Abstract
Physiological information has been successfully included in marine Species Distribution Models (SDM) before, but few have considered a previous development stage that could have affected the present-day distribution of the species at local scales. The aim of this study is to analyze the inclusion of physiological information of seedling survival on a correlative SDM based on adult present-day presences. The species were the invasive shrub Baccharis halimifolia and the native saltmarsh Juncus maritimus. For each species, five SDM were established using different approaches: using only experimentally derived physiological data, a correlative model with environmental predictors, additive combinations of presence/absence maps derived from the previous models, and a correlative model with the physiological data as a predictor variable. For B. halimifolia, the inclusion of the physiological data as a predictor variable yielded better results than with the other approaches; with J maritimus, this inclusion achieved an accuracy as high as the model with only environmental variables as predictors. The additive combinations generated less accurate models but offered possible advantages in future specialized studies. The results for B. halimifolia could extrapolate to other invasive species that rely on spreading high amounts of individuals and are more vulnerable in their early stages than in their growing and adult phases. Thus, this approach can improve the capacity for mapping invasive species’ distributions at local scales, and the conservation efforts to control biological invasions in estuaries and coastal ecosystems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akima H, Gebhardt A, Perzold T, Maechler M (2016) Interpolation of irregularly and regularly spaced data
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. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Anderson RP (2017) When and how should biotic interactions be considered in models of species niches and distributions? J Biogeogr 44:8–17. https://doi.org/10.1111/jbi.12825
Araújo MB, Alagador D, Cabeza M et al (2011) Climate change threatens European conservation areas. Ecol Lett 14:484–492. https://doi.org/10.1111/j.1461-0248.2011.01610.x
Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539. https://doi.org/10.1890/11-1930.1
Araújo MB, Anderson RP, Márcia Barbosa A et al (2019) Standards for distribution models in biodiversity assessments. Sci Adv 5:eaat4858. https://doi.org/10.1126/sciadv.aat4858
Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3:327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
Bárcena JF, García-Alba J, García A, Álvarez C (2016) Analysis of stratification patterns in river-influenced mesotidal and macrotidal estuaries using 3D hydrodynamic modelling and K-means clustering. Estuar Coast Shelf Sci 181:1–13. https://doi.org/10.1016/j.ecss.2016.08.005
Buckley LB, Waaser SA, MacLean HJ, Fox R (2011) Does including physiology improve species distribution model predictions of responses to recent climate change? Ecology 92:2214–2221. https://doi.org/10.1890/11-0066.1
Calleja F, Ondiviela B, Juanes JA (2019) Invasive potential of Baccharis halimifolia: Experimental characterization of its establishment capacity. Environ Exp Bot 162:444–454. https://doi.org/10.1016/j.envexpbot.2019.03.020
Cheaib A, Badeau V, Boe J et al (2012) Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. Ecol Lett 15:533–544. https://doi.org/10.1111/j.1461-0248.2012.01764.x
Cid A, Castanedo S, Abascal AJ et al (2014) A high resolution hindcast of the meteorological sea level component for Southern Europe: the GOS dataset. Clim Dyn 43:2167–2184. https://doi.org/10.1007/s00382-013-2041-0
de la Hoz CF, Ramos E, Puente A et al (2018) Ecological typologies of large areas. an application in the Mediterranean Sea. J Environ Manag 205:59–72. https://doi.org/10.1016/j.jenvman.2017.09.058
Deltares, (2014) Delft3D-FLOW, simulation of multidimensional hydrodynamic flows and transport phenomena, including sediments. User manual, Delft
Dormann CF, Bobrowski M, Dehling DM et al (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Glob Ecol Biogeogr 27:1004–1016. https://doi.org/10.1111/geb.12759
Dormann CF, Elith J, Bacher S et al (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop) 36:27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
Feldman AD (2000) Hydrologic modeling system HEC-HMS. Technical Reference Manual, Davis
Franco JN, Tuya F, Bertocci I et al (2018) The ‘golden kelp’ Laminaria ochroleuca under global change: integrating multiple eco-physiological responses with species distribution models. J Ecol 106:47–58. https://doi.org/10.1111/1365-2745.12810
Franklin J (2010) Mapping species distributions, First. Cambridge University Press, Cambridge
Frau D, Ondiviela B, Galván C, Juanes JA (2014) The role of the hydrodynamic regime in the distribution of the invasive shrub Baccharis halimifolia (Compositae) in Oyambre Estuary (Cantabria, Spain). Limnetica 33:1–12
Fried G, Caño L, Brunel S et al (2016) Monographs on invasive plants in Europe: Baccharis halimifolia L. Bot Lett 163:127–153. https://doi.org/10.1080/23818107.2016.1168315
Fundación Leonardo Torres Quevedo (1995) Estudio hidrodinámico, hidrológico y biológico del sistema fluvio-mareal de Oyambre. Cantabria, Santander
Galván C, Juanes JA, Puente A (2010) Ecological classification of European transitional waters in the North-East Atlantic eco-region. Estuar Coast Shelf Sci 87:442–450. https://doi.org/10.1016/j.ecss.2010.01.026
GIOC (2005) Estudio de los Recursos Hídricos de los Ríos de la Vertiente Norte de Cantabria. Santander
Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x
Guisan A, Thuiller W, Zimmermann NE (2017) Habitat suitability and distribution models. Cambridge University Press, Cambridge
Guisan A, Tingley R, Baumgartner JB et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. https://doi.org/10.1111/ele.12189
Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Cantabria IH (2017) CONVIVE-LIFE: deliverable action A2. Estudio hidrodinámico de las actuaciones de restauración, Santander
Kotta J, Vanhatalo J, Jänes H et al (2019) Integrating experimental and distribution data to predict future species patterns. Sci Rep 9:1821. https://doi.org/10.1038/s41598-018-38416-3
Leathwick JR (1998) Are New Zealand’s Nothofagus species in equilibrium with their environment? J Veg Sci 9:719–732. https://doi.org/10.2307/3237290
Martínez B, Arenas F, Trilla A et al (2015) Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Glob Chang Biol 21:1422–1433. https://doi.org/10.1111/gcb.12655
Martínez B, Viejo RM, Carreño F, Aranda SC (2012) Habitat distribution models for intertidal seaweeds: responses to climatic and non-climatic drivers. J Biogeogr 39:1877–1890. https://doi.org/10.1111/j.1365-2699.2012.02741.x
Mathewson PD, Moyer-Horner L, Beever EA et al (2017) Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates. Glob Chang Biol 23:1048–1064. https://doi.org/10.1111/gcb.13454
Mellin C, Lurgi M, Matthews S et al (2016) Forecasting marine invasions under climate change: biotic interactions and demographic processes matter. Biol Conserv 204:459–467. https://doi.org/10.1016/j.biocon.2016.11.008
Menéndez M (2008) Leaf growth, senescence and decomposition of Juncus maritimus Lam. in a coastal Mediterranean marsh. Aquat Bot 89:365–371. https://doi.org/10.1016/j.aquabot.2008.04.006
Moles AT, Westoby M (2004) Seedling survival and seed size: a synthesis of the literature. J Ecol 92:372–383. https://doi.org/10.1111/j.0022-0477.2004.00884.x
Peterson AT, Soberón J, Pearson RG et al (2011) Ecological niches and geographic distributions. Princeton University Press, New Jersey
Rodríguez L, García JJ, Carreño F, Martínez B (2019) Integration of physiological knowledge into hybrid species distribution modelling to improve forecast of distributional shifts of tropical corals. Divers Distrib. https://doi.org/10.1111/ddi.12883
Rougier T, Lassalle G, Drouineau H et al (2015) The combined use of correlative and mechanistic species distribution models benefits low conservation status Species. PLoS ONE 10:e0139194. https://doi.org/10.1371/journal.pone.0139194
Silvestri S, Marani M (2013) Salt-marsh vegetation and morphology: basic physiology, modelling and remote sensing observations. In: Fagherazzi S, Marani M, Blum LK (eds) Coastal and estuarine studies. American Geophysical Union, Washington, pp 5–25
Soberón J, Osorio-Olvera L, Peterson T (2017) Diferencias conceptuales entre modelación de nichos y modelación de áreas de distribución. Rev Mex Biodivers 88:437–441. https://doi.org/10.1016/j.rmb.2017.03.011
Somodi I, Molnár Z, Czúcz B et al (2017) Implementation and application of multiple potential natural vegetation models—a case study of Hungary. J Veg Sci 28:1260–1269. https://doi.org/10.1111/jvs.12564
Sullivan MJP, Davy AJ, Grant A, Mossman HL (2018) Is saltmarsh restoration success constrained by matching natural environments or altered succession? A test using niche models. J Appl Ecol 55:1207–1217. https://doi.org/10.1111/1365-2664.13033
Thuiller W, Lafourcade B, Engler R, Araújo MB (2009) BIOMOD—A platform for ensemble forecasting of species distributions. Ecography (Cop) 32:369–373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
Thuiller W, Richardson DM, Py Ek P et al (2005) Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Chang Biol 11:2234–2250. https://doi.org/10.1111/J.1365-2486.2005.01018.X
Valle M, Chust G, del Campo A et al (2014) Projecting future distribution of the seagrass Zostera noltii under global warming and sea level rise. Biol Conserv 170:74–85. https://doi.org/10.1016/j.biocon.2013.12.017
Van Dyke F (2008) Conservation biology. Springer, Dordrecht
Walther GR, Roques A, Hulme PE et al (2009) Alien species in a warmer world. Trends Ecol Evol 24:686–693
Weber E (2017) Invasive plant species of the world, a reference guide to environmental weeds, 2nd edn. CABI, Oxfordshire
Williams SL, Grosholz ED (2008) The invasive species challenge in estuarine and coastal environments: marrying management and science. Estuaries Coasts 31:3–20. https://doi.org/10.1007/s12237-007-9031-6
Acknowledgements
This work was partially funded by the Life-CONVIVE Project (LIFE14 NAT/ES/001213) of the LIFE Programme of the European Union and by a research Grant to IH Cantabria by the General Directory of Conservation of the Regional Government of Cantabria. FC was supported by the Universidad de Costa Rica (scholarship OAICE-CAB-09–156-2014). This work is part of the PhD thesis of FC, under the direction of BO and JAJ. The authors thank AEMET and UC for the data provided for this work (Spain02 v5 dataset, available at https://www.meteo.unican.es/datasets/spain02).
Author information
Authors and Affiliations
Contributions
All authors conceived the ideas and designed the methodology; FC developed the models, analyzed the data, and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Calleja, F., Ondiviela, B., Puente, A. et al. Can seedlings' physiological information improve vegetation distribution predictions at local scales?. Biol Invasions 22, 2509–2523 (2020). https://doi.org/10.1007/s10530-020-02266-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10530-020-02266-w