to map quantities of grass forage nutrients in the early dry season within an African savanna. Respectively 65%, 57% and 41%, of the variance in fibre, phosphorus and nitrogen concentrations were explained. We found that all grass forage nutrients show response to fire and soil. Principal component analysis, not only reduced image dimensionality, but was a useful method for removing cross-track illumination effects in the CAO imagery. To further improve the mapping of forage nutrients in the dry season we suggest that spectra within the shortwave infrared (SWIR) region, or additional relevant ancillary data, are required

Spatial distribution of lion kills determined by the water dependency of prey species

Predation risk from lions (Panthera leo) has been linked to habitat characteristics and availability and traits of prey. We separated the effects of vegetation density and the presence of drinking water by analyzing locations of lion kills in relation to rivers with dense vegetation, which offer good lion stalking opportunities, and artificial water points with low vegetation density. The spatial distribution of lion kills was studied at the Klaserie Private Nature Reserve, South Africa. The distance between 215 lion kills and the nearest water source was analyzed using generalized linear models. Lions selected medium-sized prey species. Lion kills were closer to rivers and to artificial water points than expected by random distribution of the kills. Water that attracted prey, and not the vegetation density in riverine areas, increased predation risk, with kills of buffalo (Syncerus caffer), kudu (Tragelaphus strepsiceros), and wildebeest (Connochaetes taurinus) as water-dependent prey species. Traits of prey species, including feeding type (food habits), digestion type (ruminant or nonruminant), or body size, did not explain locations of lion kills, and no seasonal patterns in lion kills were apparent. We argue that the cascading impact of lions on local mammal assemblages is spatially heterogeneou

Soil nutrient status determines how elephant utilize trees and shape environments

Elucidation of the mechanism determining the spatial scale of patch selection by herbivores has been complicated by the way in which resource availability at a specific scale is measured and by vigilance behaviour of the herbivores themselves. To reduce these complications, we studied patch selection by an animal with negligible predation risk, the African elephant. We introduce the concept of nutrient load as the product of patch size, number of patches and local patch nutrient concentration. Nutrient load provides a novel spatially explicit expression of the total available nutrients a herbivore can select from. We hypothesized that elephant would select nutrient-rich patches, based on the nutrient load per 2500m2 down to the individual plant scale, and that this selection will depend on the nitrogen and phosphorous contents of plants. We predicted that elephant would cause more adverse impact to trees of lower value to them in order to reach plant parts with higher nutrient concentrations such as bark and root. However, elephant should maintain nutrient-rich trees by inducing coppicing of trees through re-utilization of leaves. 5.Elephant patch selection was measured in a homogenous tree species stand by manipulating the spatial distribution of soil nutrients in a large field experiment using NPK fertilizer. Elephant were able to select nutrient-rich patches and utilized Colophospermum mopane trees inside these patches more than outside, at scales ranging from 2500 down to 100m2. Although both nitrogen and phosphorus contents of leaves from C. mopane trees were higher in fertilized and selected patches, patch choice correlated most strongly with nitrogen content. As predicted, stripping of leaves occurred more in nutrient-rich patches, while adverse impact such as uprooting of trees occurred more in nutrient-poor areas. Our results emphasize the necessity of including scale-dependent selectivity in foraging studies and how elephant foraging behaviour can be used as indicators of change in the availability of nutrients

Diet selection of African elephant over time shows changing optimization currency

Multiple factors determine diet selection of herbivores. However, in many diet studies selection of single nutrients is studied or optimization models are developed using only one currency. In this paper, we use linear programming to explain diet selection by African elephant based on plant availability and nutrient and deterrent content over time. Our results indicate that elephant at our study area maximized intake of phosphorus throughout the year, possibly in response to the deficiency of this nutrient in the region. After adjusting the model to incorporate the effects of this deficiency, elephant were found to maximize nitrogen intake during the wet season and energy during the dry season. We reason that the increased energy requirements during the dry season can be explained by seasonal changes in water availability and forage abundance. As forage abundance decrease into the dry season, elephant struggle to satisfy their large absolute food requirements. Adding to this restriction is the simultaneous decrease in plant and surface water availability, which force the elephant to seek out scarce surface water sources at high energy costs. During the wet season when food becomes more abundant and energy requirements are satisfied easier, elephant aim to maximize nitrogen intake for growth and reproduction. Our study contributes to the emerging theory on understanding foraging for multiple resource