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Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

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Abstract

Context

Species-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning.

Objectives

We tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats.

Methods

We compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling.

Results

Blackpoll warbler (Setophaga striata), wood thrush (Hylocichla mustelina), and Louisiana (Parkesia motacilla) and northern waterthrush (P. noveboracensis) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird (Seiurus aurocapilla), blackburnian (Setophaga fusca) and cerulean warbler (Setophaga cerulea)], as all were positively related to occupancy data.

Conclusions

LC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.

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References

  • Alldredge MW, Pollock KH, Simons TR, Collazo JA, Shriner SA (2007) Time-of-detection method for estimating abundance from point-count surveys. Auk 123:653–664

    Article  Google Scholar 

  • Ashton KG (2002) Patterns of within-species body size variation of birds: strong evidence for Bergmann’s rule. Glob Ecol Biogeogr 11:505–523

    Article  Google Scholar 

  • Balogh AL, Ryder TB, Marra PP (2011) Population demography of Gray Catbirds in the suburban matrix: sources, sinks and domestic cats. J Ornithol 152:717–726

    Article  Google Scholar 

  • Barker NKS, Fontaine PC, Cumming SG, Stralberg D, Westwood A, Bayne EM, Sólymos P, Schmiegelow FKA, Song SJ, Rugg DJ (2015) Ecological monitoring through harmonizing existing data: lessons from the Boreal Avian Modelling Project. Wildl Soc B 39:480–487

    Article  Google Scholar 

  • Barry S, Elith J (2006) Error and uncertainty in habitat models. J Appl Ecol 43:413–423

    Article  Google Scholar 

  • Barton EP, Pabian SE, Brittingham MC (2016) Bird community response to Marcellus shale gas development. J Wildl Manag 80:1301–1313

    Article  Google Scholar 

  • Boone RB, Krohn WB (1999) Modeling the occurrence of bird species: are the errors predictable? Ecol Appl 9:835–848

    Article  Google Scholar 

  • Brooks RP (1997) Improving habitat suitability index models. Wildl Soc B 25:163–167

    Google Scholar 

  • Brumm H (2004) The impact of environmental noise on song amplitude in a territorial bird. J Anim Ecol 73:434–440

    Article  Google Scholar 

  • Buehler DA, Hamel PB, Boves T (2013) Cerulean Warbler (Setophaga cerulea). In: Rodewald PG (ed) The birds of North America online. Cornell Lab of Ornithology, Ithaca. http://bna.birds.cornell.edu/bna/species/511. Accessed June 2016

  • Burnham KP, Anderson DR (2012) Model Selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York

    Google Scholar 

  • Cardoso GC (2010) Loudness of birdsong is related to the body size, syntax and phonology of passerine species. J Evolution Biol 23:212–219

    Article  CAS  Google Scholar 

  • Caro T (2010) Conservation by proxy. Island Press, Washington, DC

    Google Scholar 

  • Colorado GJ, Hamel PB, Rodewald AD, Mehlman D (2012) Advancing our understanding of Cerulean Warbler (Setophaga cerulea) in the Andes. Ornitol Neotrop 23:307–315

    Google Scholar 

  • Deluca W, Holberton R, Hunt PD, Eliason BC (2013) Blackpoll warbler (Setophaga striata). In: Rodewald PG, The birds of North America online. Cornell Lab of Ornithology, Ithaca, NY. http://bna.birds.cornell.edu/bna/species/431. Accessed July 2016

  • Edwards TC Jr, Deshler E, Foster D, Moisen GG (1996) Adequacy of wildlife habitat relation models for estimating spatial distributions of terrestrial vertebrates. Conserv Biol 10:263–270

    Article  Google Scholar 

  • Evangelista PH, Kumar S, Stohlgren TJ, Jarnevich CS, Crall AW, Norman JB III, Barnett DT (2008) Modelling invasion for a habitat generalist and a specialist plant species. Divers Distrib 14:808–817

    Article  Google Scholar 

  • Farwell LS, Wood PB, Sheehan J, George GA (2016) Shale gas development effects on the songbird community in a central Appalachian forest. Biol Conserv 201:78–91

    Article  Google Scholar 

  • Ferree C, Anderson MG (2013) A map of terrestrial habitats of the Northeastern United States: methods and approach. The Nature Conservancy, Eastern Conservation Science, Eastern Regional Office, Boston

  • Gawler SC (2008) Northeastern Terrestrial Wildlife Habitat Classification. Report to the Virginia Department of Game and Inland Fisheries on behalf of the Northeast Association of Fish and Wildlife Agencies and the National Fish and Wildlife Foundation. NatureServe, Boston

  • Gu W, Swihart RK (2003) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Conserv 116:195–203

    Article  Google Scholar 

  • Hansen AJ, Urban DL (1992) Avian response to landscape patters: the role of species’ life histories. Landscape Ecol 7:163–180

    Article  Google Scholar 

  • Hanski I, Gilpin M (1991) Metapopulation dynamics: brief history and conceptual domain. Biol J Linn Soc 42:3–16

    Article  Google Scholar 

  • Henwood K, Fabrick A (1979) A quantitative analysis of the dawn chorus temporal selection for communicatory optimiation. Am Nat 114:260–274

    Article  Google Scholar 

  • Hijmans RJ (2012) Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93:679–688

    Article  PubMed  Google Scholar 

  • Horvitz DG, Thompson DJ (1952) A generalization of sampling without replacement from a finite universe. J Am Stat Assoc 47:663–685

    Article  Google Scholar 

  • Jarnevich CS, Stohlgren TH, Kumar S, Morisette JT, Holcombe TR (2015) Caveats for correlative species distribution modeling. Ecol Inform 29:6–15

    Article  Google Scholar 

  • Jones-Farrand DT, Fearer TM, Thogmartin WE, Thompson FR III, Nelson MD, Tirpak JM (2011) Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction. Ecol Appl 21:2269–2282

    Article  PubMed  Google Scholar 

  • Kiviat E (2013) Risks to biodiversity from hydraulic fracturing for natural gas in the Marcellus and Utica shales. Ann NY Acad Sci 1286:1–14

    Article  PubMed  Google Scholar 

  • Kroodsma D (2005) The singing life of birds. Houghton Mifflin, Boston

    Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    Article  CAS  PubMed  Google Scholar 

  • Larson MA, Dijak WD, Thompson III FR, Millspaugh JJ (2003) Landscape-level habitat suitability models for twelve wildlife species in Southern Missouri. General Technical Report GTR NC-233. US Department of Agriculture, Forest Service. North Central Research Station. St. Paul, MN

  • Loman ZG, Blomberg EJ, Deluca WV, Harrison DJ, Loftin CS, Wood PB (2017) Landscape capability predicts upland game bird abundance and occurrence. J Wildl Manag 81:1110–1116

    Article  Google Scholar 

  • McGarigal K, Deluca WV, Compton BW, Plunkett EB, Grand J (2016) Designing sustainable landscapes: modeling focal species. Report to the North Atlantic Conservation Cooperative, US Fish and Wildlife Service, Northeast Region, Hadley, MA. http://jamba.provost.ads.umass.edu/web/lcc/DSL_documentation_species.pdf. Accessed September 2016

  • Nemeth E, Pieretti N, Zollinger SA, Geberzahn N, Partecke J, Miranda AC, Brumm H (2013) Bird song and anthropogenic noise: vocal constraints may explain why birds sing higher-frequency songs in cities. Proc R Soc B 280:20122798

    Article  PubMed  PubMed Central  Google Scholar 

  • North Atlantic Landscape Conservation Cooperative (2011) Executive summary. identifying representative species for the North Atlantic Landscape Conservation Cooperative (LCC). Available from https://www.fws.gov/northeast/science/pdf/Rep_Species_Executive_Summary.pdf. Accessed June 2016

  • O’Neil LJ, Roberts TH, Wakeley JS, Teaford JW (1988) A procedure to modify habitat suitability index models. Wildl Soc B 16:33–36

    Google Scholar 

  • Orians GH, Wittenberger JF (1991) Spatial and temporal scales in habitat selection. Am Nat 137:S29–S49

    Article  Google Scholar 

  • Porneluzi P, Van Horn MA, Donovan TM (2011) Ovenbird (Seiurus aurocapilla). In: Rodewald PG (ed) The birds of North America online. Cornell Lab of Ornithology, Ithaca, NY. http://bna.birds.cornell.edu/bna/species/088. Accessed July 2016

  • Powers DMW (2007) Evaluation: from precision, recall and F-factor to ROC, informedness, markedness & correlation. J Mach Learn Technol 2:37–63

    Google Scholar 

  • R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/. Accessed Sept 2015

  • Radford JQ, Bennett AF, Cheers GJ (2005) Landscape-level thresholds of habitat cover for woodland-dependent birds. Biol Conserv 124:317–337

    Article  Google Scholar 

  • Ralph CJ, Droege S, Sauer JR (1995) Managing and monitoring birds using point counts: standards and applications. In: Ralph CJ, Sauer JR, Droege S (eds) Monitoring bird populations by point counts. General technical report PSW-GTR-149. Department of Agriculture, Forest Service, Pacific Southwest Research Station, Albany, CA, pp 161–169

  • Rappole JH, McDonald MV (1994) Cause and effect in populatino declines of migratory birds. Auk 111:652–660

    Google Scholar 

  • Reilly BM, Bednarz JC, Brown JD (2014) A test of the Swainson’s Warbler habitat suitability index model. Wildl Soc B 38:297–304

    Article  Google Scholar 

  • Saab V (1999) Importance of spatial scale to habitat use by breeding birds in riparian forests: a hierarchical analysis. Ecol Appl 9:135–151

    Article  Google Scholar 

  • Sabo SR (1980) Niche and habitat relations in subalpine bird communities of the White Mountains of New Hampshire. Ecol Monogr 50:241–259

    Article  Google Scholar 

  • Sauer JR, Niven DK, Hines JE, Ziolkowski Jr DJ, Pardieck KL, Fallon JE, Link WA (2017) The North American Breeding bird survey, results and analysis 1966–2015. Version 2.07.2017. USGS Patuxent Wildlife Research Center, Laurel, MD. https://www.mbr-pwrc.usgs.gov/bbs/. Accessed Mar 2017

  • Sheehan J, Wood PB, Buehler DA, Keyser PD, Larkin JL, Rodewald AD, Wigley TB, Boves TJ, George GA, Bakermans GA, Beachy TA, Evans TA, McDermott ME, Newell FL, Perkins KA, White M (2014) Avian response to timber harvesting applied experimentally to manage Cerulean Warbler breeding populations. For Ecol Manag 321:5–18

    Article  Google Scholar 

  • Sólymos P, Lele S, Bayne E (2012) Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error. Environmetrics 23:197–205

    Article  Google Scholar 

  • Sólymos P, Matsuoka SM, Bayne EM, Lele SR, Fontaine P, Cumming SG, Stralberg D, Schmiegelow FKA, Song SJ (2013) Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods Ecol Evol 4:1047–1058

    Article  Google Scholar 

  • Sólymos P, Moreno M, Lele SR (2014) detect: analyzing wildlife data with detection error. R package version 0.3–2. http://CRAN.R-project.org/package=detect. Accessed Mar 2016

  • Stanley CQ, McKinnon EA, Fraser KC, Macpherson MP, Casbourn G, Friesen L, Marra PP, Studds C, Ryder TB, Diggs NE, Stutchbury BJM (2015) Connectivity of wood thrush breeding, wintering, and migration sits based on range-wide tracking. Conserv Biol 29:164–174

    Article  PubMed  Google Scholar 

  • Stauffer D (2002) Linking populations and habitats: where have we been? Where are we going? In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences issues of accuracy and scale. Island Press, Washington, DC, pp 53–62

    Google Scholar 

  • Sullivan BL, Wood CL, Iliff MJ, Bonney RE, Fink D, Kelling S (2009) eBird: a citizen-based bird observation network in the biological sciences. Biol Conserv 142:2282–2292

    Article  Google Scholar 

  • Sweeney JM, Dijak WD (1985) Ovenbird habitat capability model for an oak-hickory forest. Proc Ann Conf of the Southeastern Assoc Fish Wildl Agencies 39:430–438

    Google Scholar 

  • Thogmartin WE, Sauer JR, Knutson MG (2004) A hierarchical spatial model of avian abundance with application to Cerulean Warblers. Ecol Appl 14:1766–1779

    Article  Google Scholar 

  • Tirpak JM, Jones-Farrand DT, Thompson FR III, Twedt DJ, Baxter CK, Fitzgerald JA, Uihlein WB III (2009) Assessing ecoregional-scale habitat suitability index models for priority landbirds. J Wildl Manag 73:1307–1315

    Article  Google Scholar 

  • US Fish & Wildlife Service (2009) North Atlantic Landscape conservation cooperative development and operations plan. Northeast Region U.S. Fish & Wildlife Service, Hadley, MA

  • Van Horne B (1983) Density as a misleading indicator of habitat quality. J Wildl Manag 47:893–901

    Article  Google Scholar 

  • Van Horne B (2002) Approaches to habitat modeling: the tensions between pattern and process and between specificity and generality. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington DC, pp 63–72

    Google Scholar 

  • Weir JT, Wheatcroft D (2011) A latitudinal gradient in evolutionary rates of bird song complexity and length. Proc R Soc B 78:1713–1720

    Article  Google Scholar 

  • Wiley RH (1991) Associations of song properties with habitats for territorial oscine birds of eastern North America. Am Nat 138:973–993

    Article  Google Scholar 

  • Will TC, Ruth JM, Rosenberg KV, Krueper D, Hahn D, Fitzgerald J, Dettmers R, Beardmore CJ (2005) The five elements process: designing optimal landscapes to meet bird conservation objectives. Partners in flight technical series 1. Partners in flight. http://www.partnersinflight.org/pubs/ts/01-FiveElements.pdf. Accessed Oct 2015

  • Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York

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Acknowledgements

We thank the U.S.G.S. Science Support Program for funding this study. No data sets were generated in this study. Data used in this study are partially available at ScienceBase, DOI 10.5066/F76Q1W53. At the time of publication, other data sets used in this study were not available or have limited availability (i.e., ongoing work, proprietary, or sensitive). Contact sources indicated in Appendix 1 for more information about these individual data sets. We thank Kevin McGarigal, Ethan Plunkett, Joanna Grand and Brad Compton from the Designing Sustainable Landscapes Project at the UMass Amherst-Landscape Ecology Lab, the North Atlantic Landscape Conservation Cooperative, and all those who provided field data: West Virginia U. Division of Forestry and Natural Resources graduate students Kyle Aldinger, Douglas Becker, Laura Farwell, Gretchen Nareff, and Jim Sheehan; Evan Adams, Biodiversity Research Institute; Erin King and Bill Thompson, U.S. Fish & Wildlife Service; Carol Croy U. S. Forest Service; Rich Bailey, West Virginia DNR; Alan Williams, Geoffrey Sanders and Adam Kozlowski, National Park Service; Frank Ammer, Frostburg State U., Emily Thomas and Margaret Brittingham, Penn State U.; David Yeaney, Western Pennsylvania Conservancy, and David King and Tim Duclos, U. Mass Amherst. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

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Authors and Affiliations

  1. Department of Wildlife Fisheries and Conservation Biology, University of Maine, 5755 Nutting Hall, Room 210, Orono, ME, 04469, USA

    Zachary G. Loman, Daniel J. Harrison & Brian W. Rolek

  2. Department of Environmental Conservation, University of Massachusetts, 160 Holdsworth Way, Amherst, MA, 01003, USA

    William V. Deluca

  3. Maine Cooperative Fish and Wildlife Research Unit, U.S. Geological Survey, 5755 Nutting Hall, Room 210, Orono, ME, 04469, USA

    Cynthia S. Loftin

  4. U.S. Geological Survey, West Virginia Cooperative Fish and Wildlife Research Unit, West Virginia University, P. O. Box 6125, 322 Percival Hall, Morgantown, WV, 26506, USA

    Petra B. Wood

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  1. Zachary G. Loman
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Correspondence to Zachary G. Loman.

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Loman, Z.G., Deluca, W.V., Harrison, D.J. et al. Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance. Landscape Ecol 33, 77–91 (2018). https://doi.org/10.1007/s10980-017-0582-z

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