Quantifying apart what belongs together: A multi‐state species distribution modelling framework for species using distinct habitats

Methods in Ecology and Evolution - Tập 9 Số 1 - Trang 98-108 - 2018
Veronica F. Frans1,2, Amélie A. Augé3,4, Hendrik Edelhoff1, Stefan Erasmi5, Niko Balkenhol1, Jan O. Engler1,6
1Department of Wildlife Sciences, University of Göttingen, Göttingen, Germany
2Workgroup on Endangered Species University of Göttingen Göttingen Germany
3ARC Center of Excellence for Coral Reef Studies, James Cook University, Townsville, Australia
4School of Surveying, University of Otago, Dunedin, New Zealand
5Institute of Geography, University of Göttingen, Göttingen, Germany
6Zoological Research Museum Alexander Koenig, Bonn, Germany

Tóm tắt

Abstract

Species distribution models (SDMs) have been used to inform scientists and conservationists about the status and change in occurrence patterns in threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within a reachable distance. For such species, classical applications of SDMs might lead to erroneous representations of habitat suitability, as the complex relationships between predictors are lost when merging occurrence information across multiple habitats. To better account for the spatial arrangement of complementary—yet mandatory—habitat types, it is important to implement modelling strategies that partition occurrence information according to habitat use in a spatial context. Here, we address this issue by introducing a multi‐state SDM framework.

The multi‐state SDM framework stratifies occurrences according to the temporal or behavioural use of distinct habitat types, referred to as “states.” Multiple SDMs are then run for each state and statistical thresholds of presence are used to combine these separate predictions. To identify suitable sites that account for distance between habitats, two optional modules are proposed where the thresholded output is aggregated and filtered by minimum area size, or through moving windows across maximum reachable distances.

We illustrate the full use of this framework by modelling the dynamic terrestrial breeding habitat preferences of the New Zealand sea lion (NZSL) (Phocarctos hookeri), using Maxent and trialling both modules to identify suitable sites for possible recolonization.

The Maxent predictions showed excellent performance, and the multi‐state SDM framework highlighted 36–77 potential suitable breeding sites in the study area.

This framework can be applied to inform management when defining habitat suitability for species with complex changes in habitat use. It accounts for temporal and behavioural changes in distribution, maintains the individuality of each partitioned SDM, and considers distance between distinct habitat types. It also yields one final, easy‐to‐understand output for stakeholders and managers.

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Tài liệu tham khảo

10.1111/1365-2664.12714

10.1111/j.1748-7692.2011.00515.x

10.3354/meps09176

10.1163/15683909X427687

10.1016/j.dsr.2014.05.017

10.1016/j.biocon.2014.03.018

10.1080/03014223.1998.9518131

Connelly J. W., 2000, Guidelines to manage sage grouse populations and their habitats, Wildlife Society Bulletin, 28, 967

10.1111/jbi.12199

10.1890/11-0114.1

10.1071/WR9930137

Department of Conservation, 2009, New Zealand sea lion species management plan: 2009–2014

10.1016/j.ecolmodel.2008.02.006

10.1186/s40462-016-0086-5

10.1111/j.1600-0587.2009.05901.x

10.1111/j.1472-4642.2010.00725.x

10.14411/eje.2013.044

10.1016/j.biocon.2013.08.009

10.1017/CBO9780511810602

Frans V. F., 2017, Data from: Quantifying apart what belongs together: A multi‐state species distribution modeling framework for species using distinct habitats, Dryad Digital Repository

10.1111/j.1365-2664.2007.01408.x

10.1111/j.1365-2664.2012.02170.x

10.1111/j.1472-4642.2007.00342.x

10.1111/ele.12189

10.1002/rse2.7

10.1111/j.0906-7590.2006.04700.x

Hijmans R. J. Phillips S. Leathwick J. &Elith J.(2013)dismo: species distribution modeling: R package version 0.9‐3.http://CRAN.R-project.org/package=dismo[accessed 9 January 2015]

10.1002/0471722146

10.1046/j.0021-8901.2001.00668.x

10.1017/S0376892905002171

Kindlmann P., 2008, Connectivity measures: A review, Landscape Ecology, 23, 879

10.1002/ece3.557

10.1016/S0006-3207(02)00421-4

10.1071/WR9930419

10.1023/A:1008877611726

10.1111/j.0906-7590.2005.03957.x

10.1111/jbi.12058

10.7717/peerj.1122

10.1016/j.ocecoaman.2016.06.008

10.1080/03014223.2001.9518258

10.1111/j.0014-3820.2006.tb00500.x

10.1111/j.1600-0587.2013.07872.x

10.1111/2041-210X.12645

10.1046/j.1523-1739.2003.02144.x

10.1016/j.biocon.2005.12.016

10.1046/j.1523-1739.2000.98539.x

10.1016/j.tree.2007.10.009

10.1111/j.0906-7590.2008.5203.x

R Core Team, 2015, R: A language and environment for statistical computing

10.1111/j.1365-2907.2011.00186.x

10.1111/ddi.12164

10.1111/j.2041-210X.2011.00182.x

10.1016/j.gecco.2014.09.011

10.1071/WR9880595

10.1371/journal.pone.0070038

10.1046/j.1365-2486.2003.00666.x

10.1016/j.jnc.2013.08.001

10.1111/j.1472-4642.2008.00482.x

10.1111/ddi.12428

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Methods in Ecology and Evolution

Tập 9 Số 1

98-108

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