Journal of Insect Conservation

, Volume 19, Issue 3, pp 519–529 | Cite as

Point-count methods to monitor butterfly populations when traditional methods fail: a case study with Miami blue butterfly

  • Erica H. Henry
  • Nick M. Haddad
  • John Wilson
  • Phillip Hughes
  • Beth Gardner
ORIGINAL PAPER

Abstract

Established butterfly monitoring methods are designed for open habitats such as grasslands. Not all rare species occupy habitats that are easy to see across and navigate, in which cases a new approach to monitoring is necessary. We present a novel use of point transect distance sampling to monitor the Miami blue, a highly endangered butterfly that occupies dense shrub habitat. To monitor Miami blue density, we developed surveys consisting of butterfly counts in semi-circular plots. We examined the rate at which an observer detects new butterflies to determine the survey duration that meets the key assumption that butterflies are detected at their initial location. As a related secondary goal, we identified the determinants of adult flight phenology to target monitoring efforts during periods of high adult abundance. We observed peak Miami blue densities in April and July/August 2012, and July/August 2013. We estimated density using detections from a 10-sec survey, our most defensible and conservative estimate. Peak daily density estimates ranged from 592 to 680 butterflies per hectare. Adult density was related to precipitation patterns, with high densities occurring 4–6 weeks after particularly wet 4-week intervals. For butterfly species that exist in high enough densities, we recommend using point transect distance sampling in habitats where traditional methods are impossible to implement.

Keywords

Conservation Distance sampling Sub-tropics Endangered species 

References

  1. Boughton DA (2000) The dispersal system of a butterfly: a test of source-sink theory suggests the intermediate-scale hypothesis. Am Nat 156:131–144PubMedCrossRefGoogle Scholar
  2. Bried JT, Pellet J (2012) Optimal design of butterfly occupancy surveys and testing if occupancy converts to abundance for sparse populations. J Insect Conserv 16:489–499Google Scholar
  3. Brown JA, Boyce MS (1998) Line transect sampling of Karner blue butterflies (Lycaeides melissa samuelis). Environ Ecol Stat 5:81–91CrossRefGoogle Scholar
  4. Buckland ST (2006) Point-transect surveys for songbirds: robust methodologies. Auk 123:345–357CrossRefGoogle Scholar
  5. Buckland ST, Anderson DR, Burnham KP et al (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, OxfordGoogle Scholar
  6. Calhoun JV, Slotten JR, Salvato MH (2002) The rise and fall of tropical blues in Florida: Cyclargus ammon and Cyclargus thomasi bethunebakeri. Holarct Lepid 7:13–20Google Scholar
  7. Cannon P, Wilmers T, Lyons K (2010) Discovery of the imperiled Miami blue butterfly (Cyclargus Thomasi Bethunebakeri) on islands in the Florida Keys National Wildlife Refuges, Monroe County. Southeast Nat 9:847–853CrossRefGoogle Scholar
  8. Cimprich DA (2009) Effect of count duration on abundance estimates of black-capped Vireos. J Field Ornithol 80:94–100CrossRefGoogle Scholar
  9. Collins M, Knutti R, Arblaster J, Dufresne JL, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  10. Cordero-Rivera A, Perez FE, Andres JA (2002) The effect of handling damage, mobility, body size, and fluctuating asymmetry on lifetime mating success of Ischnura graellsii (rambur) (Zygoptera: Coenagrionidae). Odonatologica 31:117–128Google Scholar
  11. Daniels JC (2010) Conservation and field surveys of the endangered Miami blue butterfly (Cyclargus thomasi bethunebakeri) (Lepidoptera: Lycaenidae) report #3. Submitted to United States Fish and Wildlife Service: Florida Keys National Wildlife RefugesGoogle Scholar
  12. Daubenmire R (1959) A canopy-coverage method of vegetational analysis. Northwest Sci 33:43–64Google Scholar
  13. Dover JW, Sparks TH, Greatorex-Davies JN (1997) The importance of shelter for butterflies in open landscapes. J Insect Conserv 1:89–97CrossRefGoogle Scholar
  14. Emmel TC, Daniels JC (2008) Miami blue butterfly conservation interim annual report. Period covered: January 2007-August 31, 2008. Submitted to United States Fish and Wildlife Service: South Florida Ecological Services OfficeGoogle Scholar
  15. FNAI (2010) Guide to the natural communities of Florida, 2010th edn. Florida Natural Areas Inventory, TallahasseeGoogle Scholar
  16. Haddad NM (1999) Corridor use predicted from behaviors at habitat boundaries. Am Nat 153:215–227CrossRefGoogle Scholar
  17. Haddad NM, Baum KA (1999) An experimental test of corridor effects on butterfly densities. Ecol Appl 9:623–633CrossRefGoogle Scholar
  18. Haddad NM, Hudgens B, Damiani C et al (2008) Determining optimal population monitoring for rare butterflies. Conserv Biol 22:929–940PubMedCrossRefGoogle Scholar
  19. Hahn DA, Denlinger DL (2007) Meeting the energetic demands of insect diapause: nutrient storage and utilization. J Insect Physiol 53:760–773PubMedCrossRefGoogle Scholar
  20. Hamm CA (2013) Estimating abundance of the federally endangered Mitchell’s satyr butterfly using hierarchical distance sampling. Insect Conserv Divers 6:619–626CrossRefGoogle Scholar
  21. Henry EH, Haddad NM, Wilson JW (2012) Miami blue butterfly monitoring interim report covering September 2010–May 2012. 14. Submitted to United States Fish and Wildlife Service: Florida Keys National Wildlife RefugesGoogle Scholar
  22. Isaac NJB, Cruickshanks KL, Weddle AM et al (2011) Distance sampling and the challenge of monitoring butterfly populations. Methods Ecol Evol 2:585–594CrossRefGoogle Scholar
  23. Kuefler D, Haddad NM, Hall S et al (2008) Distribution, population structure and habitat use of the endangered Saint Francis Satyr butterfly, Neonympha mitchellii francisci. Am Midl Nat 159:298–320CrossRefGoogle Scholar
  24. Lee DC, Marsden SJ (2008) Adjusting count period strategies to improve the accuracy of forest bird abundance estimates from point transect distance sampling surveys. Ibis 150:315–325CrossRefGoogle Scholar
  25. Mackenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE (2006) Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Academic Press, LondonGoogle Scholar
  26. Mattoni R, Longcore T, Zonneveld C, Novotny V (2001) Analysis of transect counts to monitor population size in endangered insects the case of the El Segundo blue butterfly, Euphilotes bernardino allyni. J Insect Conserv 5:197–206CrossRefGoogle Scholar
  27. Moranz RA, Fuhlendorf SD, Engle DM (2014) Making sense of a prairie butterfly paradox: the effects of grazing, time since fire, and sampling period on regal fritillary abundance. Biol ConservGoogle Scholar
  28. Murphy DD (1987) Are we studying our endangered butterflies to death? J Res Lepid 26:236–239Google Scholar
  29. Nichols JD, Hines JE, Sauer JR et al (2000) A double-observer approach for estimating detection probability and abundance from point counts. Auk 117:393–408CrossRefGoogle Scholar
  30. Peak RG (2011) A field test of the distance sampling method using Golden-cheeked Warblers. J Field Ornithol 82:311–319Google Scholar
  31. Pocewicz A, Morgan P, Eigenbrode SD (2009) Local and landscape effects on butterfly density in northern Idaho grasslands and forests. J Insect Conserv 13:593–601CrossRefGoogle Scholar
  32. Pollard E (1977) A method for assessing changes in the abundance of butterflies. Biol Conserv 12:115–134CrossRefGoogle Scholar
  33. Powell AFLA, Busby WH, Kindscher K (2006) Status of the regal fritillary (Speyeria idalia) and effects of fire management on its abundance in northeastern Kansas, USA. J Insect Conserv 11:299–308CrossRefGoogle Scholar
  34. Ries L, Debinski DM (2001) Butterfly responses to habitat edges in the highly fragmented prairies of Central Iowa. J Anim Ecol 70:840–852CrossRefGoogle Scholar
  35. Ries L, Fletcher RJ Jr, Battin J, Sisk TD (2004) Ecological responses to habitat edges: mechanisms, models, and variability explained. Annu Rev Ecol Evol Syst 35:491–522Google Scholar
  36. Rosenstock SS, Anderson DR, Giesen KM et al (2002) Landbird counting techniques: current practices and an alternative. Auk 119:46–53CrossRefGoogle Scholar
  37. Ross JA, Matter SF, Roland J (2005) Edge avoidance and movement of the butterfly Parnassius smintheus in matrix and non-matrix habitat. Landsc Ecol 20:127–135CrossRefGoogle Scholar
  38. Saarinen EV, Daniels JC (2006) Miami blue butterfly larvae (Lepidoptera: Lycaenidae) and ants (Hymeoptera: Formicidae): new information on the symbionts of an endangered taxon. Fla Entomol 89:69–74CrossRefGoogle Scholar
  39. Saarinen EV, Daniels JC (2012) Using museum specimens to assess historical distribution and genetic diversity in an endangered butterfly. Anim Biol 62:337–350CrossRefGoogle Scholar
  40. Saarinen EV, Daniels JC, Maruniak JE (2009) Development and characterization of polymorphic microsatellite loci in the endangered Miami blue butterfly (Cyclargus thomasi bethunebakeri). Mol Ecol Resour 9:242–244PubMedCrossRefGoogle Scholar
  41. Schultz C, Crone E (2001) Edge-mediated dispersal behavior in a prairie butterfly. Ecology 82:1879–1892CrossRefGoogle Scholar
  42. Schultz CB, Franco AMA, Crone EE (2012) Response of butterflies to structural and resource boundaries. J Anim Ecol 81:724–734PubMedCrossRefGoogle Scholar
  43. Simons TR, Alldredge MW, Pollock KH et al (2007) Experimental analysis of the auditory detection process on avian point counts. Auk 124:986–999CrossRefGoogle Scholar
  44. Sparrow HR, Sisk TD, Ehrlich PR, Murphy DD (1994) Techniques and guidelines for monitoring neotropical butterflies. Conserv Biol 8:800–809CrossRefGoogle Scholar
  45. Thomas JA (1983) A quick method for estimating butterfly numbers during surveys. Biol Conserv 27:195–211CrossRefGoogle Scholar
  46. Thomas L, Buckland ST, Rexstad EA et al (2010) Distance software: design and analysis of distance sampling surveys for estimating population size. J Appl Ecol 47:5–14PubMedCentralPubMedCrossRefGoogle Scholar
  47. Trager MD, Daniels JC (2011) Size effects on mating and egg production in the Miami blue butterfly. J Insect Behav 24:34–43CrossRefGoogle Scholar
  48. USFWS (2012) Endangered and threatened wildlife and plants; listing of the Miami blue butterfly as endangered throughout its range; listing of the cassius blue, ceranus blue, and nickerbean blue butterflies as threatened due to similarity of appearance to the Miami blue butterfly in coastal south and central Florida. 77:20948–20986Google Scholar
  49. van Strien AJ, van Swaay CAM, Termaat T (2013) Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J Appl Ecol 50:1450–1458CrossRefGoogle Scholar
  50. Wikström L, Milberg P, Bergman K-O (2008) Monitoring of butterflies in semi-natural grasslands: diurnal variation and weather effects. J Insect Conserv 13:203–211CrossRefGoogle Scholar
  51. Williams B, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic, LondonGoogle Scholar
  52. Wolda H (1988) Insect seasonality: Why? Annu Rev Ecol Syst 19:1–18CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Erica H. Henry
    • 1
  • Nick M. Haddad
    • 1
  • John Wilson
    • 1
  • Phillip Hughes
    • 2
  • Beth Gardner
    • 3
  1. 1.Department of Biological SciencesNorth Carolina State UniversityRaleighUSA
  2. 2.Florida Keys National Wildlife RefugesUnited States Fish and Wildlife ServiceBig Pine KeyUSA
  3. 3.Department of Forestry and Environmental Resources – Fisheries, Wildlife, and Conservation Biology ProgramNorth Carolina State UniversityRaleighUSA

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