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Monitoring of climate variables such as air temperature is gaining increasing importance under climate change. This study aimed at developing an hourly gridded 3.5 × 3.5 km air temperature (Tair) data set for entire South Africa. In a Random Forest approach, MSG SEVIRI data from 2010 to 2014 were used and related to Tair measured by 78 weather stations. An external validation on new climate stations and years that were not used for model training indicated the ability of the model to predict Tair with a RMSE of 2.61 °C and a R2 of 0.89. The resulting model can be applied to the entire MSG SEVIRI time series since 2004. It hence allows for spatio-temporal pattern analysis as well as for the detection of trends which is relevant in the context of climate change.

Agriculture and the exploitation of natural resources have transformed tropical mountain ecosystems across the world, and the consequences of these transformations for biodiversity and ecosystem functioning are largely unknown1-3. Conclusions that are derived from studies in non-mountainous areas are not suitable for predicting the effects of land-use changes on tropical mountains because the climatic environment rapidly changes with elevation, which may mitigate or amplify the effects of land use4,5. It is of key importance to understand how the interplay of climate and land use constrains biodiversity and ecosystem functions to determine the consequences of global change for mountain ecosystems. Here we show that the interacting effects of climate and land use reshape elevational trends in biodiversity and ecosystem functions on Africa's largest mountain, Mount Kilimanjaro (Tanzania). We find that increasing land-use intensity causes larger losses of plant and animal species richness in the arid lowlands than in humid submontane and montane zones. Increases in land-use intensity are associated with significant changes in the composition of plant, animal and microorganism communities; stronger modifications of plant and animal communities occur in arid and humid ecosystems, respectively. Temperature, precipitation and land use jointly modulate soil properties, nutrient turnover, greenhouse gas emissions, plant biomass and productivity, as well as animal interactions. Our data suggest that the response of ecosystem functions to land-use intensity depends strongly on climate; more-severe changes in ecosystem functioning occur in the arid lowlands and the cold montane zone. Interactions between climate and land use explained-on average-54% of the variation in species richness, species composition and ecosystem functions, whereas only 30% of variation was related to single drivers. Our study reveals that climate can modulate the effects of land use on biodiversity and ecosystem functioning, and points to a lowered resistance of ecosystems in climatically challenging environments to ongoing land-use changes in tropical mountainous regions.

Bush encroachment is a highly relevant environmental issue in South African savannas, influencing ecological processes as well as the grazing capacity of the land. However the drivers of bush encroachment are not yet fully revealed which can partly be attributed to the problem that large-scale data of woody vegetation cover are missing. Using a multi-scale and a multi-sensor approach, this study aimed at providing the status of woody vegetation cover for the Molopo Area in South Africa. Training data for woody vegetation was derived from unsupervised classification of high-resolution aerial image tiles. To derive spatially continuous estimates of fractional woody cover for the entire Molopo Area, Sentinel-1 and Sentinel-2 data were applied in a machine learning based modelling approach. Therefore, a database of training samples was generated by aggregating the classified aerial image tiles to the geometry of the Sentinel data. A Random Forest algorithm with a forward feature variable selection was then trained to relate the spectral and radar information to fractional woody cover. The model was applied to make spatial predictions of fractional woody cover at 10 m resolution for the entire Molopo Area for the year 2016. Spatial cross-validation revealed a prediction error in fractional cover of 12%. The derived model and cover data show the potential for upcoming time series analysis of Sentinel-based woody cover estimates which can serve as a basis to bring new insights into the drivers of bush encroachment.

Air temperature is an important baseline parameter for terrestrial Antarctica in the context of patterns and processes in climatology, hydrology or ecology. There are still large uncertainties on how the Antarctic system responds to spatio-temporal variability of temperature. This can partly be attributed to the lack of high resolution datasets. In this paper, we present AntAir, a new dataset of gridded air temperatures in 1 km spatial and daily temporal resolution that is available since 2003. AntAir was created by modelling daily air temperature from MODIS land surface temperature using machine learning algorithms. Data from 70 weather stations was used as a reference. Daily temperatures could be estimated with a R2 of 0.91 and a RMSE of 5.07 °C validated on independent years. The performance to estimate the time series of a new spatial location was R2 = 0.78 and RMSE = 5.83 °C. Hence the high spatial and temporal resolution of the dataset as well as the high accuracy make AntAir an important baseline dataset for a wide range of applications in environmental science of Antarctica. The dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.902166 (daily, Meyer et al., 2019a) and https://doi.pangaea.de/10.1594/PANGAEA.902193 (monthly, Meyer et al., 2019b).

This first survey of rock shelters and settlements in the Bale Mountains in Ethiopia is a baseline assessment for further research into the settlement history of Africa’s largest alpine highlands. Extensive GPS-based mapping and interviews resulted in two detailed maps, a catalogue of profiles, and complete photographic documentation. In total, 331 rock shelters (four permanently inhabited, 51 seasonally inhabited, and 276 currently uninhabited) and 870 settlements (207 permanently inhabited, 449 seasonally inhabited, 214 uninhabited) were recorded together with information about the activities and livelihoods of the inhabitants of the current settlements. This 2015 study was part of the Ethiopian-European research project “The mountain exile hypothesis – how humans benefited from and re-shaped African high-altitude ecosystems during Quaternary climate changes” (DFG FOR 2358). It was designed to support future management plans in this internationally important conservation area that has recently faced increasing land-use pressure and the threat of degradation.

To understand the moisture regime at the southern slopes of Mt. Kilimanjaro, we analysed the isotopic variability of oxygen (δ18O) and hydrogen (δD) of rainfall, throughfall, and fog from a total of 2,140 samples collected weekly over 2 years at 9 study sites along an elevation transect ranging from 950 to 3,880 m above sea level. Precipitation in the Kilimanjaro tropical rainforests consists of a combination of rainfall, throughfall, and fog. We defined local meteoric water lines for all 3 precipitation types individually and the overall precipitation, δDprec = 7.45 (±0.05) × δ18Oprec + 13.61 (±0.20), n = 2,140, R2 = .91, p

Aim Gradients in climate and land use occur simultaneously in many of the Earth's ecosystems and thus collectively impact most ecological communities. Albeit climate and land use have potentially interacting effects on ecological communities that may exacerbate or ameliorate their individual effects, little is known about the effect of the climate–land use interaction on community composition. A better understanding of the interaction between climate and land use is essential to predict the impacts of environmental change on ecological communities. Location Mt. Kilimanjaro, Tanzania. Methods We quantified the community composition of bird species and feeding guilds on 64 study plots of 13 different habitat types along an elevational gradient from 870 to 4550 m a.s.l. We partitioned the variation in pairwise beta-diversity (βcc) of birds and its two additive components, abundance differences (βabu) and replacement (β−3), among the effects of temperature, land-use intensity and their interaction. Results Temperature and land use had synergistic effects on beta-diversity (βcc) of birds; that is, the combination of high temperature and high land-use intensity led to higher beta-diversity than expected from the sum of both individual effects. While temperature explained more of the variation in abundance differences (βabu), land use explained more of the variation in the replacement of individuals between species and feeding guilds (β−3), indicating that different processes drove avian beta-diversity along the temperature and land-use gradients. Main Conclusions Our results challenge previous studies that investigated the effects of climate and land use in isolation because disregarding their synergistic interaction underestimates the joint effect of climate and land use on biodiversity. A consideration of the synergy between climate and land use is essential for adequate predictions of the impact of global change on biodiversity.

Future rainfall dynamics in the Kilimanjaro region will mainly be influenced by both global climate and local land-cover change. An increase in rainfall is expected, but rising temperatures are also predicted for the ecosystem. In situ rainfall of five stations is analyzed to determine seasonal variability and multidecadal trends in the lowlands and lower elevations of the Kilimanjaro region. Monthly rainfall totals are obtained from the Tanzanian Meteorological Agency, from two mission stations, and from a sugar cane plantation. The datasets of the two mission stations cover time spans of 64 and 62 years, starting in 1940 and 1942, while rainfall data obtained from the Tanzanian Meteorological Agency and from the sugar cane plantation start in 1973 and 1974 and thus cover 40–41 years. In one out of five stations, a significant weak negative linear long-term trend in rainfall is observable, which is also evident in the other locations but is not significant. However, humid and dry decades are evident and seasonality has changed, especially during the long rains between March and May. El Niño–Southern Oscillation (ENSO) in combination with positive Indian Ocean dipole (IOD) leads to enhanced rainfall during the year of ENSO onset and the following year. During La Niña years, rainfall increases in the following year, while during the onset year rainfall patterns are more diverse. Positive IOD leads to enhanced rainfall amounts.

Species diversity is the variable most commonly studied in recent ecological research. Ecological processes, however, are driven by individuals and affected by their abundances. Understanding the variation in animal abundances along climatic gradients is important for predicting changes in ecosystem processes under global warming. High abundances make arthropods, despite their small body sizes, important actors in food webs, yet abundance distributions of major arthropod taxa along climatic gradients remain poorly documented. We sampled arthropod assemblages in disturbed and undisturbed vegetation types along an elevational gradient of 860–4550 m asl on the southern slopes of Mt. Kilimanjaro, Tanzania. In our analysis, we focused on 13 taxa of arthropods that represented three major functional groups: predators, herbivores, and decomposers. Abundance patterns were unimodal for most of the taxa and functional groups, including decomposer arthropods, and most of them peaked at low elevations in lower montane forest. When we assigned beetles to functional groups, however, decomposer beetle abundances declined almost linearly, and abundances of predator beetles (ca. 2400 m asl) and herbivore beetles (ca. 3000 m asl, undisturbed vegetation) peaked at higher elevations and exhibited unimodal patterns. Temperature, not primary productivity, was the best predictor of abundance for most of the taxa and groups. Disturbance was only of minor importance. Our results revealed different trends in the response of arthropod abundance along the elevational gradient that depended on the level of taxonomic and functional resolution. This highlights the need for more comparisons of different taxa along the same climatic gradients.

Current plant species diversity of the forest herb layer is influenced by site conditions, seed banks, stand age and historic canopy dynamics, induced for example by natural and anthropogenic disturbances. Long-term Landsat time series allow for analyzing forest canopy dynamics over several decades at a spatial resolution of 30 m. These dynamics have not been related to plant diversity in the herb layer of forests yet. Here, we related canopy layer dynamics of 132 one hectare temperate forest plots derived from Landsat time series (1985–2015) to herb layer plant species diversity observed in 2015. To quantitatively characterize the dynamics in the canopy over this period, we applied Symbolic Aggregate ApproXimation representation (SAX) to yearly NDVI time series of the plots and calculated the frequency of the representations of similar time series patterns, hereafter called SAX-metrics. We used the SAX-metrics as predictors to estimate with beta regression models the Simpson's diversity index of the herb layer of our forest plots subdivided into six forest types. Models including SAX-metrics, describing abrupt decreases in NDVI, and high, medium, or low NDVI values had pseudo R2 between 0.13 and 0.63. We conclude that disturbances, canopy closure, as well as stand age, as represented in NDVI time series, influence the diversity in the herb layer in five of the six examined forest types. Our study shows that information on historic canopy dynamics, detected from Landsat time series, can contribute to a better understanding of current herb layer diversity in some forest types. This study gives first indications on the potential of temporal metrics of Landsat time series in biodiversity research. peerReviewed

Tropical alpine ecosystems are identified as the most vulnerable to global environmental change, yet despite their sensitivity they are among the least studied ecosystems in the world. Despite its important role in constraining potential changes to the carbon balance, soil nitrogen (N) turnover and plant availability in high latitude and high altitude ecosystems is still poorly understood. Here we present a first time study on a tropical alpine Helichrysum ecosystem at Mt. Kilimanjaro, Tanzania, which lies at an altitude of 3880 m. Vegetation composition is characterized and major gross N turnover rates are investigated using the 15N pool dilution method for three different vegetation cover types. In addition greenhouse gas exchange (CO2, N2O and CH4) was manually measured using static chambers. Gross N turnover rates and soil CO2 and N2O emissions were generally lower than values reported for temperate ecosystems, but similar to tundra ecosystems. Gross N mineralization, NH4 + immobilization rates, and CO2 emissions were significantly higher on densely vegetated plots than on sparsely vegetated plots. Relative soil N retention was high and increased with vegetation cover, which suggests high competition for available soil N between microbes and plants. Due to high percolation rates, irrigation/rainfall has no impact on N turnover rates and greenhouse gas (GHG) emissions. While soil N2O fluxes were below the detection limit at all plots, soil respiration rates and CH4 uptake rates were higher at the more densely vegetated plots. Only soil respiration rates followed the pronounced diurnal course of air and soil temperature. Overall, our data show a tight N cycle dominated by closely coupled ammonification-NH4 +-immobilization, which is little prone to N losses. Warming could enhance vegetation cover and thus N turnover; however, only narrower C:N ratios due to atmospheric nitrogen deposition may open the N cycle of Helichrysum ecosystems.

Vegetation dynamics in the Kilimanjaro region, Tanzania, are subject to (i) global climate change and (ii) local land-cover change resulting from natural or anthropogenic disturbance. While recent climate models predict rising temperatures over East Africa throughout the 21st century, effects of land-use change on the local-scale water budget and, related therewith, on vegetation response are much more diverse. In addition, sea surface temperature anomalies in the Pacific (El Nino Southern Oscillation, ENSO) and Indian Ocean (Indian Ocean Dipole, IOD) are known to severely impact rainfall patterns and vegetation activity in the study area and possibly reinforce each other. Here we present long-term and seasonal vegetation dynamics derived from a GIMMS-based NDVI record resampled to 1 km spatial resolution and covering a 30-year period (1982–2011). In the long term, most of the upper mountain regions showed positive trends which was mainly attributed to vegetation recovery after disastrous fires during the outgoing 20th century. Along the western mountainside, by contrast, strong negative trends emerged as a consequence of fire-driven downward migration of Erica bush along the upper slopes and massive land conversion processes affecting the lower slopes. On the seasonal scale, a strong dependence of the regional vegetation on the effects of ENSO/IOD teleconnections became evident. Similar to previous findings on rainfall, the most beneficial effects occurred during concurrent El Nino/IOD events, while the impacts of La Nina were far less pronounced. To sum up, the newly created 1-km NDVI record proved capable of capturing long-term and seasonal vegetation patterns, which particularly applies for large-scale teleconnections, and thus provides an invaluable archive of decadal-scale vegetation dynamics in the study area.

This study introduces the set-up of a new meteorological station network on the southern slopes of Kilimanjaro, Tanzania, since 2010 and presents the recorded characteristics of air temperature, air humidity and precipitation in both a plot-based and area-wide perspectives. The station set-up follows a hierarchical approach covering an elevational as well as a land-use disturbance gradient. It consists of 52 basic stations measuring ambient air temperature and above-ground air humidity and 11 precipitation measurement sites, with recording intervals of 5 min. With respect to precipitation observations, the network extends the long-term recordings of A. Hemp who has installed and maintained up to 117 multi-month accumulating rainfall buckets in the region since 1997. The meteorological characteristics of the study region based on the derived data since 2010 are mostly in line with previous studies, although we see increased precipitation amounts at higher elevations during these years when compared with long-term means. We furthermore identify a mean annual condensation level at about 2300 m a.s.l. which has not been reported before. Finally, this is the first study to provide high resolution maps of mean monthly and mean annual temperature, humidity and precipitation for Kilimanjaro, which are of great value for geographically oriented meteorological or ecological investigations. Detailed performance statistics of the geo-statistical and machine learning techniques used for the gap filling of the recorded meteorological time series and their regionalization to the Kilimanjaro region indicate that the presented data sets provide reliable measurements of the meteorological reality at Kilimanjaro.

Agriculture and the exploitation of natural resources have transformed tropical mountain ecosystems across the world, and the consequences of these transformations for biodiversity and ecosystem functioning are largely unknown

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R 2 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R 2 = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R 2 = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.

The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities., Explaining species richness patterns is a key question in ecology. Peters et al. sample diverse plant and animal groups across elevation on Mt. Kilimanjaro to show that, while disparate factors drive distributions of individual taxa, diversity overall decreases with elevation, mostly driven by effects of temperature.

The natural abundances of stable oxygen (18O/16O) and hydrogen isotopes (2H/1H) are valuable proxies of climate changes in the past. Yet, to date no continuous δ18O and only few δ2H records are available from loess–paleosol sequences. Taking advantage of a recently developed method based on compound-specific δ18O analyses of hemicellulose sugar biomarkers in soils (Zech and Glaser, 2009. RCM 23, 3522-3532), we here present a first terrestrial δ18O biomarker record from an eolian permafrost paleosol sequence in NE-Siberia that covers the last ~ 220 ka. The δ18O values of the hemicellulose biomarkers arabinose and xylose range from 22.5 to 32.8‰ and from 21.3 to 31.9‰, respectively, and reveal systematic glacial–interglacial shifts. The modern topsoil and the interglacial paleosols exhibit more positive δ18O values, whereas the glacial paleosols are characterized by more negative δ18O values. This is in agreement with the δ2H record obtained for sedimentary n-alkane leaf wax biomarkers. We present a conceptual model for interpreting the combined δ18O and δ2H biomarker record. Based on this model, we suggest that both our δ18O and the δ2H records primarily reflect the temperature-controlled isotopic composition of paleoprecipitation modified by evaporative isotope enrichment of leaf water during transpiration. Considering fractionation factors during sugar and n-alkane biomarker biosynthesis allows reconstructing the leaf water isotopic composition and the deuterium excess of the leaf water. The deuterium excess may serve as proxy for evaporative enrichment and allows calculating relative humidity using a Craig–Gordon model. Accordingly, relative humidity in NE-Siberia was higher during marine isotope stage (MIS) 6 compared to MIS 2, 4 and 5d and thus could help explaining the much larger extent of the Late Saalian glaciation compared to the Weichselian glaciations. Using the Craig–Gordon model, we also calculated δ18O of the plant source water (δ18Osource water), which can be assumed to primarily reflect δ18O of paleoprecipitation. Our 220 ka δ18Osource water record is well in agreement with the δ18Odiatom record from Crater Lake El'gygytgyn in NE-Siberia and enables a regional paleoclimate reconstruction and interpretation. Accordingly, summer temperature was periodically warmer than at present during the Weichselian glacial period and there is a strong July insolation forcing of the summer temperature in the extremely continental study area. Overall, our study highlights the great potential of the novel hemicellulose biomarker δ18O method for paleoclimate reconstructions, especially when combined with δ2H analyses of n-alkane lipid biomarkers.

In climate science, teleconnection analysis has a long standing history as a means for describing regions that exhibit above average capability of explaining variance over time within a certain spatial domain (e.g., global). The most prominent example of a global coupled ocean-atmosphere teleconnection is the El Nin ?o Southern Oscillation. There are numerous signal decomposition methods for identifying such regions, the most widely used of which are (rotated) empirical orthogonal functions. First introduced by van den Dool, Saha, and Johansson (2000), empirical orthogonal teleconnections (EOT) denote a regression based approach that allows for straight-forward interpretation of the extracted modes. In this paper we present the R implementation of the original algorithm in the remote package. To highlight its usefulness, we provide three examples of potential use- case scenarios for the method including the replication of one of the original examples from van den Dool et al. (2000). Furthermore, we highlight the algorithms use for cross- correlations between two different geographic fields (identifying sea surface temperature drivers for precipitation), as well as statistical downscaling from coarse to fine grids (using Normalized Difference Vegetation Index fields).