Advertisement

Landscape Ecology

, Volume 33, Issue 8, pp 1319–1334 | Cite as

Effects of landscape structure and temporal habitat dynamics on wintering mallard abundance

  • John A. HerbertEmail author
  • Avishek Chakraborty
  • Luke W. Naylor
  • William S. Beatty
  • David G. Krementz
Research Article

Abstract

Context

Management of wintering waterfowl in North America requires adaptability because constant landscape and environmental change challenges existing management strategies regarding waterfowl habitat use at large spatial scales. Migratory waterfowl including mallards (Anas platyrhynchos) use the lower Mississippi Alluvial Valley (MAV) for wintering habitat, making this an important area of emphasis for improving wetland conservation strategies, while enhancing the understanding of landscape-use patterns.

Objectives

We used aerial survey data collected in the Arkansas portion of the MAV (ARMAV) to explain the abundance and distribution of mallards in relation to variable landscape conditions.

Methods

We used two-stage, hierarchical spatio-temporal models with a random spatial effect to identify covariates related to changes in mallard abundance and distribution within and among years.

Results

We found distinct spatio-temporal patterns existed for mallard distributions across the ARMAV and these distributions are dependent on the surrounding landscape structure and changing environmental conditions. Models performing best indicated seasonal surface water extent, rice field, wetland and fallow (uncultivated) fields positively influenced mallard presence. Rice fields, surface water and weather were found to influence mallard abundance. Additionally, the results suggest weather and changing surface water affects mallard presence and abundance throughout the winter.

Conclusions

Using novel datasets to identify which environmental factors drive changes in regional wildlife distribution and abundance can improve management by providing managers additional information to manage land over landscapes spanning private and public lands. We suggest our analytical approach may be informative in other areas and for other wildlife species.

Keywords

Species distribution modeling Spatial random effect Species-habitat relationships Anas platyrhynchos Waterbird Waterfowl 

Notes

Acknowledgements

This research was funded by the U.S. Geological Survey Arkansas Cooperative Fish and Wildlife Research Unit and the University of Arkansas. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. We would like to acknowledge additional funding from the Arkansas Audubon Society. High performance computing resources provided by Technology Services at Tulane University. Aerial surveys were funded by the Arkansas Game and Fish Commission and performed by AGFC employees Jason Jackson, Jason Carbaugh and J.J. Abernathy. We also thank Kristen L. Herbert, Sarah Lehnen, Michael Mitchell, and Henry T. Pittman.

Supplementary material

10980_2018_671_MOESM1_ESM.docx (97 kb)
Supplementary material 1 (DOCX 96 kb)
10980_2018_671_MOESM2_ESM.docx (28 kb)
Supplementary material 2 (DOCX 27 kb)
10980_2018_671_MOESM3_ESM.docx (54.2 mb)
Supplementary material 3 (DOCX 55499 kb)
10980_2018_671_MOESM4_ESM.docx (162 kb)
Supplementary material 4 (DOCX 161 kb)

References

  1. Albanese G, Davis CA, Compton BW (2012) Spatiotemporal scaling of North American continental interior wetlands: implications for shorebird conservation. Landscape Ecol 27:1465–1479CrossRefGoogle Scholar
  2. Albert JH, Chib S (1993) Bayesian analysis of binary and polychotomous response data. J Am Stat Assoc 88:669–679CrossRefGoogle Scholar
  3. Allen AW (1987) Habitat suitability index models: mallard (winter habitat, Lower Mississippi Valley). U.S. Fish Wildl Serv Biol Report 82(10.132)Google Scholar
  4. Allen JM, Leininger TJ, Hurd JD, Civco DL, Gelfand AE, Silander JA (2013) Socioeconomics drive woody invasive plant richness in New England, USA through forest fragmentation. Landscape Ecol 28:1671–1686CrossRefGoogle Scholar
  5. Almaraz P, Amat AJ (2004) Multi-annual spatial and numeric dynamics of the white-headed duck Oxyura leucocephala in southern Europe: seasonality, density dependence and climatic variability. J Anim Ecol 73:1013–1023CrossRefGoogle Scholar
  6. Almaraz P, Green AJ, Aguilera E, Rendon MA, Bustamante J (2012) Estimating partial observability and nonlinear climate effects on stochastic community dynamics of migratory waterfowl. J Anim Ecol 81:1113–1125CrossRefPubMedGoogle Scholar
  7. Anderson RP (2013) A framework for using niche models to estimate impacts of climate change on species distributions. Annals New York Acad Sci 1297:8–28CrossRefGoogle Scholar
  8. Ando T (2007) Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika 94:443–458CrossRefGoogle Scholar
  9. Baldassarre GA, Bolen EG (2006) Waterfowl ecology and management, 2nd edn. Krieger Publishing Company, FloridaGoogle Scholar
  10. Banerjee S, Fuentes M (2012) Bayesian modeling for large spatial datasets. Wiley Interdiscipl Rev: comput Stat 4:59–66CrossRefGoogle Scholar
  11. Banerjee S, Carlin BP, Gelfand AE (2004) Hierarchical modeling and analysis for spatial data. Chapman & Hall/CRC Press, FloridaGoogle Scholar
  12. Bastos R, Monteiro AT, Carvalho D, Gomes C, Travassos P, Honrado JP, Santos M, Cabral JA (2016) Integrating land cover structure and functioning to predict biodiversity patterns: a hierarchical modelling framework designed for ecosystem management. Landscape Ecol 31:701–710CrossRefGoogle Scholar
  13. Beatty WS, Webb EB, Kesler DC, Raedeke AH, Naylor LW, Humburg DD (2014a) Landscape effects on mallard habitat selection at multiple spatial scales during the non-breeding period. Landscape Ecol 29:989–1000CrossRefGoogle Scholar
  14. Beatty WS, Kesler DC, Webb EB, Raedeke AH, Naylor LW, Humburg DD (2014b) The role of protected area wetlands in waterfowl habitat conservation: implications for protected area network design. Biol Conser 176:144–152CrossRefGoogle Scholar
  15. Beatty WS, Kesler DC, Webb EB, Naylor LW, Raedeke AH, Humburg DD, Coluccy JM, Soulliere GJ (2017) How will predicted land-use change affect waterfowl spring stopover ecology? Inferences from an individual-based model. J Appl Ecology 54:926–934CrossRefGoogle Scholar
  16. Bellrose FC (1980) Ducks, geese and swans of North America, 3rd edn. Stackpole Books, PennsylvaniaGoogle Scholar
  17. Chakraborty A, Gelfand AE, Wilson AM, Latimer AM, Silander JA (2010) Modeling large scale species abundance with latent spatial processes. Annals of Appl Statistics 4:1403–1429CrossRefGoogle Scholar
  18. Davis BE, Afton AD, Cox RR (2011) Factors affecting winter survival of female mallards in the lower Mississippi Alluvial Valley. Waterbirds 34:186–194CrossRefGoogle Scholar
  19. Delnicki D, Reinecke KJ (1986) Mid-winter food use and body weights of mallards and wood ducks in Mississippi. J Wildl Manage 50:43–51CrossRefGoogle Scholar
  20. Drilling N, Titman R, McKinney F (2002) Mallard (Anas platyrhynchos). Account 658 the Birds of North America Online (A. Poole, ed). Ithaca: Cornell Lab of Ornithology; Retrieved from the Birds of North America http://bna.birds.cornell.edu/bna/species/658
  21. ESRI (Environmental Systems Resource Institute) (2015) ArcMap 10.3 Student Edition. ESRI, CaliforniaGoogle Scholar
  22. Faulkner S, Barrow W, Keeland B, Walls S, Telesco D (2011) Effects of conservation practices on wetland ecosystem services in the Mississippi Alluvial Valley. Ecol Appl 21(sp1):S31–S48CrossRefGoogle Scholar
  23. Fredrickson LH, Heitmeyer ME (1988) Waterfowl use of forested wetlands of the southern United States: an overview. In: Weller MW (ed) Waterfowl in winter. University of Minnesota Press, Minnesota, pp 307–323Google Scholar
  24. Gelfand AE, Schmidt AM, Wu S, Silander JA, Latimer A, Rebelo AG (2005) Modelling species diversity through species level hierarchical modelling. J Royal Stat Soc 54:1–20CrossRefGoogle Scholar
  25. Gilks WR (2005) Markov chain Monte Carlo. John Wiley & Sons, Ltd.  https://doi.org/10.1002/0470011815.b2a14021 CrossRefGoogle Scholar
  26. Green AW, Krementz DG (2008) Mallard harvest distributions in the Mississippi and Central Flyways. J Wildl Manag 72:1328–1334CrossRefGoogle Scholar
  27. Greenberg R, Marra PP (2005) Birds of two worlds: the ecology and evolution of migration. Johns Hopkins Univ Press, MarylandGoogle Scholar
  28. Guillemain M, Poysa H, Fox AD, Arzel C, Dessborn L, Ekroos J, Gunnarsson G, Holm TE, Christensen TK, Lehikoinen A, Mitchell C, Rintala J, Moller AP (2013) Effects of climate change on European ducks: what we do know and what we need to know? Wildl Biol 19:404–419CrossRefGoogle Scholar
  29. Hagy HM, Kaminski RM (2015) Determination of foraging thresholds and effects of application on energetic carrying capacity for waterfowl. PLoS ONE 10:e0118349CrossRefPubMedPubMedCentralGoogle Scholar
  30. Hagy HM, Straub JN, Schummer ML, Kaminski RM (2014) Annual variation in food densities and factors affecting wetland use by waterfowl in the Mississippi Alluvial Valley. Wildfowl Spec Issue 4:436–450Google Scholar
  31. Haig SM, Mehlman DW, Oring LW (1998) Avian movements and wetland connectivity in landscape conservation. Conserv Biol 12:749–758CrossRefGoogle Scholar
  32. Heitmeyer ME (2006) The importance of winter floods to mallards in the Mississippi Alluvial Valley. J Wildl Manag 70:101–110CrossRefGoogle Scholar
  33. Holland JD, Yang S (2016) Multi-scale studies and the ecological neighborhood. Curr Landsc Ecol Rep 4:135–145CrossRefGoogle Scholar
  34. Holland EP, Aegerter JN, Dytham C (2009) Comparing resource representations and choosing scale in heterogeneous landscapes. Landscape Ecol 24:213–227CrossRefGoogle Scholar
  35. St. James EA, Schummer ML, Kaminski RM, Burger LW (2013) Effect of weekly hunting frequency on duck abundances in Mississippi wildlife management areas. J Fish and Wildl Manag 4:144–150Google Scholar
  36. Ji W, Jeske C (2000) Spatial modeling of the geographic distribution of wildlife populations: a case study in the lower Mississippi River region. Ecol Model 132:95–104CrossRefGoogle Scholar
  37. Johnson DH (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65–71CrossRefGoogle Scholar
  38. Johnson VE (2004) A Bayesian χ2 test for goodness-of-fit. Ann Stat 32:2361–2384CrossRefGoogle Scholar
  39. Johnson MD (2007) Measuring habitat quality: a review. Condor 109:489–504CrossRefGoogle Scholar
  40. Kaminski RM, Elmberg J (2014) An introduction to habitat use and selection by waterfowl in the northern hemisphere. Wildfowl Spec Issue 4:9–16Google Scholar
  41. King SL, Keeland BD (1999) Evaluation of reforestation in the Lower Mississippi River Alluvial Valley. Restor Ecol 7:348–359CrossRefGoogle Scholar
  42. Kross JP, Kaminski RM, Reinecke KJ, Pearse AT (2007) Conserving waste rice for wintering waterfowl in the Mississippi Alluvial Valley. J Wildl Manag 72:1383–1387CrossRefGoogle Scholar
  43. Kross J, Kaminski RM, Reinecke KJ, Penny EJ, Pearse AT (2008) Moist-soil seed abundance in managed wetlands in the Mississippi Alluvial Valley. J Wildl Manag 72:707–714CrossRefGoogle Scholar
  44. Leach AG, Straub JN, Kaminski RM, Ezell A, Hawkins TS, Leininger TD (2012) Effect of winter flooding on mass and gross energy of bottomland hardwood acorns. J Wildl Manag 76:1519–1522CrossRefGoogle Scholar
  45. Leblond M, Frair J, Fortin D, Dussault C, Ouellet JP, Courtois R (2011) Assessing the influence of resource covariates at multiple spatial scales: an application to forest-dwelling caribou faced with intensive human activity. Landscape Ecol 26:1433–1446CrossRefGoogle Scholar
  46. Lehnen S (2013) Monitoring the Effects of Climate Change on Waterfowl Abundance in the Mississippi Alluvial Valley: Optimizing Sampling Efficacy and Efficiency. USGS Unpublished Report. https://ecos.fws.gov/ServCat/Reference/Profile/65874
  47. Mazerolle MJ, Villard MA (1999) Patch characteristics and landscape context as predictors of species presence and abundance: a review. Ecoscience 6:117–124CrossRefGoogle Scholar
  48. McFeeters SK (1996) The use of the Normalized Difference Water Index in the delineation of open water features. Int J Rem Sens 17:1425–1432CrossRefGoogle Scholar
  49. McGarigal K, Wan HY, Zeller KA, Timm BC, Cushman SA (2016) Multi-scale habitat selection modeling: a review and outlook. Landscape Ecol 31:1161–1175CrossRefGoogle Scholar
  50. Menne MJ, Williams CN, Vose RS (2015) United States Historical Climatology Network Daily Precipitation, and Snow Data. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. http://cdiac.ornl.gov/epubs/ndp/ushcn/ushcn.html
  51. Merow C, Smith MJ, Edwards TC, Guisan A, McMahon SM, Normand S, Thuiller W, Wuest RO, Zimmerman NE, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281CrossRefGoogle Scholar
  52. Nelms CO, Twedt DL (1996) Seed deterioration in flooded agriculture fields during winter. Wildl Soc Bull 24:85–88Google Scholar
  53. Nichols JD, Reinecke KJ, Hines JE (1983) Factors affecting the distribution of mallards wintering in the Mississippi Alluvial Valley. Auk 100:932–946Google Scholar
  54. Notaro M, Lorenz D, Hoving C, Schummer M (2014) Twenty-First-Century projections of snowfall and winter severity across central-eastern North America. J Clim 27:6526–6550CrossRefGoogle Scholar
  55. Pacifici K, Reich BJ, Miller DA, Gardner B, Stauffer G, Singh S, McKerrow A, Collazo JA (2016) Integrating multiple data sources in species distribution modeling: a framework for data fusion. Ecology 98:840–850CrossRefGoogle Scholar
  56. Pearse AT, Kaminski RM, Reinecke KJ, Dinsmore SJ (2012) Local and landscape associations between wintering dabbling ducks and wetland complexes in Mississippi. Wetlands 32:859–869CrossRefGoogle Scholar
  57. Pernollet CA, Guelmami A, Green AJ, Masip AC, Dies B, Bogliani G, Tesio F, Brogi A, Gauthier-Clerc M, Guillemain M (2015) A comparison of wintering duck numbers among European rice production areas with contrasting flood regimes. Biol Conserv 186:214–224CrossRefGoogle Scholar
  58. Pickens BA, King SL (2014) Linking multi-temporal satellite imagery to coastal wetland dynamics and bird distribution. Ecol Model 285:1–12CrossRefGoogle Scholar
  59. Potts JM, Elith J (2006) Comparing species abundance models. Ecol Model 199:153–163CrossRefGoogle Scholar
  60. Pressey RL, Cabeza M, Watts ME, Cowling RM, Wilson KA (2007) Conservation planning in a changing world. Trends Ecol Evol 22:583–592CrossRefPubMedGoogle Scholar
  61. Reinecke KJ, Barkley RC, Baxter CK (1988) Potential effects of changing water conditions on mallards wintering in the Mississippi Alluvial Valley. In: Weller MS (ed) Waterfowl in winter. University of Minnesota Press, Minneapolis, pp 325–337Google Scholar
  62. Reinecke KJ, Kaminski RM, Moorehead DJ, Hodges JD, Nassar JR (1989) Mississippi Alluvial Valley. In: Smith LM, Pederson RL, Kaminski RM (eds) Habitat management for migrating and wintering waterfowl in North America. Oxford University Press, Oxford, pp 203–224Google Scholar
  63. Royle JA, Nichols JD (2003) Estimating abundance from repeated presence–absence data or point counts. Ecology 84:777–790CrossRefGoogle Scholar
  64. Runge CA, Tulloch AI, Possingham HP, Tulloch VJ, Fuller RA (2016) Incorporating dynamic distributions into spatial prioritization. Divers Distrib 22:332–343CrossRefGoogle Scholar
  65. Schummer ML, Kaminski RM, Raedeke AH, Graber DA (2010) Weather-related indices of autumn-winter dabbling duck abundance in middle North America. J Wildl Manag 74:94–101CrossRefGoogle Scholar
  66. Smith DR, Reinecke KJ, Conroy MJ, Brown MW, Nassar JR (1995) Factors affecting visibility rate of waterfowl surveys in the Mississippi Alluvial Valley. J Wildl Manag 59:515–527CrossRefGoogle Scholar
  67. Spiegelhalter DJ, Thomas A, Best NG (2002) Bayesian measures of complexity and fit (with discussion). J Royal Stat Soc 64:540–583Google Scholar
  68. Stafford JD, Kaminski RM, Reinecke KJ, Manley SW (2006) Waste rice for waterfowl in the Mississippi Alluvial Valley. J Wildl Manag 70:61–69CrossRefGoogle Scholar
  69. Stafford JD, Kaminski RM, Reinecke KJ (2010) Avian foods, foraging and habitat conservation in world rice fields. Waterbirds 33:133–150CrossRefGoogle Scholar
  70. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Austria. https://www.R-project.org
  71. Thornton DH, Branch LC, Sunquist ME (2011) The influence of landscape, patch, and within-patch factors on species presence and abundance: a review of focal patch studies. Landscape Ecol 26:7–18CrossRefGoogle Scholar
  72. U.S. Department of Interior and Canadian Wildlife Service (1986) North American waterfowl management planGoogle Scholar
  73. U.S. Fish and Wildlife Service, Canadian Wildlife Service, Secretaria de Medio Ambiente Recursos Naturales (2012) North American waterfowl management plan 2012: People Conserving Waterfowl and WetlandsGoogle Scholar
  74. U.S. Geological Survey (USGS) (2009–2016) Data available from the USGS. http://glovis.usgs.gov/
  75. USDA National Agricultural Statistics Service (NASS) (2015) National Agriculture Statistics Service. United States Department of Agriculture. http://www.nass.usda.gov/
  76. USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (2009–2015) Published crop-specific data layer. USDA-NASS, Washington, D.C. http://nassgeodata.gmu.edu/CropScape/
  77. Wenger SJ, Freeman MC (2008) Estimating species occurrence, abundance, and detection probability using zero-inflated distributions. Ecology 89:2953–2959CrossRefPubMedGoogle Scholar
  78. Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397CrossRefGoogle Scholar
  79. Yackulic CB, Ginsberg JR (2016) The scaling of geographic ranges: implications for species distribution models. Landscape Ecol 31:1195–1208CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • John A. Herbert
    • 1
    • 6
    Email author
  • Avishek Chakraborty
    • 2
  • Luke W. Naylor
    • 3
  • William S. Beatty
    • 4
  • David G. Krementz
    • 5
  1. 1.Arkansas Cooperative Fish and Wildlife Research Unit, Department of Biological SciencesUniversity of ArkansasFayettevilleUSA
  2. 2.Department of Mathematical SciencesUniversity of ArkansasFayettevilleUSA
  3. 3.Arkansas Game & Fish CommissionLittle RockUSA
  4. 4.U.S. Fish and Wildlife Service, Marine Mammals ManagementAnchorageUSA
  5. 5.U.S. Geological Survey, Arkansas Cooperative Fish and Wildlife Research Unit, Department of Biological SciencesUniversity of ArkansasFayettevilleUSA
  6. 6.Department of Ecology and Evolutionary BiologyTulane UniversityNew OrleansUSA

Personalised recommendations