Species distribution modeling of storm-petrels (Oceanodroma furcata and O. leucorhoa) in the North Pacific and the role of dimethyl sulfide

Andreae MO, Raemdonck H (1983) Dimethyl sulfide in the surface ocean and the marine atmosphere: a global view. Science 221:744–747

Bang BG (1966) The olfactory apparatus of tubenosed birds (Procellariiformes). Acta Anat 65:391–415

Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Method Ecol Evol 93:679–688

Boersma PD, Silva MC (2001) Fork-tailed storm-petrel (Oceanodroma furcata). In: Poole A (ed) The Birds of North America, No. 569. The Academy of Natural Sciences, Philidelphia

Boersma PD, Wheelwright NT, Nerini MK, Wheelwright ES (1980) The breeding biology of the fork-tailed storm-petrel (Oceanodroma furcata). Auk 97:268–282

Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30:1145–1159

Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–231

Charlson RJ, Lovelock JE, Andreae MO, Warren SG (1987) Oceanic phytoplankton, atmospheric sulfur, cloud albedo and climate. Nature 326:655–661

Craig E, Huettmann F (2009) Using “Blackbox” algorithms such as treenet and random forests for data-mining and for finding meaningful patterns, relationships, and outliers in complex ecological data: an overview, an example using golden eagle satellite data and an outlook for a promising future. In: Wang HF (ed) Intelligent data analysis: developing new methodologies through pattern discovery and recovery. Idea Group Inc, Hershey

Crase B, Liedloff AC, Wintle BA (2012) A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35:001–010

Cunningham GB, Strauss V, Ryan PG (2008) African penguins (Spheniscus demersus) can detect dimethyl sulphide, a prey-related odour. J Exp Bio 211:3123–3127

Cushman SA (2009) Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis. In: Cushman SA, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, New York, pp 131–150

DeBose JL, Lema SC, Nevitt GA (2008) Dimethylsulfoniopropionate as a foraging cue for reef fishes. Science 319:1356

Drew GS, Piatt JF (2005) North Pacific Pelagic Seabird Database (NPPSD): compiling datasets and creating an archive, accessible database, and Pelagic Seabird Atlas. US Geological Survey, Anchorage

Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Ani Ecol 77:802–813

Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absences models. Env Conserv 24:38–49

Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

Friedman JH (2002) Stochastic gradient boosting. Comp Stat Dat Anal 38:367–378

Grömping U (2009) Variable importance assessment in regression: linear regression versus random forest. Am Stat 63:308–318

Grubb TC (1979) Olfactory guidance of Leach’s storm petrel to the breeding island. Wilson Bull 91:143–145

Hegel TM, Cushman SA, Evans J, Huettmann F (2009) Current state of the art for statistical modelling of species distributions. In: Cushman SA, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, New York, pp 273–309

Hijmans RJ (2012) Cross-validation of species distribution models: removing spatial bias and calibration with a null model. Ecologicals 93:679–688

Huettmann F, Diamond AW (2006) Large-scale effects on the spatial distribution of seabirds in the Northwest Atlantic. Landsc Ecol 21:1089–1108

Humphries GRW (2008) The ecological niche of storm-petrels in the North Pacific and a global model of dimethylsulfide concentration. University of Alaska Fairbanks, Dissertation

Huntington CE, Butler RG, Mauck RA (1996) Leach’s storm petrel (Oceanodroma leucorhoa). In: Poole A (ed) The Birds of North America, No. 233. The Academy of Natural Sciences, Philadelphia

IOC (1998) Global Temperature-salinity profile programme (GTSPP)—overview and future. IOC technical Series, 49, Intergovernmental Oceanographic Commission, Paris

Levitus S, Sato S, Maillard C, Mikhailov N, Caldwell P, Dooley H (2005) Building Ocean Profile-Plankton databases for climate and ecosystem research. NOAA Technical Report NESDIS 117, US Government Printing Office, Washington

Longhurst AR (1998) Ecological geography of the sea. Academic Press, San Diego

Nevitt G (1999) Olfactory foraging in Antarctic seabirds: a species-specific attraction to krill odors. Mar Ecol Prog Ser 177:235–241

Nevitt G (2008) Sensory ecology on the high seas: the odor world of the procellariiform seabirds. J Exp Bio 211:1706–1713

Nevitt G (2011) The neuroecology of dimethyl sulfide: a global-climate regulator turned marine infochemical. Integr Comp Biol. doi: 10.1093/icb/icr093

Nevitt GA, Bonadonna F (2005) Seeing the world through the nose of a bird: new developments in the sensory ecology of procellariiform seabirds. Mar Ecol Prog Ser 287:292–295

Nevitt GA, Haberman K (2002) Behavioral attraction of Leach’s storm-petrels (Oceanodroma leucorhoa) to dimethyl sulfide. J Exp Bio 206:1497–1501

Nevitt GA, Veit RR, Kareiva P (1995) Dimethyl sulphide as a foraging cue for Antarctic procellariiform seabirds. Nature 376:680–682

Nur N, Jahncke J, Herzog MP, Howar J, Hyrenbach DK, Zamon JE, Ainley DG, Wiens JA, Morgan K, Balance LT, Stralber D (2011) Where the wild things are: predicting hotspots of seabird aggregations in the California current system. Ecol App 21(6):2241–2257

Onley D, Scofield P (2007) Albatrosses, petrels & shearwaters of the world. Princeton University Press, Princeton

Oppel S, Meirinho A, Ramirez I, Gardner B, O’Connell A, Miller PI, Louzao M (2011) Comparison of five modeling techniques to predict the spatial distribution and abundance of seabirds. Biol Conserv. doi: 10.1016/j.biocon.2011.11.013

Schneider DC, Piatt JF (1986) Scale-dependent correlation of seabirds with schooling fish in a coastal ecosystem. Mar Ecol Prog Ser 32:237–246

Stefels J, Steinke M, Turner S, Malin G, Belviso S (2007) Environmental constraints on the production and removal of the climatically active gas dimethylsulphide (DMS) and implications for ecosystem modelling. Biogeochemistry 83:245–274

Steinke M, Stefels J, Stamhuis E (2006) Dimethyl sulfide triggers search behavior in copepods. Lim Ocean 51:1925–1930

Thomas L, Buckland ST, Burnham KP, Anderson DR, Laake JL, Borchers DL, Strindberg S (2002) Distance sampling. Encyc Env 1:544–552

Tremblay Y, Bertrand S, Henry RW, Kappes MA, Costa DP, Shaffer SA (2009) Analytical approaches to investigating seabird-environment interactions: a review. Mar Ecol Prog Ser 391:153–163

Van Buskirk RW, Nevitt GA (2007) The influence of developmental environment on the evolution of olfactory foraging behavior in procellariiform seabirds. J Evol Bio 21:67–76

Wilbur HM (1969) The breeding biology of Leach’s petrel Oceanodroma leucorhoa. Auk 86:433–442

Yang G-P, Tsunogai S (2005) Biogeochemistry of dimethylsulfide (DMS) and dimethylsulfoniopropionate (DMSP) in the surface microlayer of the western North Pacific. Deep-Sea Res I 52:553–567