Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T16:45:37.925Z Has data issue: false hasContentIssue false

Using invaded-range species distribution modeling to estimate the potential distribution of Linaria species and their hybrids in the U.S. northern Rockies

Published online by Cambridge University Press:  19 July 2019

Kevin R. McCartney
Affiliation:
Graduate Student, Colorado State University, Fort Collins, CO, USA
Sunil Kumar
Affiliation:
Natural Resource Ecology Laboratory and Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO, USA
Sharlene E. Sing
Affiliation:
Research Entomologist, Rocky Mountain Research Station, USDA Forest Service, Bozeman, MT, USA
Sarah M. Ward*
Affiliation:
Associate Professor, Colorado State University, Fort Collins, CO, USA
*
Author for correspondence: Sarah M. Ward, Colorado State University, Fort Collins, CO 80523-1499. (Email: sarah.ward@colostate.edu)

Abstract

Invasive populations of Dalmation toadflax [Linaria dalmatica (L.) Mill.] and yellow toadflax (Linaria vulgaris Mill.) are widespread throughout the Intermountain West, where gene flow between these nonnative species is producing vigorous and fertile hybrids. These hybrid toadflax populations are less responsive to herbicides than either parent species, and biocontrol agents routinely released on L. dalmatica and L. vulgaris often fail to establish on hybrid hosts. Early detection of hybrid Linaria populations is therefore essential for effective management, but resources are limited for scouting large expanses of range and wildland. We used species distribution modeling to identify environmentally suitable areas for these invasive Linaria taxa in Montana, Wyoming, and Colorado. Areas suitable for hybrid Linaria establishment were estimated using two different modeling approaches: first, based on known hybrid occurrence and associated environmental conditions, and second, based on zones environmentally suitable for co-occurrence of the parent species. This also allowed comparison of different model outputs, especially relevant when modeling emerging invasives, such as novel hybrids, with minimal occurrence data. Combining the two model outputs identified areas at greatest risk of hybrid Linaria invasion, including parts of north-central Montana, where model estimates indicate the hybrid may spread without prior co-invasion of the parents. Potential hybrid hot spots were also identified in western Montana; northwestern, northeastern, and southeastern Wyoming; and the Western Slope and Front Range of Colorado. Despite relatively few confirmed occurrences of hybrid populations to date, our results indicate that extensive spread of hybrid populations is possible within the studied area. Model-based maps of potential Linaria distributions will allow area weed managers to direct limited resources more effectively for locating and controlling these invaders.

Type
Research Article
Copyright
© Weed Science Society of America, 2019 

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.)

Footnotes

Associate Editor: Catherine Jarnevich, U.S. Geological Survey

References

Alex, J (1962) The taxonomy, history, and distribution of Linaria dalmatica . Can J Bot 40:295307 CrossRefGoogle Scholar
Arnold, RM (1982) Pollination, predation and seed set in Linaria vulgaris (Scrophulariaceae). Am Midl Nat 107:360369 CrossRefGoogle Scholar
Austin, M, Smith, T (1989) A new model for the continuum concept. Plant Ecol 83:3547 CrossRefGoogle Scholar
Benito, BM, Martinez-Ortega, MM, Munoz, LM, Lorite, J, Penas, J (2009) Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses. Biodivers Conserv 18:25092520 CrossRefGoogle Scholar
Blumenthal, DM, Norton, AP, Cox, SE, Hardy, EM, Liston, GE, Kennaway, L, Booth, DT, Derner, JD (2012) Linaria dalmatica invades south-facing slopes and less grazed areas in grazing-tolerant mixed-grass prairie. Biol Invasions 14:395404 CrossRefGoogle Scholar
Boria, RA, Olson, LE, Goodman, SM, Anderson, RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275:7377 CrossRefGoogle Scholar
Boswell, A, Sing, SE, Ward, SM (2016) Plastid DNA analysis reveals cryptic hybridization in invasive Dalmatian toadflax (Linaria dalmatica) populations. Invasive Plant Sci Manag 9:112120 CrossRefGoogle Scholar
Bradley, BA, Marvin, DC (2011) Using expert knowledge to satisfy data needs: mapping invasive plant distributions in the western United States. West N Am Nat 71:302315 CrossRefGoogle Scholar
Broennimann, O, Fitzpatrick, MC, Pearman, PB, Petitpierre, B, Pellissier, L, Yoccoz, NG, Thuiller, W, Fortin, MJ, Randin, C, Zimmermann, NE, Graham, CH, Guisan, A (2012) Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecol Biogeogr 21:481497 CrossRefGoogle Scholar
Bromberg, JE, Kumar, S, Brown, CS, Stohlgren, TJ (2011) Distributional changes and range predictions of downy brome (Bromus tectorum) in Rocky Mountain National Park. Invasive Plant Sci Manag 4:173182 CrossRefGoogle Scholar
Brown, JL (2014) SDMtoolbox: a Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol Evol 5:694700 CrossRefGoogle Scholar
Brown, LS (2008) Genetic Variation of the Invasive Linaria dalmatica in Its Introduced Range in Western North America and the Impact of Its Predominant Biological Control Agent, Mecinus janthinus. MS thesis. Moscow, ID: University of Idaho. 91 pGoogle Scholar
Carter, L, Young, N (2011) Identifying Niche Overlap from Maxent Model Predictions. http://ibis.colostate.edu/WebContent/WS/ColoradoView/TutorialsDownloads/Niche_Overlap_v4.pdf. Accessed: May 5, 2016 Google Scholar
Choudhury, MR, Deb, P, Singha, H, Chakdar, B, Medhi, M (2016) Predicting the probable distribution and threat of invasive Mimosa diplotricha Suavalle and Mikania micrantha Kunth in a protected tropical grassland. Ecol Eng 97:2331 CrossRefGoogle Scholar
Dormann, CF (2007) Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecol Biogeogr 16:129138 Google Scholar
Dormann, CF, Elith, J, Bacher, S, Buchmann, C, Carl, G, Carré, G, García Marquéz, JR, Gruber, B, Lafourcade, B, Leitão, PJ, Münkemüller, T, McClean, C, Osborne, PE, Reineking, B, Schróder, B, Skidmore, AK, Zurell, D, Lautenbach, S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:2746 CrossRefGoogle Scholar
Elith, J, Kearney, M, Phillips, S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330342 CrossRefGoogle Scholar
Elith, J, Leathwick, JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677697 CrossRefGoogle Scholar
Fourcade, Y, Engler, JO, Rodder, D, Secondi, J (2014) Mapping species distributions with Maxent using a geographically biased sample of presence data: a performance assessment of methods for correcting sample bias. PLoS ONE 9:e97122 CrossRefGoogle Scholar
Gaskin, JF (2017) The role of hybridization in facilitating tree invasion. AoB Plants 9:plw079Google Scholar
Global Biodiversity Information Facility (2016) www.gbif.org. Accessed: December 10, 2016Google Scholar
Green, RH (1971) A multivariate statistical approach to the Hutchinsonian niche: bivalve molluscs of central Canada. Ecology 52:543556 CrossRefGoogle ScholarPubMed
Guisan, A, Graham, CH, Elith, J, Huettmann, F, Distri, NS (2007a) Sensitivity of predictive species distribution models to change in grain size. Divers Distrib 13:332340 CrossRefGoogle Scholar
Guisan, A, Zimmermann, NE, Elith, J, Graham, CH, Phillips, S, Peterson, AT (2007b) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77:615630 CrossRefGoogle Scholar
Hengl, T, Mendes de Jesus, J, Heuvelink, GBM, Gonzalez, MR, Kilibarda, M, Blagoti, A, Shangguan, W, Wright, MN, Geng, X, Bauer-Marschallinger, B, Guevara, MA, Vargas, R, MacMillan, RA, Batjes, NH, Leenaars, JGB, Ribeiro, E, Wheeler, I, Mantel, S, Kempen, B (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12:e0169748CrossRefGoogle ScholarPubMed
Hernandez, PA, Graham, CH, Master, LL, Albert, DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773785 Google Scholar
Homer, C, Dewitz, J, Yang, LM, Jin, S, Danielson, P, Xian, G, Coulston, J, Herold, N, Wickham, J, Megown, K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm Eng Remote Sens 81:345354 Google Scholar
Hovick, SM, Campbell, LG, Snow, AA, Whitney, KD (2012) Hybridization alters early life-history traits and increases plant colonization success in a novel region. Am Nat 179:192203 CrossRefGoogle Scholar
Hovick, SM, Whitney, KD (2014) Hybridization is associated with increased fecundity and size in invasive taxa: meta-analytic support for the hybridization-invasion hypothesis. Ecol Lett 17:14641477 CrossRefGoogle ScholarPubMed
iDigBio (2016) http://www.iDigBio.org. Accessed: December 10, 2016Google Scholar
Jarnevich, CS, Holcombe, TR, Barnett, DT, Stohlgren, TJ, Kartesz, JT (2010) Forecasting weed distributions using climate data: a GIS early warning tool. Invasive Plant Sci Manag 3:365375 CrossRefGoogle Scholar
Jarnevich, CS, Stohlgren, TJ, Kumar, S, Morisette, JT, Holcombe, TR (2015) Caveats for correlative species distribution modeling. Ecol Inform 29:615 CrossRefGoogle Scholar
Jazwa, M, Jedrzejczak, E, Klichowska, E, Pliszko, A (2018) Predicting the potential distribution area of Solidago × niederederi (Asteraceae). Turkish J Bot 42:5156 CrossRefGoogle Scholar
Jimenez-Valverde, A, Peterson, AT, Soberon, J, Overton, JM, Aragon, P, Lobo, JM (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13:27852797 CrossRefGoogle Scholar
Jovanovic, S, Hlavati-Sirka, V, Lakusic, D, Jogan, N, Nikolic, T, Anastasiu, P, Vladimirov, V, Sinzar-Sekulic, J (2018) Reynoutria niche modelling and protected area prioritization for restoration and protection from invasion: a southeastern Europe case study. J Nat Conserv (Jena) 41:115 CrossRefGoogle Scholar
Kaplan, H, van Niekerk, A, Le Roux, JJ, Richardson, DM, Wilson, JRU (2014) Incorporating risk mapping at multiple spatial scales into eradication management plans. Biol Invasions 16:691703 CrossRefGoogle Scholar
Koncki, NG, Aronson, MFJ (2015) Invasion risk in a warmer world: modeling range expansion and habitat preferences of three nonnative aquatic invasive plants. Invasive Plant Sci Manag 8:436450 CrossRefGoogle Scholar
Kumar, S, Neven, LG, Yee, WL (2014) Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment. Ecosphere 5:86 CrossRefGoogle Scholar
Kumar, S, Stohlgren, TJ (2009) MaxEnt modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 1:9498 Google Scholar
Lestina, J, Cook, M, Kumar, S, Morisette, J, Ode, PJ, Peairs, F (2016) MODIS imagery improves pest risk assessment: a case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA. Environ Entomol 45:13431351 Google Scholar
Liu, CR, White, M, Newell, G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778789 CrossRefGoogle Scholar
Lobo, JM, Jimenez-Valverde, A, Real, R (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145151 CrossRefGoogle Scholar
López-Alvarez, D, Manzaneda, AJ, Rey, PJ, Giraldo, P, Benavente, E, Allainguillaume, J, Mur, L, Caicedo, AL, Hazen, SP, Breiman, A (2015) Environmental niche variation and evolutionary diversification of the Brachypodium distachyon grass complex species in their native circum-Mediterranean range. Am J Bot 102:10731088 CrossRefGoogle ScholarPubMed
Mack, RN (2003) Plant naturalizations and invasions in the eastern United States: 1634-1860. Ann Mo Bot Gard 90:7790 CrossRefGoogle Scholar
Mack, RN, Simberloff, D, Lonsdale, WM, Evans, H, Clout, M, Bazzaz, FA (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689710 CrossRefGoogle Scholar
Mainali, KP, Warren, DL, Dhileepan, K, McConnachie, A, Strathie, L, Hassan, G, Karki, D, Shrestha, BB, Parmesan, C (2015). Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Global Change Biol 21:44644480 CrossRefGoogle ScholarPubMed
Maxwell, BD, Backus, V, Hohmann, MG, Irvine, KM, Lawrence, P, Lehnhoff, EA, Rew, LJ (2012) Comparison of transect-based standard and adaptive sampling methods for invasive plant species. Invasive Plant Sci Manag 5:178193 CrossRefGoogle Scholar
Merow, C, Smith, MJ, Silander, JA (2013) A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter. Ecography 36:10581069 Google Scholar
Mesgaran, MB, Lewis, MA, Ades, PK, Donohue, K, Ohadi, S, Li, C, Cousens, RD (2016) Hybridization can facilitate species invasions, even without enhancing local adaptation. Proc Natl Acad Sci USA 113:1021010214 CrossRefGoogle ScholarPubMed
Milne, RI, Abbott, RJ (2000) Origin and evolution of invasive naturalized material of Rhododendron ponticum L. in the British Isles. Mol Ecol 9:541556 Google ScholarPubMed
[MRLC] Multi-Resolution Land Characteristics Consortium (2016) MRLC NLCD Mapping Tools https://www.mrlc.gov/tools. Accessed: January 14, 2017Google Scholar
Ndlovu, P, Mutanga, O, Sibanda, M, Odidndi, J, Rushwort, I (2018) Modelling potential distribution of bramble (Rubus cuneifolius) using topographic, bioclimatic and remotely sensed data in the KwaZulu-Natal Drakensberg, South Africa. Appl Geogr 4:7180 Google Scholar
Olson, A, Paul, J, Freeland, JR (2009) Habitat preferences of cattail species and hybrids (Typha spp.) in eastern Canada. Aquat Bot 91:6770 CrossRefGoogle Scholar
Ort, BS, Thornton, WJ (2016) Changes in the population genetics of an invasive Spartina after 10 years of management. Biol Invasions 18:22672281 CrossRefGoogle Scholar
Parepa, M, Fischer, M, Krebs, C, Bossdorf, O (2014) Hybridization increases invasive knotweed success. Evol Appl 7:413420 CrossRefGoogle ScholarPubMed
Pearson, RG, Raxworthy, CJ, Nakamura, M, Peterson, AT (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102117 CrossRefGoogle Scholar
Peterson, AT, Soberon, J,Pearson, RG, Anderson, AP, Martinez-Meyer, E, Nakamura, M, Araujo, MB (2011) Ecological Niches and Geographic Distributions. Princeton, NJ: Princeton University Press. 328 pCrossRefGoogle Scholar
Phillips, SJ, Anderson, RP, Schapire, RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231259 CrossRefGoogle Scholar
Phillips, SJ, Dudik, M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161175 CrossRefGoogle Scholar
Rauber, RB, Cipriotti, PA, Collantes, MB, Martini, JP, Frers, E (2016) Regional suitability assessment for the mouseear hawkweed (Hieracium pilosella) invasion in Patagonian rangelands. Invasive Plant Sci Manag 9:242251 CrossRefGoogle Scholar
Rebelo, H, Jones, G (2010) Ground validation of presence-only modelling with rare species: a case study on barbastelles Barbastella barbastellus (Chiroptera: Vespertilionidae). J Appl Ecol 47:410420 CrossRefGoogle Scholar
Rieseberg, LH, Archer, MA, Wayne, RK (1999) Transgressive segregation, adaptation and speciation. Heredity 83:363372 CrossRefGoogle ScholarPubMed
Rieseberg, LH, Kim, SC, Randell, RA, Whitney, KD, Gross, BL, Lexer, C, Clay, K (2007) Hybridization and the colonization of novel habitats by annual sunflowers. Genetica 129:149165 CrossRefGoogle ScholarPubMed
Rodder, D, Schmidtlein, S, Veith, M, Lotters, S (2009) Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS ONE 4:e7843 Google ScholarPubMed
Saner, MA, Clements, DR, Hall, MR, Doohan, DJ, Crompton, CW (1995) The biology of Canadian weeds. 105. Linaria vulgaris Mill. Can J Plant Sci 75:525537 CrossRefGoogle Scholar
Schierenbeck, KA, Ellstrand, NC (2009) Hybridization and the evolution of invasiveness in plants and other organisms. Biol Invasions 11:10931105 CrossRefGoogle Scholar
SEINet (2016) http://swbiodiversity.org/seinet/collections/. Accessed: November 22, 2016Google Scholar
Shafii, B, Price, W, Prather, TS, Lass, LW, Thill, D (2003) Predicting the likelihood of yellow starthistle (Centaurea solstitialis) occurrence using landscape characteristics. Weed Sci 51:748751 CrossRefGoogle Scholar
Sing, SE, De Clerck-Floate, RA, Hansen, RW, Pearce, H, Randall, CB, Toševski, I, Ward, SM (2016) Biology and Biological Control of Dalmatian and Yellow Toadflax. Morgantown, WV: USDA Forest Service, Forest Health Technology Enterprise Team, FHTET-2016-01 CrossRefGoogle Scholar
Sutton, DA (1988) A Revision of the Tribe Antirrhineae. Oxford, UK: Oxford University Press. 575 pGoogle Scholar
Sutton, JR, Stohlgren, TJ, Beck, KG (2007) Predicting yellow toadflax infestations in the Flat Tops Wilderness of Colorado. Biol Invasions 9:783793 CrossRefGoogle Scholar
Syfert, MM, Smith, MJ, Coomes, DA (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE 8:e55158 CrossRefGoogle ScholarPubMed
Turner MFS (2012) Viability and Invasive Potential of Hybrids Between Yellow Toadflax (Linaria vulgaris) and Dalmatian Toadflax (Linaria dalmatica). Ph.D dissertation. Fort Collins, CO: Colorado State University. 148 pGoogle Scholar
University of Colorado-Boulder Museum of Natural History (2016) https://www.colorado.edu/cumuseum/research-collections/botany-section-university-herbarium-colo. Accessed: December 13, 2016Google Scholar
University of Georgia (2016) Center for Invasive Species and Ecosystem Health Early Detection and Distribution Mapping System http://www.eddmaps.org. Accessed: November 22, 2016Google Scholar
University of Washington (2016) Consortium of Pacific Northwest Herbaria http://www.pnwherbaria.org/. Accessed: November 23, 2016Google Scholar
University of Wyoming (2016) Rocky Mountain Region Digital Herbarium. https://www-lib.uwyo.edu/digitalherbaria/. Accessed: November 25, 2016Google Scholar
[USDA-NRCS] U.S. Department of Agriculture-Natural Resources Conservation Service (2018) The PLANTS Database. http://plants.usda.gov Accessed: June 8, 2017Google Scholar
[USGS] U.S. Geological Survey (2016a) EarthExplorer https://earthexplorer.usgs.gov. Accessed: December 15, 2016Google Scholar
[USGS] U.S. Geological Survey (2016b) Processes Distributed Archive Center https://lpdaac.usgs.gov. Accessed: December 13, 2016Google Scholar
Vaclavik, T, Meentemeyer, RK (2009) Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions? Ecol Model 220:32483258 CrossRefGoogle Scholar
Veloz, SD (2009) Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J Biogeogr 36:22902299 CrossRefGoogle Scholar
Visser, H, de Nijs, T (2006) The map comparison kit. Environ Model Software 21:346358 CrossRefGoogle Scholar
Vujnovic, K, Wein, RW (1997) The biology of Canadian weeds. 106. Linaria dalmatica (L.) Mill. Can J Plant Sci 77:483491 CrossRefGoogle Scholar
Wang, TL, Hamann, A, Spittlehouse, DL, Murdock, TQ (2012) ClimateWNA-high-resolution spatial climate data for western North America. J Appl Meteorol Clim 51:1629 CrossRefGoogle Scholar
Ward, SM, Fleischmann, CE, Turner, MF, Sing, SE (2009) Hybridization between invasive populations of Dalmatian toadflax (Linaria dalmatica) and yellow toadflax (Linaria vulgaris). Invasive Plant Sci Manag 2:369378 CrossRefGoogle Scholar
Ward, SM, Reid, SD, Harrington, J, Sutton, J, Beck, KG (2008) Genetic variation in invasive populations of yellow toadflax (Linaria vulgaris) in the western United States. Weed Sci 56:394399 CrossRefGoogle Scholar
Warren, DL, Glor, RE, Turelli, M (2010) ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33:607611 Google Scholar
Warren, DL, Seifert, SN (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335342 CrossRefGoogle ScholarPubMed
West, AM, Kumar, S, Brown, CS, Stohlgren, TJ, Bromberg, J (2016) Field validation of an invasive species Maxent model. Ecol Inform 36:126134 CrossRefGoogle Scholar
[WRCC] Western Regional Climate Center (2016) Climate Narratives of the State. http://www.wrcc.dri.edu/climate-narratives. Accessed: October 31, 2016Google Scholar
Wisz, MS, Hijmans, RJ, Li, J, Peterson, AT, Graham, CH, Guisan, A, NCEAS Predicting Species Distributions Working Group (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763773 CrossRefGoogle Scholar
York, P, Evangelista, P, Kumar, S, Graham, J, Flather, C, Stohlgren, T (2011) A habitat overlap analysis derived from Maxent for tamarisk and the south-western willow flycatcher. Front Earth Sci 5:120129 CrossRefGoogle Scholar
Zapfe, L, Freeland, JR (2015) Heterosis in invasive F-1 cattail hybrids (Typha x glauca). Aquat Bot 125:4447 CrossRefGoogle Scholar
Supplementary material: File

McCartney supplementary material

McCartney supplementary material 1

Download McCartney supplementary material(File)
File 2.7 MB