Comparison of habitat models for scarcely detected species

Akaike, 1974, A new look at the statistical model identification, IEEE Trans. Automatic Control, 19, 716, 10.1109/TAC.1974.1100705 Austin, 2002, Spatial prediction of species distribution: an interface between ecological theory and statistical modelling, Ecol. Model., 157, 101, 10.1016/S0304-3800(02)00205-3 Authier, 2016, Variable selection and accurate predictions in habitat modelling: a shrinkage approach, Ecography, 39, 001 Bailey, 2009, Using marine mammal habitat modelling to identify priority conservation zones within a marine protected area, Mar. Ecol. Prog. Ser., 378, 279, 10.3354/meps07887 Barry, 2002, Generalized additive modelling and zero inflated count data, Ecol. Model., 157, 179, 10.1016/S0304-3800(02)00194-1 Becker, 2010, Comparing California current cetacean-habitat models developed using in situ and remotely sensed sea surface temperature data, Mar. Ecol. Prog. Ser., 413, 163, 10.3354/meps08696 Brotons, 2004, Presence‐absence versus presence‐only modelling methods for predicting bird habitat suitability, Ecography, 4, 437, 10.1111/j.0906-7590.2004.03764.x Buckland, 2001 Cañadas, 2005, Habitat preference modelling as a conservation tool: proposals for marine protected areas for cetaceans in southern Spanish waters, Aquat. Conserv. Mar. Freshw. Ecosyst., 15, 495, 10.1002/aqc.689 Cañadas, 2009, 7, 201 Deng, 2005, Score tests for zero-inflation and over-dispersion in generalized linear models, Stat. Sin., 257 Dobbie, 2001, Theory & methods: modelling correlated zero-inflated count data, ANZ J. Stat., 43, 431 Dunn, 2005, Series evaluation of Tweedie exponential dispersion model densities, Stat. Comput., 15, 267, 10.1007/s11222-005-4070-y Elith, 2009, Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models, Ecography, 32, 66, 10.1111/j.1600-0587.2008.05505.x Elith, 2009, Species distribution models: ecological explanation and prediction across space and time, Annu. Rev. Ecol. Evol. Systematics, 40, 677, 10.1146/annurev.ecolsys.110308.120159 Elith, 2006, Novel methods improve prediction of species’ distributions from occurrence data, Ecography, 29, 129, 10.1111/j.2006.0906-7590.04596.x Elith, 2011, A statistical explanation of MaxEnt for ecologists, Divers. Distributions, 17, 43, 10.1111/j.1472-4642.2010.00725.x Ferguson, 2006, Predicting Cuvier’s (Ziphius cavirostris) and Mesoplodon beaked whale population density from habitat characteristics in the eastern tropical Pacific Ocean, J. Cetacean Res. Manag., 7, 287, 10.47536/jcrm.v7i3.738 Gormley, 2011, Using presence-only and presence-absence data to estimate the current and potential distributions of established invasive species, J. Appl. Ecol., 48, 25, 10.1111/j.1365-2664.2010.01911.x Guisan, 2005, Predicting species distribution: offering more than simple habitat models, Ecol. Lett., 8, 993, 10.1111/j.1461-0248.2005.00792.x Guisan, 2000, Predictive habitat distribution models in ecology, Ecol. Model., 135, 147, 10.1016/S0304-3800(00)00354-9 Hammond, 2013, Cetacean abundance and distribution in European Atlantic shelf waters to inform conservation and management, Biol. Conserv., 164, 107, 10.1016/j.biocon.2013.04.010 Hastie, 1986, Generalized additive models, Stat. Sci., 3, 297, 10.1214/ss/1177013604 Hegel, 2010, Current state of the art for statistical modelling of species distributions, 273 Hirzel, 2002, Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?, Ecology, 83, 2027, 10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2 King, 2007, When can history Be our guide? the pitfalls of counterfactual inference, Int. Stud. Quaterly, 51, 183, 10.1111/j.1468-2478.2007.00445.x Kleiber, 2016, Visualizing count data regressions using rootograms, Am. Statistician, 1 Lambert, C., Virgili, A., Pettex, E., Delavenne, J., Toison, V., Blanck, A., Ridoux, V. (in review) Habitat modelling predictions highlight seasonal relevance of Marine Protected Areas for marine megafauna. Deep Sea Research II, Special Issue European Marine Megafauna. Lambert, C., Pettex, E., Dorémus, G., Laran, S., Stephan, E., Van Canneyt, O., Ridoux, V. (in press). How does ocean seasonality drive habitat preferences of highly mobile top predators? Part II: the eastern North-Atlantic. Deep-Sea Research II, Special Issue European Marine Megafauna. Lambert, 1992, Zero-Inflated Poisson regression, with an application to defects in manufacturing, Technometrics, 34, 1, 10.2307/1269547 Laran, S., Authier, M., Blanck, A., Dorémus, G., Falchetto, H., Monestiez, P., Pettex, E., Stephan, E., Van Canneyt, O., Ridoux, V. (in review). Using large scale survey to investigate seasonal variations in seabird distribution and abundance. Part II: the Bay of Biscay and the English Channel, Deep Sea Research Part II, Special Issue European Marine Megafauna. Lindén, 2011, Using the negative binomial distribution to model overdispersion in ecological data, Ecology, 92, 1414, 10.1890/10-1831.1 MacKenzie, 2002, Estimating site occupancy rates when detection probabilities are less than one, Ecology, 83, 2248, 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 MacLeod, 2009, Changes in the occurrence of common dolphins, striped dolphins and harbour porpoises in the English Channel and Bay of Biscay, J. Mar. Biol. Assoc. U. K., 89, 1059, 10.1017/S0025315408002828 Mannocci, 2014, Predicting cetacean and seabird habitats across a productivity gradient in the South Pacific gyre, Prog. Oceanogr., 120, 383, 10.1016/j.pocean.2013.11.005 Mannocci, 2014, Predicting top predator habitats in the Southwest Indian Ocean, Ecography, 37, 261, 10.1111/j.1600-0587.2013.00317.x Mannocci, 2015, Extrapolating cetacean densities beyond surveyed regions: habitat-based predictions in the circumtropical belt, J. Biogeogr., 42, 1267, 10.1111/jbi.12530 Martin, 2005, Zero tolerance ecology: improving ecological inference by modelling the source of zero observations, Ecol. Lett., 8, 1235, 10.1111/j.1461-0248.2005.00826.x Merow, 2013, A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter, Ecography, 36, 1058, 10.1111/j.1600-0587.2013.07872.x Minami, 2007, Modeling shark bycatch: the zero-inflated negative binomial regression model with smoothing, Fish. Res., 84, 210, 10.1016/j.fishres.2006.10.019 Monk, 2010, Habitat suitability for marine fishes using presence-only modelling and multibeam sonar, Mar. Ecol. Prog. Ser., 420, 157, 10.3354/meps08858 Murphy, 2013, Review of new information on other matters relevant for small cetacean conservation population size, distribution, structure and causes of any changes marine megavertebrates adrift: a framework for the interpretation of stranding data in a monitoring p, Oceanogr. Mar. Biol., 51, 193 Pearce, 2006, Modelling distribution and abundance with presence-only data, J. Appl. Ecol., 43, 405, 10.1111/j.1365-2664.2005.01112.x Pearce, 2000, Evaluating the predictive performance of habitat models developed using logistic regression, Ecol. Model., 133, 225, 10.1016/S0304-3800(00)00322-7 Phillips, 2008, Modeling of species distribution with Maxent: new extensions and a comprehensive evalutation, Ecography, 31, 161, 10.1111/j.0906-7590.2008.5203.x Phillips, 2004, A maximum entropy approach to species distribution modeling, Twenty-first International Conference on Machine Learning − ICML ’04, 10.1145/1015330.1015412 Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol. Model., 190, 231, 10.1016/j.ecolmodel.2005.03.026 Previmer (2014). Previmer—Observation et prévisions côtières. Catalogue version 2.1. R Core Team, 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/.all. Redfern, 2006, Techniques for cetacean − habitat modeling, Mar. Ecol. Prog. Ser., 310, 271, 10.3354/meps310271 Richards, 2008, Dealing with overdispersed count data in applied ecology, J. Appl. Ecol., 45, 218, 10.1111/j.1365-2664.2007.01377.x Ridout, 1998, Models for count data with many zeros, Int. Biom. Conf., 1 Segurado, 2004, An evaluation of methods for modelling species distributions, J. Biogeogr., 31, 1555, 10.1111/j.1365-2699.2004.01076.x Syphard, 2009, Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors, Ecography, 32, 907, 10.1111/j.1600-0587.2009.05883.x Tsoar, 2007, A comparative evaluation of presence-only methods for modelling species distribution, Divers. Distributions, 13, 397, 10.1111/j.1472-4642.2007.00346.x Vilchis, 2006, Pelagic habitat of seabirds in the eastern tropical Pacific: effects of foraging ecology on habitat selection, Mar. Ecol. Prog. Ser., 315, 279, 10.3354/meps315279 Virgili, A., Lambert, C., Pettex, E., Dorémus, G., Van Canneyt, O., Ridoux, V. (in review). Predicting seasonal variations in coastal seabird habitats in the English Channel and the Bay of Biscay. Deep Sea Research II, Special Issue European Marine Megafauna. Warton, 2005, Many zeros does not mean zero inflation: comparing the goodness-of-fit of parametric models to multivariate abundance data, Environmetrics, 16, 275, 10.1002/env.702 Wenger, 2014, Estimating species occurrence, abundance, and detection probability using zero-inflated distributions, Ecology, 89, 2953, 10.1890/07-1127.1 Wisz, 2008, Effects of sample size on the performance of species distribution models, Divers. Distributions, 14, 763, 10.1111/j.1472-4642.2008.00482.x Wood, 2006 Wood, S., 2013. mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation. Retrieved 7 July 2014, from http://cran.r-project.org/web/packages/mgcv/index.html. Yackulic, 2013, Presence-only modelling using MAXENT: when can we trust the inferences?, Methods Ecol. Evol., 4, 236, 10.1111/2041-210x.12004 Zaniewski, 2002, Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns, Ecol. Model., 157, 261, 10.1016/S0304-3800(02)00199-0 Zeileis, 2007, 53