Potential of Airborne LiDAR Derived Vegetation Structure for the Prediction of Animal Species Richness at Mount Kilimanjaro

The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results

Airorne LiDAR and Hyperspectral data of the southern slopes of Mt. Kilimanjaro, Tanzania

The LiDAR and hyperspectral data set were acquired during two missions with a Riegl LMS-Q780 sensor and a HYSPEX imaging spectrometer (400 nm to 1,000 nm; 158 bands). The first campaign was flown between March 25 to March 27 2015 and covered the study area below 2,500 m a.s.l, the second between November 10 and November 13 2016 and covered the remaining study plots. To read more about the LiDAR data please refer: doi:10.3390/rs14030786

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

Monitoring of education for sustainable development in Germany – insights from early childhood education, school and higher education

Singer-Brodowski M, Brock A, Etzkorn N, Otte I. Monitoring of education for sustainable development in Germany – insights from early childhood education, school and higher education. Environmental Education Research. 2019;25(4):492-507

Seasonal and long-term vegetation dynamics from 1-km GIMMS-based NDVI time series at Mt. Kilimanjaro, Tanzania

This dataset consists of GeoTIFF fille for GIMMS NDVI downscaled record (1982–2011) that was resampled from 8 km to 1 km spatial resolution

Raw climate station data for the southern slopes of Kilimanjaro, Tanzania

The climate station network was set up on the southern slopes of Kilimanjaro, Tanzania, in 2010 and presents the recorded characteristics of air temperature, air humidity, and precipitation from both a plot-based and area-wide perspectives. The station set-up followed a hierarchical approach covering an elevation as well as a land-use disturbance gradient. It consisted 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 extended the long-term recordings of A. Hemp which has installed and maintained up to 117 multi-month accumulating rainfall buckets in the region since 1997

Monthly maps of air temperature and air humidity of the southern slopes of Kilimanjaro, Tanzania

Monthly maps of air temperature and air humidity of the southern slopes of Kilimanjaro, Tanzania. The dataset is part of our study on eco‐meteorological characteristics of the southern slopes of Kilimanjaro, Tanzania ([https://doi.org/10.1002/joc.4552]). (1) ta200_kriging.zip The dataset contains interpolate monthly air temperature maps using universal kriging with elevation, aspect, slope, sky‐view factor and mean monthly normalized difference vegetation index (NDVI) as external drift variables. This corresponds to step 5 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. (2) rh200_kriging.zip The dataset contains interpolate monthly air humidity maps using universal kriging with elevation, aspect, slope, sky‐view factor and mean monthly normalized difference vegetation index (NDVI) as external drift variables. This corresponds to step 5 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. (3) ta200_kriging_multi-year_average.zip and rh200_kriging_multi-year_average.zip The dataset contains multi-year monthly averages from (1) and (2) and a map of the multi-year annual mean air temperature and humidity. This corresponds to step 6 in chapter 3.1 of [https://doi.org/10.1002/joc.4552]. For the datasets (1) and (2), we used 5-min measurements between 2011 and 2014 of 52 climate stations that distributed across the southern slopes of Mt. Kilimanjaro. We aggregated the 5-min measurements to hourly observations and filled existing gaps of up to 1 year through multivariate regression using the five nearest stations. We aggregated the gap-filled hourly data to daily averages if at least 22 h of valid records exist for that day at a specific station. Finally, we aggregated the daily averages to monthly averages if a single month has at least 20 valid daily records

Stable isotope composition of atmospheric water input between 2012 and 2014 at the southern slopes of Mt. Kilimanjaro, Tanzania

The dataset provides detailed information about the stable isotope composition of different precipitation types (rainfall, fog, throughfall). It was manually collected on up to 9 study plots generally on a weekly basis between November 2012 and November 2014.The following map shows the distribution of the study plots on the southern slopes of Mt. Kilimanjaro. The study plots span across an altitude gradient rising from 950 m to nearly 4,000 m a.s.l. The plot IDs are the ones used within the respective research group. Moisture sources (- 96 hours) of the isotope samples were estimated using backward trajectory computations with the HYSPLIT model (https://www.ready.noaa.gov/HYSPLIT.php) and the R opentraj package (Thalles Santos Silva (2014). opentraj: Tools for Creating and Analysing Air Trajectory Data. R package version 1.0. https://cran.r-project.org/package=opentraj). Reanalysis data was taken from NCEP/NCAR version 2 ( https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/reanalysis-1-reanalysis-2)

Data from: Heterogeneous patterns of abundance of epigeic arthropod taxa along a major elevation gradient

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