• This record comes from PubMed

Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion

. 2022 Aug 30 ; 11 (9) : . [epub] 20220830

Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic

Document type Journal Article

Grant support
DSI-NRF Centre of Excellence for Invasion Biology
89967 Millennium Trust, the National Research Foundation of South Africa
CZ.02.2.69/0.0/0.0/18_053/0017850 Mobility 2020 project (Ministry of Education, Youth and Sports of the Czech Republic)
RVO 67985939 long-term research development project (Czech Academy of Sciences)

Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.

See more in PubMed

Pyšek P., Křivánek M., Jarošík V. Planting intensity, residence time, and species traits determine invasion success of alien woody species. Ecology. 2009;90:2734–2744. doi: 10.1890/08-0857.1. PubMed DOI

Haubrock P.J., Cuthbert R.N., Tricarico E., Diagne C., Courchamp F., Gozlan R.E. The recorded economic costs of alien invasive species in Italy. NeoBiota. 2021;67:247. doi: 10.3897/neobiota.67.57747. DOI

Renault D., Manfrini E., Leroy B., Diagne C., Ballesteros-Mejia L., Angulo E., Courchamp F. Biological invasions in France: Alarming costs and even more alarming knowledge gaps. NeoBiota. 2021;67:191. doi: 10.3897/neobiota.67.59134. DOI

Cuthbert R.N., Bartlett A.C., Turbelin A.J., Haubrock P.J., Diagne C., Pattison Z., Catford J.A. Economic costs of biological invasions in the United Kingdom. NeoBiota. 2021;67:299–328. doi: 10.3897/neobiota.67.59743. DOI

Haubrock P.J., Turbelin A.J., Cuthbert R.N., Novoa A., Taylor N.G., Angulo E., Courchamp F. Economic costs of invasive alien species across Europe. NeoBiota. 2021;67:153–190. doi: 10.3897/neobiota.67.58196. DOI

Seebens H., Blackburn T.M., Dyer E.E., Genovesi P., Hulme P.E., Jeschke J.M., Essl F. No saturation in the accumulation of alien species worldwide. Nat. Commun. 2017;8:1–9. doi: 10.1038/ncomms14435. PubMed DOI PMC

Rouget M., Robertson M.P., Wilson J.R., Hui C., Essl F., Renteria J.L., Richardson D.M. Invasion debt–quantifying future biological invasions. Divers. Distrib. 2016;22:445–456. doi: 10.1111/ddi.12408. DOI

Kowarik I. Time lags in biological invasions with regard to the success and failure of alien species. Plant Invasions Gen. Asp. Spec. Probl. 1995:15–38.

Wilson J.R., Panetta F.D., Lindgren C. Detecting and Responding to Alien Plant Incursions. Cambridge University Press; Cambridge, UK: 2016.

Encarnação J., Teodósio M.A., Morais P. Citizen science and biological invasions: A review. Front. Environ. Sci. 2021;8:303. doi: 10.3389/fenvs.2020.602980. DOI

Hui C., Richardson D.M. Invasion Dynamics. Oxford University Press; Oxford, UK: 2017.

Elith J. Predicting distributions of invasive species. Invasive Species Risk Assess. Manag. 2017;10:93–129.

Thomas C.D. Climate, climate change and range boundaries. Divers. Distrib. 2010;16:488–495. doi: 10.1111/j.1472-4642.2010.00642.x. DOI

Häkkinen H., Hodgson D., Early R. Plant naturalizations are constrained by temperature but released by precipitation. Glob. Ecol. Biogeogr. 2021;31:504–521. doi: 10.1111/geb.13443. DOI

Roura-Pascual N., Bas J.M., Thuiller W., Hui C., Krug R.M., Brotons L. From introduction to equilibrium: Reconstructing the invasive pathways of the Argentine ant in a Mediterranean region. Glob. Change Biol. 2009;15:2101–2115. doi: 10.1111/j.1365-2486.2009.01907.x. DOI

Donaldson J.E., Hui C., Richardson D.M., Robertson M.P., Webber B.L., Wilson J.R. Invasion trajectory of alien trees: The role of introduction pathway and planting history. Glob. Change Biol. 2014;20:1527–1537. doi: 10.1111/gcb.12486. PubMed DOI

Melbourne B.A., Hastings A. Highly variable spread rates in replicated biological invasions: Fundamental limits to predictability. Science. 2009;325:1536–1539. doi: 10.1126/science.1176138. PubMed DOI

Drenovsky R.E., Grewell B.J., D’antonio C.M., Funk J.L., James J.J., Molinari N., Richards C.L. A functional trait perspective on plant invasion. Ann. Bot. 2012;110:141–153. doi: 10.1093/aob/mcs100. PubMed DOI PMC

Daehler C.C. Variation in self-fertility and the reproductive advantage of self-fertility for an invading plant (Spartina alterniflora) Evol. Ecol. 1998;12:553–568. doi: 10.1023/A:1006556709662. DOI

Pyšek P. Is there a taxonomic pattern to plant invasions? Oikos. 1998;82:282–294. doi: 10.2307/3546968. DOI

Schurr F.M., Pagel J., Cabral J.S., Groeneveld J., Bykova O., O’Hara R.B., Zimmermann N.E. How to understand species’ niches and range dynamics: A demographic research agenda for biogeography. J. Biogeogr. 2012;39:2146–2162. doi: 10.1111/j.1365-2699.2012.02737.x. DOI

Louvrier J., Papaïx J., Duchamp C., Gimenez O. A mechanistic–statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France. Spat. Stat. 2020;36:100428. doi: 10.1016/j.spasta.2020.100428. DOI

Roques L., Desbiez C., Berthier K., Soubeyrand S., Walker E., Klein E.K., Papaïx J. Emerging strains of watermelon mosaic virus in Southeastern France: Model-based estimation of the dates and places of introduction. Sci. Rep. 2021;11:1–11. doi: 10.1038/s41598-021-86314-y. PubMed DOI PMC

Rejmánek M., Richardson D.M. What attributes make some plant species more invasive? Ecology. 1996;77:1655–1661. doi: 10.2307/2265768. DOI

Higgins S.I., Richardson D.M. Predicting plant migration rates in a changing world: The role of long-distance dispersal. Am. Nat. 1999;153:464–475. doi: 10.1086/303193. PubMed DOI

Caswell H., Lensink R., Neubert M.G. Demography and dispersal: Life table response experiments for invasion speed. Ecology. 2003;84:1968–1978. doi: 10.1890/02-0100. DOI

Pemberton R.W., Liu H. Marketing time predicts naturalization of horticultural plants. Ecology. 2009;90:69–80. doi: 10.1890/07-1516.1. PubMed DOI

Castro-Díez P., Godoy O., Saldaña A., Richardson D.M. Predicting invasiveness of Australian acacias on the basis of their native climatic affinities, life history traits and human use. Divers. Distrib. 2011;17:934–945.

Caswell H. Matrix Population Models. Volume 1 Sinauer; Sunderland, MA, USA: 2000.

Stott I., Townley S., Hodgson D.J. A framework for studying transient dynamics of population projection matrix models. Ecol. Lett. 2011;14:959–970. doi: 10.1111/j.1461-0248.2011.01659.x. PubMed DOI

Qiu T., Aravena M.C., Andrus R., Ascoli D., Bergeron Y., Berretti R., Clark J.S. Is there tree senescence? The fecundity evidence. Proc. Natl. Acad. Sci. USA. 2021;118:e2106130118. doi: 10.1073/pnas.2106130118. PubMed DOI PMC

Wilson J.R.U., Richardson D.M., Rouget M., Procheş Ş., Amis M.A., Henderson L., Thuiller W. Residence time and potential range: Crucial considerations in modelling plant invasions. Divers. Distrib. 2007;13:11–22. doi: 10.1111/j.1366-9516.2006.00302.x. DOI

Caley P., Groves R.H., Barker R. Estimating the invasion success of introduced plants. Divers. Distrib. 2008;14:196–203. doi: 10.1111/j.1472-4642.2007.00440.x. DOI

Williamson M., Dehnen-Schmutz K., Kühn I., Hill M., Klotz S., Milbau A., Pyšek P. The distribution of range sizes of native and alien plants in four European countries and the effects of residence time. Divers. Distrib. 2009;15:158–166. doi: 10.1111/j.1472-4642.2008.00528.x. DOI

Cook A., Marion G., Butler A., Gibson G. Bayesian inference for the spatio-temporal invasion of alien species. Bull. Math. Biol. 2007;69:2005–2025. doi: 10.1007/s11538-007-9202-4. PubMed DOI

Clark J.S., Scher C.L., Swift M. The emergent interactions that govern biodiversity change. Proc. Natl. Acad. Sci. USA. 2020;117:17074–17083. doi: 10.1073/pnas.2003852117. PubMed DOI PMC

West M., Harrison P.J. Bayesian Forecasting and Dynamic Models. 2nd ed. Springer; New York, NY, USA: 1997.

Miller D.A., Pacifici K., Sanderlin J.S., Reich B.J. The recent past and promising future for data integration methods to estimate species’ distributions. Methods Ecol. Evol. 2019;10:22–37. doi: 10.1111/2041-210X.13110. DOI

Hastie T., Fithian W. Inference from presence-only data; the ongoing controversy. Ecography. 2013;36:864–867. doi: 10.1111/j.1600-0587.2013.00321.x. PubMed DOI PMC

Alasbahi R.H., Melzig M.F. Plectranthus barbatus: A review of phytochemistry, ethnobotanical uses and pharmacology-Part 1. Planta Med. 2010;76:653–661. doi: 10.1055/s-0029-1240898. PubMed DOI

Phillips L.A., Greer C.W., Farrell R.E., Germida J.J. Field-scale assessment of weathered hydrocarbon degradation by mixed and single plant treatments. Appl. Soil Ecol. 2009;42:9–17. doi: 10.1016/j.apsoil.2009.01.002. DOI

Botella C., Joly A., Monestiez P., Bonnet P., Munoz F. Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection. PLoS ONE. 2020;15:e0232078. doi: 10.1371/journal.pone.0232078. PubMed DOI PMC

Loveland T.R., Reed B.C., Brown J.F., Ohlen D.O., Zhu Z., Yang L.W.M.J., Merchant J.W. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 2000;21:1303–1330. doi: 10.1080/014311600210191. DOI

Harris I., Jones P.D., Osborn T.J., Lister D.H. Updated high-resolution grids of monthly climatic observations—The CRU TS3.10 Dataset. Int. J. Climatol. 2014;34:623–642. doi: 10.1002/joc.3711. DOI

Fick S.E., Hijmans R.J. WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017;37:4302–4315. doi: 10.1002/joc.5086. DOI

Bellard C., Thuiller W., Leroy B., Genovesi P., Bakkenes M., Courchamp F. Will climate change promote future invasions? Glob. Chang. Biol. 2013;19:3740–3748. doi: 10.1111/gcb.12344. PubMed DOI PMC

Araújo M.B., Pearson R.G., Thuiller W., Erhard M. Validation of species–climate impact models under climate change. Glob. Chang. Biol. 2005;11:1504–1513. doi: 10.1111/j.1365-2486.2005.01000.x. DOI

Chalmandrier L., Hartig F., Laughlin D.C., Lischke H., Pichler M., Stouffer D.B., Pellissier L. Linking functional traits and demography to model species-rich communities. Nat. Commun. 2021;12:1–9. doi: 10.1038/s41467-021-22630-1. PubMed DOI PMC

McLean P., Gallien L., Wilson J.R.U., Gaertner M., Richardson D.M. Small urban centres as launching sites for plant invasions in natural areas: Insights from South Africa. Biol. Invasions. 2017;19:3541–3555. doi: 10.1007/s10530-017-1600-4. DOI

Potgieter L.J., Douwes E., Gaertner M., Measey G.J., Paap T., Richardson D.M. Biological invasions in South Africa’s urban ecosystems: Patterns, processes, impacts and management. In: Van Wilgen B.W., Measey J., Richardson D.M., Wilson J.R.U., Zengeya T.A., editors. Biological Invasions in South Africa. Springer; Berlin/Heidelberg, Germany: 2020. pp. 275–309.

Pages M., Fischer A., van der Wal R., Lambin X. Empowered communities or “cheap labour”? Engaging volunteers in the rationalised management of invasive alien species in Great Britain. J. Environ. Manag. 2019;229:102–111. doi: 10.1016/j.jenvman.2018.06.053. PubMed DOI

Catterall S., Cook A.R., Marion G., Butler A., Hulme P.E. Accounting for uncertainty in colonisation times: A novel approach to modelling the spatio-temporal dynamics of alien invasions using distribution data. Ecography. 2012;35:901–911. doi: 10.1111/j.1600-0587.2011.07190.x. DOI

Groom Q., Adriaens T., Bertolino S., Poelen J.H., Reeder D., Richardson D.M., Simmons N. Holistic understanding of contemporary ecosystems requires integration of data on domesticated, captive, and cultivated organisms. [(accessed on 29 August 2022)];Biodivers. Data J. 2021 9:e65371. doi: 10.3897/BDJ.9.e65371. Available online: https://bdj.pensoft.net/article/65371/ PubMed DOI PMC

Li E., Parker S.S., Pauly G.B., Randall J.M., Brown B.V., Cohen B.S. An urban biodiversity assessment framework that combines an urban habitat classification scheme and citizen science data. Front. Ecol. Evol. 2019;7:277. doi: 10.3389/fevo.2019.00277. DOI

Aikio S., Duncan R.P., Hulme P.E. Lag-phases in alien plant invasions: Separating the facts from the artefacts. Oikos. 2010;119:370–378. doi: 10.1111/j.1600-0706.2009.17963.x. DOI

Nelson G., Ellis S. The history and impact of digitization and digital data mobilization on biodiversity research. Philos. Trans. R. Soc. B. 2019;374:20170391. doi: 10.1098/rstb.2017.0391. PubMed DOI PMC

Randin C.F., Dirnböck T., Dullinger S., Zimmermann N.E., Zappa M., Guisan A. Are niche-based species distribution models transferable in space? J. Biogeogr. 2006;33:1689–1703. doi: 10.1111/j.1365-2699.2006.01466.x. DOI

Elliott-Graves A. The problem of prediction in invasion biology. Biol. Philos. 2016;31:373–393. doi: 10.1007/s10539-015-9504-0. DOI

Cole D.J. Parameter Redundancy and Identifiability. Chapman and Hall; London, UK: 2020. Bayesian Identifiability; pp. 101–153.

MacKenzie D.I., Nichols J.D., Lachman G.B., Droege S., Royle J.A., Langtimm C.A. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 2002;83:2248–2255. doi: 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2. DOI

Rosenthal J.S. Optimal proposal distributions and adaptive MCMC. Handb. Markov Chain. Monte Carlo. 2011;4:93–112.

Hartig F. BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics. 2017. [(accessed on 29 August 2022)]. R package version 0.1-7. CRAN. Available online: https://cran.r-project.org/web/packages/BayesianTools/index.html.

Rosenthal M., Glew R. Medical Biochemistry. 1st ed. Wiley; Hoboken, NJ, USA: 2011. [(accessed on 29 August 2022)]. Original work published 2011. Available online: https://www.perlego.com/book/1008615/medical-biochemistry-pdf.

Brooks S.P., Gelman A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 1998;7:434–455.

Jiménez-Valverde A. Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Glob. Ecol. Biogeogr. 2012;21:498–507. doi: 10.1111/j.1466-8238.2011.00683.x. DOI

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...