Latitude-enhanced species-area relationships for conservation planning
Species-area relationship models are useful in conservation planning; however these models could be strengthened with the addition of a latitudinal factor.
We built latitude-enhanced species-area relationship models to predict species richness for a variety of common taxa in the eastern United States at local to regional scales.
We used data from complete surveys of East Coast parks in the United States to build latitude-enhanced species-area relationship models for amphibians, birds, freshwater fish, mammals, marine fish, plants, and reptiles. We used data from the published literature and United States Fish and Wildlife Refuges to independently test the accuracy of the models. We demonstrated the utility of all modeled taxa within selected East Coast Protected Areas of the United States.
Our models explained 35–91% of the variation in surveyed species richness, with marine fish, freshwater fish and reptile models exhibiting the strongest relationships (pseudo-R2 = 0.91, 0.66, and 0.70, respectively). Latitude had the strongest influence in the amphibian model. During accuracy testing, all taxa exhibited significant agreement between observed and predicted species richness and explained 75–97% of the variation. Our demonstration showed that for two similarly sized US Protected Areas, the parcel l.25° lower in latitude would likely have one more bird species, four more plant species, and an additional amphibian species.
The latitude term added value to the species-area relationship models for most taxa and proved useful for conservation and urban planning in local to regional sized areas of the East Coast of the United States.
KeywordsModel Negative binomial regression Species richness Taxa Urban Vertebrates
This study was supported by funding from the United States Department of the Interior, National Park Service [Cooperative agreement: P14AC01473]. We would like to thank the people who helped in the collection of data for this project, namely: Jennifer Meller, Glen Kandia, and Joseph Rua. Ellen Creveling of The Nature Conservancy generously shared data for model construction and we very much appreciate her willingness to collaborate on this project. We would also like to thank Charles Yackulic for valuable statistical modeling input, Glen Kandia for editing assistance, and to the anonymous reviewers for their comments that helped to improve the presentation.
- Branch SA (1985) National estuarine inventory data atlas, vol 1. Physical and hydrologic characteristics. Ocean Assessments Division, National Ocean Service, National Oceanic and Atmospheric Administration, Rockville, p 103Google Scholar
- Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA, Brovkin V, Cox PM, Fisher V, Foley JA, Friend AD, Kucharik C (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob Change Biol 7(4):357–373CrossRefGoogle Scholar
- Estuarine Living Marine Resources Database (2016) http://www8.nos.noaa.gov/biogeo_public/elmr.aspx. Accessed 7 July 2016
- Gotelli NJ (1995) A primer of ecology. Sinauer Associates, SunderlandGoogle Scholar
- Hester RT (2006) Design for ecological democracy. MIT Press, CambridgeGoogle Scholar
- Huyck Preserve (2015) https://www.huyckpreserve.org/visit-us.html. Accessed 21 July 2015
- Integrated Resource Management Applications (2015) National Park Service IRMA Portal. https://irma.nps.gov. Accessed 15 Dec 2015
- Jury SH, Field JD, Stone SL, Nelson DM, Monaco ME (1994) Distribution and abundance of fishes and invertebrates in North Atlantic estuaries. ELMR Rep. No. 13. NOAA/NOS Strategic Environmental Assessments Division, Silver Spring, MDGoogle Scholar
- Kiviat E, MacDonald K (2004) Biodiversity patterns and conservation in the Hackensack Meadowlands, New Jersey. Urban Habitats 2(1):28–61Google Scholar
- Lomolino MV (2001) The species-area relationship: new challenges for an old pattern. Prog Phys Geogr 25(1):1–21Google Scholar
- North Carolina Division of Parks (2015) http://www.dpr.ncparks.gov/nrid/public.php. Accessed 21 July 2015
- Sanderson EW (2013) Mannahatta: a natural history of New York City. Abrams, New YorkGoogle Scholar
- Sanderson EW, Orton P, Fischbach J, Knopman D, Roberts H, Solecki WD, Fitzpatrick J, Wilson R (2016) Computational modelling of the Jamaica Bay system. In: Sanderson EW, Solecki WD, Waldman JR, Parris AS (eds) Prospects for resilience: insights from New York City’s Jamaica Bay. Island Press, Washington, D.CCrossRefGoogle Scholar
- Schemske DW (2009) Biotic interactions and speciation in the tropics. In: Butlin R, Bridle J, Schluter D (eds) Speciation and patterns of diversity. Cambridge University Press, CambridgeGoogle Scholar
- Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D’Erchia F, Edwards Jr TC, Ulliman J (1993) Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monographs, pp 3–41Google Scholar
- Stillwater Township. 2015. http://www.stillwatertownshipnj.com/uploads/StillwaterBrochure-FINAL_9.20.2012.pdf. Accessed 21 July 2015
- Stonybrook-Millstone Watershed Association. 2015. http://thewatershed.org/resource-center/reports-and-materials/conservation/. Accessed 21 July 2015
- United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC) (2002) Protected Areas Database v5.0. UNEP-WCMC, Cambridge, United KingdomGoogle Scholar
- United States Bureau of the Census (2000) Statistical abstract of the United States. US Government Printing Office, Washington, DCGoogle Scholar
- United States Fish and Wildlife Service (2018) http://www.fws.gov/refuges. Accessed 14 Dec 2018
- United States Geological Survey, Gap Analysis Program (2011) National Land Cover, Version 2Google Scholar
- United States Geological Survey, Gap Analysis Program (2018) Protected areas database of the United States. https://gapanalysis.usgs.gov/padus/data/download/. Accessed 23 July 2018