Papers read in 2016

  1. Ovaskainen, O., Abrego, N., Halme, P. & Dunson, D. Using latent variable models to identify large networks of species-to-species associations at different spatial scales. Methods Ecol. Evol. (2015). doi: 10.1111/2041-210X.12501

  2. Ovaskainen, O., Roy, D. B., Fox, R. & Anderson, B. J. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods Ecol. Evol. (2015). doi: 10.1111/2041-210X.12502

  3. Thorson, J. T. et al. Spatial factor analysis: a new tool for estimating joint species distributions and correlations in species range. Methods Ecol. Evol. 6, 627–637 (2015). doi: 10.1111/2041-210X.12359

  4. Golding, N. & Purse, B. V. Fast and flexible Bayesian species distribution modelling using Gaussian processes. Methods Ecol. Evol. (2016). doi: 10.1111/2041-210X.12523

  5. Lyons, S. K. et al. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529, 80–83 (2016). doi: 10.1038/nature16447

  6. Elayouty, A., Scott, M., Miller, C., Waldron, S. & Franco-Villoria, M. Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial CO2. Environ. Ecol. Stat. 1–23 (2015). doi: 10.1007/s10651-015-0329-4

  7. Harris, D. J. Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol. 6, 465–473 (2015). doi: 10.1111/2041-210X.12332

  8. Harald Baayen, R., van Rij, J., de Cat, C. & Wood, S. N. Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models. arXiv [stat.AP] (2016). at

  9. Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature (2016). doi:10.1038/nature16524

  10. Micheli, F. et al. The Dual Nature of Community Variability. Oikos 85, 161–169 (1999). doi: 10.2307/3546802

  11. Wood, S. N. Just Another Gibbs Additive Modeller: Interfacing JAGS and mgcv. arXiv [stat.CO] (2016). at

  12. Soued, C., del Giorgio, P. A., & R. Maranger. Nitrous oxide sinks and emissions in boreal aquatic networks in Québec. Nature Geoscience (2016) doi: 10.1038/ngeo2611

  13. Blonder, B. Do Hypervolumes Have Holes? American Naturalist (2016, in press) doi: 10.1086/685444

  14. Harris, D. J. Estimating species interactions from observational data with Markov networks. bioRxiv (2015). doi: 10.1101/018861

  15. Warton, D. I. et al. So Many Variables: Joint Modeling in Community Ecology. Trends Ecol. Evol. (2015). doi: 10.1016/j.tree.2015.09.007

  16. Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016). doi: 10.1038/ngeo2578

  17. Letcher, B. H. et al. A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ 4, e1727 (2016). doi: 10.7717/peerj.1727

  18. Spanbauer, T. L. et al. Prolonged instability prior to a regime shift. PLoS One 9, e108936 (2014). doi: 10.1371/journal.pone.0108936

  19. Yenni, G., Adler, P. & Ernest, M. Do persistent rare species experience stronger negative frequency dependence than common species? bioRxiv 040360 (2016). doi: 10.1101/040360

  20. Cahill, N., Rahmstorf, S. & Parnell, A. C. Change points of global temperature. Environmental Research Letters 10, 084002 (2015). doi: 10.1088/1748-9326/10/8/084002

  21. Pya, N. & Wood, S. N. A note on basis dimension selection in generalized additive modelling. arXiv [stat.ME] (2016). at

  22. Lawton, J. H. Patterns in Ecology. Oikos 75, 145–147 (1996). doi: 10.2307/3546237

  23. Jorgensen, J. C., Ward, E. J., Scheuerell, M. D. & Zabel, R. W. Assessing spatial covariance among time series of abundance. Ecol. Evol. (2016). doi: 10.1002/ece3.2031

  24. Scheuerell, M. D. et al. Analyzing large-scale conservation interventions with Bayesian hierarchical models: a case study of supplementing threatened Pacific salmon. Ecol. Evol. 5, 2115–2125 (2015). doi: 10.1002/ece3.1509

  25. Fahrmeir, L. & Kneib, T. On the identification of trend and correlation in temporal and spatial regression. In Recent Advances in Linear Models and Related Areas 1–27 (Physica-Verlag HD, 2008). doi: 10.1007/978-3-7908-2064-5_1

  26. Boettiger, C. & Hastings, A. Quantifying limits to detection of early warning for critical transitions. J. R. Soc. Interface 9, 2527–2539 (2012). doi: 10.1098/rsif.2012.0125

  27. Drake, J. M. Early warning signals of stochastic switching. Proc. Biol. Sci. 280, 20130686 (2013). doi: 10.1098/rspb.2013.0686

  28. Boettiger, C. & Hastings, A. No early warning signals for stochastic transitions: insights from large deviation theory. Proc. Biol. Sci. 280, 20131372 (2013). doi: 10.1098/rspb.2013.1372

  29. Ganguli, B. & Wand, M. P. Feature Significance in Geostatistics. J. Comput. Graph. Stat. 13, 954–973 (2004). doi:10.1198/106186004X12515

  30. Ganguli, B. & Wand, M. P. Feature significance in generalized additive models. Stat. Comput. 17, 179–192 (2007). doi: 10.1007/s11222-006-9011-x

  31. de Valpine, P. & Harmon-Threatt, A. N. General models for resource use or other compositional count data using the Dirichlet-multinomial distribution. Ecology 94, 2678–2687 (2013). doi: 10.1890/12-0416.1

  32. Kammann, E. E. & Wand, M. P. Geoadditive models. J. R. Stat. Soc. Ser. C Appl. Stat. 52, 1–18 (2003). doi: 10.1111/1467-9876.00385

  33. Haslbeck, J. M. B. & Waldorp, L. J. mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data. arXiv [stat.AP] (2015). at

  34. O’Brien, J. D. & Record, N. The power and pitfalls of Dirichlet-multinomial mixture models for ecological count data. bioRxiv 045468 (2016). doi: 10.1101/045468

  35. Marra, G. & Radice, R. A Bivariate Copula Additive Model for Location, Scale and Shape. arXiv [stat.ME] (2016). at

  36. Wood, S. N. P-splines with derivative based penalties and tensor product smoothing of unevenly distributed data. Stat. Comput. 1–5 (2016). doi:10.1007/s11222-016-9666-x

  37. Kleiber, C. & Zeileis, A. Visualizing Count Data Regressions Using Rootograms. arXiv [stat.AP] (2016). at

  38. Hart, S. P., Schreiber, S. J. & Levine, J. M. How variation between individuals affects species coexistence. Ecology Letters (2016). doi: 10.1111/ele.12618

  39. Barros, C., Thuiller, W., Georges, D., Boulangeat, I. & Münkemüller, T. N-dimensional hypervolumes to study stability of complex ecosystems. Ecology Letters 19, 729–742 (2016). doi: 10.1111/ele.12617

  40. Brewer, M. J., Butler, A. & Cooksley, S. L. The relative performance of AIC, AICC and BIC in the presence of unobserved heterogeneity. Methods Ecol. Evol. 7, 679–692 (2016). doi: 10.1111/2041-210X.12541

  41. de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecology Letters 16, 617–625 (2013). dio: 10.1111/ele.12088

  42. Ye, H., & Sugihara, G. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science, 353(6302), 922–925 (2016) doi: 10.1126/science.aag0863

  43. ter Braak, C. J. F. & Schaffers, A. P. Co-Correspondence Analysis: a New Ordination Method to Relate Two Community Compositions. Ecology 85, 834–846 (2004). doi: 10.1890/03-0021

  44. Birks, H. J. B. Challenges in the presentation and analysis of plant-macrofossil stratigraphical data. Veg. Hist. Archaeobot. 23, 309–330 (2014). doi: 10.1007/s00334-013-0430-2

  45. Donchyts, G. et al. Earth’s surface water change over the past 30 years. Nat. Clim. Chang. 6, 810–813 (2016). doi: 10.1038/nclimate3111

  46. Lemoine, N. P. et al. Underappreciated problems of low replication in ecological field studies. Ecology (2016). doi: 10.1002/ecy.1506

  47. Beissinger, S. R. et al. Incorporating Imperfect Detection into Joint Models of Communities: A response to Warton et al. Trends Ecol. Evol. 31, 736–737 (2016). doi: 10.1016/j.tree.2016.07.009

  48. Warton, D. I. et al. Extending Joint Models in Community Ecology: A Response to Beissinger et al. Trends Ecol. Evol. 31, 737–738 (2016). doi: 10.1016/j.tree.2016.07.009

  49. Ernest, S. k. M. et al. Zero Sum, the Niche, and Metacommunities: Long‐Term Dynamics of Community Assembly. American Naturalist 172, E257–E269 (2008). doi: 10.1086/592402

  50. Paerl, H. W. et al. It takes two to tango: When and where dual nutrient (N & P) reductions are needed to protect lakes and downstream ecosystems. Environ. Sci. Technol. (2016). doi: 10.1021/acs.est.6b02575

  51. Schindler, D. W., Carpenter, S. R., Chapra, S. C., Hecky, R. E. & Orihel, D. M. Reducing Phosphorus to Curb Lake Eutrophication is a Success. Environ. Sci. Technol. 50, 8923–8929 (2016). doi: 10.1021/acs.est.6b02204

  52. Brooks, J. R. et al. Stable isotope estimates of evaporation : inflow and water residence time for lakes across the United States as a tool for national lake water quality assessments. Limnol. Oceanogr. 59, 2150–2165 (2014). doi: 10.4319/lo.2014.59.6.2150

  53. D. Vinebrooke, R. et al. Impacts of multiple stressors on biodiversity and ecosystem functioning: the role of species co-tolerance. Oikos 104, 451–457 (2004). doi: 10.1111/j.0030-1299.2004.13255.x

  54. Desjardins-Proulx, P., Laigle, I., Poisot, T. & Gravel, D. Ecological Interactions and the Netflix Problem. bioRxiv 089771 (2016). doi: 10.1101/089771

  55. Dunn, P. K. Occurrence and quantity of precipitation can be modelled simultaneously. Int. J. Climatol. 24, 1231–1239 (2004). doi: 10.1002/joc.1063