Advertisement

Biological Invasions

, Volume 20, Issue 8, pp 2257–2271 | Cite as

Analyzing remotely sensed structural and chemical canopy traits of a forest invaded by Prunus serotina over multiple spatial scales

  • Michael Ewald
  • Sandra Skowronek
  • Raf Aerts
  • Klara Dolos
  • Jonathan Lenoir
  • Manuel Nicolas
  • Jens Warrie
  • Tarek Hattab
  • Hannes Feilhauer
  • Olivier Honnay
  • Carol X. Garzón-López
  • Guillaume Decocq
  • Ruben Van De Kerchove
  • Ben Somers
  • Duccio Rocchini
  • Sebastian Schmidtlein
Original Paper

Abstract

Non-native invasive plant species can influence ecosystem functioning over broad spatial scales, but most research on ecosystem impacts has focused on the plot level covering sampling units of only a few square meters or less. We used a multi-scale approach to analyze structural and leaf chemical vegetation traits depending on the presence of non-native American black cherry (Prunus serotina) in a mixed deciduous forest at the plot level and at the forest stand level. Trait data were derived from remotely sensed maps of leaf area index (LAI), wood volume as well as canopy leaf nitrogen concentration (Nmass), phosphorous concentration (Pmass), and N:P ratio. Differences in these traits were compared between invaded and non-invaded areas at the plot level using 264 sampling units with a size of 25 m × 25 m and in 4119 forest management units (mean area: 7.6 ± 5.1 ha). Observed patterns between invaded and non-invaded areas were similar at both spatial scales. Invaded areas were characterized by less wood volume, indicating that lower standing biomass promotes the occurrence of P. serotina. In contrast, LAI did not differ between invaded and non-invaded areas. Furthermore, the presence of P. serotina trees had an impact on the chemical composition of the forest canopy by decreasing leaf N:P. While for Pmass, we found no differences in between invaded and non-invaded areas, for Nmass we observed an invasion effect, though only at the plot level. Using remotely sensed trait data proved valuable to evaluate the spatial relevance of invasion impacts over large areas.

Keywords

Alien plants Foliar stoichiometry Hyperspectral Imaging spectroscopy LiDAR 

Notes

Acknowledgements

This study is part of the project DIARS (Detection of invasive plant species and assessment of their impact on ecosystem properties through remote sensing) funded by the ERA-Net BiodivERsA, with the national funders: ANR (Agence Nationale de la Recherche); BelSPO (Belgian Federal Science Policy Office); and DFG (Deutsche Forschungsgemeinschaft). Michael Ewald is funded through the DFG research grant SCHM 2153/9-1. Statistical models were calculated using the computational resource bwUniCluster funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the universities of the state of Baden-Württemberg, Germany, within the framework program bwHPC. The authors would like to thank the Office National des Forêts for providing airborne LiDAR data. We also wish to thank Jérôme Piat, Luc Croisé, Fabien Spicher and Anthony Viaud for their help during field work as well as Javier Lopatin for proofreading.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10530_2018_1700_MOESM1_ESM.pdf (3.7 mb)
Supplementary material 1 (PDF 3772 kb)
10530_2018_1700_MOESM2_ESM.pdf (81 kb)
Supplementary material 2 (PDF 80 kb)
10530_2018_1700_MOESM3_ESM.jpg (4 mb)
Supplementary material 3 (JPEG 4111 kb)

References

  1. Aerts R, Ewald M, Nicolas M et al (2017) Invasion by the alien tree Prunus serotina alters ecosystem functions in a temperate deciduous forest. Front Plant Sci 8:179.  https://doi.org/10.3389/fpls.2017.00179 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Aguilera AG, Alpert P, Dukes JS, Harrington R (2010) Impacts of the invasive plant Fallopia japonica (Houtt.) on plant communities and ecosystem processes. Biol Invasions 12:1243–1252.  https://doi.org/10.1007/s10530-009-9543-z CrossRefGoogle Scholar
  3. Asner GP, Vitousek PM (2005) Remote analysis of biological invasion and biogeochemical change. Proc Natl Acad Sci USA 102:4383–4386.  https://doi.org/10.1073/pnas.0500823102 CrossRefPubMedGoogle Scholar
  4. Asner GP, Martin RE, Anderson CB, Knapp DE (2015) Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens Environ 158:15–27.  https://doi.org/10.1016/j.rse.2014.11.011 CrossRefGoogle Scholar
  5. Bjornstad ON (2016) ncf: spatial nonparametric covariance functions. R package version 1.1-7Google Scholar
  6. Bradley BA (2014) Remote detection of invasive plants: a review of spectral, textural and phenological approaches. Biol Invasions 16:1411–1425.  https://doi.org/10.1007/s10530-013-0578-9 CrossRefGoogle Scholar
  7. Chabrerie O, Roulier F, Hoeblich H et al (2007) Defining patch mosaic functional types to predict invasion patterns in a forest landscape. Ecol Appl 17:464–481CrossRefPubMedGoogle Scholar
  8. Chabrerie O, Verheyen K, Saguez R, Decocq G (2008) Disentangling relationships between habitat conditions, disturbance history, plant diversity, and American black cherry (Prunus serotina Ehrh.) invasion in a European temperate forest. Divers Distrib 14:204–212.  https://doi.org/10.1111/j.1472-4642.2007.00453.x CrossRefGoogle Scholar
  9. Chapuis-Lardy L, Vanderhoeven S, Dassonville N et al (2006) Effect of the exotic invasive plant Solidago gigantea on soil phosphorus status. Biol Fertil Soils 42:481–489.  https://doi.org/10.1007/s00374-005-0039-4 CrossRefGoogle Scholar
  10. Closset-Kopp D, Saguez R, Decocq G (2010) Differential growth patterns and fitness may explain contrasted performances of the invasive Prunus serotina in its exotic range. Biol Invasions 13:1341–1355.  https://doi.org/10.1007/s10530-010-9893-6 CrossRefGoogle Scholar
  11. Dassonville N, Vanderhoeven S, Vanparys V et al (2008) Impacts of alien invasive plants on soil nutrients are correlated with initial site conditions in NW Europe. Oecologia 157:131–140.  https://doi.org/10.1007/s00442-008-1054-6 CrossRefPubMedGoogle Scholar
  12. Dormann CF, McPherson JM, Araújo MB et al (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628.  https://doi.org/10.1111/j.2007.0906-7590.05171.x CrossRefGoogle Scholar
  13. Ehrenfeld JG (2010) Ecosystem consequences of biological invasions. Annu Rev Ecol Evol Syst 41:59–80.  https://doi.org/10.1146/annurev-ecolsys-102209-144650 CrossRefGoogle Scholar
  14. Eviner VT, Garbach K, Baty JH, Hoskinson SA (2012) Measuring the effects of invasive plants on ecosystem services: challenges and prospects. Invasive Plant Sci Manag 5:125–136.  https://doi.org/10.1614/IPSM-D-11-00095.1 CrossRefGoogle Scholar
  15. Fassnacht FE, Hartig F, Latifi H et al (2014) Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens Environ 154:102–114.  https://doi.org/10.1016/j.rse.2014.07.028 CrossRefGoogle Scholar
  16. Fisher JL, Veneklaas EJ, Lambers H, Loneragan WA (2006) Enhanced soil and leaf nutrient status of a Western Australian Banksia woodland community invaded by Ehrharta calycina and Pelargonium capitatum. Plant Soil 284:253–264.  https://doi.org/10.1007/s11104-006-0042-z CrossRefGoogle Scholar
  17. Fridley JD, Stachowicz JJ, Naeem S et al (2007) The invasion paradox: reconciling pattern and process in species invasions. Ecology 88:3–17.  https://doi.org/10.1890/0012-9658(2007)88[3:TIPRPA]2.0.CO;2 CrossRefPubMedGoogle Scholar
  18. Gaertner M, Breeyen AD, Hui C, Richardson DM (2009) Impacts of alien plant invasions on species richness in Mediterranean-type ecosystems: a meta-analysis. Prog Phys Geogr 33:319–338.  https://doi.org/10.1177/0309133309341607 CrossRefGoogle Scholar
  19. Gaertner M, Biggs R, Te Beest M et al (2014) Invasive plants as drivers of regime shifts: identifying high-priority invaders that alter feedback relationships. Divers Distrib 20:733–744.  https://doi.org/10.1111/ddi.12182 CrossRefGoogle Scholar
  20. Güsewell S (2004) N:P ratios in terrestrial plants: variation and functional significance. New Phytol 164:243–266.  https://doi.org/10.1111/j.1469-8137.2004.01192.x CrossRefGoogle Scholar
  21. Halarewicz A, Pruchniewicz D (2015) Vegetation and environmental changes in a Scots pine forest invaded by Prunus serotina: what is the threat to terricolous bryophytes? Eur J For Res 134:793–801.  https://doi.org/10.1007/s10342-015-0890-2 CrossRefGoogle Scholar
  22. Hattab T, Garzón-López CX, Ewald M et al (2017) A unified framework to model the potential and realized distributions of invasive species within the invaded range. Divers Distrib 23:806–819.  https://doi.org/10.1111/ddi.12566 CrossRefGoogle Scholar
  23. Huang C, Asner GP (2009) Applications of remote sensing to alien invasive plant studies. Sensors 9:4869–4889.  https://doi.org/10.3390/s90604869 CrossRefPubMedGoogle Scholar
  24. Jäger H, Alencastro MJ, Kaupenjohann M, Kowarik I (2013) Ecosystem changes in Galápagos highlands by the invasive tree Cinchona pubescens. Plant Soil 371:629–640.  https://doi.org/10.1007/s11104-013-1719-8 CrossRefGoogle Scholar
  25. Jonard M, Fürst A, Verstraeten A et al (2015) Tree mineral nutrition is deteriorating in Europe. Glob Change Biol 21:418–430.  https://doi.org/10.1111/gcb.12657 CrossRefGoogle Scholar
  26. Kattenborn T, Fassnacht FE, Pierce S et al (2017) Linking plant strategies and plant traits derived by radiative transfer modelling. J Veg Sci 28:717–727.  https://doi.org/10.1111/jvs.12525 CrossRefGoogle Scholar
  27. Kumar L, Sinha P, Taylor S, Alqurashi AF (2015) Review of the use of remote sensing for biomass estimation to support renewable energy generation. J Appl Remote Sens 9:097696.  https://doi.org/10.1117/1.JRS.9.097696 CrossRefGoogle Scholar
  28. Kurokawa H, Peltzer DA, Wardle DA (2010) Plant traits, leaf palatability and litter decomposability for co-occurring woody species differing in invasion status and nitrogen fixation ability. Funct Ecol 24:513–523.  https://doi.org/10.1111/j.1365-2435.2009.01676.x CrossRefGoogle Scholar
  29. Kurten EL, Snyder CP, Iwata T, Vitousek PM (2008) Morella cerifera invasion and nitrogen cycling on a lowland Hawaiian lava flow. Biol Invasions 10:19–24.  https://doi.org/10.1007/s10530-007-9101-5 CrossRefGoogle Scholar
  30. Lee MR, Bernhardt ES, van Bodegom PM et al (2017) Invasive species’ leaf traits and dissimilarity from natives shape their impact on nitrogen cycling: a meta-analysis. New Phytol 213:128–139.  https://doi.org/10.1111/nph.14115 CrossRefPubMedGoogle Scholar
  31. Liao C, Peng R, Luo Y et al (2008) Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. New Phytol 177:706–714.  https://doi.org/10.1111/j.1469-8137.2007.02290.x CrossRefPubMedGoogle Scholar
  32. Mellert KH, Göttlein A (2012) Comparison of new foliar nutrient thresholds derived from van den Burg’s literature compilation with established central European references. Eur J For Res 131:1461–1472.  https://doi.org/10.1007/s10342-012-0615-8 CrossRefGoogle Scholar
  33. Ollinger SV (2011) Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol 189:375–394.  https://doi.org/10.1111/j.1469-8137.2010.03536.x CrossRefPubMedGoogle Scholar
  34. Parker IM, Simberloff D, Lonsdale WM et al (1999) Impact: toward a framework for understanding the ecological effects of invaders. Biol Invasions 1:3–19.  https://doi.org/10.1023/A:1010034312781 CrossRefGoogle Scholar
  35. Pauchard A, Shea K (2006) Integrating the study of non-native plant invasions across spatial scales. Biol Invasions 8:399–413.  https://doi.org/10.1007/s10530-005-6419-8 CrossRefGoogle Scholar
  36. Pinheiro J, Bates D, DebRoy S et al (2016) nlme: linear and nonlinear mixed effects models. R package version 3.1-131Google Scholar
  37. Powell KI, Chase JM, Knight TM (2011) A synthesis of plant invasion effects on biodiversity across spatial scales. Am J Bot 98:539–548.  https://doi.org/10.3732/ajb.1000402 CrossRefPubMedGoogle Scholar
  38. Powell KI, Chase JM, Knight TM (2013) Invasive plants have scale-dependent effects on diversity by altering species-area relationships. Science 339:316–318.  https://doi.org/10.1126/science.1226817 CrossRefPubMedGoogle Scholar
  39. Pyšek P, Jarošík V, Hulme PE et al (2012) A global assessment of invasive plant impacts on resident species, communities and ecosystems: the interaction of impact measures, invading species’ traits and environment. Glob Change Biol 18:1725–1737.  https://doi.org/10.1111/j.1365-2486.2011.02636.x CrossRefGoogle Scholar
  40. R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  41. Richardson SJ, Peltzer DA, Allen RB, McGlone MS (2005) Resorption proficiency along a chronosequence: responses among communities and within species. Ecology 86:20–25.  https://doi.org/10.1890/04-0524 CrossRefGoogle Scholar
  42. Rocchini D, Andreo V, Förster M et al (2015) Potential of remote sensing to predict species invasions: a modelling perspective. Prog Phys Geogr Earth Environ 39:283–309.  https://doi.org/10.1177/0309133315574659 CrossRefGoogle Scholar
  43. Sardans J, Peñuelas J (2015) Trees increase their P:N ratio with size. Glob Ecol Biogeogr J Macroecol 24:147–156.  https://doi.org/10.1111/geb.12231 CrossRefGoogle Scholar
  44. Sardans J, Alonso R, Carnicer J et al (2016) Factors influencing the foliar elemental composition and stoichiometry in forest trees in Spain. Perspect Plant Ecol Evol Syst 18:52–69.  https://doi.org/10.1016/j.ppees.2016.01.001 CrossRefGoogle Scholar
  45. Singh A, Serbin SP, McNeil BE et al (2015) Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecol Appl 25:2180–2197.  https://doi.org/10.1890/14-2098.1 CrossRefPubMedGoogle Scholar
  46. Skowronek S, Ewald M, Isermann M et al (2016) Mapping an invasive bryophyte species using hyperspectral remote sensing data. Biol Invasions.  https://doi.org/10.1007/s10530-016-1276-1 CrossRefGoogle Scholar
  47. Skowronek S, Van De Kerchove R, Rombouts B et al (2018) Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data. Int J Appl Earth Obs Geoinf 68:61–72.  https://doi.org/10.1016/j.jag.2018.02.001 CrossRefGoogle Scholar
  48. Starfinger U, Kowarik I, Rode M, Schepker H (2003) From desirable ornamental plant to pest to accepted addition to the flora?—the perception of an alien tree species through the centuries. Biol Invasions 5:323–335.  https://doi.org/10.1023/B:BINV.0000005573.14800.07 CrossRefGoogle Scholar
  49. Stricker KB, Hagan D, Flory SL (2015) Improving methods to evaluate the impacts of plant invasions: lessons from 40 years of research. AoB Plants 7:plv028.  https://doi.org/10.1093/aobpla/plv028 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Talkner U, Meiwes KJ, Potočić N et al (2015) Phosphorus nutrition of beech (Fagus sylvatica L.) is decreasing in Europe. Ann For Sci 72:919–928.  https://doi.org/10.1007/s13595-015-0459-8 CrossRefGoogle Scholar
  51. Terwei A, Zerbe S, Zeileis A et al (2013) Which are the factors controlling tree seedling establishment in North Italian floodplain forests invaded by non-native tree species? For Ecol Manag 304:192–203.  https://doi.org/10.1016/j.foreco.2013.05.003 CrossRefGoogle Scholar
  52. Thiele J, Kollmann J, Markussen B, Otte A (2009) Impact assessment revisited: improving the theoretical basis for management of invasive alien species. Biol Invasions 12:2025–2035.  https://doi.org/10.1007/s10530-009-9605-2 CrossRefGoogle Scholar
  53. Thorpe AS, Archer V, DeLuca TH (2006) The invasive forb, Centaurea maculosa, increases phosphorus availability in Montana grasslands. Appl Soil Ecol 32:118–122.  https://doi.org/10.1016/j.apsoil.2005.02.018 CrossRefGoogle Scholar
  54. Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186:795–816.  https://doi.org/10.1111/j.1469-8137.2010.03284.x CrossRefPubMedGoogle Scholar
  55. Van Kleunen M, Weber E, Fischer M (2010) A meta-analysis of trait differences between invasive and non-invasive plant species. Ecol Lett 13:235–245.  https://doi.org/10.1111/j.1461-0248.2009.01418.x CrossRefPubMedGoogle Scholar
  56. Vanhellemont M, Verheyen K, Keersmaeker LD et al (2008) Does Prunus serotina act as an aggressive invader in areas with a low propagule pressure? Biol Invasions 11:1451–1462.  https://doi.org/10.1007/s10530-008-9353-8 CrossRefGoogle Scholar
  57. Verrelst J, Camps-Valls G, Muñoz-Marí J et al (2015) Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—a review. ISPRS J Photogramm Remote Sens 108:273–290.  https://doi.org/10.1016/j.isprsjprs.2015.05.005 CrossRefGoogle Scholar
  58. Vicente JR, Pinto AT, Araújo MB et al (2013) Using life strategies to explore the vulnerability of ecosystem services to invasion by alien plants. Ecosystems 16:678–693.  https://doi.org/10.1007/s10021-013-9640-9 CrossRefGoogle Scholar
  59. Vilà M, Weiner J (2004) Are invasive plant species better competitors than native plant species?: evidence from pair-wise experiments. Oikos 105:229–238CrossRefGoogle Scholar
  60. Vilà M, Espinar JL, Hejda M et al (2011) Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol Lett 14:702–708.  https://doi.org/10.1111/j.1461-0248.2011.01628.x CrossRefPubMedGoogle Scholar
  61. Weidenhamer JD, Callaway RM (2010) Direct and indirect effects of invasive plants on soil chemistry and ecosystem function. J Chem Ecol 36:59–69.  https://doi.org/10.1007/s10886-009-9735-0 CrossRefPubMedGoogle Scholar
  62. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397.  https://doi.org/10.2307/2389612 CrossRefGoogle Scholar
  63. Windham L, Ehrenfeld JG (2003) Net impact of a plant invasion on nitrogen-cycling processes within a brackish tidal marsh. Ecol Appl 13:883–896.  https://doi.org/10.1890/02-5005 CrossRefGoogle Scholar
  64. Wright IJ, Reich PB, Westoby M et al (2004) The worldwide leaf economics spectrum. Nature 428:821–827.  https://doi.org/10.1038/nature02403 CrossRefPubMedGoogle Scholar
  65. Zheng G, Moskal LM (2009) retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors. Sensors 9:2719–2745.  https://doi.org/10.3390/s90402719 CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Michael Ewald
    • 1
  • Sandra Skowronek
    • 2
  • Raf Aerts
    • 3
  • Klara Dolos
    • 1
  • Jonathan Lenoir
    • 4
  • Manuel Nicolas
    • 5
  • Jens Warrie
    • 3
  • Tarek Hattab
    • 4
    • 6
  • Hannes Feilhauer
    • 2
  • Olivier Honnay
    • 3
  • Carol X. Garzón-López
    • 4
    • 7
  • Guillaume Decocq
    • 4
  • Ruben Van De Kerchove
    • 8
  • Ben Somers
    • 9
  • Duccio Rocchini
    • 10
    • 11
    • 12
  • Sebastian Schmidtlein
    • 1
  1. 1.Institute of Geography and GeoecologyKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Institute of GeographyFAU Erlangen-NurembergErlangenGermany
  3. 3.Ecology, Evolution and Biodiversity Conservation SectionUniversity of Leuven (KU Leuven)LouvainBelgium
  4. 4.UR “Ecologie et Dynamique des Systèmes Anthropisés” (EDYSAN, FRE 3498 CNRS)Université de Picardie Jules VerneAmiens Cedex 1France
  5. 5.Département Recherche et DéveloppementOffice National des ForêtsFontainebleauFrance
  6. 6.UMR MARBECInstitut Français de Recherche pour l’Exploitation de la MerSèteFrance
  7. 7.Ecology and Vegetation Physiology Group (EcoFiv)Universidad de los AndesBogotáColombia
  8. 8.VITO Flemish Institute for Technological ResearchMolBelgium
  9. 9.Department of Earth and Environmental SciencesKU LeuvenLouvainBelgium
  10. 10.Department of Biodiversity and Molecular Ecology, Research and Innovation CentreFondazione Edmund MachSan Michele all’AdigeItaly
  11. 11.Center Agriculture Food EnvironmentUniversity of TrentoS. Michele all’AdigeItaly
  12. 12.Centre for Integrative BiologyUniversity of TrentoPovoItaly

Personalised recommendations