Biologically informed ecological niche models for an example pelagic, highly mobile species
DOI:
https://doi.org/10.1515/eje-2017-0006Keywords:
Boosted regression trees, digital accessible knowledge, distribution modelling, Maxent, minimum volume ellipsoids, pelagic seabird distribution, Diomedea exulansAbstract
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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applications in biodiversity analysis. Trends in Ecology & Evolution,
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at-sea areas for seabird conservation: an example using
Northern Gannets breeding in the North East Atlantic. Biological
Conservation, 156, 43–52.
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areas and ocean basin management. Aquatic Conservation—
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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)
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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.
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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.
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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
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How to Cite
Ingenloff, K. (2017). Biologically informed ecological niche models for an example pelagic, highly mobile species. European Journal of Ecology, 3(1), 55-75. https://doi.org/10.1515/eje-2017-0006
