@article{23155,
  abstract     = {In this work we establish several monotonicity and decomposition results in the framework of random regular graphs. Among other results, we
show that, for a wide range of parameters d1 ≤ d2, there exists a coupling of
G(n,d1) and G(n,d2) satisfying that G(n,d1) ⊆ G(n,d2) with high probability, confirming a conjecture of Gao, Isaev and McKay in a new regime.
Our contributions include new tools for analysing contiguity and total variation distance between random regular graph models, a novel procedure for
generating unions of random edge-disjoint perfect matchings, and refined estimates of Gao’s bounds on the number of perfect matchings in random regular graphs. In addition, we make progress towards another conjecture of Isaev,
McKay, Southwell and Zhukovskii.},
  author       = {Hollom, Lawrence and Lichev, Lyuben and Mond, Adva and Portier, Julien and Wang, Yiting},
  issn         = {2168-8737},
  journal      = {Annals of Applied Probability},
  keywords     = {Random regular graphs, coupling, contiguity, total variation distance, decomposition},
  number       = {5},
  pages        = {4716--4738},
  publisher    = {Institute of Mathematical Statistics},
  title        = {{Monotonicity and decompositions of random regular graphs}},
  doi          = {10.1214/26-AAP2345},
  volume       = {36},
  year         = {2026},
}

@article{22585,
  abstract     = {Most of the currently experienced global environmental changes (rising atmospheric CO2 concentrations, warming, altered amount and pattern of precipitation, and increased nutrient load) directly or indirectly affect ecosystem surface energy balance and plant transpiration. As a consequence, the relative humidity of the air surrounding the vegetation changes, thus creating a feedback loop whose net effect on transpiration and finally productivity is not trivial to quantify. Forcedly, in any global change experiment with the above drivers, we can only treat small plots, or ‘islands’, of vegetation. This means that the treated plots will likely experience the ambient humidity conditions influenced by the surrounding, non-treated vegetation. Experimental assessments of global change effects will thus systematically lack modifications originating from these potentially important feedback mechanisms, introducing a bias of unknown magnitude in all measurements of processes directly or indirectly depending on plant transpiration. We call this potential bias the ‘island effect’. Here, we discuss its implications in various global change experiments with plants. We also suggest ways to complement experiments using modelling approaches and observational studies. Ultimately, there is no obvious solution to deal with the island effect in field experiments and only models can provide an estimate of modification of responses by these feedbacks. However, we suggest that increasing the awareness of the island effect among both experimental researchers and modellers will greatly improve the interpretation of vegetation responses to global change.},
  author       = {Leuzinger, Sebastian and Fatichi, Simone and Cusens, Jarrod and Körner, Christian and Niklaus, Pascal A.},
  issn         = {2041-2851},
  journal      = {AoB Plants},
  keywords     = {DGVM, Elevated CO2, FACE, Hydrology, Land–atmosphere coupling, Vegetation feedback effects, Warming},
  publisher    = {Oxford University Press},
  title        = {{The ‘island effect’ in terrestrial global change experiments: a problem with no solution?}},
  doi          = {10.1093/aobpla/plv092},
  volume       = {7},
  year         = {2015},
}