References
Anderson, M.J. (2008) Animal-sediment relationships re-visited: Characterising species’ distributions along an environmental gradient using canonical analysis and quantile regression splines. Journal of Experimental Marine Biology and Ecology , 366, 16–27.
Araújo, M.B., Cabeza, M., Thuiller, W., Hannah, L. & Williams, P.H. (2004) Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology , 10, 1618–1626.
Austin, M. (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling , 200, 1–19.
Beguería, S. & Vicente-Serrano, S.M. (2017) SPEI: calculation of the standardised precipitation-evapotranspiration index.
Belsey, D.A. (1991) Conditioning diagnostics, collinearity and weak data in regression , Wiley.
Bezeng, B.S., Morales-Castilla, I., van der Bank, M., Yessoufou, K., Daru, B.H. & Davies, T.J. (2017) Climate change may reduce the spread of non-native species. Ecosphere , 8, e01694.
BirdLife International and Handbook of the Birds of the World (2016) Bird species distribution maps of the world. Version 6.0.
Bissinger, J.E., Montagnes, D.J.S., Harples, J. & Atkinson, D. (2008) Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression. Limnology and Oceanography , 53, 487–493.
de Boer, W.F., van Langevelde, F., Prins, H.H.T., de Ruiter, P.C., Blanc, J., Vis, M.J.P. et al. (2013) Understanding spatial differences in African elephant densities and occurrence, a continent-wide analysis. Biological Conservation , 159, 468–476.
Brennan, A., Cross, P.C. & Creel, S. (2015) Managing more than the mean: using quantile regression to identify factors related to large elk groups. Journal of Applied Ecology , 52, 1656–1664.
Broennimann, O., Fitzpatrick, M.C., Pearman, P.B., Petitpierre, B., Pellissier, L., Yoccoz, N.G. et al. (2012) Measuring ecological niche overlap from occurrence and spatial environmental data: Measuring niche overlap. Global Ecology and Biogeography , 21, 481–497.
Brooks, J.R., Barnard, H.R., Coulombe, R. & McDonnell, J.J. (2010) Ecohydrologic separation of water between trees and streams in a Mediterranean climate. Nature Geoscience , 3, 100–104.
Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach , 2nd edn. Springer-Verlag, New York.
Burril, E.A., Wilson, A.M., Turner, J.A., Pugh, S.A., Menlove, J., Christiansen, G. et al. (2018) The Forest Inventory and Analysis Database: database description and user guide version 8.0 for Phase 2.
Cade, B.S. & Noon, B.R. (2003) A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment , 1, 412–420.
Cade, B.S., Noon, B.R. & Flather, C.H. (2005) Quantile regression reveals hidden bias and uncertainty in habitat models. Ecology , 86, 786–800.
Cade, B.S., Terrell, J.W. & Schroeder, R.L. (1999) Estimating effects of limiting factors with regression quantiles. Ecology , 80, 311–323.
Carrascal, L.M., Villén-Pérez, S. & Palomino, D. (2016) Preferred temperature and thermal breadth of birds wintering in peninsular Spain: the limited effect of temperature on species distribution. PeerJ , 4, e2156.
Carroll, M.J., Dennis, P., Pearce-Higgins, J.W. & Thomas, C.D. (2011) Maintaining northern peatland ecosystems in a changing climate: effects of soil moisture, drainage and drain blocking on craneflies.Global Change Biology , 17, 2991–3001.
Carvalho, L., McDonald, C., de Hoyos, C., Mischke, U., Phillips, G., Borics, G. et al. (2013) Sustaining recreational quality of European lakes: minimizing the health risks from algal blooms through phosphorus control. Journal of Applied Ecology , 50, 315–323.
Dallas, T., Decker, R.R. & Hastings, A. (2017) Species are not most abundant in the centre of their geographic range or climatic niche.Ecology Letters , 20, 1526–1533.
Didham, R.K. (2006) Modelling and predicting invertebrate abundance along environmental gradients. The Weta , 31, 1–10.
Ehrlén, J. & Morris, W.F. (2015) Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters, 18, 303–314.
Farias, A.A., Soares, C.P.B., Leite, H.G. & da Silva, G.F. (2021) Quantile regression: prediction of growth and yield for a eucalyptus plantation in northeast Brazil. European Journal of Forest Research, 140, 983–989.
Fornaroli, R., Cabrini, R., Sartori, L., Marazzi, F., Vracevic, D., Mezzanotte, V. et al. (2015) Predicting the constraint effect of environmental characteristics on macroinvertebrate density and diversity using quantile regression mixed model. Hydrobiologia, 742, 153–167.
Gibbons, D.W., Donald, P.F., Bauer, H.-G., Fornasari, L. & Dawson, I.K. (2007) Mapping avian distributions: the evolution of bird atlases. Bird Study, 54, 324–334.
Greenberg, J.A., Santos, M.J., Dobrowski, S.Z., Vanderbilt, V.C. & Ustin, S.L. (2015) Quantifying Environmental Limiting Factors on Tree Cover Using Geospatial Data. PLOS ONE, 10, e0114648.
Hiddink, J.G. & Kaiser, M.J. (2005) Implications of Liebig’s law of the minimum for the use of ecological indicators based on abundance.Ecography , 28, 264–271.
Holt, R.D. (1987) Population dynamics and evolutionary processes: the manifold roles of habitat selection. Evolutionary Ecology , 1, 331–347.
Howard, C., Stephens, P.A., Pearce‐Higgins, J.W., Gregory, R.D. & Willis, S.G. (2014) Improving species distribution models: the value of data on abundance. Methods in Ecology and Evolution , 5, 506–513.
Howard, C., Stephens, P.A., Pearce-Higgins, J.W., Gregory, R.D. & Willis, S.G. (2015) The drivers of avian abundance: patterns in the relative importance of climate and land use. Global Ecology and Biogeography , 24, 1249–1260.
Huston, M.A. (2002) Introductory essay: critical issues for improving predictions . Predicting Species Occurrences: Issues of Accuracy and Scale , Island Press, Covelo, California.
IUCN (2019) Guidelines for Using the IUCN Red List Categories and Criteria. Version 14.
Jarema, S.I., Samson, J., Mcgill, B.J. & Humphries, M.M. (2009) Variation in abundance across a species’ range predicts climate change responses in the range interior will exceed those at the edge: a case study with North American beaver. Global Change Biology , 15, 508–522.
Johnston, A., Fink, D., Reynolds, M.D., Hochachka, W.M., Sullivan, B.L., Bruns, N.E. et al. (2015) Abundance models improve spatial and temporal prioritization of conservation resources. Ecological Applications , 25, 1749–1756.
Kearney, M. & Porter, W. (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecology Letters , 12, 334–350.
Kim, D.-S. (1999) A Standardization Technique to Reduce the Problem of Multicollinearity in Polynomial Regression Analysis. Bulletin of the International Statistical Institute , 52nd Session.
Kneib, T. (2013) Beyond mean regression. Statistical Modelling , 13, 275–303.
Koenker, R. (2019) quantreg: quantile regression.
Konrad, C.P., Brasher, A.M.D. & May, J.T. (2008) Assessing streamflow characteristics as limiting factors on benthic invertebrate assemblages in streams across the western United States. Freshwater Biology , 53, 1983–1998.
Lancaster, J. & Belyea, L.R. (2006) Defining the limits to local density: alternative views of abundance-environment relationships.Freshwater Biology , 51, 783–796.
Liebig, J.V. (1840) Die organische Chemie in Chemie in ihrer Anwendung auf Agricultur und Physiologie (Organic chemistry in its applications to agriculture and physiology). , Friedrich Vieweg und Sohn Publ. Co., Braunschweig, Germany.
McClain, C. & Rex, M. (2001) The relationship between dissolved oxygen concentration and maximum size in deep-sea turrid gastropods: an application of quantile regression. Marine Biology , 139, 681–685.
Milne, A.E., Wheeler, H.C. & Lark, R.M. (2006) On testing biological data for the presence of a boundary. Annals of Applied Biology , 149, 213–222.
Neter, J. (1996) Applied linear statistical models , McGraw-Hill Education.
Orsini, N. & Bottai, M. (2011) Logistic Quantile Regression in Stata.The Stata Journal: Promoting communications on statistics and Stata , 11, 327–344.
Pigeon, K.E., Cardinal, E., Stenhouse, G.B. & Côté, S.D. (2016) Staying cool in a changing landscape: the influence of maximum daily ambient temperature on grizzly bear habitat selection. Oecologia , 181, 1101–1116.
R Core Team (2019) R: A language and environment for statistical computing , R Foundation for Statistical Computing, Vienna, Austria.
Ronquillo, C., Alves-Martins, F., Mazimpaka, V., Sobral-Souza, T., Vilela-Silva, B., G. Medina, N. et al. (2020) Assessing spatial and temporal biases and gaps in the publicly available distributional information of Iberian mosses. Biodiversity Data Journal , 8, e53474.
Rosenzweig, M.L. & Winakur, J. (1969) Population Ecology of Desert Rodent Communities: Habitats and Environmental Complexity.Ecology , 50, 558–572.
Sagarin, R.D. & Gaines, S.D. (2002) The “abundant centre” distribution: to what extent is it a biogeographical rule? Ecology Letters , 5, 137–147.
Scharf, F.S., Juanes, F. & Sutherland, M. (1998) Inferring ecological relationships from the edges of scatter diagrams: comparison of regression techniques. Ecology , 79, 448–460.
Schoener, T.W. (1974) Resource Partitioning in Ecological Communities.Science , 185, 27–39.
See, K.E., Ackerman, M.W., Carmichael, R.A., Hoffmann, S.L. & Beasley, C. (2021) Estimating carrying capacity for juvenile salmon using quantile random forest models. Ecosphere , 12.
Sheffield, J., Goteti, G. & Wood, E.F. (2006) Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling. Journal of Climate , 19, 3088–3111.
Soberón, J.M. (2010) Niche and area of distribution modeling: a population ecology perspective. Ecography , 33, 159–167.
Sprengel, C. (1828) Von den Substanzen der Ackerkrume und des Untergrundes (About the substances in the plow layer and the subsoil).Journal für Technische und Ökonomische Chemie , 423–474, and 42–99, 313–352, and 397–421.
Stanke, H., Finley, A.O., Weed, A.S., Walters, B.F. & Domke, G.M. (2020) rFIA: An R package for estimation of forest attributes with the US Forest Inventory and Analysis database. Environmental Modelling & Software , 127, 104664.
Stralberg, D., Carroll, C., Pedlar, J.H., Wilsey, C.B., McKenney, D.W. & Nielsen, S.E. (2018) Macrorefugia for North American trees and songbirds: Climatic limiting factors and multi-scale topographic influences. Global Ecology and Biogeography , 27, 690–703.
Strimas-Mackey, M., Hochachka, W.M., Ruiz-Gutierrez, V., Robinson, O.J., Miller, E.T., Auer, T. et al. (2020) Best Practices for Using eBird Data v1.0 , Zenodo.
Sullivan, B.L., Wood, C.L., Iliff, M.J., Bonney, R.E., Fink, D. & Kelling, S. (2009) eBird: A citizen-based bird observation network in the biological sciences. Biological Conservation , 142, 2282–2292.
Sweka, J.A. & Mackey, G. (2010) A Functional Relationship Between Watershed Size and Atlantic Salmon Parr Density. Journal of Fish and Wildlife Management , 1, 3–10.
Symonds, M.R.E. & Moussalli, A. (2011) A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behavioral Ecology and Sociobiology , 65, 13–21.
Thomson, J.D., Weiblen, G., Thomson, B.A., Alfaro, S. & Legendre, P. (1996) Untangling Multiple Factors in Spatial Distributions: Lilies, Gophers, and Rocks. Ecology , 77, 1698–1715.
Thornthwaite, C.W. (1948) An Approach toward a Rational Classification of Climate. Geographical Review , 38, 55.
Vaz, S., Martin, C.S., Eastwood, P.D., Ernande, B., Carpentier, A., Meaden, G.J. et al. (2007) Modelling species distributions using regression quantiles. Journal of Applied Ecology , 45, 204–217.
Villén-Pérez, S., Carrascal, L.M. & Palomino, D. (2022) Cambio climático, hábitats y Red Natura 2000: el futuro de las aves comunes en España , Uno Editorial, Madrid.
Villén‐Peréz, S., Heikkinen, J., Salemaa, M. & Mäkipää, R. (2020) Global warming will affect the maximum potential abundance of boreal plant species. Ecography , 43, 801–811.
Wilson, K.A., Westphal, M.I., Possingham, H.P. & Elith, J. (2005) Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation , 122, 99–112.
Zang, C.S., Buras, A., Esquivel‐Muelbert, A., Jump, A.S., Rigling, A. & Rammig, A. (2020) Standardized drought indices in ecological research: Why one size does not fit all. Global Change Biology , 26, 322–324.