Biologically informed ecological niche models for an example pelagic, highly mobile species

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Background: Although pelagic seabirds are broadly recognised as indicators of the health of marine systems, numerous gaps exist in knowledge of their at-sea distributions at the species level. These gaps have profound negative impacts on the robustness of marine conservation policies. Correlative modelling techniques have provided some information, but few studies have explored model development for non-breeding pelagic seabirds. Here, I present a first phase in developing robust niche models for highly mobile species as a baseline for further development. Methodology: Using observational data from a 12-year time period, 217 unique model parameterisations across three correlative modelling algorithms (boosted regression trees, Maxent and minimum volume ellipsoids) were tested in a time-averaged approach for their ability to recreate the at-sea distribution of non-breeding Wandering Albatrosses (Diomedea exulans) to provide a baseline for further development. Principle Findings/Results: Overall, minimum volume ellipsoids outperformed both boosted regression trees and Maxent. However, whilst the latter two algorithms generally overfit the data, minimum volume ellipsoids tended to underfit the data. Conclusions: The results of this exercise suggest a necessary evolution in how correlative modelling for highly mobile species such as pelagic seabirds should be approached. These insights are crucial for understanding seabird-environment interactions at macroscales, which can facilitate the ability to address population declines and inform effective marine conservation policy in the wake of rapid global change.

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  • Anderson R.P. (2003) Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia:Muridae) in Venezuela. Journal of Biogeography 30 591-605.

  • Barbet-Massin M. Jiguet F. Albert C.H. & Thuiller W. (2012) Selecting pseudo-absences for species distribution models: how where and how many? Methods in Ecology and Evolution 3 327-338.

  • Barve N. Barve V. Jiménez-Valverde A. Lira-Noriega A. Maher S.P. Peterson A.T. et al. (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222 1810-1819.

  • Beck J. Ballesteros-Mejia L. Nagel P. & Kitching I.J. (2013) Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species’ ranges? Diversity and Distributions 19 1043-1050.

  • Beck J. Boller M. Erhardt A. & Schwanghart W. (2014) Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics 19 10-15.

  • Bellier E.G. Certain G. Planque B. Monestiez P. & Bretagnolle V. (2010) Modelling habitat selection at multiple scales with multivariate geostatistics: an application to seabirds in open sea. Oikos 119 988-999.

  • Birdlife International and Natureserve (2015b) Marine IBA e-Atlas:

  • Burg T.M. & Croxall J.P. (2004) Global population structure and taxonomy of the Wandering Albatross species complex. Molecular Ecology 13 2345-2355.

  • Catry P. Lemos R.T. Brickle P. Phillips R.A. Matias R. & Granadeiro J.P. (2013) Predicting the distribution of a threatened albatross: the importance of competition fisheries and annual variability. Progress in Oceanography 110 1-10.

  • Ceia F.R. Phillips R.A. Ramos J.A. Cherel Y. Vieira R.P. Richard P. et al. (2012) Short- and long-term consistency in the foraging niche of Wandering Albatrosses. Marine Biology 159 1581-1591.

  • Chambers G.K. Moeke C. Steel R. & Trueman J.W. (2009) Phylogenetic analysis of the 24 named albatross taxa based on full mitochondrial cytochrome b DNA sequences. Notornis 56 82-94.

  • Clay T.A. Manica A. Ryan P. G. Silk J.R.D. Croxall J.P. Ireland L. & Phillips R.A. (2016) Proximate drivers of spatial segregation in non-breeding albatrosses. Scientific Reports 6 29932.

  • Coble P.G. (2007) Marine optical biogeochemistry: The chemistry of ocean color. Chemical Reviews 107 402-418.

  • Croxall J.P. Butchart S.H.M. Lascelles B. Stattersfield A.J. Sullivan B. Symes A. et al. (2012) Seabird conservation status threats and priority actions: a global assessment. Bird Conservation International 22 1-34.

  • Doney S.C. Ruckelshaus M. Duffy J.E. Barry J.P. Chan F. English C.A. et al. (2012) Climate change impacts on marine ecosystems. Annual Review of Marine Science 4 11-37.

  • Elith J. Graham C.H. Anderson R.P. Dudík M. Ferrier S. Guisan A. et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29 129-151.

  • Elith J. Leathwick J.R. & Hastie T. (2008) A working guide to boosted regression trees. Journal of Animal Ecology 77 802-813.

  • Elith J. Phillips S.J. Hastie T. Dudík M. Chee Y.E. & Yates C.J. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17 43-57.

  • Game E.T. Grantham H.S. Hobday A.J. Pressey R.L. Lombard A.T. Beckley L.E. et al. (2009) Pelagic protected areas: the missing dimension in ocean conservation. Trends in Ecology & Evolution 24 360-369.

  • Graham C.H. Ferrier S. Huettman F. Moritz C. & Peterson A.T. (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution 19 497-503.

  • Grecian W.J. Witt M.J. Attrill M.J. Bearhop S. Godley B.J. Grémillet D. et al. (2012) A novel projection technique to identify important at-sea areas for seabird conservation: an example using Northern Gannets breeding in the North East Atlantic. Biological Conservation 156 43-52.

  • Hyrenbach K.D. Forney K.A. & Dayton P.K. (2000) Marine protected areas and ocean basin management. Aquatic Conservation-Marine and Freshwater Ecosystems 10 437-458.

  • Hyrenbach K.D. Veit R.R. Weimerskirch H. Metzl N. & Hunt G.L. (2007) Community structure across a large-scale ocean productivity gradient: marine bird assemblages of the southern Indian Ocean. Deep-Sea Research Part I-Oceanographic Research Papers 54 1129-1145.

  • Iucn (2016) IUCN Red List of Threatened Species v2015-4:

  • Kramer-Schadt S. Niedballa J. Pilgrim J.D. Schroder B. Lindenborn J. Reinfelder V. et al. (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions 19 1366-1379.

  • Krüger L. Ramos J.A. Xavier J.C. Grémillet D. González‐Solís J. Petry M.V. Phillips R.A. Wanless R.M. & Paiva V.H. (2017) Projected distributions of Southern Ocean albatrosses petrels and fisheries as a consequence of climatic change. Ecography

  • Lascelles B.G. Langham G.M. Ronconi R.A. & Reid J.B. (2012) From hotspots to site protection: identifying Marine Protected Areas for seabirds around the globe. Biological Conservation 156 5-14.

  • Lewison R. Oro D. Godley B. Underhill L. Bearhop S. Wilson R.P. et al. (2012) Research priorities for seabirds: improving conservation and management in the 21st century. Endangered Species Research 17 93-121.

  • Lobo J.M. Jiménez-Valverde A. & Real R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17 145-151.

  • Louzao M. Pinaud D. Péron C. Delord K. Wiegand T. & Weimerskirch H. (2011) Conserving pelagic habitats: seascape modelling of an oceanic top predator. Journal of Applied Ecology 48 121-132.

  • Louzao M. Aumont O. Hothorn T. Wiegand T. & Weimerskirch H. (2013) Foraging in a changing environment: habitat shifts of an oceanic predator over the last half century. Ecography 36 57-67.

  • Mateo R.G. De La Estrella M. Felicísimo Á.M. Munoz J. & Guisan A. (2013) A new spin on a compositionalist predictive modelling framework for conservation planning: a tropical case study in Ecuador. Biological Conservation 160 150-161.

  • Mcgowan J. Hines E. Elliott M. Howar J. Dransfield A. Nur N. et al. (2013) Using seabird habitat modeling to inform marine spatial planning in central California’s National Marine Sanctuaries. PLoS One 8 e71406.

  • Merow C. Smith M.J. & Silander J.A. (2013) A practical guide to Max-Ent for modeling species’ distributions: what it does and why inputs and settings matter. Ecography 36 1058-1069.

  • Milot E. Weimerskirch H. & Bernatchez L. (2008) The seabird paradox: dispersal genetic structure and population dynamics in a highly mobile but philopatric albatross species. Molecular Ecology 17 1658-1673.

  • Nelson N.B. & Siegel D.A. (2013) The global distribution and dynamics of chromophoric dissolved organic matter. Annual Review of Marine Science 5 447-476.

  • Nunn G.B. Cooper J. Jouventin P. Robertson C.J.R. & Robertson G.G. (1996) Evolutionary relationships among extant albatrosses (Procellariiformes: Diomedeidae) established from complete cytochrome-B gene sequences. Auk 113 784-801.

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

  • Oppel S. Meirinho A. Ramírez I. Gardner B. O’connell A.F. Miller P.I. et al. (2012) Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biological Conservation 156 94-104.

  • Owens H.L. Campbell L.P. Dornak L.L. Saupe E.E. Barve N. Soberon J. et al. (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecological Modelling 263 10-18.

  • Peterson A.T. Martínez-Campos C. Nakazawa Y. & Martínez-Meyer E. (2005) Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases. Transactions of the Royal Society of Tropical Medicine and Hygiene 99 647-655.

  • Peterson A.T. (2006) Uses and requirements of ecological niche models and related distribution models. Biodiversity Informatics 3 59-72.

  • Peterson A.T. Papeş M. & Soberón J. (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling 213 63-72.

  • Phillips R.A. Silk J.R.D. Croxall J.P. Afanasyev V. & Bennett V.J. (2005) Summer distribution and migration of nonbreeding albatrosses: individual consistencies and implications for conservation. Ecology 86 2386-2396.

  • Phillips S.J. Anderson R.P. & Schapire R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190 231-259.

  • Phillips S.J. Dudík M. Elith J. Graham C.H. Lehmann A. Leathwick J. et al. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudoabsence data. Ecological Applications 19 181-197.

  • Piatt J.F. Sydeman W.J. & Wiese F. (2007) Introduction: a modern role for seabirds as indicators. Marine Ecology Progress Series 352 199-204.

  • Prince P.A. Wood A.G. Barton T. & Croxall J.P. (1992) Satellite tracking of Wandering Albatrosses (Diomedea exulans) in the South Atlantic. Antarctic Science 4 31-36.

  • Qiao H.J. Soberón J. & Peterson A.T. (2015) No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods in Ecology and Evolution 6 1126-1136.

  • Quillfeldt P. Engler J.O. Silk J.R. Phillips R.A. (2017) Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black‐browed albatrosses. Journal of Avian Biology

  • R Development Core Team (2009) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing:

  • Rains D. Weimerskirch H. & Burg T.M. (2011) Piecing together the global population puzzle of Wandering Albatrosses: genetic analysis of the Amsterdam albatross Diomedea amsterdamensis. Journal of Avian Biology 42 69-79.

  • Ramos R. Sanz V. Militao T. Bried J. Neves V.C. Biscoito M. et al. (2015) Leapfrog migration and habitat preferences of a small oceanic seabird Bulwer’s petrel (Bulweria bulwerii). Journal of Biogeography 42 1651-1664.

  • Roberts J.J. Best B.D. Dunn D.C. & Halpin P.N. (2010) Marine Geospatial Ecology Tools: an integrated framework for eological geoprocessing with ArcGIS Python R MATLAB and C++. Environmental Modelling and Software 25 1197-1207.

  • Rodríguez J.P. Brotons L. Bustamante J. & Seoane J. (2007) The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions 13 243-251.

  • Saupe E.E. Barve V. Myers C.E. Soberόn J. Barve N. Hensz C.M. et al. (2012) Variation in niche and distribution model performance: the need for a priori assessment of key causal factors. Ecological Modelling 237 11-22.

  • Scales K.L. Miller P.I. Ingram S.N. Hazen E.L. Bograd S.J. & Phillips R.A. (2016) Identifying predictable foraging habitats for a wide-ranging marine predator using ensemble ecological niche models. Diversity and Distributions 22 212-224.

  • Shcheglovitova M. & Anderson R.P. (2013) Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling 269 9-17.

  • Soberón J. & Peterson A.T. (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics 2 1-10.

  • Sousa-Baena M.S. Garcia L.C. & Peterson A.T. (2014) Completeness of digital accessible knowledge of the plants of Brazil and priorities for survey and inventory. Diversity and Distributions 20 369-381.

  • Thiebot J.B. Lescroel A. Pinaud D. Trathan P.N. & Bost C.A. (2011) Larger foraging range but similar habitat selection in non-breeding versus breeding sub-Antarctic penguins. Antarctic Science 23 117-126.

  • Urtizberea A. Dupont N. Rosland R. & Aksnes D.L. (2013) Sensitivity of euphotic zone properties to CDOM variations in marine ecosystem models. Ecological Modelling 256 16-22.

  • Wakefield E.D. Phillips R.A. & Matthiopoulos J. (2009) Quantifying habitat use and preferences of pelagic seabirds using individual movement data: a review. Marine Ecology Progress Series 391 165-182.

  • Wakefield E.D. Phillips R.A. Trathan P.N. Arata J. Gales R. Huin N. et al. (2011) Habitat preference accessibility and competition limit the global distribution of breeding Black-browed Albatrosses. Ecological Monographs 81 141-167.

  • Weimerskirch H. Inchausti P. Guinet C. & Barbraud C. (2003) Trends in bird and seal populations as indicators of a system shift in the Southern Ocean. Antarctic Science 15 249-256.

  • Weimerskirch H. Gault A. & Cherel Y. (2005) Prey distribution and patchiness: factors in foraging success and efficiency of Wandering Albatrosses. Ecology 86 2611-2622.

  • Weimerskirch H. Åkesson S. & Pinaud D. (2006) Postnatal dispersal of Wandering Albatrosses Diomedea exulans: implications for the conservation of the species. Journal of Avian Biology 37 23-28.

  • Weimerskirch H. Jouventin P. Mougin J.L. Stahl J.C. & Vanbeveren M. (1985) Banding recoveries and the dispersal of seabirds breeding in French Austral and Antarctic Territories. Emu 85 22-33.

  • Weimerskirch H. Louzao M. De Grissac S. & Delord K. (2012) Changes in wind pattern alter albatross distribution and life-history traits. Science 335 211-214.

  • Yesson C. Brewer P.W. Sutton T. Caithness N. Pahwa J.S. Burgess M. et al. (2007) How global is the Global Biodiversity Information Facility? PLoS One 2 e1124.

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