Abstract Kilimanjaro has a large variety of forest types over an altitudinal range of 3000 m containing over 1200 vascular plant species. Montane Ocotea forests occur on the wet southern slope. Cassipourea and Juniperus forests grow on the dry northern slope. Subalpine Erica forests at 4100 m represent the highest elevation cloud forests in Africa. In contrast to this enormous biodiversity, the degree of endemism is low. However, forest relicts in the deepest valleys of the cultivated lower areas suggest that a rich forest flora inhabited Mt Kilimanjaro in the past, with restricted‐range species otherwise only known from the Eastern Arc mountains. The low degree of endemism on Kilimanjaro may result from destruction of lower altitude forest rather than the relatively young age of the mountain. Another feature of the forests of Kilimanjaro is the absence of a bamboo zone, which occurs on all other tall mountains in East Africa with a similarly high rainfall. Sinarundinaria alpina stands are favoured by elephants and buffaloes. On Kilimanjaro these megaherbivores occur on the northern slopes, where it is too dry for a large bamboo zone to develop. They are excluded from the wet southern slope forests by topography and humans, who have cultivated the foothills for at least 2000 years. This interplay of biotic and abiotic factors could explain not only the lack of a bamboo zone on Kilimanjaro but also offers possible explanations for the patterns of diversity and endemism. Kilimanjaro's forests can therefore serve as a striking example of the large and long‐lasting influence of both animals and humans on the African landscape.
Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements. The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8 m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features. Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree. Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.
This dataset contains the digitized treatments in Plazi based on the original journal article Hemp, Claudia, Heller, Klaus-Gerhard, Warchalowska-Śliwa, Elzbieta, Hemp, Andreas (2016): Spotted males, uniform females and the lowest chromosome number in Tettigoniids recorded: Review of the genus Gonatoxia Karsch (Orthoptera, Phaneropterinae). Deutsche Entomologische Zeitschrift 2: 271-286, DOI: http://dx.doi.org/10.3897/dez.63.10799, URL: http://dx.doi.org/10.3897/dez.63.10799
Abstract. Lake Challa (3°19' S, 37°42' E) is a steep-sided crater lake situated in equatorial East Africa, a tropical semi-arid area with bimodal rainfall pattern. Plants in this region are exposed to a prolonged dry season and we investigated if (1) these plants show spatial variability and temporal shifts in their water source use; (2) seasonal differences in the isotopic composition of precipitation are reflected in xylem water; and (3) plant family, growth form, leaf phenology, habitat and season influence the xylem-to-leaf water deuterium enrichment. In this study, the δ2H and δ18O of precipitation, lake water, groundwater, plant xylem water and plant leaf water were measured across different plant species, seasons and plant habitats in the vicinity of Lake Challa. We found that plants rely mostly on water from the "short" rains falling from October to December (northeastern monsoon), as these recharge the soil after the long dry season. This plant-available water pool is only slightly replenished by the "long" rains falling from February to May (southeastern monsoon), in agreement with the "two water world" hypothesis according to which plants rely on a static water pool while a mobile water pool recharges the groundwater. Trees at the lake shore and on the crater rim use more evaporated water than shrubs in the same habitats, suggesting that trees tap water from the topsoil where the nutrient content is highest. Plants at the lake shore rely on a water source admixed with lake water. The enrichment in deuterium from xylem water to leaf water averages 24 ± 28 ‰. According to our results, plant species and their associated leaf phenology are the primary factors influencing this enrichment factor, while growth form and season have negligible effects.
Significance Identifying and explaining regional differences in tropical forest dynamics, structure, diversity, and composition are critical for anticipating region-specific responses to global environmental change. Floristic classifications are of fundamental importance for these efforts. Here we provide a global tropical forest classification that is explicitly based on community evolutionary similarity, resulting in identification of five major tropical forest regions and their relationships: ( i ) Indo-Pacific, ( ii ) Subtropical, ( iii ) African, ( iv ) American, and ( v ) Dry forests. African and American forests are grouped, reflecting their former western Gondwanan connection, while Indo-Pacific forests range from eastern Africa and Madagascar to Australia and the Pacific. The connection between northern-hemisphere Asian and American forests is confirmed, while Dry forests are identified as a single tropical biome.
Abstract Aim To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships. Location Global. Time period Recent. Major taxa studied Trees. Methods We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community‐weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes). Results Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within‐biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models. Main conclusions Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within‐biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.