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Impact of biased sampling effort and spatial uncertainty of locations on models of plant invasion patterns in Croatia

  • Andreja Radović
  • Stefan Schindler
  • David Rossiter
  • Toni Nikolić
Original Paper
  • 32 Downloads

Abstract

Very frequently biological databases are used for analysing distribution of different taxa. These databases are usually the result of variable sampling effort and location uncertainty. The aim of this study was to test the influence of geographically biased sampling effort and spatial uncertainty of locations on models of species richness. For this purpose, we assessed the pattern of invasive alien plants in Croatia using the Flora Croatica Database. The procedure applied in testing of the sensitivity of models consisted of sample area sectioning into coherent ecological classes (hereinafter Gower classes). The quadrants were then ranked based on sampling effort per class. This resulted in creation of models using varying numbers of quadrants whose performance was tested with independent validation points. From this the best fitting model was determined, as well as a threshold of sampling effort. The data from quadrants with sampling effort below the threshold were considered too unreliable for modelling. Further, spatial uncertainty was simulated by adding a random term to each location and re-running the models using the simulated locations. Biased sampling effort and spatial uncertainty of locations had similar effects on model performance in terms of the magnitude of the affected area, as in both cases 7% of the quadrants showed statistically significant deviations in alien plant species richness. The model using only on the quandrants with the highest 35% quantile sampling effort best balanced the sampling effort per quadrant and overall geographical coverage. It predicted a mean number of 3.2 invasive alien plant species per quadrant for the Alpine region, 5.2 for the Continental, 6.1 for the Mediterranean and 5.3 for the Pannonian region of Croatia. Thus, the observational databases can be considered as a reliable source for species richness models and, most likely, for other types of species distribution models, given that their limitations are accounted for in the data selection process. In order to obtain precise estimates of species richness it is required to sample the whole range of ecological conditions of the study area.

Keywords

Biodiversity databases Balkans Data quality Regression kriging Spatial analysis 

Notes

Acknowledgements

This study was prepared under Grant 119-1191193-1227 of the Croatian Ministry of Science, Education and Sports. We would like to thank to two referees for improving previous versions of the manuscript.

References

  1. Allouche O, Kalyuzhny M, Moreno-Rueda G, Pizarro M, Kadmon R (2012) Area-heterogeneity trade-off and the diversity of ecological communities. Proc Natl Acad Sci (USA) 109:17495–17500CrossRefGoogle Scholar
  2. Araújo MB (2003) The coincidence of people and biodiversity in Europe. Glob Ecol Biogeogr 12(1):5–12CrossRefGoogle Scholar
  3. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688CrossRefGoogle Scholar
  4. Bellard C, Thuiller W, Leroy B, Genovesi P, Bakkenes M, Courchamp F (2013) Will climate change promote future invasions? Glob Change Biol 19(12):3740–3748CrossRefGoogle Scholar
  5. Bini LM, Diniz-Filho JAF, Rangel TF, Bastos RP, Pinto MP (2006) Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot. Divers Distrib 12:475–482CrossRefGoogle Scholar
  6. Boršić I, Milović M, Dujmović I, Bogdanović S, Cigić P, Rešetnik I, Nikolić T, Mitić B (2008) Preliminary check-list of invasive alien plant species(ias) in Croatia. Natura Croatica 17:55–71Google Scholar
  7. Brenning A (2008) Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. In: Boehner J, Blaschke T, Montanarella L (eds) SAGA—seconds out (= Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie vol 19, pp 23–32Google Scholar
  8. Brooks ML, Dantonio CM, Richardson DM, Grace JB, Keeley JE, DiTomaso JM, Hobbs RJ, Pellant M, Pyke D (2004) Effects of invasive alien plants on fire regimes. Bioscience 54(7):677–688CrossRefGoogle Scholar
  9. Chao A, Tsung-Jen S (2004) Nonparametric prediction in species sampling. J Agric Environ Stat 9:253–269CrossRefGoogle Scholar
  10. Chefaoui RM, Hortal J, Lobo JM (2005) Potential distribution modelling, niche characterization and conservation status assessment using GIS tools: a case study of Iberian Copris species. Biol Conserv 122:327–338CrossRefGoogle Scholar
  11. Chytry M, Maskell LC, Pyšek P, Vila M, Font X, Smart SM (2008) Habitat invasions by alien plants: a quantitative comparison among Mediterranean, subcontinental and oceanic regions of Europe. J Appl Ecol 448–458Google Scholar
  12. Crall AW, Jarnevich CS, Panke B, Young N, Renz M, Morisette J (2013) Using habitat suitability models to target invasive plant species surveys. Ecol Appl 23(1):60–72CrossRefPubMedGoogle Scholar
  13. Dennis RLH, Thomas CD (2000) Bias in butterfly distribution maps: the influence of hot spots and recorder’s home range. J Insect Conserv 4:73–77CrossRefGoogle Scholar
  14. Draper NR, Smith H (1998) Applied regression analysis, 3rd edn. Wiley-Interscience, New YorkGoogle Scholar
  15. Dukes JS, Mooney HA (1999) Does global change increase the success of biological invaders? Trends Ecol Evol 14(4):135–139CrossRefPubMedGoogle Scholar
  16. Fady B, Cyrille C (2010) Macroecological patterns of species and genetic diversity in vascular plants of the Mediterranean basin. Divers Distrib 16:53–64CrossRefGoogle Scholar
  17. Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404CrossRefGoogle Scholar
  18. Ferrier S, Watson G, Pearce J, Drielsma M (2002) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast new south wales, Species-level modelling. Biodivers Conserv 11:2275–2307CrossRefGoogle Scholar
  19. Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9(5):97122CrossRefGoogle Scholar
  20. Garcillán PP, Ezcurra E, Riemann H (2003) Distribution and species richness of woody dryland legumes in baja california, mexico. J Veg Sci.  https://doi.org/10.1016/j.tree.2013.01.014 Google Scholar
  21. Genovesi P, Shine C (2003) European strategy on invasive alien species: convention on the conservation of European wildlife and habitats (Bern convention). Council of EuropeGoogle Scholar
  22. Gower JC (1971) A general coefficient of similarity and some of its properties. Biometrics 27:857–874CrossRefGoogle Scholar
  23. Griffiths HI, Kryštufek B, Reed JM (eds) (2004) Balkan biodiversity. Pattern and process in the European hotspot. Kluwer Academic Publishers, Dordrecht, p 377Google Scholar
  24. Guisan A, Rahbek C (2011) Sesam—a new framework integrating macroecological and species distribution models for predicting spatio temporal patterns of species assemblages. J Biogeogr.  https://doi.org/10.1111/j.1365-2699.2011.02550.x
  25. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  26. Hengl T (2009) A practical guide to geostatistical mapping. LULU 291 ppGoogle Scholar
  27. Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Comput Geosci 33:1301–1315CrossRefGoogle Scholar
  28. Hewitt GM (2011) Mediterranean peninsulas. The evolution of hotspots. In: Zachos F, Habel CJ (eds) Biodiversity hotspots. Distribution and protection of conservation priority areas. Springer, Berlin, pp 123–147Google Scholar
  29. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978CrossRefGoogle Scholar
  30. Hortal J, Lobo JM (2005) An ED-based protocol for optimal sampling of biodiversity. Biodivers Conserv 14:2913–2947CrossRefGoogle Scholar
  31. Hortal J, Lobo JM (2006) Towards synecological framework for systematic conservation planning. Biodivers Inf 16–45Google Scholar
  32. Hortal J, Lobo JM (2011) Can species richness patterns be interpolated from a limited number of well-known areas? Mapping diversity using GLM and kriging. Nat Conserv 9:200–207CrossRefGoogle Scholar
  33. Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biomet J 50:346–363CrossRefGoogle Scholar
  34. Hunter ML, Yonzon (1993) Altitudinal distributions of birds, mammals, people, forests and parks in Nepal. Conserv Biol 7(2):420–423CrossRefGoogle Scholar
  35. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkCrossRefGoogle Scholar
  36. Kery M, Schmid H (2004) Monitoring programs need to take into account imperfect species detectability. Basic Appl Ecol 5:65–73CrossRefGoogle Scholar
  37. Kéry M, Gardner B, Monnerat C (2010) Predicting species distributions from checklist data using site-occupancy models. J Biogeogr 37:1851–1862Google Scholar
  38. Liebhold AM, Rossi RE, Kemp WP (1993) Geostatistics and geographic information systems in applied insect ecology. Annu Rev Entomol 38:303–327CrossRefGoogle Scholar
  39. Lobo JM (2008a) Database records as a surrogate for sampling effort provide higher species richness estimations. Biodivers Conserv 17(4):873–881CrossRefGoogle Scholar
  40. Lobo JM (2008b) More complex distribution models or more representative data? Biodivers Inf 5:14–19Google Scholar
  41. Mack RN, Simberloff D, Lonsdale WM, Evans H, Clout M, Bazzaz F (2000) Biotic invasions: causes, epidemiology, global consequences and control. Ecol Appl 10:689–710CrossRefGoogle Scholar
  42. Mckinney ML (2001) Effects of human population, area and time on non-native plant and fish diversity of the us. Biol Conserv 243–252Google Scholar
  43. Meng Q (2014) Regression kriging versus geographically weighted regression for spatial interpolation. Int J Adv Remote Sens GIS 3(1):606–615Google Scholar
  44. Mitić B, Boršić I, Dujmović I, Bogdanović S, Milović M, Cigić P, Rešetnik I, Nikolić T (2008) Alien flora of Croatia: proposals for standards in terminology, criteria and related database. Natura Croatica 17(2):73–90Google Scholar
  45. Nikolic T, Bukovec D, Šopf J, Jelaska SD (1998) Mapping of Croatian flora—possibilities and standards. Natura Croatica 7:1–62Google Scholar
  46. Nikolić T, Mitić B, Milašinović B, Jelaska SD (2013) Invasive alien plants in Croatia as a threat to biodiversity of south-eastern Europe: distributional patterns and range size. CR Biol 336:109–121CrossRefGoogle Scholar
  47. Nikolić T, Mitić B, Ruščić M, Milašinović B (2014) Diversity, knowledge and spatial distribution of the vascular flora of Croatia. Plant Biosyst 148(4):591–601CrossRefGoogle Scholar
  48. Pauchard A, Kueffer K, Dietz H, Daehler CC, Alexander J, Edvards PJ, Arevalo JR, Cavieres LA, Guisan A, Haider S, Jakobs G, Mcdougall K, Millar CI, Naylor BJ, Parks CG, Rew LJ, Seipel T (2009) Ain’t no mountain high enough: plant invasions reaching new elevations. Front Ecol Environ 7(9):479–486CrossRefGoogle Scholar
  49. Pebesma EJ (2004) Multivariable geostatistics in s: the gstat package. Comput Geosci 30(7):683–691CrossRefGoogle Scholar
  50. Pebesma EJ (2006) The role of external variables and GIS databases in geostatistical analysis. Trans GIS 10:615–632CrossRefGoogle Scholar
  51. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19(1):181–197CrossRefPubMedGoogle Scholar
  52. Pimentel D, Zuniga R, Morrison D (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol Econ 52:273–288CrossRefGoogle Scholar
  53. Pino J, Font X, Carbó J, Jové M, Pallarès L (2005) Large-scale correlates of alien plant invasion in Catalonia (NE of Spain). Biol Conserv 122:339–350CrossRefGoogle Scholar
  54. Rejmanek M (1999) Invasive plant species and invasible ecosystems. In: Sandlund OT, Schei PJ, Viken A (eds) Invasive species and bio-diversity anagement. Kluwer Academic Publishers, Dordrecht, pp 79–102CrossRefGoogle Scholar
  55. Robertson MP, Cumming GS, Erasmus BFN (2010) Getting the most out of atlas data. Divers Distrib 16:363–375CrossRefGoogle Scholar
  56. Rocchini D, Hortal J, Lengyel S, Lobo JM, Jimenez-Valverde A, Bacaro G, Chiarucci A (2011) Uncertainty in species distribution mapping and the need for maps of ignorance. Prog Phys Geogr 35:211–226CrossRefGoogle Scholar
  57. Roy H (2016) Invasive species: control wildlife pathogens too. Nature 530:281CrossRefPubMedGoogle Scholar
  58. R Core Team (2012) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.r-project.org/
  59. Sala OE, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Hanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, Poff NL, Sykes MT, Walker BH, Walker M, Wall DH (2000) Global biodiversity scenarios for the year 2100. Science 287(5459):1770–1774CrossRefPubMedGoogle Scholar
  60. Santos AMC, Jones OR, Quicke DLJ, Hortal J (2010) Assessing the reliability of biodiversity databases: identifying evenly inventoried island parasitoid faunas (Hymenoptera: Ichneumonoidea) worldwide. Insect Conserv Divers 3:72–82.  https://doi.org/10.1111/j.1752-4598.2010.00079.x CrossRefGoogle Scholar
  61. Scalera R, Genovesi P, Essl F, Rabitsch W (2012) The impacts of invasive alien species in Europe. European Environment Agency, CopenhagenGoogle Scholar
  62. Schindler S, von Wehrden H, Poirazidis K, Wrbka T, Kati V (2013) Multiscale performance of landscape metrics as indicators of species richness of plants, insects and vertebrates. Ecol Ind 31:41–48CrossRefGoogle Scholar
  63. Schindler S, Staska B, Adam M, Rabitsch W, Essl F (2015) Alien species and public health impacts in Europe: a literature review. NeoBiota 27:1–23CrossRefGoogle Scholar
  64. Simberloff D (2013) Biological invasions: What’s worth fighting and what can be won? Ecol Eng 65:112–121CrossRefGoogle Scholar
  65. SINP (2009) National habitat classification scheme. [Nacionalna klasifikacija staništa] (in Croatian)Google Scholar
  66. Stein A, Gerstner K, Kreftt H (2014) Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol Lett 17:866–880CrossRefPubMedGoogle Scholar
  67. Stohlgren TJ, Schnase JL (2006) Risk analysis for biological hazards: what we need to know about invasive species. Risk Anal 26:163–173CrossRefPubMedGoogle Scholar
  68. Taramarcaz P, Lambelet C, Clot B, Keimer C, Hauser C (2005) Ragweed (Ambrosia) progression and its health risks: Will Switzerland resist this invasion? Swiss Med Week 135:538–548Google Scholar
  69. Thuiller W, Richardson MD, Midgley GF (2007) Will climate change promote alien plant invasions? In: Nentwig W (ed) Ecological studies biological invasions. Springer, BerlinGoogle Scholar
  70. Tittensor DP, Walpole M, Hill SLL, Boyce DG, Britten GL, Burgess ND, Butchart SHM, Leadley PW, Regan EC, Alkemade R, Baumung R, Bellard C, Bouwman L, Bowles-Newark NJ, Chenery AM, Cheung WWL, Christensen V, Cooper HD, Crowther AR, Dixon MJR, Galli A, Gaveau V, Gregory RD, Gutierrez NL, Hirsch TL, Höft R, Januchowski-Hartley SR, Karmann M, Krug CB, Leverington FJ, Loh J, Kutsch Lojenga R, Malsch K, Marques A, Morgan DHW, Mumby PJ, Newbold T, Noonan-Mooney K, Pagad SN, Parks BC, Pereira HM, Robertson T, Rondinini C, Santini L, Scharlemann JPW, Schindler S, Sumaila UR, The SLS, van Kolck J, Visconti P, Ye Y (2014) A mid -term analysis of progress towards international biodiversity targets. Science 346(6206):241–244CrossRefPubMedGoogle Scholar
  71. Tscharntke T, Tylianakis JM, Rand TA, Didham RK, Fahrig L, Batary P, Bengtsson J, Clough Y, Crist TO, Dormann CF, Ewers RM, Frund J, Holt RD, Holzschuh A, Klein AM, Kleijn D, Kremen C, Landis DA (2012) Landscape moderation of biodiversity patterns and processes— eight hypotheses. Biol Rev 87:661–685CrossRefPubMedGoogle Scholar
  72. Vanderwal J, Falconi L, Januchowski S, Shoo L, Storlie C (2012) Sdmtools: species distribution modelling tools: tools for processing data associated with species distribution modelling exercises. R package version 1.1-13. http://cran.r-project.org/package=sdmtools
  73. Wang K, Zhang C, Li W (2012) Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter. GISci Remote Sens 49(6):915–932CrossRefGoogle Scholar
  74. Woodward FI (1987) Climate and plant distribution. Cambridge University Press, CambridgeGoogle Scholar
  75. Zachos FE, Habel CJ (eds) (2011) Biodiversity hotspots. Distribution and protection of conservation Priority areas. Springer, Berlin, p 546Google Scholar
  76. Zaniewski AE, Lehmann A, Overton JM (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model 157:261–280CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied Geoinformatics and Spatial Planning, Faculty of Environmental SciencesCzech University of Life Sciences PraguePragueCzech Republic
  2. 2.Environment Agency AustriaViennaAustria
  3. 3.Division of Conservation Biology, Vegetation and Landscape EcologyUniversity of ViennaViennaAustria
  4. 4.New York State College of Agriculture and Life SciencesCornell UniversityIthacaUSA
  5. 5.Faculty of Geo-Information Science and Earth ObservationFormerly of University of TwenteEnschedeThe Netherlands
  6. 6.ZagrebCroatia

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