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Environmental Biology of Fishes

, Volume 98, Issue 6, pp 1525–1539 | Cite as

Nonlinear and density-dependent fish habitat selection across physiochemical gradients in an invasive macrophyte habitat

  • Kyle L. WilsonEmail author
  • Micheal S. Allen
  • Robert N. M. Ahrens
  • Michael D. Netherland
Article

Abstract

Invasive macrophytes drive substantial changes to freshwater fish habitats. Such changes can influence how fish select for habitats, but fish–habitat relationships in many invasive macrophytes are often poorly understood at micro-scales. Fish habitat use is influenced by dissolved oxygen (DO) and habitat complexity, but this response can be nonlinear and dependent upon fish density. This study tested whether microhabitat use of sunfish (Lepomis spp.) in invasive macrophyte beds was density-dependent. Fish were sampled with underwater video point counts in six 0.405 ha experimental ponds with surface-matted hydrilla Hydrilla verticillata and stocked with varying densities of sunfishes. Regression models were used to evaluate the key drivers for fish habitat selection across DO, complexity and fish densities. Both fish occurrence and fish counts were positively influenced by DO and negatively influenced by habitat complexity, but the fish counts-DO relationship was dome-shaped and both fish occurrence and counts depended upon fish densities. For example, at high fish densities, fish used low and high DO and high macrophyte complexity; at low fish densities, fish avoided such areas. Fish counts peaked at intermediate DO and low macrophyte complexity. Density-dependent fish habitat selection appeared to mitigate detrimental effects of invasive macrophytes on fish habitats, indicating that fish can and do use ‘inhospitable’ habitats with potential positive population growth. Understanding density-dependent habitat selection is needed when evaluating the quality of habitats, as apparently inhospitable habitats can be utilized when fish density is high.

Keywords

Lepomis macrochirus Hydrilla verticillata Aquatic plant management Fish–macrophyte relationships Freshwater fisheries Underwater video 

Notes

Acknowledgments

The authors thank the Florida Fish and Wildlife Conservation Commission Invasive Plant Management Section for financial support and the U.S. Geological Survey Southeastern Ecological Science Center for access to the experimental ponds. This research was permitted under the Institutional Animal Care and Use Committee permit #USGS/SESC 2011–09 under Category I (no direct impact on animals). The authors thank the University of Florida’s Dan Gwinn for advice on analyses and Jeremy Slade for help on project development as well as Erin Bradshaw, Nicholas Cole, Zack Slagle, Simone Nageon de Lestang, and Antonio Malouf for field collections and video analysis.

References

  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723CrossRefGoogle Scholar
  2. Allison PD, Waterman RP (2002) Fixed-effects negative binomial regression models. Sociol Methodol 32:247–265CrossRefGoogle Scholar
  3. Bachmann RW, Jones BL, Fox DD, Hoyer MV, Bull LA, Canfield DE Jr (1996) Relations between trophic state indicators and fish in Florida (U.S.A.) lakes. Can J Fish Aquat Sci 53:842–855CrossRefGoogle Scholar
  4. Barrientos CA, Allen MS (2008) Fish abundance and community composition in native and non-native plants following hydrilla colonization at Lake Izabel, Guatemala. Fish Manag Ecol 15:99–106CrossRefGoogle Scholar
  5. Bayley PB, Austen DJ (2002) Capture efficiency of a boat electrofisher. Trans Am Fish Soc 131:435–451CrossRefGoogle Scholar
  6. Bettoli PW, Maceina MJ, Noble RL, Betsill RK (1992) Piscivory in largemouth bass as a function of aquatic vegetation abundance. N Am J Fish Manag 12:509–516CrossRefGoogle Scholar
  7. Bettoli PW, Maceina MJ, Noble RL, Betsill RK (1993) Response of a reservoir fish community to aquatic vegetation removal. N Am J Fish Manag 13:110–124CrossRefGoogle Scholar
  8. Bowes G, Holaday AS, Haller WT (1972) Seasonal variation in the biomass, tuber density, and photosynthetic metabolism of hydrilla in three Florida lakes. J Aquat Plant Manag 17:61–65Google Scholar
  9. Bult TP, Riley SC, Haedrick RL, Gibson RJ, Heggenes J (1999) Density-dependent habitat selection by juvenile Atlantic salmon (Salmo salar) in experimental riverine habitats. Can J Fish Aquat Sci 56:1298–1306CrossRefGoogle Scholar
  10. Bunch AJ, Allen MS, Gwinn DC (2010) Spatial and temporal hypoxia dynamics in dense emergent macrophytes in a Florida lake. Wetlands 30:429–435CrossRefGoogle Scholar
  11. Burleson ML, Wilhelm DR, Smatresk NJ (2001) The influence of fish size on the avoidance of hypoxia and oxygen selection by largemouth bass. J Fish Biol 59:1336–1349Google Scholar
  12. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304CrossRefGoogle Scholar
  13. Cameron AC, Windmeijer FA (1996) R-squared measures for count data regression models with applications to health-care utilization. J Bus Econ Stat 14:209–220Google Scholar
  14. Caraco N, Cole J, Findlay S, Wigand C (2006) Vascular plants as engineers of oxygen in aquatic systems. Bioscience 56:219–225CrossRefGoogle Scholar
  15. Chick JH, Mclvor CC (1994) Patterns in the abundance and composition of fishes among beds of different macrophytes: viewing a littoral zone as a landscape. Can J Fish Aquat Sci 51:2873–2882CrossRefGoogle Scholar
  16. Coetzee JA, Hill MP, Schlange D (2009) Potential spread of the invasive plant Hydrilla verticillata in South Africa based on anthropogenic spread and climate suitability. Biol Invasions 11:801–812CrossRefGoogle Scholar
  17. Colle DE, Shireman JV (1980) Coefficients of condition for largemouth bass, bluegill, and redear sunfish in hydrilla infested lakes. Trans Am Fish Soc 109:521–531CrossRefGoogle Scholar
  18. Colon-Guad JC, Kelso WE, Rutherford DW (2004) Spatial distribution of macroinvertebrates inhabiting hydrilla and coontail beds in the Atchafalaya Basin, Louisiana. J Aquat Plant Manag 42:85–91Google Scholar
  19. Crowder LB, Cooper WE (1982) Habitat structural complexity and the interaction between bluegills and their prey. Ecology 63:1802–1813CrossRefGoogle Scholar
  20. Dibble ED, Killgore KJ, Harrell SL (1996) Assessment of fish-plant interactions. Am Fish Soc Symp 16:357–372Google Scholar
  21. Diehl S (1992) Fish predation and benthic community structure: the role of omnivory and habitat complexity. Ecology 73:1646–1661CrossRefGoogle Scholar
  22. Diniz-Filho JAF, Bini LM, Hawkins BA (2003) Spatial autocorrelation and red herrings in geographical ecology. Glob Ecol Biogeogr 12:53–64CrossRefGoogle Scholar
  23. Dunham JB, Cade BS, Terrell JW (2002) Influences of spatial and temporal variation on fish–habitat relationships defined by regression quantiles. Trans Am Fish Soc 131:86–98CrossRefGoogle Scholar
  24. Eby LA, Crowder LB, McClellan CM, Peterson CH, Powers MJ (2005) Habitat degradation from intermittent hypoxia: impacts of demersal fishes. Mar Ecol Prog Ser 291:249–261CrossRefGoogle Scholar
  25. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  26. Fretwell SD, Lucas HJ Jr (1970) On territorial behavior and other factors influencing habitat distribution in birds. Acta Biotheor 19:16–36CrossRefGoogle Scholar
  27. FWC (2011) Background information for the fish and wildlife conservation commission’s position on hydrilla management. State of Florida Fish and Wildlife Conservation Commission, Tallahassee, 8 ppGoogle Scholar
  28. Gilliam JF, Fraser DF (1987) Habitat selection under predation hazard: test of a model with foraging minnows. Ecology 68:1856–1862CrossRefGoogle Scholar
  29. Hall DJ, Werner EE (1977) Seasonal distribution and abundance of fishes in the littoral zone of a Michigan lake. Trans Am Fish Soc 106:545–555CrossRefGoogle Scholar
  30. Hershner C, Havens KJ (2008) Managing invasive aquatic plants in a changing system: strategic consideration of ecosystem services. Conserv Biol 22:544–550CrossRefPubMedGoogle Scholar
  31. Johnson JA, Newman RM (2011) A comparison of two methods for sampling biomass of aquatic plants. J Aquat Plant Manag 49:1–8Google Scholar
  32. Killgore KJ, Hoover JJ (2001) Effects of hypoxia on fish assemblages in a vegetated waterbody. J Aquat Plant Manag 39:40–44Google Scholar
  33. Kramer DL (1987) Dissolved oxygen and fish behavior. Environ Biol Fish 18:81–92Google Scholar
  34. Langeland KA (1996) Hydrilla verticillata (L.F.) Royle (Hydrocharitaceae), “The perfect aquatic weed”. Castanea 61:293–304Google Scholar
  35. Lillie RA, Budd J (1992) Habititat architecture of Myriophyllum spicatum L. as an index to habitat quality for fish and macroinvertebrates. J Freshw Ecol 7:113–125CrossRefGoogle Scholar
  36. Lindberg WJ, Frazer TK, Portier KM, Vose F, Loftin J, Murie DJ et al (2006) Density-dependent habitat selection and performance by a large mobile reef fish. Ecol Appl 16:731–746CrossRefPubMedGoogle Scholar
  37. Martin TH, Crowder LB, Dumas CF, Burkholder JM (1992) Indirect effects of fish on macrophytes in Bays Mountain Lake: evidence for a littoral trophic cascade. Oecologia 89:476–581CrossRefGoogle Scholar
  38. Miranda LE, Hodges KB (2000) Role of aquatic vegetation coverage on hypoxia and sunfish abundance in bays of a eutrophic reservoir. Hydrobiologia 427:51–57CrossRefGoogle Scholar
  39. Miranda LE, Driscoll MP, Allen MS (2000) Transient physicochemical microhabitats facilitate fish survival in inhospitable aquatic plant stands. Freshw Biol 44:617–628CrossRefGoogle Scholar
  40. Morris DW (1987) Tests of density-dependent habitat selection in a patchy environment. Ecol Monogr 57:269–281CrossRefGoogle Scholar
  41. Osenberg CW, Mittelbach GG, Wainwright PC (1992) Two-stage life histories in fish: the interaction between juvenile competition and adult performance. Ecology 73:255–267CrossRefGoogle Scholar
  42. Peterson AT, Papes M, Kluza DA (2003) Predicting the potential invasive distributions of four alien plant species in North America. Weed Sci 51:863–868CrossRefGoogle Scholar
  43. R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. http://www.R-project.org
  44. Robb T, Abrahams MV (2003) Variation in tolerance to hypoxia in a predator and prey species: an ecological advantage of being small? J Fish Biol 62:1067–1081CrossRefGoogle Scholar
  45. Sammons SM, Maceina MJ, Partridge DG (2003) Changes in behaviour, movement, and home ranges of largemouth bass following large-scale hydrilla removal in Lake Seminole, Georga. J Aquat Plant Manag 41:31–38Google Scholar
  46. Savino JF, Stein RA (1982) Predator–prey interaction between largemouth bass and bluegills as influenced by simulated, submersed vegetation. Trans Am Fish Soc 111:255–266CrossRefGoogle Scholar
  47. Schultz R, Dibble E (2012) Effects of invasive macrophytes on freshwater fish and macroinvertebrate communities: the role of invasive plant traits. Hydrobiologia 684:1–14CrossRefGoogle Scholar
  48. Smart J, Sutherland WJ, Watkinson AR, Gill JA (2004) A new means of presenting the results of logistic regression. Bull Ecol Soc Am 85:100–102CrossRefGoogle Scholar
  49. Stubben CJ, Milligan BG (2007) Estimating and analyzing demographic nodels using the popbio package in R. J Stat Softw 22:11Google Scholar
  50. Theel HJ, Dibble ED, Madsen JD (2008) Differential influence of a monotypic and diverse native aquatic plant bed on a macroinvertebrate assemblage; an experimental implication of exotic plant induced habitat. Hydrobiologia 600:77–87CrossRefGoogle Scholar
  51. Van Horne B (1983) Density as a misleading indicator of habitat quality. J Wildl Manag 47:893–901CrossRefGoogle Scholar
  52. VanderKooy KE, Rakocinski CF, Heard RW (2000) Trophic relationships of three sunfishes (Lepomis spp.) in an estuarine bayou. Estuaries 23:621–632CrossRefGoogle Scholar
  53. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New YorkCrossRefGoogle Scholar
  54. Ver Hoef JM, Boveng PL (2007) Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology 88:2766–2772CrossRefPubMedGoogle Scholar
  55. Werner EE, Hall DJ (1988) Ontogenetic habitat shifts in bluegill: the foraging rate-predation risk trade-off. Ecology 69:1352–1366CrossRefGoogle Scholar
  56. Wilson KL (2013) Assessing fish communities in dense submersed aquatic vegetation habitats using underwater video cameras. Thesis, The University of FloridaGoogle Scholar
  57. Wilson KL, Allen MS, Ahrens RNM, Netherland MD (2014) Use of underwater video to assess freshwater fish populations in dense submersed aquatic vegetation. Mar Freshw Res 66:10–22. doi: 10.1071/MF13230 CrossRefGoogle Scholar
  58. Yokota K, Kiyota M, Okamura H (2009) Effect of bait species and color on sea turtle bycatch and fish catch in a pelagic longline fishery. Fish Res 97:53–58CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Kyle L. Wilson
    • 1
    • 3
    Email author
  • Micheal S. Allen
    • 1
  • Robert N. M. Ahrens
    • 1
  • Michael D. Netherland
    • 2
  1. 1.School of Forest Resources and Conservation, Program of Fisheries and Aquatic SciencesUniversity of FloridaGainesvilleUSA
  2. 2.US Army Engineer Research and Development CenterGainesvilleUSA
  3. 3.Department of Biological SciencesUniversity of CalgaryCalgaryCanada

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