Evaluation of machine learning methods for predicting eradication of aquatic invasive species

Original Paper
  • 36 Downloads

Abstract

In the work, we evaluate the performance of machine learning approaches for predicting successful eradication of aquatic invasive species (AIS) and assess the extent to which eradication of an invasive species depends on the certain specified ecological features of the target ecosystem and/or features that characterize the planned intervention. We studied the outcomes of 143 planned attempts for eradicating AIS, where each attempt was described by ecological and eradication-strategy-related features of the target ecosystem. We considered several machine learning approaches to determine whether one could produce a classifier that accurately predicts weather an invasive species will be eradicated. To assess each learner’s performance, we examined its tenfold cross-validated prediction accuracy as well as the false positive rate, the F-measure, and the Area Under the ROC Curve. We also used Kaplan–Meier survival analysis to determine which features are relevant to predicting the time required for each eradication program. Across the five typical machine learning approaches, our analysis suggests that learners trained by the decision tree work well, and have the best performance. In particular, by examining the trained decision tree model, we found that if an occupied area was not large and/or containments of AIS dispersal were employed, the eradication of AIS was likely to be successful. We also trained decision tree models over only the ecological features and found that their performances were comparable with that of models trained using all features. As our trained decision tree models are accurate, decision makers can use them to estimate the result of the proposed actions before they commit to which specific strategy should be applied.

Keywords

Aquatic species Machine learning Survival analysis Ecological features Planned intervention 

Notes

Acknowledgements

MAL acknowledges support from a Canadian Research Chair, an NSERC Discovery Grant and a Killiam Research Fellowship. RG acknowledges support from NSERC and AMII. YX acknowledges support from the Simon foundations. We thank Boris Beric, David Drolet and Huge MacIsaac for their contribution on data collection and useful comments. This work was partially supported by the Alberta Innovates Centre for Machine Learning, the Canadian Aquatic Invasive Species Network, the Natural Sciences and Engineering Research Council of Canada.

References

  1. Akers P (2009) Hydrilla eradication program progress report 2009. Technical report, California Department of Food and AgricultureGoogle Scholar
  2. Barahona-Segovia R, Grez A, Bozinovic F (2015) Testing the hypothesis of greater eurythermality in invasive than in native ladybird species: from physiological performance to life-history strategies. Ecol Entomol 41(2):182–191CrossRefGoogle Scholar
  3. Boets P, Landuyt D, Everaert G, Broekx S, Goethals P (2015) Evaluation and comparison of data-driven and knowledge-supported Bayesian belief networks to assess the habitat suitability for alien macroinvertebrates. Environ Model Softw 74:92–103CrossRefGoogle Scholar
  4. Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and regression trees. Taylor & Francis, LondonGoogle Scholar
  5. Cambray J (2003) Impact on indigenous species biodiversity caused by the globalisation of alien recreational freshwater fisheries. In: Martens K (ed) Aquatic biodiversity: a celebratory volume in honour of Henri J. Dumont. Springer, Dordrecht, pp 217–230CrossRefGoogle Scholar
  6. Caudron A, Champigneulle A (2011) Multiple electrofishing as a mitigate tool for removing nonnative atlantic brown trout (Salmo trutte l.) threatening a native mediterranean brown trout population. Eur J Widlife Res 5(3):575–583CrossRefGoogle Scholar
  7. Cooling M, Hartley S, Sim D, Lester P (2011) The widespread collapse of an invasive species: Argentine ants (Linepithema humile) in New Zealand. Biol Lett 8:430–433CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cox D, Oakes D (1984) Analysis of survival data. Chapman & Hall/CRC, LondonGoogle Scholar
  9. Drake D, Mercader R, Dobson T, Mandrak E (2015) Can we predict risky human behaviour involving invasive species? A case study of the release of fishes to the wild. Biol Invasions 17:309–326CrossRefGoogle Scholar
  10. Drolet D, Locke A, Lewis MA, Davidson J (2014) User-friendly and evidence-based tool to evaluate probability of eradication of aquatic non-indigenous species. J Appl Ecol 51(4):1050–1056CrossRefGoogle Scholar
  11. Drolet D, Locke A, Lewis MA, Davidson J (2015) Evidence-based tool surpasses expert opinion in predicting probability of eradication of aquatic nonindigenous species. Ecol Appl 25(2):441–450CrossRefPubMedGoogle Scholar
  12. Eilers J, Truemper H, Jackson L, Eilers B, Loomis D (2011) Eradication of an invasive cyprinid (Gila bicolor) to achieve water quality goals in Diamond Lake, Oregon (USA). Ecol Appl 27:194–204Google Scholar
  13. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874CrossRefGoogle Scholar
  14. Ferri C, Flach P, Hernandez-Orallo J (2002) Learning decision trees using the area under the ROC curve. In: Proceeding ICML ’02 proceedings of the nineteenth international conference on machine learning, pp 139–146Google Scholar
  15. Fielding A (1999) Machine learning methods for ecological applications. Springer, New YorkCrossRefGoogle Scholar
  16. Gurevitch J, Padilla DK (2004) Are invasive species a major cause of extinctions? Trends Ecol Evol 19(9):470–474CrossRefPubMedGoogle Scholar
  17. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I (2009) The weka data mining software: an update. SIGKDD Explor 11(1):10–18CrossRefGoogle Scholar
  18. Houlahan J, Findlay CS (2004) Effect of invasive plant species on temperate wetland plant diversity. Conserv Biol 18(4):1132–1138CrossRefGoogle Scholar
  19. Kaukeinen D (1983) Vertebrate pest control and management materials: fourth symposium. ASTM International, PhiladelphiaCrossRefGoogle Scholar
  20. Keller R, Kocev D, Dzeroski S (2011) Trait-based risk assessment for invasive species: high performance across diverse taxonomic groups, geographic ranges and machine learning/statistical tools. Divers Distrib 17(3):451–461CrossRefGoogle Scholar
  21. Klein J, Moeschberger M (1997) Survival analysis—techniques for censored and truncated data—statistics for biology and health. Springer, New YorkGoogle Scholar
  22. Kleinbaum D, Klein M (2005) Survival analysis: statistics for biology and health, 2nd edn. Springer, New YorkGoogle Scholar
  23. Kolar C, Lodge D (2001) Progress in invasion biology: predicting invaders. Trends Ecol Evol 16:199–204CrossRefPubMedGoogle Scholar
  24. Kolar C, Lodge DM (2002) Ecological predictions and risk assessment for alien fishes in North America. Science 298(5596):1233–1236CrossRefPubMedGoogle Scholar
  25. Kulp M, Moore S (2000) Multiple electrofishing removals for eliminating rainbow trout in a small southern appalachian stream. N Am J Fish Manag 20(1):259–266CrossRefGoogle Scholar
  26. Lawless J (2002) Statistical models and methods for lifetime data. Wiley-Interscience, HobokenCrossRefGoogle Scholar
  27. Lawrence J (2005) Introduction to neural networks, 2nd edn. California Scientific Software Press, CaliforniaGoogle Scholar
  28. Lek S, Guacgan J (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 120:65–73CrossRefGoogle Scholar
  29. Lockwood J, Cassey P, Blackburn T (2005) The role of propagule pressure in explaining species invasions. Trends Ecol Evol 20:223–228CrossRefPubMedGoogle Scholar
  30. Mantel N (1966) Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50(3):163–170PubMedGoogle Scholar
  31. Mantel N, Haenszel W (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22(4):719–748PubMedGoogle Scholar
  32. Massey F (1951) The Kolmogorov–Smirnov test for goodness of fit. J Am Stat Assoc 46(253):68–78CrossRefGoogle Scholar
  33. McDonald J (2014) Handbook of biological statistics, 3rd edn. Sparky House Publishing, BaltimoreGoogle Scholar
  34. Miller L (1956) Table of percentage points of Kolmogorov statistics. J Am Stat Assoc 51(273):111–121CrossRefGoogle Scholar
  35. Mitchell T (1997) Machine learning. Mc-Graw-Hill Companies Inc, New YorkGoogle Scholar
  36. Nagar L, Shenkar N (2016) Temperature and salinity sensitivity of the invasive ascidian Microcosmus exasperatus Heller, 1878. Aquat Invasions 11(1):33–43CrossRefGoogle Scholar
  37. Olden J, Jackson D (2002) Illuminating the black box; understanding variable contributions in artificial neural networks. Ecol Model 154:135–150CrossRefGoogle Scholar
  38. Olden J, Lawler J, Poff N (2008) Machine learning methods without tears: a primer for ecologists. Q Rev Biol 83(2):171–193CrossRefPubMedGoogle Scholar
  39. Peto R, Peto J (1972) Asymptotically efficient rank invariant test procedures. J R Stat Soc Ser A 135(2):185–207CrossRefGoogle Scholar
  40. Powers D (2011) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37–63Google Scholar
  41. Pu R, Gong P, Tian Y, Miao X, Carruthers R, Anderson G (2008) Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environ Monit Assess 140(1–3):15–32CrossRefPubMedGoogle Scholar
  42. Pullin A, Knight T, Stone D, Charman K (2004) Do conservation managers use scientific evidence to support their decision-making? Biol Conserv 119:245–252CrossRefGoogle Scholar
  43. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc, San FranciscoGoogle Scholar
  44. Raymond B, McInnes J, Dambacher J, Way S, Bergstrom D (2011) Qualitative modelling of invasive species eradication on subantarctic macquarie island. J Appl Ecol 48(1):181–191CrossRefGoogle Scholar
  45. Reichard S, Hamilton C (1997) Predicting invasions of woody plants introduced into North America. Conserv Biol 11(1):193–203CrossRefGoogle Scholar
  46. Ricciardi A, Neves RJ, Richard J, Rasmussen J (1998) Impending extinctions of North American freshwater mussels (Unionoida) following the zebra mussel (Dreissena polymorpha) invasion. J Anim Ecol 67(4):613–619CrossRefGoogle Scholar
  47. Rowe D, Champion P (1994) Biomanipulation of plants and fish to restore Lake Parkinson: a case study and its implications. In: Collier K (ed) Restoration of aquatic habitat. Selected papers from the second day of the New Zealand Limnological Society 1993 annual conference, pp 53–65Google Scholar
  48. Van-Dyke J, Leslie JA, Nall L (1984) The effects of the grass carp on the aquatic macrophytes of four Florida lakes. J Aquat Plant Manag 22:87–95Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematical SciencesUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Center for Mathematical Biology, Department of Mathematical and Statistical SciencesUniversity of AlbertaEdmontonCanada
  4. 4.Department of Biological SciencesUniversity of AlbertaEdmontonCanada

Personalised recommendations