Landscape dynamics of floral resources affect the supply of a biodiversity-dependent cultural ecosystem service
- 685 Downloads
Cultural ecosystem services, many of which depend on biodiversity, are recognized as important but seldom quantified biophysically across landscapes. Furthermore, many ecosystem service models are static, and the supply of cultural ecosystem services may be misrepresented if seasonal shifts in biotic communities are ignored.
We modeled landscape dynamics of wildflower blooms in a temperate montane landscape to determine (1) how floral resources (wildflower species richness, abundance, timing, and presence of charismatic species) changed over the growing season, (2) how projected wildflower viewing hotspots varied over space and time, and (3) how spatial shifts in floral resources affected potential public access to wildflower viewing.
Data were collected at 63 sites across a rural-to-urban gradient in the Southern Appalachian Mountains (USA). Generalized linear models were used to identify factors affecting floral resources at two temporal scales. Floral resources were projected across the landscape and hotspots of wildflower viewing were quantified using overlay analysis.
Floral resources were affected by topoedaphic conditions, climate, and surrounding building density and changed seasonally. Seasonal models revealed locational shifts in ecosystem service hotspots, which changed the proportion of hotspots accessible to the public and identified wildflower-viewing opportunities unnoticed by static models.
Relationships between landscape gradients, biodiversity, and ecosystem service supply varied seasonally, and our models identified cultural ecosystem service hotspots otherwise obscured by simple proxies. Landscape models of biodiversity-based cultural ecosystem services should include seasonal dynamics of biotic communities to avoid under- or over-emphasizing the importance of particular locations in ecosystem service assessments.
KeywordsCultural services Ecosystem service capacity Temporal pattern Wildflowers Nature-based recreation
We would like to thank the landowners who granted us access to their properties. W. Hansen, J. Qiu, and C. Ziter provided useful advice during the development of this paper. Assistance in the data compilation was provided by G. Lancaster, J. Mackie, and Z. Hane. M. Hopey assisted with data collection. Thank you to E. Damschen, C. Kucharik, V. Radeloff, and B. Zuckerberg for providing comments that improved the study and its interpretation. We appreciate the constructive comments from three anonymous reviews on an earlier version of this manuscript. This study was funded by the National Science Foundation Long-term Ecological Research Program (grants DEB-0823293 and DEB-1440485).
- Bates D, Maechler M, Bolker B, Walker S (2015) lme4: linear mixed-effects models using S4 classes. R package version 1.1-8. RGoogle Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
- Cordell HK (2012) Outdoor recreation trends and futures: a technical document supporting the Forest Service 2010 RPA assessmentGoogle Scholar
- Daniel TC, Muhar A, Arnberger A, Aznar O, Boyd JW, Chan KMA, Costanza R, Elmqvist T, Flint CG, Gobster PH, Gret-Regamey A, Lave R, Muhar S, Penker M, Ribe RG, Schauppenlehner T, Sikor T, Soloviy I, Spierenburg M, Taczanowska K, Tam J, von der Dunk A (2012) Contributions of cultural services to the ecosystem services agenda. Proc Natl Acad Sci USA 109:8812–8819CrossRefPubMedPubMedCentralGoogle Scholar
- Feld CK, da Silva PM, Sousa JP, de Bello F, Bugter R, Grandin U, Hering D, Lavorel S, Mountford O, Pardo I, Paertel M, Roembke J, Sandin L, Jones KB, Harrison P, Martins da Silva P, Paulo Sousa J, PÃrtel M, RÃmbke J, Bruce Jones KB (2009) Indicators of biodiversity and ecosystem services: a synthesis across ecosystems and spatial scales. Oikos 118:1862–1871CrossRefGoogle Scholar
- Gesch D, Oimoen M, Greenlee S, Nelson C, Steuck M, Tyler D (2002) The National Elevation Dataset. Photogramm Eng Remote Sens 68:5–11Google Scholar
- GroWNC (2013) GroWNC regional plan: final report. AshevilleGoogle Scholar
- Hijmans R, van Etten J (2015) Raster: geographic analysis and modeling with raster data. R Packag. version 2.4-20Google Scholar
- Homer C, Fry J, Barnes C (2012) The national land cover database. US Geol Surv Fact Sheet 3020(4):1–4Google Scholar
- Kareiva P, Tallis H, Ricketts TH, Daily GC, Polasky S (eds) (2011) Natural capital: theory and practice of mapping ecosystem services. Oxford University Press, OxfordGoogle Scholar
- Kremen C, Williams NM, Aizen MA, Gemmill-Herren B, LeBuhn G, Minckley R, Packer L, Potts SG, Roulston T, Steffan-Dewenter I, Vazquez DP, Winfree R, Adams L, Crone EE, Greenleaf SS, Keitt TH, Klein A-M, Regetz J, Ricketts TH, Vázquez DP, Winfree R, Adams L, Crone EE, Greenleaf SS, Keitt TH, Klein A-M, Regetz J, Ricketts TH (2007) Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol Lett 10:299–314CrossRefPubMedGoogle Scholar
- Luck GW, Harrington R, Harrison PA, Kremen C, Pam M, Bugter ROB, Dawson TP, De Bello F, Díaz S, Feld CK, Haslett JR, Hering D, Kontogianni A, Lavorel S, Rounsevell M, Samways J, Sandin L, Settele J, Sykes MT, Van Den Hove S, Zobel M, Berry PMAMM, de Bello F, Diaz S, Samways MJ, van den Hove S, Vandewalle M (2009) Quantifying the contribution of organisms to the provision of ecosystem services. Bioscience 59:223–235CrossRefGoogle Scholar
- Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: multiscale assessments. Island Press, Washington, DCGoogle Scholar
- Nicholson E, Mace GM, Armsworth PR, Atkinson G, Buckle S, Clements T, Ewers RM, Fa JE, Gardner TA, Gibbons J, Grenyer R, Metcalfe R, Mourato S, Muuls M, Osborn D, Reuman DC, Watson C, Milner-Gulland EJ (2009) Priority research areas for ecosystem services in a changing world. J App Ecol 46:1139–1144Google Scholar
- Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2015) Vegan: community ecology package. University of Oulu, OuluGoogle Scholar
- SAMAB (1996) The Southern Appalachian Assessment. Terrestrial resources technical report 5. AtlantaGoogle Scholar
- Sharp R, Tallis HT, Ricketts T, Guerry AD, Wood SA, Chaplin-Kramer R, Nelson E, Ennaanay D, Wolny S, Olwero N, Vigerstol K, Pennington D, Mendoza G, Aukema J, Foster J, Forrest J, Cameron D, Arkema K, Lonsdorf K, Kennedy EC, Verutes G, Kim CK, Guannel G, Papenfus M, Toft J, Marsik M, Bernhardt J, Griffin R, Glowinski K, Chaumont N, Perelman A, Lacayo M, Mandle L, Hamel P, Vogl AL, Rogers L, Bierbower W (2016) InVEST User Guide. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund, MinneapolisGoogle Scholar
- Soil Survey Staff (2013) Soil survey geographic (SSURGO) database. http://sdmdataaccess.nrcs.usda.gov
- TEEB (2009) The economics of ecosystems and biodiversity (TEEB) for National and International Policy Makers. Econ Ecosyst Biodivers 1–47Google Scholar
- Thornton P, Thornton M, Mayer B, Wilhelmi N, Wei Y, Coo R (2012) Daymet: daily surface weather on a 1 km grid for North America, 1980–2012. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge. http://daymet.ornl.gov/
- Vogler JB, Shoemaker DA, Dorning M, Meentemeyer RK (2010) Mapping historical development patterns and forecasting urban growth in Western North Carolina: 1976–2030. Charlotte, NCGoogle Scholar
- Watson A, Williams D, Roggenbuck J, Daigle J (1992) Visitor characteristics and preferences for three national forest wildernesses in the South. Page Research Paper INT-455Google Scholar
- Wear DN (2011) Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA assessment. USDA Forest Service, AshevilleGoogle Scholar