Tim Appelhans

Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level.

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.

Mapping Daily Air Temperature for Antarctica Based on MODIS LST

Spatial predictions of near-surface air temperature ( T a i r ) 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 a i r 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 a i r 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 a i r using LST and the month of the year as predictor variables. Using the trained model, T a i r 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 a i r from LST ( R 2 of 0.64, RMSE of 11.02 ∘ C). Using the trained model allowed creating time series of T a i r over Antarctica for 2013. Extending the training data by including more years will allow developing time series of T a i r from 2000 on.

Precipitation Estimates from MSG SEVIRI Daytime, Nighttime, and Twilight Data with Random Forests

AbstractA 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 dete...

A Comparative Study of Cross-Product NDVI Dynamics in the Kilimanjaro Region—A Matter of Sensor, Degradation Calibration, and Significance

While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p < 0.05 ( 52.6 % ) than for p < 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends.

Mesoscale controls on particulate matter pollution for a mega city in a semi-arid mountainous environment: Tehran, Iran

Tehran, the mega-city capital of Iran, suffers from high concentrations of PM10 throughout the year. Emissions from transport combined with mesoscale atmospheric features related to the mountainous terrain lead to a distinctive diurnal pattern in concentrations. Air quality data from monitoring stations show that the highest concentrations of PM10 are in the morning and evening periods, associated with peak traffic volumes and transition in local meteorology from a stable nocturnal down-slope flow to a daytime upslope regime. There is a clear north-south gradient in PM10 associated with the transport by down-slope winds. A year-long simulation with The Air Pollution Model (TAPM) confirms the mesoscale meteorological regime over Tehran. Simulation results indicate that the peaks in traffic flow and the transition between meteorological regimes contribute to daily PM10 peaks, with the transition playing a relatively minor role.

Spatial Patterns of Sea Surface Temperature Influences on East African Precipitation as Revealed by Empirical Orthogonal Teleconnections

East Africa is characterized by a rather dry annual precipitation climatology with two distinct rainy seasons. In order to investigate sea surface temperature driven precipitation anomalies for the region we use the algorithm of empirical orthogonal teleconnection analysis as a data mining tool. We investigate the entire East African domain as well as 5 smaller sub-regions mainly located in areas of mountainous terrain. In searching for influential sea surface temperature patterns we do not focus any particular season or oceanic region. Furthermore, we investigate different time lags from zero to twelve months. The strongest influence is identified for the immediate (i.e. non-lagged) influences of the Indian Ocean in close vicinity to the East African coast. None of the most important modes are located in the tropical Pacific Ocean, though the region is sometimes coupled with the Indian Ocean basin. Furthermore, we identify a region in the southern Indian Ocean around the Kerguelen Plateau which has not yet been reported in the literature with regard to precipitation modulation in East Africa. Finally, it is observed that not all regions in East Africa are equally influenced by the identified patterns.

Multidecadal Trends and Interannual Variability of Rainfall as Observed from Five Lowland Stations at Mt. Kilimanjaro, Tanzania

AbstractFuture 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 evid...

Modelling emission trends from non-constant time series of PM10 concentrations in Christchurch, New Zealand

This paper presents an attempt to model the trend of emissions through analysis of a time series of PPM10 concentrations in Christchurch, New Zealand. Emissions are not constant over time, but show high seasonality. Fluctuations are removed by creating a time series in which concentrations do not show dependency on ambient air temperature. Remaining meteorological influences are removed through multiple linear regression. Finally, a moving average filter is applied to reveal the low-frequency trend in the residuals of the meteorologically adjusted time series. The modelled trend shows a peak in emissions in 2001-2002 with a steady decrease thereafter.

High-Resolution MaxEnt Modelling of Habitat Suitability for Maternity Colonies of the Barbastelle Bat Barbastella barbastellus (Schreber, 1774) in Rhineland-Palatinate, Germany

The barbastelle bat Barbastella barbastellus (Schreber, 1774), probably one of the rarest of western European bat species, has suffered from substantial population declines over the last several decades. In fact, it was believed to be extinct within the federal state of Rhineland-Palatinate (western Germany) until the discovery of a maternity colony in 2004. More reproduction sites have since been found, which demonstrates a substantial knowledge gap about the actual distribution and abundance of the species in Rhineland-Palatinate. Suitable habitats for maternity colonies are crucial for the survival of a population and knowledge of their location is critical for conservation. We modelled the suitability of habitats for use by maternity colonies in Rhineland-Palatinate based on high-resolution data of the forest structure and roosting sites of maternity colonies, using the presence-only machine learning approach MaxEnt. In addition to statistical tests of the model performance, we analysed general occurrence surveys from the last few years for evidence of barbastelle and conducted an in-situ survey on one of the sites identified as highly suitable by the model, but for which no occurrence records exist. On this site, we discovered a new maternity colony. Analysis of third-party surveys resulted in two recently discovered colonies, which shows the barbastelles range is not restricted to the area south of the Moselle River. The results of our study along with the scattered pattern of potentially suitable locations for maternity colonies in the region challenge previous assumptions of the geographic distribution of barbastelle in Rhineland-Palatinate. This study demonstrates the potential of habitat suitability modelling in conservation ecology and the results may provide a basis for future preservation strategies in the region.

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

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 (Rnet) 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 Rnet, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R2 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 (R2 = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R2 = 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.