@article{ArvidssonPerezRodriguezMuellerRoeber2011,
  author    = {Arvidsson, Samuel Janne and Perez-Rodriguez, Paulino and M{\"u}ller-R{\"o}ber, Bernd},
  title     = {A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modeling for robust quantification of genotype effects},
  series = {New phytologist : international journal of plant science},
  volume    = {191},
  journal   = {New phytologist : international journal of plant science},
  number    = {3},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0028-646X},
  doi       = {10.1111/j.1469-8137.2011.03756.x},
  pages     = {895 -- 907},
  year      = {2011},
  abstract  = {To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes. The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times. Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2\%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9. We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.},
  language  = {en}
}
@misc{NikoloskivanDongen2011,
  author    = {Nikoloski, Zoran and van Dongen, Joost T.},
  title     = {Modeling alternatives for interpreting the change in oxygen-consumption rates during hypoxic conditions},
  series = {New phytologist : international journal of plant science},
  volume    = {190},
  journal   = {New phytologist : international journal of plant science},
  number    = {2},
  publisher = {Wiley-Blackwell},
  address   = {Malden},
  issn      = {0028-646X},
  doi       = {10.1111/j.1469-8137.2011.03674.x},
  pages     = {273 -- 276},
  year      = {2011},
  language  = {en}
}
@article{StillmanRailsbackGiskeetal.2015,
  author    = {Stillman, Richard A. and Railsback, Steven Floyd and Giske, Jarl and Berger, Uta and Grimm, Volker},
  title     = {Making Predictions in a Changing World: The Benefits of Individual-Based Ecology},
  series = {Bioscience},
  volume    = {65},
  journal   = {Bioscience},
  number    = {2},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0006-3568},
  doi       = {10.1093/biosci/biu192},
  pages     = {140 -- 150},
  year      = {2015},
  abstract  = {Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research.},
  language  = {en}
}
@article{HornBecherJohstetal.2020,
  author    = {Horn, Juliane and Becher, Matthias A. and Johst, Karin and Kennedy, Peter J. and Osborne, Juliet L. and Radchuk, Viktoriia and Grimm, Volker},
  title     = {Honey bee colony performance affected by crop diversity and farmland structure},
  series = {Ecological applications},
  volume    = {31},
  journal   = {Ecological applications},
  number    = {1},
  publisher = {Wiley Periodicals LLC},
  address   = {Washington DC},
  issn      = {1939-5582},
  doi       = {10.1002/eap.2216},
  pages     = {1 -- 22},
  year      = {2020},
  abstract  = {Forage availability has been suggested as one driver of the observed decline in honey bees. However, little is known about the effects of its spatiotemporal variation on colony success. We present a modeling framework for assessing honey bee colony viability in cropping systems. Based on two real farmland structures, we developed a landscape generator to design cropping systems varying in crop species identity, diversity, and relative abundance. The landscape scenarios generated were evaluated using the existing honey bee colony model BEEHAVE, which links foraging to in-hive dynamics. We thereby explored how different cropping systems determine spatiotemporal forage availability and, in turn, honey bee colony viability (e.g., time to extinction, TTE) and resilience (indicated by, e.g., brood mortality). To assess overall colony viability, we developed metrics,P(H)andP(P,)which quantified how much nectar and pollen provided by a cropping system per year was converted into a colony's adult worker population. Both crop species identity and diversity determined the temporal continuity in nectar and pollen supply and thus colony viability. Overall farmland structure and relative crop abundance were less important, but details mattered. For monocultures and for four-crop species systems composed of cereals, oilseed rape, maize, and sunflower,P(H)andP(P)were below the viability threshold. Such cropping systems showed frequent, badly timed, and prolonged forage gaps leading to detrimental cascading effects on life stages and in-hive work force, which critically reduced colony resilience. Four-crop systems composed of rye-grass-dandelion pasture, trefoil-grass pasture, sunflower, and phacelia ensured continuous nectar and pollen supply resulting in TTE > 5 yr, andP(H)(269.5 kg) andP(P)(108 kg) being above viability thresholds for 5 yr. Overall, trefoil-grass pasture, oilseed rape, buckwheat, and phacelia improved the temporal continuity in forage supply and colony's viability. Our results are hypothetical as they are obtained from simplified landscape settings, but they nevertheless match empirical observations, in particular the viability threshold. Our framework can be used to assess the effects of cropping systems on honey bee viability and to develop land-use strategies that help maintain pollination services by avoiding prolonged and badly timed forage gaps.},
  language  = {en}
}