@article{NathanMonkArlinghausetal.2022,
  author    = {Nathan, Ran and Monk, Christopher T. and Arlinghaus, Robert and Adam, Timo and Al{\´o}s, Josep and Assaf, Michael and Baktoft, Henrik and Beardsworth, Christine E. and Bertram, Michael G. and Bijleveld, Allert and Brodin, Tomas and Brooks, Jill L. and Campos-Candela, Andrea and Cooke, Steven J. and Gjelland, Karl O. and Gupte, Pratik R. and Harel, Roi and Hellstrom, Gustav and Jeltsch, Florian and Killen, Shaun S. and Klefoth, Thomas and Langrock, Roland and Lennox, Robert J. and Lourie, Emmanuel and Madden, Joah R. and Orchan, Yotam and Pauwels, Ine S. and Riha, Milan and R{\"o}leke, Manuel and Schl{\"a}gel, Ulrike and Shohami, David and Signer, Johannes and Toledo, Sivan and Vilk, Ohad and Westrelin, Samuel and Whiteside, Mark A. and Jaric, Ivan},
  title     = {Big-data approaches lead to an increased understanding of the ecology of animal movement},
  series = {Science},
  volume    = {375},
  journal   = {Science},
  number    = {6582},
  publisher = {American Assoc. for the Advancement of Science},
  address   = {Washington},
  issn      = {0036-8075},
  doi       = {10.1126/science.abg1780},
  pages     = {734 -- +},
  year      = {2022},
  abstract  = {Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.},
  language  = {en}
}
@article{PohleAdamBeumer2022,
  author    = {Pohle, Jennifer and Adam, Timo and Beumer, Larissa},
  title     = {Flexible estimation of the state dwell-time distribution in hidden semi-Markov models},
  series = {Computational statistics \& data analysis},
  volume    = {172},
  journal   = {Computational statistics \& data analysis},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-9473},
  doi       = {10.1016/j.csda.2022.107479},
  pages     = {15},
  year      = {2022},
  abstract  = {Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN.},
  language  = {en}
}
@article{TiegsCostelloIskenetal.2019,
  author    = {Tiegs, Scott D. and Costello, David M. and Isken, Mark W. and Woodward, Guy and McIntyre, Peter B. and Gessner, Mark O. and Chauvet, Eric and Griffiths, Natalie A. and Flecker, Alex S. and Acuna, Vicenc and Albarino, Ricardo and Allen, Daniel C. and Alonso, Cecilia and Andino, Patricio and Arango, Clay and Aroviita, Jukka and Barbosa, Marcus V. M. and Barmuta, Leon A. and Baxter, Colden V. and Bell, Thomas D. C. and Bellinger, Brent and Boyero, Luz and Brown, Lee E. and Bruder, Andreas and Bruesewitz, Denise A. and Burdon, Francis J. and Callisto, Marcos and Canhoto, Cristina and Capps, Krista A. and Castillo, Maria M. and Clapcott, Joanne and Colas, Fanny and Colon-Gaud, Checo and Cornut, Julien and Crespo-Perez, Veronica and Cross, Wyatt F. and Culp, Joseph M. and Danger, Michael and Dangles, Olivier and de Eyto, Elvira and Derry, Alison M. and Diaz Villanueva, Veronica and Douglas, Michael M. and Elosegi, Arturo and Encalada, Andrea C. and Entrekin, Sally and Espinosa, Rodrigo and Ethaiya, Diana and Ferreira, Veronica and Ferriol, Carmen and Flanagan, Kyla M. and Fleituch, Tadeusz and Shah, Jennifer J. Follstad and Frainer, Andre and Friberg, Nikolai and Frost, Paul C. and Garcia, Erica A. and Lago, Liliana Garcia and Garcia Soto, Pavel Ernesto and Ghate, Sudeep and Giling, Darren P. and Gilmer, Alan and Goncalves, Jose Francisco and Gonzales, Rosario Karina and Graca, Manuel A. S. and Grace, Mike and Grossart, Hans-Peter and Guerold, Francois and Gulis, Vlad and Hepp, Luiz U. and Higgins, Scott and Hishi, Takuo and Huddart, Joseph and Hudson, John and Imberger, Samantha and Iniguez-Armijos, Carlos and Iwata, Tomoya and Janetski, David J. and Jennings, Eleanor and Kirkwood, Andrea E. and Koning, Aaron A. and Kosten, Sarian and Kuehn, Kevin A. and Laudon, Hjalmar and Leavitt, Peter R. and Lemes da Silva, Aurea L. and Leroux, Shawn J. and Leroy, Carri J. and Lisi, Peter J. and MacKenzie, Richard and Marcarelli, Amy M. and Masese, Frank O. and Mckie, Brendan G. and Oliveira Medeiros, Adriana and Meissner, Kristian and Milisa, Marko and Mishra, Shailendra and Miyake, Yo and Moerke, Ashley and Mombrikotb, Shorok and Mooney, Rob and Moulton, Tim and Muotka, Timo and Negishi, Junjiro N. and Neres-Lima, Vinicius and Nieminen, Mika L. and Nimptsch, Jorge and Ondruch, Jakub and Paavola, Riku and Pardo, Isabel and Patrick, Christopher J. and Peeters, Edwin T. H. M. and Pozo, Jesus and Pringle, Catherine and Prussian, Aaron and Quenta, Estefania and Quesada, Antonio and Reid, Brian and Richardson, John S. and Rigosi, Anna and Rincon, Jose and Risnoveanu, Geta and Robinson, Christopher T. and Rodriguez-Gallego, Lorena and Royer, Todd V. and Rusak, James A. and Santamans, Anna C. and Selmeczy, Geza B. and Simiyu, Gelas and Skuja, Agnija and Smykla, Jerzy and Sridhar, Kandikere R. and Sponseller, Ryan and Stoler, Aaron and Swan, Christopher M. and Szlag, David and Teixeira-de Mello, Franco and Tonkin, Jonathan D. and Uusheimo, Sari and Veach, Allison M. and Vilbaste, Sirje and Vought, Lena B. M. and Wang, Chiao-Ping and Webster, Jackson R. and Wilson, Paul B. and Woelfl, Stefan and Xenopoulos, Marguerite A. and Yates, Adam G. and Yoshimura, Chihiro and Yule, Catherine M. and Zhang, Yixin X. and Zwart, Jacob A.},
  title     = {Global patterns and drivers of ecosystem functioning in rivers and riparian zones},
  series = {Science Advances},
  volume    = {5},
  journal   = {Science Advances},
  number    = {1},
  publisher = {American Assoc. for the Advancement of Science},
  address   = {Washington},
  issn      = {2375-2548},
  doi       = {10.1126/sciadv.aav0486},
  pages     = {8},
  year      = {2019},
  abstract  = {River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth's biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented "next-generation biomonitoring" by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.},
  language  = {en}
}
@article{MettlerMuehlhausHemmeetal.2014,
  author    = {Mettler, Tabea and M{\"u}hlhaus, Timo and Hemme, Dorothea and Sch{\"o}ttler, Mark Aurel and Rupprecht, Jens and Idoine, Adam and Veyel, Daniel and Pal, Sunil Kumar and Yaneva-Roder, Liliya and Winck, Flavia Vischi and Sommer, Frederik and Vosloh, Daniel and Seiwert, Bettina and Erban, Alexander and Burgos, Asdrubal and Arvidsson, Samuel Janne and Schoenfelder, Stephanie and Arnold, Anne and Guenther, Manuela and Krause, Ursula and Lohse, Marc and Kopka, Joachim and Nikoloski, Zoran and M{\"u}ller-R{\"o}ber, Bernd and Willmitzer, Lothar and Bock, Ralph and Schroda, Michael and Stitt, Mark},
  title     = {Systems analysis of the response of photosynthesis, metabolism, and growth to an increase in irradiance in the photosynthetic model organism chlamydomonas reinhardtii},
  series = {The plant cell},
  volume    = {26},
  journal   = {The plant cell},
  number    = {6},
  publisher = {American Society of Plant Physiologists},
  address   = {Rockville},
  issn      = {1040-4651},
  doi       = {10.1105/tpc.114.124537},
  pages     = {2310 -- 2350},
  year      = {2014},
  abstract  = {We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R-2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance.},
  language  = {en}
}