Skip to main content

The Role of Assumptions in Predictions of Habitat Availability and Quality

  • Chapter
  • First Online:
Predictive Species and Habitat Modeling in Landscape Ecology

Abstract

Abstracting a complex reality into ecological models composed of maps, diagrams, and mathematical equations forces modelers to organize information, distinguish essential from superfluous components, and define relationships among variables. Within this context, an assumption is a premise, stated or unstated, which characterizes model variables and relationships as essential or irrelevant to the model’s ­setting and purpose. For example, assumptions about how species interact with their environment at a specific time and place can be used to justify the thematic, spatial, and temporal extent and grain of the input data, given a model’s intended application. These assumptions also justify the use or rejection of specific model variables, parameters, and mathematical functions describing the relationship between focal species and their environment. Furthermore, models improve over time through incremental steps of testing assumptions as hypotheses to establish empirical knowledge. Hence, the utility of any habitat model is both empowered by and limited by its assumptions. Therefore it is critical that project objectives and ecological theory inform assumptions, rather than allowing these decisions to be driven by data availability and knowledge gaps.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, Matthysen E (2003) The application of “least-cost” modelling as a functional landscape model. Landsc Urban Plan 64:233–247

    Article  Google Scholar 

  • Adriaenssens V, De Baets B, Goethals PLM, De Pauw N (2004) Fuzzy rule-based models for decision support in ecosystem management. Sci Total Environ 319:1–12

    Article  CAS  PubMed  Google Scholar 

  • Amstrup SC, Marcot BM, Douglas DC (2007) Forecasting the range-wide status of polar bears at selected times in the 21st Century. U.S. Geological Survey Administrative Report.

    Google Scholar 

  • Angelstam P, Roberge J-M, Lõhmus A, Bergmanis M, Brazaitis G, Dönz-Beuss M, Edenius L, Koskinski Z, Kurlavicius P, Lārmanis V, Lūkins M, Mikusinski G, Račinskis E, Strazds M, Tryjanowski P (2004) Habitat modelling as a tool for landscape-scale conservation – a review of parameters for focal forest birds. Ecol Bull 51:427–453.

    Google Scholar 

  • Antrop M (2007) The preoccupation of landscape research with land use and cover. In: Wu J, Hobbs R (eds) Key topics in landscape ecology. Cambridge University Press, Cambridge.

    Google Scholar 

  • Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688.

    Article  Google Scholar 

  • Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Modell 200:1–19.

    Article  Google Scholar 

  • Badyaev AV, Martin TE, Etges WJ (1996) Habitat sampling and habitat selection by female wild Turkeys: ecological correlates and reproductive consequences. Auk 113:636–646.

    Google Scholar 

  • Bélisle M (2005) Measuring landscape connectivity: the challenge of behavioral landscape ecology. Ecology 86:1988–1995.

    Article  Google Scholar 

  • Bunn AG, Urban DL, Keitt TH (2000) Landscape connectivity: a conservation application of graph theory. J Environ Manage 59:265–278.

    Article  Google Scholar 

  • Bürgi MA, Hersperger M, Schneeberger N (2004) Driving forces of landscape change – current and new directions. Landsc Ecol 19:857–868.

    Article  Google Scholar 

  • Camus PA, Lima M (2002) Populations, metapopulations, and the open-closed dilemma: the conflict between operational and natural population concepts. Oikos 97:433–438.

    Article  Google Scholar 

  • Castilla G, Larkin K, Linke J, Hay GJ (2009) The impact of thematic resolution on the patch-mosaic model of natural landscapes. Landsc Ecol 24:15–23.

    Article  Google Scholar 

  • Cox GW, Ricklefs RE (1977) Species diversity and ecological release in Caribbean land bird faunas. Oikos 28:113–122.

    Article  Google Scholar 

  • De Wan AA, Sullivan PJ, Lembo AJ, Smith CR, Maerz JC, Lassoie JP, Richmond ME (2009) Using occupancy models of forest breeding birds to prioritize conservation planning. Biol Conserv 142:982–991.

    Article  Google Scholar 

  • Dunn AG, Majer JD (2007) In response to the continuum model for fauna research: a hierarchical, patch-based model of spatial landscape patterns. Oikos 116:1413–1418.

    Article  Google Scholar 

  • Dutilleul P, Legendre P (1993) Spatial heterogeneity against heteroscedasticity: an ecological paradigm versus a statistical concept. Oikos 66:152–171.

    Article  Google Scholar 

  • Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40:677–697.

    Article  Google Scholar 

  • Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson KS, Scachetti-Pereira R, Schapire RE, Soberón J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151.

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in ­conservation presence/absence models. Environ Conserv 24:38–49.

    Article  Google Scholar 

  • Fischer J, Lindenmayer DB (2006) Beyond fragmentation: the continuum model for fauna research and conservation in human-modified landscapes. Oikos 112:473–480.

    Article  Google Scholar 

  • Fitzpatrick MC, Weltzin JF, Sanders NJ, Dunn RR (2007) The biogeography of prediction error: why does the introduced range of fire ant over-predict its native range? Glob Ecol Biogeogr 16:24–33.

    Article  Google Scholar 

  • Fox D (2007) Back to the no-analog future? Science 316:823–825.

    Article  CAS  PubMed  Google Scholar 

  • Fretwell SD, Lucas HL (1969) On territorial behavior and other factors influencing habitat distribution in birds I: theoretical development. Acta Biotheoretica 19:16–36.

    Article  Google Scholar 

  • Gallant AL (2009) What you should know about land-cover data. J Wildl Manage 73:796–805.

    Article  Google Scholar 

  • Gallant AL, Loveland TR, Sohl TL, Napton DE (2004) Using an ecoregional framework to ­analyze land-cover and land-use dynamics. Environ Manage 34:S89–S110.

    Article  PubMed  Google Scholar 

  • Garshelis DL (2000) Delusions in habitat evaluation: measuring use, selection, and importance. In: Boitani L, Fuller TK (eds) Research techniques in animal ecology: controversies and ­consequences. Columbia Unievsrity Press, New York.

    Google Scholar 

  • Gillson L (2009) Landscapes in time and space. Landsc Ecol 24:149–155.

    Article  Google Scholar 

  • Goss-Custard JD, Stillman RH (2008) Individual-based models and the management of shorebird populations. Nat Resour Model 21:3–71.

    Article  Google Scholar 

  • Graham CH, Moritz C, Williams SE (2006) Habitat history improves prediction of biodiversity in rainforest fauna. Proc Natl Acad Sci USA 103:632–636.

    Article  CAS  PubMed  Google Scholar 

  • Grimm V, Railsback SF (2005) Individual-based modelling and ecology. Princeton University Press, Princeton.

    Google Scholar 

  • Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Conserv 116:195–203.

    Article  Google Scholar 

  • Haddad NM, Bowne DR, Cunningham A, Danielson BJ, Levey DJ, Sargent S, Spira T (2003) Corridor use by diverse taxa. Ecology 84:609–615.

    Article  Google Scholar 

  • Harris G (2007) Seeking sustainability in an age of complexity. Cambridge University Press, New York.

    Google Scholar 

  • Harwood J (2000) Risk assessment and decision analysis in conservation. Biol Conserv 95:219-226.

    Article  Google Scholar 

  • Heinrich B (1979) Bumblebee economics. Harvard University Press, Boston.

    Google Scholar 

  • Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecol Modell 199:142–152.

    Article  Google Scholar 

  • Hixon MA, Pacala SW, Sandin SA (2002) Population regulation: historical context and contemporary challenges of open vs. closed systems. Ecology 83:1490–1508.

    Article  Google Scholar 

  • Hjermann DØ (2000) Analyzing habitat selection in animals without well-defined home ranges. Ecology 81:1462–1468.

    Article  Google Scholar 

  • Hobbs NT, Hanley TA (1990) Habitat evaluation: do use/availability data reflect carrying ­capacity? J Wildl Manage 54:515–522.

    Article  Google Scholar 

  • Hunsaker CT, Graham RL, Suter GW, II, O’Neill RV, Barnthouse LW, Gardner RH (1990) Assessing ecological risk on a regional scale. Environ Manage 14:325–332.

    Article  Google Scholar 

  • Jager HI, King JW (2004) Spatial uncertainty and ecological models. Ecosystems 7:841–847.

    Article  Google Scholar 

  • Johnson MP (2005) Is there confusion over what is meant by “open population?” Hydrobiologia 544:333–338.

    Article  Google Scholar 

  • Keane RM, Crawley MJ (2002) Exotic plant invasions and the enemy release hypothesis. Trends Ecol Evol 17:164–170.

    Article  Google Scholar 

  • Koenig WD (1999) Spatial autocorrelation of ecological phenomena. Trends Ecol Evol 14:22–26.

    Article  PubMed  Google Scholar 

  • Lennon JJ (2000) Red-shifts and red herrings in geographical ecology. Ecography 23:101–113.

    Article  Google Scholar 

  • Lima SL, Zollner PA (1996) Towards a behavioral ecology of ecological landscapes. Trends Ecol Evol 11:131–135.

    Article  Google Scholar 

  • Loveland TR, Sohl TL, Stehman SV, Gallant AL, Sayler KL, Napton DE (2002) A strategy for estimating rates of recent United States land cover changes. Photogram Eng Remote Sensing 68:1091–1099.

    Google Scholar 

  • MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey LL, Hines JE (2006) Occupancy estimation and modeling. Elsevier, Oxford.

    Google Scholar 

  • Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20:703–726.

    Article  Google Scholar 

  • Manning AD, Lindenmayer DB, Nix HA (2004) Continua and umwelt: novel perspectives on viewing landscapes. Oikos 104:621–628.

    Article  Google Scholar 

  • Manning AD, Fischer J, Felton A, Newell B, Steffen W, Lindenmayer DB (2009) Landscape fluidity – a unifying perspective for understanding and adapting to global change. J Biogeogr 36:193–199.

    Article  Google Scholar 

  • McGarigal K, Cushman SA (2005) The gradient concept of landscape structure. In: Wiens JA, Moss MR (eds) Issues and perspectives in landscape ecology. Cambridge University Press, Cambridge.

    Google Scholar 

  • Mouton AM, De Baets B, Goethals PLM (2009) Knowledge-based versus data-driven fuzzy habitat suitability models for river management. Environ Model Software 24:982–993.

    Article  Google Scholar 

  • Olden JD, Schooley RL, Monroe JB, Poff NL (2004) Context-dependent perceptual ranges and their relevance to animal movements in landscapes. J Anim Ecol 73:1190–1194.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ovaskainen O (2004) Habitat-specific movement parameters estimated using mark-recapture data and a diffusion model. Ecology 85:242–257.

    Article  Google Scholar 

  • Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Modell 133:225–245.

    Article  Google Scholar 

  • Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371.

    Article  Google Scholar 

  • Petitgas P (2001) Geostatistics in fisheries survey design and stock assessment: models, variances and applications. Fish Fish 2:231–249.

    Google Scholar 

  • Pulliam HR (1988) Sources, sinks, and population regulation. Am Nat 132:652–661.

    Article  Google Scholar 

  • Pulliam RH, Danielson BJ (1991) Sources, sinks, and habitat selection: a landscape perspective on population dynamics. Am Nat 137:S50–S66.

    Article  Google Scholar 

  • Rhemtulla JM, Mladenoff DJ, Clayton MK (2007) Regional land-cover conversion in the U.S. upper Midwest: magnitude of change and limited recovery (1850–1935–1993). Landsc Ecol 22:57–75.

    Article  Google Scholar 

  • Rocchini D, Ricotta C (2007) Are landscapes as crisp as we may think? Ecol Modell 204:535–539

    Article  Google Scholar 

  • Rondinini C, Wilson KA, Boitani L, Grantham H, Possingham HP (2006) Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett 9:1136–1145.

    Article  PubMed  Google Scholar 

  • Royle JA, Nichols JD (2003) Estimating abundance from repeated presence-absence data or point counts. Ecology 84:770–790.

    Article  Google Scholar 

  • Royle JA, Nichols JD, Kery M (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos 110:353–359.

    Article  Google Scholar 

  • Shifley SR, Rittenhouse CD, Millspaugh JJ (2009) Validation of landscape-scale decision support models that predict vegetation and wildlife dynamics. In: Millspaugh JJ, Thompson FR (eds) Models for planning wildlife conservation in large landscapes. Elsevier, New York.

    Google Scholar 

  • Segurado P, Araújo MB (2004) An evaluation of methods for modelling species distributions. J Biogeogr 31:1555–1568.

    Article  Google Scholar 

  • Soberón J, Peterson AT (2004) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform 2:1–10.

    Google Scholar 

  • Soulé ME, Estes JA, Miller B, Honnold DL (2005) Strongly interacting species: conservation policy, management, and ethics. BioSci 55:168–176.

    Article  Google Scholar 

  • Starfield AM (1997) A pragmatic approach to modeling for wildlife management. J Wildl Manage 61:261–270.

    Article  Google Scholar 

  • Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, Princeton, New Jersey.

    Google Scholar 

  • Tang SM, Gustafson EJ (1997) Perception of scale in forest management planning: challenges and implications. Landsc Urban Plan 39:1–9.

    Article  Google Scholar 

  • Thogmartin WE, Gallant A, Fox T, Knutson MG, Suárez M (2004a) Commentary: a cautionary tale regarding use of the 1992 National Land Cover Dataset. Wildl Soc Bull 32:960–968.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Thomas CD, Kunin WE (1999) The spatial structure of populations. J Anim Ecol 68:647–657.

    Article  Google Scholar 

  • Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Modell 203:312–318.

    Article  Google Scholar 

  • Urban D, Keitt T (2001) Landscape connectivity: a graph-theoretic perspective. Ecology 82:1205–1218.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Vassallo MI, Rice JC (1982) Ecological release and ecological flexibility in habitat use and ­foraging of an insular avifauna. Wilson Bull 94:139–155.

    Google Scholar 

  • Von Holle B, Motzkin G (2007) Historical land use and environmental determinants of nonnative plant distribution in coastal southern New England. Biol Conserv 136:33–43.

    Article  Google Scholar 

  • Wiens JA, Stenseth NC, Van Horne B, Ims RA (1993) Ecological mechanisms and landscape ecology. Oikos 66:369–380.

    Article  Google Scholar 

  • Wiens JA,Van Horne B, Noon BR (2002) Integrating landscape structure and scale into natural resources management. In: Liu J, Taylor WW (eds) Integrating landscape ecology into ­natural resources management. Cambridge University Press, New York.

    Google Scholar 

  • Williams BK, Nichols JD, Conroy MJ (2002) Analysis and management of animal populations. Academic Press, San Diego, California.

    Google Scholar 

  • Williams JW, Jackson ST (2007) Novel climates, no-analog communities, and ecological ­surprises. Front Ecol Environ 5:475–482.

    Article  Google Scholar 

  • Willis KJ, Araújo MB, Bennet KD, Figueroa-Rangel B, Froyd CA, Myers N (2007) How can a knowledge of the past help to conserve the future? Biodiversity conservation and the relevance of long-term ecological studies. Phil Trans R Soc B 362:175–187.

    Article  PubMed  Google Scholar 

  • Woodcock CE, Gopal S (2000) Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int J Geogr Inf Sci 14:153–172.

    Article  Google Scholar 

  • Zimmerman GS, LaHaye WS, Gutiérrez RJ (2003) Empirical support for a despotic distribution in a California spotted owl population. Behav Ecol 14:433–437.

    Article  Google Scholar 

  • Zollner PA, Lima SL (2005) Behavioral trade-offs when dispersing across a patchy landscape. Oikos 108:219–230.

    Article  Google Scholar 

Download references

Acknowledgments

We thank N. Haddad, A. McKerrow, J. Collazo, M. Iglecia, and two anonymous reviewers who provided insightful comments at various stages of this manuscript’s development.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Edward J. Laurent .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+BUsiness Media, LLC

About this chapter

Cite this chapter

Laurent, E.J., Drew, C.A., Thogmartin, W.E. (2011). The Role of Assumptions in Predictions of Habitat Availability and Quality. In: Drew, C., Wiersma, Y., Huettmann, F. (eds) Predictive Species and Habitat Modeling in Landscape Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7390-0_5

Download citation

Publish with us

Policies and ethics