Transferability and scalability of species distribution models: a test with sedentary marine invertebrates

被引:8
|
作者
Eger, Aaron M. [1 ,5 ]
Curtis, Janelle M. R. [2 ]
Fortin, Marie-Josee [3 ]
Cote, Isabelle M. [4 ]
Guichard, Frederic [1 ]
机构
[1] McGill Univ, Dept Biol, Montreal, PQ H3A 1B1, Canada
[2] Fisheries & Oceans Canada, Pacific Biol Stn, 3190 Hammond Bay Rd, Nanaimo, BC V9T 6N7, Canada
[3] Univ Toronto, Dept Ecol & Evolutionary Biol, Toronto, ON M5S 3G5, Canada
[4] Simon Fraser Univ, Dept Biol Sci, Earth Ocean Res Grp, Burnaby, BC V5A 1S6, Canada
[5] Univ Victoria, Dept Biol, Victoria, BC V8P 5C2, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
HABITAT SUITABILITY; SAMPLE-SIZE; SPATIAL SCALE; SEA-URCHIN; PERFORMANCE; CONSERVATION; PREDICTIONS; SENSITIVITY; ACCURACY; CURVES;
D O I
10.1139/cjfas-2016-0129
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
We found the predictive accuracy of species distribution models (SDMs) for sedentary marine invertebrates to be dependent on the methodology of their application. We explored three applications of SDMs: first a model tested at a scale smaller than at which it was trained (downscaled), second a model tested at scale larger than its training scale (upscaled), and third a model tested at the same scale but outside the extent for which it was trained (transferred). The accuracies of these models were compared with the "reference" models that were trained and tested at the same scale and extent. We found that downscaled SDMs had higher predictive accuracy than reference SDMs. Transferred and upscaled models had lower predictive accuracy than their reference counterparts but still performed better than random, making them potentially acceptable alternatives where information is lacking for imminent decisions or in cost-restricted scenarios. Our results provide insights into the techniques available for researchers and managers developing SDMs at varying scales, with different species, and with different levels of initial information.
引用
收藏
页码:766 / 778
页数:13
相关论文
共 50 条
  • [21] Poor Transferability of Species Distribution Models for a Pelagic Predator, the Grey Petrel, Indicates Contrasting Habitat Preferences across Ocean Basins
    Torres, Leigh G.
    Sutton, Philip J. H.
    Thompson, David R.
    Delord, Karine
    Weimerskirch, Henri
    Sagar, Paul M.
    Sommer, Erica
    Dilley, Ben J.
    Ryan, Peter G.
    Phillips, Richard A.
    PLOS ONE, 2015, 10 (03):
  • [22] Effects of species traits and environmental predictors on performance and transferability of ecological niche models
    Regos, Adrian
    Gagne, Laura
    Alcaraz-Segura, Domingo
    Honrado, Joao P.
    Dominguez, Jesus
    SCIENTIFIC REPORTS, 2019, 9 (1)
  • [23] A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps
    Tehrani, Nasrin Amini
    Naimi, Babak
    Jaboyedoff, Michel
    ECOLOGICAL INFORMATICS, 2022, 69
  • [24] Uncertainty associated with survey design in Species Distribution Models
    Tessarolo, Geiziane
    Rangel, Thiago F.
    Araujo, Miguel B.
    Hortal, Joaquin
    DIVERSITY AND DISTRIBUTIONS, 2014, 20 (11) : 1258 - 1269
  • [25] Scaling down distribution maps from atlas data: a test of different approaches with virtual species
    Bombi, Pierluigi
    D'Amen, Manuela
    JOURNAL OF BIOGEOGRAPHY, 2012, 39 (04) : 640 - 651
  • [26] Identifying marine invasion hotspots using stacked species distribution models
    Lyons, Devin A.
    Ben Lowen, J.
    Therriault, Thomas W.
    Brickman, David
    Guo, Lanli
    Moore, Andrea M.
    Pena, M. Angelica
    Wang, Zeliang
    DiBacco, Claudio
    BIOLOGICAL INVASIONS, 2020, 22 (11) : 3403 - 3423
  • [27] Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines
    Cord, Anna F.
    Klein, Doris
    Gernandt, David S.
    Perez de la Rosa, Jorge A.
    Dech, Stefan
    JOURNAL OF BIOGEOGRAPHY, 2014, 41 (04) : 736 - 748
  • [28] The effect of positional error on fine scale species distribution models increases for specialist species
    Gabor, Lukas
    Moudry, Vitezslav
    Lecours, Vincent
    Malavasi, Marco
    Bartak, Vojtech
    Fogl, Michal
    Simova, Petra
    Rocchini, Duccio
    Vaclavik, Tomas
    ECOGRAPHY, 2020, 43 (02) : 256 - 269
  • [29] Distribution models and species discovery: the story of a new Solanum species from the Peruvian Andes
    Saerkinen, Tiina
    Gonzales, Paul
    Knapp, Sandra
    PHYTOKEYS, 2013, 31 : 1 - 20
  • [30] Preferential habitats prediction in syngnathids using species distribution models
    Hernandez-Urcera, J.
    Murillo, F. J.
    Regueira, M.
    Cabanellas-Reboredo, M.
    Planas, M.
    MARINE ENVIRONMENTAL RESEARCH, 2021, 172