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.

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Aim Understanding the mechanisms controlling variation in species richness along environmental gradients is one of the most important objectives in ecology. Resource availability is often considered as the major driver of animal diversity. However, in ectotherms, temperature might play a predominant role as it modulates metabolic rates and the access of animals to resources. Here, we investigate the relative importance of resource availability and temperature in determining the diversity pattern of bees along a 3.6‐km elevational gradient. Location Mount Kilimanjaro, Tanzania. Methods We assessed bee species richness and abundance with pan traps and floral resources with transect records on 60 study sites which were equally distributed over six near‐natural and six disturbed habitat types along an elevational gradient from 870 to 4550 m a.s.l. We used path analysis to disentangle the effects of temperature, precipitation, floral resource abundance, bee abundance and land use on bee species richness. In addition, we monitored flower visitation rates during transect walks at different elevations to evaluate the temperature dependence of bee–flower interactions. Results Bee species richness continuously declined with elevation in natural and disturbed habitats. While the abundance of floral resources had a significant but only weak effect on species richness, the effect of temperature was strong. Temperature had a strong positive effect on species richness that was not mediated by bee abundance and an indirect effect via bee abundances. We observed higher levels of bee–flower interactions at higher temperatures, supporting the hypothesis that temperature limits diversity by constraining resource exploitation in ectotherms. Main conclusions Temperature and the availability of resources shape species richness patterns along environmental gradients. In ectothermic organisms like bees temperature seems to have the more important role, as it both limits the access to resources (abundance‐mediated effect) and accelerates other (abundance‐independent) ecological and evolutionary processes that drive the maintenance and origination of diversity.

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.

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A new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3–8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data.

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Spatial predictions of near-surface air temperature (T air ) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather stations in Antarctica is sparse, remote sensing methods have large potential due to their capabilities and accessibility. Based on the MODIS land surface temperature (LST) data, T air at the exact time of satellite overpass was modelled at a spatial resolution of 1 km using data from 32 weather stations. The performance of a simple linear regression model to predict T air from LST was compared to the performance of three machine learning algorithms: Random Forest (RF), generalized boosted regression models (GBM) and Cubist. In addition to LST, auxiliary predictor variables were tested in these models. Their relevance was evaluated by a Cubist-based forward feature selection in conjunction with leave-one-station-out cross-validation to reduce the impact of spatial overfitting. GBM performed best to predict T air using LST and the month of the year as predictor variables. Using the trained model, T air could be estimated with a leave-one-station-out cross-validated R 2 of 0.71 and a RMSE of 10.51 • C. However, the machine learning approaches only slightly outperformed the simple linear estimation of T air from LST (R 2 of 0.64, RMSE of 11.02 • C). Using the trained model allowed creating time series of T air over Antarctica for 2013. Extending the training data by including more years will allow developing time series of T air from 2000 on.