@article{PielstroemRoces2013,
  author    = {Pielstr{\"o}m, Steffen and Roces, Flavio},
  title     = {Sequential Soil Transport and Its Influence on the Spatial Organisation of Collective Digging in Leaf-Cutting Ants},
  series = {PLoS ONE},
  journal   = {PLoS ONE},
  doi       = {10.1371/journal.pone.0057040},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96275},
  year      = {2013},
  abstract  = {The Chaco leaf-cutting ant Atta vollenweideri (Forel) inhabits large and deep subterranean nests composed of a large number of fungus and refuse chambers. The ants dispose of the excavated soil by forming small pellets that are carried to the surface. For ants in general, the organisation of underground soil transport during nest building remains completely unknown. In the laboratory, we investigated how soil pellets are formed and transported, and whether their occurrence influences the spatial organisation of collective digging. Similar to leaf transport, we discovered size matching between soil pellet mass and carrier mass. Workers observed while digging excavated pellets at a rate of 26 per hour. Each excavator deposited its pellets in an individual cluster, independently of the preferred deposition sites of other excavators. Soil pellets were transported sequentially over 2 m, and the transport involved up to 12 workers belonging to three functionally distinct groups: excavators, several short-distance carriers that dropped the collected pellets after a few centimetres, and long-distance, last carriers that reached the final deposition site. When initiating a new excavation, the proportion of long-distance carriers increased from 18\% to 45\% within the first five hours, and remained unchanged over more than 20 hours. Accumulated, freshly-excavated pellets significantly influenced the workers' decision where to start digging in a choice experiment. Thus, pellets temporarily accumulated as a result of their sequential transport provide cues that spatially organise collective nest excavation.},
  language  = {en}
}
@article{HuserRohwedderApostolopoulouetal.2012,
  author    = {Huser, Annina and Rohwedder, Astrid and Apostolopoulou, Anthi A. and Widmann, Annekathrin and Pfitzenmaier, Johanna E. and Maiolo, Elena M. and Selcho, Mareike and Pauls, Dennis and von Essen, Alina and Gupta, Tript and Sprecher, Simon G. and Birman, Serge and Riemensperger, Thomas and Stocker, Reinhard F. and Thum, Andreas S.},
  title     = {The Serotonergic Central Nervous System of the Drosophila Larva: Anatomy and Behavioral Function},
  series = {PLoS One},
  volume    = {7},
  journal   = {PLoS One},
  number    = {10},
  doi       = {10.1371/journal.pone.0047518},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-130437},
  pages     = {e47518},
  year      = {2012},
  abstract  = {The Drosophila larva has turned into a particularly simple model system for studying the neuronal basis of innate behaviors and higher brain functions. Neuronal networks involved in olfaction, gustation, vision and learning and memory have been described during the last decade, often up to the single-cell level. Thus, most of these sensory networks are substantially defined, from the sensory level up to third-order neurons. This is especially true for the olfactory system of the larva. Given the wealth of genetic tools in Drosophila it is now possible to address the question how modulatory systems interfere with sensory systems and affect learning and memory. Here we focus on the serotonergic system that was shown to be involved in mammalian and insect sensory perception as well as learning and memory. Larval studies suggested that the serotonergic system is involved in the modulation of olfaction, feeding, vision and heart rate regulation. In a dual anatomical and behavioral approach we describe the basic anatomy of the larval serotonergic system, down to the single-cell level. In parallel, by expressing apoptosis-inducing genes during embryonic and larval development, we ablate most of the serotonergic neurons within the larval central nervous system. When testing these animals for naive odor, sugar, salt and light perception, no profound phenotype was detectable; even appetitive and aversive learning was normal. Our results provide the first comprehensive description of the neuronal network of the larval serotonergic system. Moreover, they suggest that serotonin per se is not necessary for any of the behaviors tested. However, our data do not exclude that this system may modulate or fine-tune a wide set of behaviors, similar to its reported function in other insect species or in mammals. Based on our observations and the availability of a wide variety of genetic tools, this issue can now be addressed.},
  language  = {en}
}
@article{DhillonDahmsKuebertFlocketal.2023,
  author    = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Liepa, Adomas and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias},
  title     = {Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany},
  series = {Remote Sensing},
  volume    = {15},
  journal   = {Remote Sensing},
  number    = {6},
  issn      = {2072-4292},
  doi       = {10.3390/rs15061651},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311092},
  year      = {2023},
  abstract  = {Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and -0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.},
  language  = {en}
}