Advertisement

Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies

  • Hsiao-Hsuan Wang
  • Carissa L. Wonkka
  • Michael L. Treglia
  • William E. Grant
  • Fred E. Smeins
  • William E. Rogers
Original Paper

Abstract

The conservation of rare species is typically challenging because of incomplete knowledge about their biology and distributions. Species distribution models (SDMs) have emerged as an important tool for improving the efficiency of rare species conservation. However, these models must include biologically relevant predictor variables at scales appropriate for discriminating suitable and unsuitable habitat. We used a species distribution modelling tool, maximum entropy (Maxent), to assess the relative influence of biologically relevant topographic characteristics, land cover features, geological formations, and edaphic factors on the occurrence of the endangered endemic orchid Spiranthes parksii (Navasota ladies’ tresses). Our final model produced an excellent AUC value (0.984), with the permutation importance to model fit of predictor variables representing topographic characteristics, land cover features, geological formations, and edaphic factors summing to 8.17, 35.12, 10.43, and 46.28%, respectively. Local-scale edaphic variables were the most informative, with soil taxonomic units explaining the highest amount of variance (36.40%) of all variables included in the model. These results document the importance of local edaphic characteristics in discriminating between suitable and unsuitable habitat for S. parksii, and emphasize the importance of including local-scale edaphic factors in SDMs for species such as S. parksii with specialized habitat requirements and close relationships with other organisms.

Keywords

Conservation planning Endangered species Navasota ladies’ tresses Restoration Scale Species distribution models 

Notes

Acknowledgements

We thank the many Texas A&M University undergraduate and graduate students who provided assistance with field work. We acknowledge the City of Bryan/College Station-Brazos Valley Solid Waste Management Agency for logistical assistance with field studies. We also thank the City of Bryan/College Station-Brazos Valley Solid Waste Management Agency, the Texas Department of Transportation, and the Ladybird Johnson Wildflower Center (Austin, TX)—Endangered Species Conservation Grant Program Award #12419 for providing funding. Finally, we thank the anonymous reviewer and Associate Editor for their time and effort, and the manuscript is greatly improved as a result of their comments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Supplementary material

10531_2018_1645_MOESM1_ESM.pdf (34 kb)
Supplementary material 1 (PDF 33 kb)
10531_2018_1645_MOESM2_ESM.pdf (220 kb)
Supplementary material 2 (PDF 220 kb)
10531_2018_1645_MOESM3_ESM.pdf (11 kb)
Supplementary material 3 (PDF 11 kb)

References

  1. Ariza MC (2013) Mycorrhizal associations, life history, and habitat characteristics of the endangered terrestrial orchid Spiranthes parksii Corell and sympatric congener Spiranthes cernua: Implications for conservation. PhD dissertation. Department of Ecosystem Science and Management. Texas A&M University, College Station, TX, pp. 279Google Scholar
  2. Austin MP, Van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8CrossRefGoogle Scholar
  3. Bazzaz F (1991) Habitat selection in plants. Am Nat 137:S116–S130CrossRefGoogle Scholar
  4. Brundrett M (2004) Diversity and classification of mycorrhizal associations. Biol Rev 79:473–495CrossRefGoogle Scholar
  5. Brundrett MC (2007) Role of symbiotic relationships in Australian terrestrial orchid conservation. Aust Plant Conserv 15:2–7Google Scholar
  6. Ciccolini V, Bonari E, Pellegrino E (2015) Land-use intensity and soil properties shape the composition of fungal communities in Mediterranean peaty soils drained for agricultural purposes. Biol Fertil Soils 51:719–731CrossRefGoogle Scholar
  7. Coudun C, Gégout JC, Piedallu C, Rameau JC (2006) Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. J Biogeogr 33:1750–1763CrossRefGoogle Scholar
  8. del Mar Alguacil M, Torres MP, Montesinos-Navarro A, Roldán A (2016) Soil characteristics driving arbuscular mycorrhizal fungal communities in semiarid Mediterranean soils. Appl Environ Microbiol 82:3348–3356CrossRefGoogle Scholar
  9. Diez JM (2007) Hierarchical patterns of symbiotic orchid germination linked to adult proximity and environmental gradients. J Ecol 95:159–170CrossRefGoogle Scholar
  10. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  11. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813CrossRefGoogle Scholar
  12. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57CrossRefGoogle Scholar
  13. ESRI (2011) ArcGIS Desktop: Release 10. Environmental Systems Research Institute, Redlands, CAGoogle Scholar
  14. Ettema CH, Wardle DA (2002) Spatial soil ecology. Trends Ecol Evol 17:177–183CrossRefGoogle Scholar
  15. Ettema CH, Rathbun SL, Coleman DC (2000) On spatiotemporal patchiness and the coexistence of five species of Chronogaster (Nematoda: Chronogasteridae) in a riparian wetland. Oecologia 125:444–452CrossRefGoogle Scholar
  16. Fry JG, Xian SJ, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011) Completion of the 2006 national land cover database for the conterminous United States. Photogramm Eng Remote Sens 77:858–864Google Scholar
  17. Gesch DB (2007) The National Elevation Dataset. In: Maune D (ed) Digital elevation model technologies and applications: the DEM users manual, 2nd edn. American Society for Photogrammetry and Remote Sensing, Bethesda, MD, pp 99–118Google Scholar
  18. Gogol-Prokurat M (2011) Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol Appl 21:33–47CrossRefGoogle Scholar
  19. Grundel R, Jean RP, Frohnapple KJ, Glowacki GA, Scott PE, Pavlovic NB (2010) Floral and nesting resources, habitat structure, and fire influence bee distribution across an open-forest gradient. Ecol Appl 20:1678–1692CrossRefGoogle Scholar
  20. Hazard C, Gosling P, Van Der Gast CJ, Mitchell DT, Doohan FM, Bending GD (2013) The role of local environment and geographical distance in determining community composition of arbuscular mycorrhizal fungi at the landscape scale. ISME J 7:498CrossRefGoogle Scholar
  21. Homer CG, Dewitz JA, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold ND, 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 81:345–354Google Scholar
  22. Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New YorkCrossRefGoogle Scholar
  23. Krupnick GA, McCormick MK, Mirenda T, Whigham DF (2013) The status and future of orchid conservation in north America. Ann Mo Bot Gard 99:180–198CrossRefGoogle Scholar
  24. Lemke D, Hulme PE, Brown JA, Tadesse W (2011) Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA. For Ecol Manag 262:139–149CrossRefGoogle Scholar
  25. Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73:1943–1967CrossRefGoogle Scholar
  26. Luoto M, Heikkinen R (2008) Disregarding topographical heterogeneity biases species turnover assessments based on bioclimatic models. Glob Change Biol 14:483–494CrossRefGoogle Scholar
  27. McCormick MK, Lee Taylor D, Juhaszova K, Burnett RK, Whigham DF, O’Neill JP (2012) Limitations on orchid recruitment: not a simple picture. Mol Ecol 21:1511–1523CrossRefGoogle Scholar
  28. 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 36:1058–1069CrossRefGoogle Scholar
  29. Oehl F, Laczko E, Bogenrieder A, Stahr K, Bösch R, van der Heijden M, Sieverding E (2010) Soil type and land use intensity determine the composition of arbuscular mycorrhizal fungal communities. Soil Biol Biochem 42:724–738CrossRefGoogle Scholar
  30. Phillips SJ, Dudík M, Schapire RE (2004) A maximum entropy approach to species distribution modeling. In: Proceedings of the twenty-first international conference on Machine learning. ACM, pp. 83Google Scholar
  31. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  32. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed 20 Feb 2017
  33. Randin CF, Engler R, Normand S, Zappa M, Zimmermann NE, Pearman PB, Vittoz P, Thuiller W, Guisan A (2009) Climate change and plant distribution: local models predict high-elevation persistence. Glob Change Biol 15:1557–1569CrossRefGoogle Scholar
  34. Randklev C, Wang H-H, Groce J, Grant WE, Robertson S, Wilkins N (2015) Land use relationships for a rare freshwater mussel species (Family: Unionidae) endemic to central Texas. J Fish Wildl Manag.  https://doi.org/10.3996/012015-jfwm-003 CrossRefGoogle Scholar
  35. Schemske DW, Husband BC, Ruckelshaus MH, Goodwillie C, Parker IM, Bishop JG (1994) Evaluating approaches to the conservation of rare and endangered plants. Ecology 75:584–606CrossRefGoogle Scholar
  36. Soil Data Mart (2013) U.S. General Soil Map (STATSGO2). http://soildatamart.nrcs.usda.gov. USDA/NRCS. Accessed 25 07 2013
  37. Soil Survey Staff (2016) Soil Survey Geographic (SSURGO) Database. https://sdmdataaccess.sc.egov.usda.gov. Natural Resources Conservation Service, United States Department of Agriculture. Accessed: June 10 2016
  38. Stoeser DB, Shock N, Green GN, Dumonceaux GM, Heran WD (2013) A digital geologic map database for the state of Texas: U.S. geological survey data seriesGoogle Scholar
  39. Swarts ND, Dixon KW (2009) Terrestrial orchid conservation in the age of extinction. Ann Bot 104:543–556CrossRefGoogle Scholar
  40. Treglia ML, Fisher RN, Fitzgerald LA (2015) Integrating multiple distribution models to guide conservation efforts of an endangered toad. PLoS ONE 10:e0131628CrossRefGoogle Scholar
  41. Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landsc Ecol 3:153–162CrossRefGoogle Scholar
  42. Wang H-H, Wonkka CL, Treglia ML, Grant WE, Smeins FE, Rogers WE (2015) Species distribution modelling for conservation of an endangered endemic orchid. AoB Plants 7:plv039CrossRefGoogle Scholar
  43. Wonkka CL, Rogers WE, Smeins FE, Hammons JR, Ariza MC, Haller SJ (2012) Biology, ecology, and conservation of Navasota ladies-tresses (Spiranthes parksii Correll): an endangered terrestrial orchid of Texas. Native Plants J 13:236–243CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Wildlife and Fisheries SciencesTexas A&M UniversityCollege StationUSA
  2. 2.Department of Agronomy and HorticultureUniversity of NebraskaLincolnUSA
  3. 3.New York City ProgramThe Nature ConservancyNew YorkUSA
  4. 4.Department of Ecosystem Science and ManagementTexas A&M UniversityCollege StationUSA

Personalised recommendations