Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-19T06:09:20.838Z Has data issue: false hasContentIssue false
  • English
  • Français

Robustness and accuracy of Maxent niche modelling for Lactuca species distributions in light of collecting expeditions

Published online by Cambridge University Press:  18 September 2014

M. M. P. Cobben ,
R. van Treuren ,
N. P. Castañeda-Álvarez ,
C. K. Khoury ,
C. Kik  and
T. J. L. van Hintum
M. M. P. Cobben*
Affiliation:
Centre for Genetic Resources, The Netherlands (CGN), PO Box 16, 6700 AAWageningen, The Netherlands Netherlands Institute of Ecology (NIOO-KNAW), PO Box 50, 6700 ABWageningen, The Netherlands
R. van Treuren
Affiliation:
Centre for Genetic Resources, The Netherlands (CGN), PO Box 16, 6700 AAWageningen, The Netherlands
N. P. Castañeda-Álvarez
Affiliation:
International Center for Tropical Agriculture (CIAT), Apartado Aéreo 6713, Calí, Colombia
C. K. Khoury
Affiliation:
International Center for Tropical Agriculture (CIAT), Apartado Aéreo 6713, Calí, Colombia Centre for Crop Systems Analysis, Wageningen University, PO Box 16, 6700 AAWageningen, The Netherlands
C. Kik
Affiliation:
Centre for Genetic Resources, The Netherlands (CGN), PO Box 16, 6700 AAWageningen, The Netherlands
T. J. L. van Hintum
Affiliation:
Centre for Genetic Resources, The Netherlands (CGN), PO Box 16, 6700 AAWageningen, The Netherlands
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Niche modelling software can be used to assess the probability of detecting a population of a plant species at a certain location. In this study, we used the distribution of the wild relatives of lettuce (Lactuca spp.) to investigate the applicability of Maxent species distribution models for collecting missions. Geographic origin data of genebank and herbarium specimens and climatic data of the origin locations were used as input. For Lactuca saligna, we varied the input data by omitting the specimens from different parts of the known distribution area to assess the robustness of the predicted distributions. Furthermore, we examined the accuracy of the modelling by comparing the predicted probabilities of population presence against recent expedition data for the endemic Lactuca georgica and the cosmopolitan Lactuca serriola. We found Maxent to be quite robust in its predictions, although its usefulness was higher for endemic taxa than for more widespread species. The exclusion of occurrence data from the perceived range margins of the species can result in important information about local adaptation to distinct climatic conditions. We discuss the potential for enhanced use of Maxent in germplasm collecting planning.

Keywords

collecting expeditiongenebankLactucaMaxentniche modellingplant genetic resourcesspecies distribution modelling
Type
Research Article
Information
Plant Genetic Resources , Volume 13 , Issue 2 , August 2015 , pp. 153 - 161
Copyright
Copyright © NIAB 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Afonin, A and Greene, SL (1999) Germplasm collecting using modern geographic information technologies: directions explored by the N.I. Vavilov Institute of Plant Industry. In: Greene, SL and Guarino, L (eds) Linking Genetic Resources and Geography: Emerging Strategies for Conserving and Using Crop Biodiversity. CSSA Special Publication 27. Madison, WI: American Society of Agronomy, pp. 75–85.Google Scholar
Alexander, JM (2013) Evolution under changing climates: climatic niche stasis despite rapid evolution in a non-native plant. Proceedings of the Royal Society B 280: 20131446.CrossRefGoogle Scholar
Araújo, MB and Guisan, A (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography 33: 1677–1688.Google Scholar
Araújo, MB and New, M (2007) Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22: 42–47.CrossRefGoogle ScholarPubMed
D'Andrea, L, Broennimann, O, Kozlowski, G, Guisan, A, Morin, X, Keller-Senften, J and Felber, F (2009) Climate change, anthropogenic disturbance and the northward range expansion of Lactuca serriola (Asteraceae). Journal of Biogeography 36: 1573–1587.Google Scholar
Elith, J and Leathwick, JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40: 677–697.Google Scholar
Elith, J, Graham, CH, Anderson, RP, Dudík, M, Ferrier, S, Guisan, A, Hijmans, RJ, Huettmann, F, Leathwick, JR, Lehmann, A, Li, J, Lohmann, LG, Loiselle, BA, Manion, G, Moritz, C, Nakamura, M, Nakazawa, Y, Overton, JMM, Townsend Peterson, A, Phillips, SJ, Richardson, K, Scachetti-Pereira, R, Schapire, RE, Soberón, J, Williams, S, Wisz, MS and Zimmermann, NE (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129–151.Google Scholar
Greene, SL, Hart, TC and Afonin, A (1999) Using geographic information to acquire wild crop germplasm for ex situ collections: I. map development and field use. Crop Science 39: 836–842.CrossRefGoogle Scholar
Grime, JP (1977) Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. The American Naturalist 111: 1169–1194.CrossRefGoogle Scholar
GRIN(2014) http://www.ars-grin.gov/cgi-bin/npgs/html/taxon.pl?21359.Google Scholar
Guarino, L (1995) Mapping the ecogeographic distribution of biodiversity. In: Guarino, L, Ramanatha Rao, V and Reid, R (eds) Collecting Plant Genetic Diversity Technical Guidelines. Wallingford, UK: CAB International, pp. 287–327.Google Scholar
Guisan, A and Thuiller, W (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993–1009.Google Scholar
Hijmans, RJ, Guarino, L and Mathur, P (2012) DIVA-GIS vs 7.5. A geographic information system for the analysis of species distribution data Software and manual available at www.diva-gis.org.Google Scholar
Hirzel, AH, Hausser, J, Chessel, D and Perrin, N (2002) Ecological-niche factor analysis: how to compute habitat-suitability map without absence data. Ecology 83: 2027–2036.Google Scholar
Jarvis, A, Williams, K, Williams, D, Guarino, L, Caballero, PJ and Mottram, G (2005) Use of GIS for optimizing a collecting mission for a rare wild pepper (Capsicum flexuosum Sendtn.) in Paraguay. Genetic Resources and Crop Evolution 52: 671–682.CrossRefGoogle Scholar
Lebeda, A, Dolezalová, I, Feráková, V and Astley, D (2004a) Geographical distribution of wild Lactuca species (Asteraceae, Lactuceae). The Botanical Review 70: 328–356.Google Scholar
Lebeda, A, Dolezalová, I and Astley, D (2004b) Representation of wild Lactuca spp. (Asteraceae, Lactuceae) in world genebank collections. Genetic Resources and Crop Evolution 51: 167–174.Google Scholar
Lebeda, A, Dolezalová, I, Křıástková, E, Kitner, M, Petrželová, I, Mieslerová, B and Novotná, A (2009b) Wild Lactuca germplasm for lettuce breeding: current status, gaps and challenges. Euphytica 170: 15–34.Google Scholar
Maxted, N, Dulloo, E, Ford-Lloyd, BV, Iriondo, JM and Jarvis, A (2008) Gap analysis: a tool for complementary genetic conservation assessment. Diversity and Distributions 14: 1018–1030.Google Scholar
Parra-Quijano, M, Iriondo, JM and Torres, E (2012) Improving representativeness of genebank collections through species distribution models, gap analysis and ecogeographical maps. Biodiversity and Conservation 21: 79–96.Google Scholar
Phillips, SJ, Anderson, RP and Schapire, RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259.CrossRefGoogle Scholar
Ramírez-Villegas, J, Khoury, C, Jarvis, A, Debouck, DG and Guarino, L (2010) A gap analysis methodology for collecting crop gene pools: a case study with Phaseolus beans. PLoS ONE 5: e13497.CrossRefGoogle ScholarPubMed
Stockwell, D and Peters, D (1999) The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13: 143–158.Google Scholar
Thuiller, W, Lafourcade, B, Engler, R and Araújo, MB (2009) BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32: 369–373.CrossRefGoogle Scholar
Van Treuren, R, Coquin, P and Lohwasser, U (2012) Genetic resources collections of leafy vegetables (lettuce, spinach, chicory, artichoke, asparagus, lamb's lettuce, rhubarb and rocket salad): composition and gaps. Genetic Resources and Crop Evolution 59: 981–997.CrossRefGoogle Scholar
VanDerWal, J, Shoo, LP, Graham, C and Williams, SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecological Modelling 220: 589–594.CrossRefGoogle Scholar
Zohary, D (1991) The wild genetic resources of cultivated lettuce (Lactuca sativa L.). Euphytica 53: 31–35.Google Scholar
Supplementary material: File

Cobben Supplementary Material

Supplementary Material

Download Cobben Supplementary Material(File)
File 800.1 KB