Skip to main content

Advertisement

Springer Nature Link
Log in

Do community-level models account for the effects of biotic interactions? A comparison of community-level and species distribution modeling of Rocky Mountain conifers

  • Published:
Plant Ecology Aims and scope Submit manuscript

Abstract

Community-level models (CLMs) aim to improve species distribution modeling (SDM) methods by attempting to explicitly incorporate the influences of interacting species. However, the ability of CLMs to appropriately account for biotic interactions is unclear. We applied CLM and SDM methods to predict the distributions of three dominant conifer tree species in the U.S. Rocky Mountains and compared CLM and SDM predictive accuracy as well as the ability of each approach to accurately reproduce species co-occurrence patterns. We specifically evaluated the performance of two statistical algorithms, MARS and CForest, within both CLM and SDM frameworks. Across all species, differences in SDM and CLM predictive accuracy were slight and can be attributed to differences in model structure rather than accounting for the effects of biotic interactions. In addition, CLMs generally over-predicted species co-occurrence, while SDMs under-predicted co-occurrence. Our results demonstrate no real improvement in the ability of CLMs to account for biotic interactions relative to SDMs. We conclude that alternative modeling approaches are needed in order to accurately account for the effects of biotic interactions on species distributions.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232

    Article  Google Scholar 

  • Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Glob Ecol Biogeogr 16:743–753

    Article  Google Scholar 

  • Araújo MB, Pearson RG, Rahbek C (2005a) Equilibrium of species’ distributions with climate. Ecography 28:693–695

    Article  Google Scholar 

  • Araújo MB, Pearson RG, Thuiller W, Erhard M (2005b) Validation of species-climate impact models under climate change. Glob Change Biol 11:1504–1513

    Article  Google Scholar 

  • Araújo MB, Rozenfeld A, Rahbek C, Marquet PA (2011) Using species co-occurrence networks to assess the impacts of climate change. Ecography 34:897–908

    Article  Google Scholar 

  • Bahn V, McGill BJ (2013) Testing the predictive performance of distribution models. Oikos 122:321–331

    Article  Google Scholar 

  • Bartlein PJ, Whitlock C, Shafer SL (1997) Future climate in the Yellowstone National Park region and its potential impact on vegetation. Conserv Biol 11:782–792

    Article  Google Scholar 

  • Baselga A, Araújo MB (2009) Individualistic vs community modelling of species distributions under climate change. Ecography 32:55–65

    Article  Google Scholar 

  • Baselga A, Araújo MB (2010) Do community-level models describe community variation effectively? J Biogeogr 37:1842–1850

    Google Scholar 

  • Bell DM, Bradford JB, Lauenroth WK (2014) Early indicators of change: divergent climate envelopes between tree life stages imply range shifts in the western United States. Glob Ecol Biogeogr 23:168–180

    Article  Google Scholar 

  • Blois JL, Gotelli NJ, Behrensmeyer AK, Faith JT, Lyons SK, Williams JW, Amatangelo KL, Bercovici A, Du A, Eronen DJ, Graves GR, Jud N, Labandeira C, Looy CV, McGill B, Patterson D, Potts R, Riddle B, Terry R, Tóth A, Villaseñor A, Wing S (2014) A framework for evaluating the influence of climate, dispersal limitation, and biotic interactions using fossil pollen associations across the late Quaternary. Ecography 37:1095–1108

    Google Scholar 

  • Boulangeat I, Gravel D, Thuiller W (2012) Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances. Ecol Lett 15:584–593

    Article  PubMed  PubMed Central  Google Scholar 

  • Case TJ, Holt RD, McPeek MA, Keitt H (2005) The community context of species’ borders: ecological and evolutionary perspectives. Oikos 108:28–46

    Article  Google Scholar 

  • Chatfield BS (2008) How to find the one that got away. Predicting the distribution of temperate demersal fish from environmental variables. PhD Thesis, School of Earth and Geographical Sciences, University of Western Australia, Perth

  • Clark JS, Gelfand AE, Woodall CW, Zhu K (2014) More than the sum of the parts: forest climate response from joint species distribution models. Ecol Appl 24:990–999

    Article  PubMed  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scales. Education and Psychological Measurement 20:37–46

    Article  Google Scholar 

  • Copenhaver-Parry PE, Cannon E (2016) The relative influences of climate and competition on tree growth along montane ecotones in the Rocky Mountains. Oecologia. doi:10.1007/s00442-016-3565-x

    PubMed  Google Scholar 

  • Craven P, Wahba G (1979) Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math 31:317–403

    Google Scholar 

  • Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García Marquéz JR, Gruber B, Lafourcade B, Leitāo PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:027–046

    Article  Google Scholar 

  • Elith J, Graham CH, Anderson RP, Dudik 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 K, Scachetti-Pereira R, Schapire RE, Soberón J, Wisz S, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Article  Google Scholar 

  • Evans JS, Oakleaf J, Cushman SA, Theobald D (2014) An ArcGIS toolbox for surface gradient and geomorphometric modeling, version 2.0-0. Available http://evansmurphy.wix.com/evansspatial. Accessed 2015

  • Ferrier S, Guisan A (2006) Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404

    Article  Google Scholar 

  • Friedman JH (1991) Multivariate adaptive regression splines. Annal Stat 19:1–67

    Article  Google Scholar 

  • Friedman JH (1993) Fast MARS. Technical Report No. 110: Department of Statistics, Stanford University

  • Gibson J, Moisen G, Frescino T, Edwards TC Jr (2014) Using publicly available forest inventory data in climate-based models of tree species distribution: examining effects of true versus altered location coordinates. Ecosystems 17:43–53

    Article  Google Scholar 

  • Godsoe W, Harmon LJ (2012) How do species interactions affect species distribution models? Ecography 35:811–820

    Article  Google Scholar 

  • Guisan A, Rahbek C (2011) SESAM- a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. J Biogeogr 38:1433–1444

    Article  Google Scholar 

  • Hardy OJ (2008) Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. J Ecol 96:914–926

    Article  Google Scholar 

  • HilleRisLambers J, Harsch MA, Ettinger AK, Ford KR, Theobald EJ (2013) How will biotic interactions influence climate change-induced range shifts? Ann N Y Acad Sci 1297:112–125

    PubMed  Google Scholar 

  • Holt RD (2009) Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc Natl Acad Sci 106:19659–19665

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hothorn T, Buehlmann P, Dudoit S, Molinaro A, Van Der Laan M (2006a) Survival ensembles. Biostatistics 7:355–373

    Article  PubMed  Google Scholar 

  • Hothorn T, Hornik K, Zeileis A (2006b) Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15:651–674

    Article  Google Scholar 

  • Jackson ST, Betancourt JL, Booth RK, Gray ST (2009) Ecology and the ratchet of events: climate variability, niche dimensions, and species distributions. Proc Natl Acad Sci 106:19685–19692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kissling WD, Dormann CF, Groenveld J, Hickler T, Kühn I, McInemy GJ, Montoya JM, Römermann C, Schiffers K, Schurr FM, Singer A, Svenning JC, Zimmermann NE, O’Hara RB (2012) Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents. J Biogeogr 39:2163–2178

    Article  Google Scholar 

  • Klanderud K, Totland Ø (2005) Simulated climate change altered dominance hierarchies and diversity of an alpine biodiversity hotspot. Ecology 86:2047–2054

    Article  Google Scholar 

  • Leathwick J (2009) Are New Zealand’s Nothofagus species in equilibrium with their environment? J Veg Sci 9:719–732

    Article  Google Scholar 

  • Leathwick JR, Elith J, Hastie T (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecol Model 199:188–196

    Article  Google Scholar 

  • Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393

    Article  Google Scholar 

  • Madon B, Warton DI, Araújo MB (2013) Community-level vs species-specific approaches to model selection. Ecography 36:1291–1298

    Article  Google Scholar 

  • Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence-absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931

    Article  Google Scholar 

  • McCaughey WW, Schmidt WC, Shearer RC (1985) Seed dispersal characteristics of conifers of the inland Mountain West. In: Shearer RC. Proceedings: Conifer Tree Seed in the Inland Mountain West Symposium. April 1985, Missoula. General Technical Report INT-203. USDA Forest Service Intermountain Research Station, Ogden, pp 50–62

  • Meddens AJH, Hicke JA, Ferguson CA (2012) Spatiotemporal patterns of observed bark beetle-caused tree mortality in British Columbia and the western United States. Ecol Appl 22:1876–1891

    Article  PubMed  Google Scholar 

  • Meier ES, Kienast F, Pearman PB, Svenning JC, Thuiller W, Araújo MB, Guisan A, Zimmermann NE (2010) Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography 33:1038–1048

    Article  Google Scholar 

  • Meineri E, Skarpaas O, Vandvik V (2012) Modeling alpine plant distributions at the landscape scale: do biotic interactions matter? Ecol Model 231:1–10

    Article  Google Scholar 

  • Milborrow S (2015) Derived from mda:mars by T. Hastie and R. Tibshirani. Uses A. Miller’s Fortran utilities with T. Lumley’s leaps wrapper. Earth: multivariate adaptive regression splines. R package version 4.2.0. http://CRAN.R-project.org/package=earth

  • Morueta-Holme N, Blonder B, Sandel B, McGill B, Peet RK, Ott JE, Violle C, Enqust BJ, Jorgensen PM, Svenning JC (2015) A network approach for inferring species association from co-occurrence data. Ecography. doi:10.1111/ecog.01892

    Google Scholar 

  • Murphy MA, Evans JS, Storfer A (2010) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91:252–261

    Article  PubMed  Google Scholar 

  • Normand S, Zimmermann NE, Schurr FM, Lischke H (2014) Demography as the basis for understanding and predicting range dynamics. Ecography 37:1149–1154

    Article  Google Scholar 

  • Olden JD, Joy MK, Death RG (2006) Rediscovering the species in community-wide predictive modeling. Ecol Appl 16:1449–1460

    Article  PubMed  Google Scholar 

  • Peet RK (1981) Forest vegetation of the Colorado front range: composition and dynamics. Vegetatio 45:3–75

    Article  Google Scholar 

  • Pellissier L, Pradervand JN, Pottier J, Dubuis A, Maiorano L, Guisan A (2012) Climate-based empirical models show biased predictions of butterfly communities along environmental gradients. Ecography 35:684–692

    Article  Google Scholar 

  • Peterson TA, Papes M, Eaton M (2007) Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30:550–560

    Article  Google Scholar 

  • Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modeling species simultaneously with a joint species distribution model (JSDM). Methods Ecol Evol 5:397–406

    Article  Google Scholar 

  • R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, URL http://www.R-project.org/

  • Rehfeldt GL (2006) A spline model of climate for the Western United States. General Technical Report RMRS-GTR-165. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins

  • Renkonen O (1938) Statistich-ökologische Utersuchungen über die terrestrische Käferwelt der finnischen Bruchmoore. Annales Zoologici Societatis Zoologicae-Botanicae Fennicae ‘Vanamo’ 6:1–231

    Google Scholar 

  • Roberts DW, Cooper SV (1989) Concepts and techniques of vegetation mapping. In: Land classifications based on vegetation: applications for resource management. USDA Forest Service GTR INT-257, Ogden, pp 90–96

  • Rödder D, Engler JO (2011) Quantitative metrics of overlaps in Grinnellian niches: advances and possible drawbacks. Glob Ecol Biogeogr 20:915–927

    Article  Google Scholar 

  • Rouget M, Richardson DM, Lavorel S, Vayreda J, Gracia C, Milton SJ (2001) Determinants of distribution of six Pinus species in Catalonia, Spain. J Veg Sci 12:491–502

    Article  Google Scholar 

  • Sahney S, Benton MJ (2008) Recovery from the most profound mass extinction of all time. Proc R Soc B 275:759–765

    Article  PubMed  PubMed Central  Google Scholar 

  • Schubert JK, Bottjer DJ (1995) Aftermath of the Permian-Triassic mass extinction event: paleoecology of lower triassic carbonates in the western USA. Palaeogeogr Palaeoclimatol Palaeoecol 116:1–39

    Article  Google Scholar 

  • Smith WB (2002) Forest inventory and analysis: a national inventory and monitoring program. Environ Pollut 116:S233–S242

    Article  CAS  PubMed  Google Scholar 

  • Suttle KB, Thomsen MA, Power ME (2007) Species interactions reverse grassland responses to changing climate. Science 315:640–642

    Article  CAS  PubMed  Google Scholar 

  • Svenning JC, Gravel D, Holt RD, Schurr FM, Thuiller W, Münkemüller T, Schiffers KH, Dullinger S, Edwards TC Jr, Hickler T, Higgins SI, Nabel JEMS, Pagel J, Normand S (2014) The influence of interspecific interactions on species range expansion rates. Ecography 37:1198–1209

    Article  PubMed  PubMed Central  Google Scholar 

  • Swab RM, Regan HM, Matthies D, Becker U, Bruun HH (2015) The role of demography, intra-species variation, and species distribution models in species’ projections under climate change. Ecography 38:221–230

    Article  Google Scholar 

  • Tylianakis JM, Didham RK, Bascompte J, Wardle DA (2008) Global change and species interactions in terrestrial ecosystems. Ecol Lett 11:1351–1363

    Article  PubMed  Google Scholar 

  • Warren DL, Glor RE, Turelli M (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62:2868–2883

    Article  PubMed  Google Scholar 

  • Webb CO, Ackerly DD, McPeek MA, Donoghue MJ (2002) Phylogenies and community ecology. Annu Rev Ecol Syst 33:475–505

    Article  Google Scholar 

  • Wisz MS, Pottier J, Kissling WD, Pellissier L, Lenoir J, Damgaard CF, Dormann CF, Forchhammer MC, Grytnes JA, Guisan A, Heikkinen RK, Høye TT, Kühn I, Luoto M, Maiorano L, Nilsson MC, Normand S, Öckinger E, Schmidt NM, Termansen M, Timmermann A, Wardle DA, Aastrup P, Svenning JC (2013) The role of biotic interactions in shaping distributions and realized assemblages of species: implications for species distribution modelling. Biol Rev 88:15–30

    Article  PubMed  PubMed Central  Google Scholar 

  • Woodall CW, Oswalt CM, Westfall JA, Perry CH, Nelson MD, Finley AO (2010) Selecting tree species for testing climate change migration hypotheses using forest inventory data. For Ecol Manag 259:778–785

    Article  Google Scholar 

  • Worth JRP, Williamson GJ, Sakaguchi S, Nevill PG, Jorden GJ (2014) Environmental niche modelling fails to predict Last Glacial Maximum refugia: niche shifts, microrefugia or incorrect palaeoclimate estimates? Glob Ecol Biogeogr 23:1186–1197

    Article  Google Scholar 

  • Woudenberg SW, Conkling BL, O’Connell BM, LaPoint EB, Turner JA, Waddell KL (2010) The forest inventory and analysis database: database description and user manual version 4.0 for phase 2. General Technical Report RMRS-GTR-245. USDA Forest Service Rocky Mountain Research Station, Fort Collins

Download references

Acknowledgments

The authors would like to thank Chris Woodall and Brian Walters for providing the data used in this study, as well as the students of the 2014 Advanced Spatial Analysis course at the University of Wyoming for their feedback and assistance. We are also grateful to two anonymous reviewers whose comments substantially improved the quality of this manuscript. P. Copenhaver-Parry was supported by a National Science Foundation Fellowship (G-K12 Project #0841298) and a fellowship from the Wyoming NASA Space Grant Consortium during the development of this manuscript.

Author information

Authors and Affiliations

  1. Program in Ecology and Department of Botany, University of Wyoming, 3165, 1000 E. University Avenue, Laramie, WY, 82071, USA

    Paige E. Copenhaver-Parry & Daniel B. Tinker

  2. Wyoming Geographic Information Sciences Center and Department of Geography, University of Wyoming, 3371, 1000 E. University Avenue, Laramie, WY, 82071, USA

    Shannon E. Albeke

Authors
  1. Paige E. Copenhaver-Parry
    View author publications

    Search author on:PubMed Google Scholar

  2. Shannon E. Albeke
    View author publications

    Search author on:PubMed Google Scholar

  3. Daniel B. Tinker
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Paige E. Copenhaver-Parry.

Additional information

Communicated by James D. A. Millington.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Copenhaver-Parry, P.E., Albeke, S.E. & Tinker, D.B. Do community-level models account for the effects of biotic interactions? A comparison of community-level and species distribution modeling of Rocky Mountain conifers. Plant Ecol 217, 533–547 (2016). https://doi.org/10.1007/s11258-016-0598-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11258-016-0598-5

Keywords