@article{LudwigReissmannBeneckeetal.2015,
  author    = {Ludwig, Arne and Reissmann, Monika and Benecke, Norbert and Bellone, Rebecca and Sandoval-Castellanos, Edson and Cieslak, Michael and Gonz{\´a}lez-Fortes, Gloria M. and Morales-Muniz, Arturo and Hofreiter, Michael and Pruvost, Melanie},
  title     = {Twenty-five thousand years of fluctuating selection on leopard complex spotting and congenital night blindness in horses},
  series = {Philosophical transactions of the Royal Society of London : B, Biological sciences},
  volume    = {370},
  journal   = {Philosophical transactions of the Royal Society of London : B, Biological sciences},
  number    = {1660},
  publisher = {Royal Society},
  address   = {London},
  issn      = {0962-8436},
  doi       = {10.1098/rstb.2013.0386},
  pages     = {7},
  year      = {2015},
  abstract  = {Leopard complex spotting is inherited by the incompletely dominant locus, LP, which also causes congenital stationary night blindness in homozygous horses. We investigated an associated single nucleotide polymorphism in the TRPM1 gene in 96 archaeological bones from 31 localities from Late Pleistocene (approx. 17 000 YBP) to medieval times. The first genetic evidence of LP spotting in Europe dates back to the Pleistocene. We tested for temporal changes in the LP associated allele frequency and estimated coefficients of selection by means of approximate Bayesian computation analyses. Our results show that at least some of the observed frequency changes are congruent with shifts in artificial selection pressure for the leopard complex spotting phenotype. In early domestic horses from Kirklareli-Kanligecit (Turkey) dating to 2700-2200 BC, a remarkably high number of leopard spotted horses (six of 10 individuals) was detected including one adult homozygote. However, LP seems to have largely disappeared during the late Bronze Age, suggesting selection against this phenotype in early domestic horses. During the Iron Age, LP reappeared, probably by reintroduction into the domestic gene pool from wild animals. This picture of alternating selective regimes might explain how genetic diversity was maintained in domestic animals despite selection for specific traits at different times.},
  language  = {en}
}
@article{DeCahsanNagelSchedinaetal.2020,
  author    = {De Cahsan, Binia and Nagel, Rebecca and Schedina, Ina-Maria and King, James J. and Bianco, Pier G. and Tiedemann, Ralph and Ketmaier, Valerio},
  title     = {Phylogeography of the European brook lamprey (Lampetra planeri) and the European river lamprey (Lampetra fluviatilis) species pair based on mitochondrial data},
  series = {Journal of fish biology},
  volume    = {96},
  journal   = {Journal of fish biology},
  number    = {4},
  publisher = {Wiley-Blackwell},
  address   = {Oxford [u.a.]},
  issn      = {0022-1112},
  doi       = {10.1111/jfb.14279},
  pages     = {905 -- 912},
  year      = {2020},
  abstract  = {The European river lamprey Lampetra fluviatilis and the European brook lamprey Lampetra planeri (Block 1784) are classified as a paired species, characterized by notably different life histories but morphological similarities. Previous work has further shown limited genetic differentiation between these two species at the mitochondrial DNA level. Here, we expand on this previous work, which focused on lamprey species from the Iberian Peninsula in the south and mainland Europe in the north, by sequencing three mitochondrial marker regions of Lampetra individuals from five river systems in Ireland and five in southern Italy. Our results corroborate the previously identified pattern of genetic diversity for the species pair. We also show significant genetic differentiation between Irish and mainland European lamprey populations, suggesting another ichthyogeographic district distinct from those previously defined. Finally, our results stress the importance of southern Italian L. planeri populations, which maintain several private alleles and notable genetic diversity.},
  language  = {en}
}
@article{MooijTrolleJeppesenetal.2010,
  author    = {Mooij, Wolf M. and Trolle, Dennis and Jeppesen, Erik and Arhonditsis, George B. and Belolipetsky, Pavel V. and Chitamwebwa, Deonatus B. R. and Degermendzhy, Andrey G. and DeAngelis, Donald L. and Domis, Lisette Nicole de Senerpont and Downing, Andrea S. and Elliott, J. Alex and Fragoso Jr, Carlos Ruberto and Gaedke, Ursula and Genova, Svetlana N. and Gulati, Ramesh D. and H{\aa}kanson, Lars and Hamilton, David P. and Hipsey, Matthew R. and 't Hoen, Jochem and H{\"u}lsmann, Stephan and Los, F. Hans and Makler-Pick, Vardit and Petzoldt, Thomas and Prokopkin, Igor G. and Rinke, Karsten and Schep, Sebastiaan A. and Tominaga, Koji and Van Dam, Anne A. and Van Nes, Egbert H. and Wells, Scott A. and Janse, Jan H.},
  title     = {Challenges and opportunities for integrating lake ecosystem modelling approaches},
  series = {Aquatic ecology},
  volume    = {44},
  journal   = {Aquatic ecology},
  publisher = {Springer Science + Business Media B.V.},
  address   = {Dordrecht},
  issn      = {1573-5125},
  doi       = {10.1007/s10452-010-9339-3},
  pages     = {633 -- 667},
  year      = {2010},
  abstract  = {A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.},
  language  = {en}
}