Predicting worldwide invasiveness for four major problematic decapods: An evaluation of using different calibration sets

Ecography

Volumn 34, Issue 3, 2011, Pages 448-459

  Capinha, César ^a   Leung, Brian ^b   Anastácio, Pedro ^a  

^a UNIVERSITY OF ÉVORA   (Portugal)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL INVASION; CALIBRATION; CRUSTACEAN; ECOLOGICAL MODELING; INVASIVE SPECIES; NICHE; NUMERICAL MODEL; POPULATION STRUCTURE; UNCERTAINTY ANALYSIS;

CHERAX DESTRUCTOR; ERIOCHEIR SINENSIS; PACIFASTACUS LENIUSCULUS; PROCAMBARUS CLARKII;

EID: 79957803292     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/j.1600-0587.2010.06369.x     Document Type: Article

References (52)

1. Anastácio, P. M. et al. 1999. CRISP (crayfish and rice integrated system of production): 2. Modelling crayfish (Procambarus clarkii) population dynamics. Ecol. Model. 123: 5-16.
2. Araújo, M. B. et al. 2005. Reducing uncertainty in projections of extinction risk from climate change. Global Ecol. Biogeogr. 14: 538-529.
3. Bahn, V. and McGill, B. J. 2007. Can niche-based distribution models outperform spatial interpolation?. Global Ecol. Biogeogr. 16: 733-742.
4. Beaumont, L. J. et al. 2009. Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Divers. Distrib. 15: 409-420.
5. Broennimann, O. and Guisan, A. 2008. Predicting current and future biological invasions: both native and invaded ranges matter. Biol. Lett. 4: 585-589.
6. Broennimann, O. et al. 2007. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10: 701-709.
7. Brotons, L. et al. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437-448.
8. Burnham, K. P. and Anderson, D. R. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer.
9. Chefaoui, R. M. and Lobo, J. M. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Model. 210: 478-486.
10. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20: 37-46.
11. Correia, A. and Anastácio, P. 2008. Shifts in aquatic macroinvertebrate biodiversity associated with the presence and size of an alien crayfish. Ecol. Res. 23: 729-734.
12. Côté, I. M. and Reynolds, J. D. 2002. Conservation biology: predictive ecology to the rescue?. Science 298: 1181-1182.
13. Cruz, M. et al. 2008. Collapse of the amphibian community of the Paul do Boquilobo Natural Reserve (central Portugal) after the arrival of the exotic American crayfish Procambarus clarkii. Herpetol. J. 18: 197-204.
14. Cumberlidge, N. 2009. Freshwater crabs and shrimps (Crustacea: Decapoda) of the Nile Basin. - In: Dumont, H. J. (ed.), The Nile. Springer, pp. 547-561.
15. DAISIE. 2008. Handbook of alien species in Europe. Springer.
16. Dittel, A. I. and Epifanio, C. E. 2009. Invasion biology of the Chinese mitten crab Eriochier sinensis: a brief review. J. Exp. Mar. Biol. Ecol. 374: 79-92.
17. Elith, J. et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
18. Elith, J. et al. 2010. The art of modelling range-shifting species. Methods Ecol. Evol. in press.
19. Fitzpatrick, M. et al. 2007. The biogeography of prediction error: why does the introduced range of the fire ant over-predict its native range?. Global Ecol. Biogeogr. 16: 24-33.
20. Gollasch, S. 2006. NOBANIS - invasive alien species fact sheet -Eriocheir sinensis. Online Database of the North European and Baltic Network on Invasive Alien Species - NOBANIS .
21. Gutiérrez-Yurrita, P. J. et al. 1999. The status of crayfish populations in Spain and Portugal. - In: Gherardi, F. and Holdich, D. M. (eds), Crayfish in Europe as alien species: how to make the best of a bad situation?. A. A. Balkema, pp. 161-192.
22. Hanley, J. A. and McNeil, B. J. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36.
23. Hayes, K. and Barry, S. 2008. Are there any consistent predictors of invasion success?. Biol. Invasions 10: 483-506.
24. Hirzel, A. H. et al. 2001. Assessing habitat-suitability models with a virtual species. Ecol. Model. 145: 111-121.
25. Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 33: 159-174.
26. Lek, S. and Guégan, J. F. 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Model. 120: 65-73.
27. Leung, B. and Mandrak, N. E. 2007. The risk of establishment of aquatic invasive species: joining invasibility and propagule pressure. Proc. R. Soc. B 274: 2603-2609.
28. Lobo, J. M. 2008. More complex distribution models or more representative data?. Biodivers. Inform. 5: 15-19.
29. Loo, S. E. et al. 2007. Forecasting New Zealand mudsnail invasion range: model comparisons using native and invaded ranges. Ecol. Appl. 17: 181-189..
30. Lowe, S. et al. 2000. 100 of the world's worst invasive alien species. A selection from the global invasive species database. ISSG, SSC and IUCN.
31. Lynas, J. et al. 2004. Is the yabby, Cherax destructor (Parastacidae) in Western Australia an ecological threat?. Freshwater Crayfish 14: 37-44.
32. Marmion, M. et al. 2009. Evaluation of consensus methods in predictive species distribution modeling. Divers. Distrib. 15: 59-69.
33. Mau-Crimmins, T. M. et al. 2006. Can the invaded range of a species be predicted sufficiently using only native-range data? Lehmann lovegrass (Eragrostis lehmanniana) in the southwestern United States. Ecol. Model. 193: 736-746.
34. Mitchell, T. D. and Jones, P. D. 2005. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol. 25: 693-712.
35. Myers, N. et al. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853-858.
36. Nyström, P. 2002. Ecology. - In: Holdich, D. M. (ed.), Biology of freshwater crayfish. Wiley-Blackwell, pp. 192-235.
37. Özesmia, S. L. et al. 2006. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecol. Model. 195: 83-93.
38. Pearce, J. L. and Boyce, M. S. 2006. Modelling distribution and abundance with presence-only data. J. Appl. Ecol. 43: 405-412.
39. Pearman, P. B. et al. 2008. Niche dynamics in space and time. Trends Ecol. Evol. 23: 149-158.
40. Phillips, S. J. 2008. Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography 31: 272-278.
41. Phillips, S. J. et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19: 181-197.
42. Segurado, P. and Araújo, M. B. 2004. An evaluation of methods for modelling species distributions. J. Biogeogr. 31: 1555-1568.
43. Semple, D. B. et al. 1995. Cherax destructor, C. tenuimanus and C. quadricarinatus (Decapoda: Parastacidae): a comparative review of biological traits relating to aquaculture potential. Freshwater Crayfish 8: 495-503.
44. Souty-Grosset, C. et al (eds), Atlas of crayfish in Europe. Museum national d'Histoire naturelle 2006.
45. Steiner, F. M. et al. 2008. Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species. Divers. Distrib. 14: 538-545.
46. Stohlgren, T. J. et al. 2010. Ensemble habitat mapping of invasive plant species. Risk Anal. 30: 224-235.
47. Thuiller, W. et al. 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Change Biol. 11: 2234-2250.
48. VanDerWal, J. et al. 2009. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know?. Ecol. Model. 220: 589-594.
49. Verdin, K. and Jenson, S. 1996. Development of continental scale DEMs and extraction of hydrographic features. - Proceedings of the Third Conference on GIS and Environmental Modeling. Univ. of California.
50. Welk, E. et al. 2002. Present and potential distribution of invasive garlic mustard (Alliaria petiolata) in North America. Divers. Distrib. 8: 219-233.
51. Wilson, J. R. U. et al. 2007. Residence time and potential range: crucial considerations in modelling plant invasions. Divers. Distrib. 13: 11-22.
52. Witten, I. H. and Frank, E. 2005. Data mining: practical machine learning tools and techniques, 2nd ed. Morgan Kaufmann.