Wetlands Ecology and Management

, Volume 23, Issue 6, pp 1039–1047 | Cite as

Automated analysis of temperature variance to determine inundation state of wetlands

  • Thomas L. Anderson
  • Jennifer L. Heemeyer
  • William E. Peterman
  • Michael J. Everson
  • Brittany H. Ousterhout
  • Dana L. Drake
  • Raymond D. Semlitsch
Original Paper

Abstract

Monitoring the inundation state (wet or dry) of wetlands is critical to understanding aquatic community structure but can be costly and labor-intensive. We tested the ability of temperature data from cost-effective iButton data loggers to reflect the inundation state of wetlands in central Missouri, based on our hypothesis that dry ponds would show greater daily temperature variance than ponds that remained inundated with water. We evaluated this method with two experiments in large outdoor mesocosms, and in existing natural wetlands in which we had deployed iButtons. True inundation state from pond visits was compared to predicted inundation state over different temperature variance thresholds expected to delineate wet or dry ponds. We confirmed that the daily temperature variances of dry iButtons were higher than that of iButtons under water, as expected, but that variance was influenced by factors such as canopy cover. We also describe an automated procedure that can be used to determine whether a pond was wet or dry with greater than 80 % accuracy. Using this approach, changes in inundation state, the number of days wet and dry, and the number of drying and filling events can be calculated. Several caveats are also provided that should be considered prior to using this method to maximize the accuracy in assessing inundation state.

Keywords

Drying Hydroperiod iButton Inundation Pond Water level Temperature data logger 

Supplementary material

11273_2015_9439_MOESM1_ESM.docx (738 kb)
Online Appendix A (DOCX 738 kb)
11273_2015_9439_MOESM2_ESM.docx (2 mb)
Online Appendix B (DOCX 2012 kb)
11273_2015_9439_MOESM3_ESM.docx (1.3 mb)
Online Appendix C (DOCX 1318 kb)

References

  1. Anderson TL, Semlitsch RD (2014) High intraguild predator density induces thinning effects on and increases temporal overlap with prey populations. Popul Ecol 56:265–273CrossRefGoogle Scholar
  2. Anderson TL, Ousterhout BH, Drake DL, Peterman WE, Semlitsch RD (2015) Life history differences influence the impacts of drought on two pond-breeding salamanders. Ecol Appl. doi:10.1890/14-2096.1 PubMedGoogle Scholar
  3. Angilletta M Jr, Krochmal A (2003) TECHNIQUES—the Thermochron: a truly miniature and inexpensive temperature-logger. Herpetol Rev 34:31–32Google Scholar
  4. Babbitt KJ (2005) The relative importance of wetland size and hydroperiod for amphibians in southern New Hampshire, USA. Wetl Ecol Manag 13:269–279CrossRefGoogle Scholar
  5. Babbitt KJ, Baber MJ, Tarr TL (2003) Patterns of larval amphibian distribution along a wetland hydroperiod gradient. Can J Zool 81:1539–1552CrossRefGoogle Scholar
  6. Brooks RT (2009) Potential impacts of global climate change on the hydrology and ecology of ephemeral freshwater systems of the forests of the northeastern United States. Clim Change 95:469–483CrossRefGoogle Scholar
  7. Correa-Araneda F, Urrutia J, Soto-Mora Y, Figueroa R, Hauenstein E (2012) Effects of the hydroperiod on the vegetative and community structure of freshwater forested wetlands, Chile. J Freshw Ecol 27:459–470CrossRefGoogle Scholar
  8. Development Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  9. Gamble DL, Mitsch WJ (2009) Hydroperiods of created and natural vernal pools in central Ohio: a comparison of depth and duration of inundation. Wetl Ecol Manag 17:385–395CrossRefGoogle Scholar
  10. Grayson KL, Dorcas ME (2004) Seasonal temperature variation in the painted turtle (Chrysemys picta). Herpetologica 60:325–336CrossRefGoogle Scholar
  11. Hubbart J, Link T, Campbell C, Cobos D (2005) Evaluation of a low-cost temperature measurement system for environmental applications. Hydrol Process 19:1517–1523CrossRefGoogle Scholar
  12. Korfel CA, Mitsch WJ, Hetherington TE, Mack JJ (2010) Hydrology, physiochemistry, and amphibians in natural and created vernal pool wetlands. Restor Ecol 18:843–854CrossRefGoogle Scholar
  13. Oke TR (2002) Boundary layer climates. Routledge, LondonGoogle Scholar
  14. Pechmann JH, Scott DE, Gibbons JW, Semlitsch RD (1989) Influence of wetland hydroperiod on diversity and abundance of metamorphosing juvenile amphibians. Wetl Ecol Manag 1:3–11CrossRefGoogle Scholar
  15. Peterman WE, Anderson TL, Drake DL, Ousterhout BH, Semlitsch RD (2014) Maximizing pond biodiversity across the landscape: a case study of larval ambystomatid salamanders. Anim Conserv. 17:275–285Google Scholar
  16. Roznik EA, Alford RA (2012) Does waterproofing Thermochron iButton dataloggers influence temperature readings? J Therm Biol 37:260–264CrossRefGoogle Scholar
  17. Semlitsch R, Scott D, Pechmann J, Gibbons J (1996) Structure and dynamics of an amphibian community: evidence from a 16-year study of a natural pond. In: Cody ML, Smallwood JA (eds) Long-term studies of vertebrate communities. Academic Press, San Diego, pp 217–248CrossRefGoogle Scholar
  18. Skelly DK (1996) Pond drying, predators, and the distribution of Pseudacris tadpoles. Copeia 1996:599–605CrossRefGoogle Scholar
  19. Sowder C, Steel EA (2012) A note on the collection and cleaning of water temperature data. Water 4:597–606CrossRefGoogle Scholar
  20. Wellborn GA, Skelly DK, Werner EE (1996) Mechanisms creating community structure across a freshwater habitat gradient. Annu Rev Ecol Syst 27:337–363CrossRefGoogle Scholar
  21. Williams DD (2005) The biology of temporary waters. Oxford University Press, OxfordCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Thomas L. Anderson
    • 1
  • Jennifer L. Heemeyer
    • 1
  • William E. Peterman
    • 1
    • 2
  • Michael J. Everson
    • 1
  • Brittany H. Ousterhout
    • 1
  • Dana L. Drake
    • 1
  • Raymond D. Semlitsch
    • 1
  1. 1.Division of Biological SciencesUniversity of MissouriColumbiaUSA
  2. 2.Illinois Natural History Survey, Prairie Research InstituteUniversity of IllinoisChampaignUSA

Personalised recommendations