Facilitation of management plan development via spatial classification of areas invaded by alien invasive plant

  • Takeshi OsawaEmail author
  • Munemitsu Akasaka
  • Naoki Kachi
Original Paper


Propagule supply and habitat suitability strongly influence the success of invasive alien plants. Thus, an invaded area is likely to have an adequate propagule supply, a suitable habitat, or both for species persistence. Based on this idea, we classified invaded areas into four categories as follows but with establishment still occurring in some cases: Class 1, adequate propagule supply and habitat suitability; Class 2, adequate propagule supply but limited habitat suitability; Class 3, limited propagule supply and adequate habitat suitability; and Class 4, mid- to low-level propagule supply and habitat suitability. We propose a framework for the classification of invaded areas into these four classes and present a case study in which this framework was applied. Classifying target areas in this manner could facilitate more efficient and practical management planning, thereby saving time and resources. We selected the alien shrub Leucaena leucocephala L. (Fabaceae) as a model species, which has invaded the Nakodo-jima Island in the Ogasawara Archipelago of Japan. We developed a species distribution model by incorporating proxy variables for propagule supply and habitat suitability as well as submodels for propagule supply or habitat suitability. Using these submodels, we estimated the levels of propagule supply and habitat suitability in each, and classified the current distribution range appropriately. Using these classifications, land managers could set priorities to concentrate their efforts to efficiently control target species.


Ecosystem management Habitat suitability Leucaena leucocephala Propagule pressure Resource allocation Species distribution model 



We thank Dr. A. Mizuguchi for several useful discussions for this study. Two anonymous reviewers provided us the constructive suggestions. This study was supported partly by JSPS KAKENHI Grant Number 16H01794.


  1. Andrew ME, Ustin SL (2010) The effects of temporally variable dispersal and landscape structure on invasive species spread. Ecol Appl 20:593–608. CrossRefGoogle Scholar
  2. Aung T, Koike F (2015) Identification of invasion status using a habitat invasibility assessment model: the case of Prosopis species in the dry zone of Myanmar. J Arid Environ 120:87–94. CrossRefGoogle Scholar
  3. Barnett DT, Stohlgren TJ, Jarnevich CS et al (2007) The art and science of weed mapping. Environ Monit Assess 132:235–252. CrossRefGoogle Scholar
  4. Burnham KP, Anderson DR (2003) Model selection and multimodel inference: a practical information-theoretic approach. Springer, BerlinGoogle Scholar
  5. Chiou C-R, Wang H-H, Chen Y-J et al (2013) Modeling potential range expansion of the invasive shrub Leucaena leucocephala in the Hengchun Peninsula, Taiwan. Invasive Plant Sci Manag 6:492–501. CrossRefGoogle Scholar
  6. Davies K, Sheley R (2007) A conceptual framework for preventing the spatial dispersal of invasive plants. Weed Sci 55:178–184CrossRefGoogle Scholar
  7. Foxcroft L, Richardson D, Rouget M (2009) Patterns of alien plant distribution at multiple spatial scales in a large national park: implications for ecology, management and monitoring. Divers Distrib 15:367–378CrossRefGoogle Scholar
  8. Fukasawa K, Koike F, Tanaka N, Otsu K (2009) Predicting future invasion of an invasive alien tree in a Japanese oceanic island by process-based statistical models using recent distribution maps. Ecol Res 24:965–975. CrossRefGoogle Scholar
  9. Funakoshi M (1979) Formation of Leucaena leucocephala, forest in the Ogasawara Islands. Annu Rep Ogasawara Res (in Japanese) 13:59–72Google Scholar
  10. Giljohann KM, Hauser CE, Williams NSG, Moore JL (2011) Optimizing invasive species control across space: Willow invasion management in the Australian Alps. J Appl Ecol 48:1286–1294. CrossRefGoogle Scholar
  11. Greiner M (1995) Two-graph receiver operating characteristic (TG-ROC): a Microsoft-EXCEL template for the selection of cut-off values in diagnostic tests. J Immunol Methods 185:145–146CrossRefGoogle Scholar
  12. Grevstad FS (2005) Simulating control strategies for a spatially structured weed invasion: Spartina alterniflora (Loisel) in Pacific Coast estuaries. Biol Invasions 7:665–677CrossRefGoogle Scholar
  13. Grice A, Clarkson J, Calvert M (2011) Geographic differentiation of management objectives for invasive species: a case study of Hymenachne amplexicaulis in Australia. Environ Sci Policy 14:986–997CrossRefGoogle Scholar
  14. Gupta B, Huang B (2014) Mechanism of salinity tolerance in plants: physiological, biochemical, and molecular characterization. Int J Genomics Article IDGoogle Scholar
  15. Hata K, Kato H, Kachi N (2010a) Litter of an alien tree, Casuarina equisetifolia, inhibits seed germination and initial growth of a native tree on the Ogasawara Islands (subtropical oceanic islands). J For Res 15:384–390. CrossRefGoogle Scholar
  16. Hata K, Suzuki JI, Kachi N (2010b) Fine-scale spatial distribution of seedling establishment of the invasive plant, Leucaena leucocephala, on an oceanic island after feral goat extermination. Weed Res 50:472–480. CrossRefGoogle Scholar
  17. Hata K, Osawa T, Hiradate S, Kachi N (2018) Soil erosion alters soil chemical properties and limits grassland plant establishment on an oceanic island even after goat eradication. Restor Ecol 1:2. Google Scholar
  18. Hauser CE, McCarthy MA (2009) Streamlining “search and destroy”: cost-effective surveillance for invasive species management. Ecol Lett 12:683–692. CrossRefGoogle Scholar
  19. Hiebert RD (1997) Prioritizing invasive plants and planning for management. In: Luken JO, Thieret JW (eds) Assessment and management of plant invasions. Series on environmental management. Springer, New York, NY.
  20. Humston R, Mortensen D (2005) Anthropogenic forcing on the spatial dynamics of an agricultural weed: the case of the common sunflower. J Appl Ecol 42:863–872CrossRefGoogle Scholar
  21. Januchowski-Hartley SR, Visconti P, Pressey RL (2011) A systematic approach for prioritizing multiple management actions for invasive species. Biol Invasions 13:1241–1253. CrossRefGoogle Scholar
  22. Jiménez-Valverde A, Peterson A, Soberón J (2011) Use of niche models in invasive species risk assessments. Biol Invasions 13:2785–2797CrossRefGoogle Scholar
  23. Kluth S, Kruess A, Tscharntke T (2003) Influence of mechanical cutting and pathogen application on the performance and nutrient storage of Cirsium arvense. J Appl Ecol 40:334–343CrossRefGoogle Scholar
  24. Knight AT, Cowling RM, Rouget M et al (2008) Knowing but not doing: selecting priority conservation areas and the research—implementation gap. Conserv Biol 22:610–617CrossRefGoogle Scholar
  25. Kumschick S, Bacher S, Dawson W et al (2012) A conceptual framework for prioritization of invasive alien species for management according to their impact. NeoBiota 15:69–100. CrossRefGoogle Scholar
  26. Lichstein J, Simons T, Shriner S (2002) Spatial autocorrelation and autoregressive models in ecology. Ecol Monogr 72:445–463CrossRefGoogle Scholar
  27. Lowe S, Browne M, Boudjelas S, De PM (2000) 100 of the world’s worst invasive alien species: a selection from the global invasive species database. IUCN, AucklandGoogle Scholar
  28. Masters R, Sheley R (2001) Principles and practices for managing rangeland invasive plants. J Range Manag 54:502–517CrossRefGoogle Scholar
  29. McDonald-Madden E, Chades I (2011) Allocating conservation resources between areas where persistence of a species is uncertain. Ecol Appl 21:844–858CrossRefGoogle Scholar
  30. Moilanen A, Wilson K, Possingham H (2009) Spatial conservation prioritization: quantitative methods and computational tools. Oxford University Press, OxfordGoogle Scholar
  31. Murray B, Phillips M (2010) Investment in seed dispersal structures is linked to invasiveness in exotic plant species of south-eastern Australia. Biol Invasions 12:2265–2275CrossRefGoogle Scholar
  32. Osawa T, Ito K (2015) A rapid method for constructing precaution maps based on a simple virtual ecology model: a case study on the range expansion of the invasive aquatic species Limnoperna fortunei. Popul Ecol 57:529–538. CrossRefGoogle Scholar
  33. Osawa T, Mitsuhashi H, Niwa H (2013) Many alien invasive plants disperse against the direction of stream flow in riparian areas. Ecol Complex 15:26–32. CrossRefGoogle Scholar
  34. Osawa T, Hata K, Kachi N (2016a) Eradication of feral goats enhances expansion of the invasive shrub Leucaena leucocephala on Nakoudo-jima, an oceanic island. Weed Res 56:168–178CrossRefGoogle Scholar
  35. Osawa T, Okawa S, Kurokawa S, Ando S (2016b) Generating an agricultural risk map based on limited ecological information: a case study using Sicyos angulatus. Ambio 45:895–903. CrossRefGoogle Scholar
  36. Pichancourt J, Chadès I, Firn J (2012) Simple rules to contain an invasive species with a complex life cycle and high dispersal capacity. J Appl Ecol 49:52–62CrossRefGoogle Scholar
  37. Prendergast JR, Quinn RM, Lawton JH (1999) The gaps between theory and practice in selecting nature reserves. Conserv Biol 13:484–492CrossRefGoogle Scholar
  38. Pyšek P, Richardson DM (2010) Invasive species, environmental change and management, and health. Annu Rev Environ Resour 35:25–55. CrossRefGoogle Scholar
  39. Shaw D (2005) Remote sensing and site-specific weed management. Front Ecol Environ 3:526–532CrossRefGoogle Scholar
  40. Shea K, Possingham H, Murdoch W (2002) Active adaptive management in insect pest and weed control: intervention with a plan for learning. Ecol Appl 12:927–936.[0927:AAMIIP]2.0.CO;2 CrossRefGoogle Scholar
  41. Simberloff D, Rejmánek M (2011) Encyclopedia of biological invasions. University of California Press, BerkeleyGoogle Scholar
  42. Swets J (1988) Measuring the accuracy of diagnostic systems. Science (80-) 240:1285–1293CrossRefGoogle Scholar
  43. van Wilgen BW, Forsyth GG, Le Maitre DC et al (2012) An assessment of the effectiveness of a large, national-scale invasive alien plant control strategy in South Africa. Biol Conserv 148:28–38CrossRefGoogle Scholar
  44. Vilà M, Basnou C, Pyšek P, Josefsson M (2010) How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front Ecol Environ 8:135–144CrossRefGoogle Scholar
  45. Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementations. NESUG Proc HealGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tokyo Metropolitan UniversityHachioujiJapan
  2. 2.Institute of AgricultureTokyo University of Agriculture and TechnologyFuchuJapan
  3. 3.Institute for Agro-environmental SciencesNAROTsukubaJapan

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