@article{ForkuorHounkpatinWelpetal.2017,
  author    = {Forkuor, Gerald and Hounkpatin, Ozias K.L. and Welp, Gerhard and Thiel, Michael},
  title     = {High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models},
  series = {PLOS One},
  volume    = {12},
  journal   = {PLOS One},
  number    = {1},
  doi       = {10.1371/journal.pone.0170478},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180978},
  pages     = {21},
  year      = {2017},
  abstract  = {Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.},
  language  = {en}
}
@article{DrescherKleinSchmittetal.2019,
  author    = {Drescher, Nora and Klein, Alexandra-Maria and Schmitt, Thomas and Leonhardt, Sara Diana},
  title     = {A clue on bee glue: New insight into the sources and factors driving resin intake in honeybees (Apis mellifera)},
  series = {PLoS ONE},
  volume    = {14},
  journal   = {PLoS ONE},
  number    = {2},
  doi       = {10.1371/journal.pone.0210594},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200935},
  pages     = {e0210594},
  year      = {2019},
  abstract  = {Honeybees (Apis mellifera) are threatened by numerous pathogens and parasites. To prevent infections they apply cooperative behavioral defenses, such as allo-grooming and hygiene, or they use antimicrobial plant resin. Resin is a chemically complex and highly variable mixture of many bioactive compounds. Bees collect the sticky material from different plant species and use it for nest construction and protection. Despite its importance for colony health, comparatively little is known about the precise origins and variability in resin spectra collected by honeybees. To identify the botanical resin sources of A. mellifera in Western Europe we chemically compared resin loads of individual foragers and tree resins. We further examined the resin intake of 25 colonies from five different apiaries to assess the effect of location on variation in the spectra of collected resin. Across all colonies and apiaries, seven distinct resin types were categorized according to their color and chemical composition. Matches between bee-collected resin and tree resin indicated that bees used poplar (Populus balsamifera, P. x canadensis), birch (Betula alba), horse chestnut (Aesculus hippocastanum) and coniferous trees (either Picea abies or Pinus sylvestris) as resin sources. Our data reveal that honeybees collect a comparatively broad and variable spectrum of resin sources, thus assuring protection against a variety of antagonists sensitive to different resins and/or compounds. We further unravel distinct preferences for specific resins and resin chemotypes, indicating that honeybees selectively search for bioactive resin compounds.},
  language  = {en}
}