A species distribution model for Paracentrotus lividus: predicted projections of habitat suitability

Abstract

Event Abstract Back to Event A species distribution model for Paracentrotus lividus: predicted projections of habitat suitability Ana Filipa Costa1*, Vania Freitas1, Joana Campos1* and Francisco Arenas1 1 Interdisciplinary Center for Marine and Environmental Research, Abel Salazar Institute of Biomedical Sciences, University of Porto, Portugal INTRODUCTION The European purple sea urchin Paracentrotus lividus is the most common echinoid in Portugal (Gago et al. 2001), being present in the rocky intertidal and shallow subtidal habitats. It is a species with recognized importance both from an ecological and an economic point of view. In Portugal, for instance, the commercial exploitation of the European purple sea urchins, P. lividus, has increased more than 2000% since 2010 (INE 2011; INE 2018). However, sea urchin’s populations are exposed to several threats ranging from climate change (Miller et al. 2018) to overfishing (Ouréns et al. 2015). Sea surface temperature has been increasing in the North Atlantic for decades (IPCC, 2014), which influences the metabolism and fitness of ectotherms as well as its distribution area (Shpigel et al. 2004; Byrne et al. 2009; Angilletta 2009; Schulte 2015). Understanding the impacts of future climate change on species distribution is key to help formulating conservation policies to reduce the economic and ecological risks of biodiversity loss. Species distribution models (SDMs) that characterize the species’ niches based on environmental variables and species location data, are an important tool to predict species occurrence under particular climate change scenarios. In this study we used two algorithms (MaxEnt and Biomod2), as SDM tools, to assess the current and the future predicted habitat suitability of P. lividus in accordance with the worst climate change scenario, anticipating a reduction of its distribution area in the Atlantic coast. MATERIAL AND METHODS Species distribution model Paracentrotus lividus data occurrence was collected from existing online databases such as Ocean Biogeographic Information System (OBIS) and Global Biodiversity Information Facility (GBIF). Additional records were added from a literature review using the Web of Science. Duplicate records were eliminated using R code. A total of 266 georeferenced occurrence records were used in the SDM, for a given distribution area that considered European and North Africa shores. These coastal areas were masked using a bathymetric raster to include only a depth range from 0 to 200 meters (Bertocci et al. 2010). Data about environmental predictors were downloaded as raster layers from the online repository BIO-Oracle and then environmental predictors were selected in accordance with the potential influence on the distribution of P. lividus. Since correlation among environmental drivers is a potential problem in species distribution modelling (Elith et al. 2010), we only used predictors for which pairwise Pearson correlations between variables were less than 0.85: salinity, pH, maximum annual seawater temperature, minimum annual seawater temperature and seawater temperature range. The SDMs were constructed using two different algorithms. First, we used Maximum Entropy Modelling (MaxEnt software) (Phillips 2005) where the MaxEnt algorithm aims to maximize the entropy of the species probability distribution (Merow et al. 2013). This algorithm fits complex models as linear combinations of basic functions and we ran the models using the linear and quadratic features (Elith et al. 2011). Additionally, we built a generalized linear model (GLM) using the R package Biomod2, a regression-like method that relates presence records and pseudo absences with environmental layers (Guisan et al. 2017). The contribution of each predictor was examined using the permutation importance and percent contribution coefficients from MaxEnt software, as well as with the variable importance function of Biomod2. In the first case, the performance of the model was evaluated in accordance with the predicted area under the curve (AUC), where values higher than 0.85 indicated a good discrimination power (Phillips et al. 2006). Internal data-splitting validation was applied to confirm the variable importance of the final predictors in the training data (70% of presence = points) and the consistency of the above evaluation metric (AUC). MaxEnt was used to determine the habitat suitability index for all the study areas with the environmental conditions registered from 2002 to 2009, as well as to obtain future distribution projections by using rasters of forecasted physical conditions under the Representative Concentration Pathways (RCP) 8.5, the pathway with the highest greenhouse gas emissions (IPCC 2014). The layers extracted from Bio-Oracle contained the information from the UKMO-HadCM3 model, which represented the most severe among those provided by Bio-Oracle (Meehl et al. 2007). RESULTS Environmental predictors The species distribution models included the five initial predictors (i.e. salinity, pH, maximum annual seawater temperature, minimum annual seawater temperature and seawater temperature range). However, after the selection procedure, only three predictors were considered as most relevant in the modelling distribution of this species: salinity, seawater minimum temperature and seawater maximum temperature (Table 1). MaxEnt “functional responses” help to identify the thresholds of the species for each predictor (Figure 1). Thresholds were not clear in the functional responses to minimum and maximum temperatures. At minimum temperatures below 5˚C, the presence of the species clearly reduces (Figure 1a) but no upper threshold was identified for maximum temperatures (Figure 1b). The functional response to salinity (Figure 1c) supports that the species is marine with a low probability of occurrence at salinities below 30 ppm. Habitat suitability The predictive accuracy of both models had a high evaluation score, with AUC = 0.897 for MaxEnt and AUC > 0.950 for Biomod2. The model prediction for the current distribution shows a species with affinities for fully marine warm waters with higher habitat suitability in the Mediterranean and southern shores of the Atlantic regions (Figure 2a). Future projections using the worst Representative Concentration Pathways (RCP) scenario (>900 CO2 ppm) suggest a general reduction of habitat suitability of the overall European population, except in the Black Sea. In Portugal the favourable index of 0.5 reduces to 0.2 by 2100 (Figure 2b). DISCUSSION Future of sea urchin population The response of a species to global warming depends on how different populations are affected by increasing temperature throughout the species’ geographic range (Gardiner et al 2010). Physiological thresholds and correlative functional responses from species distribution models performed quite accordingly in shaping the response of Paracentrotus lividus to temperature. Thus, our confidence in the performance of our modelling exercise is high. However, the species distribution model used did not take into account biological predictors such as food availability and/or biotic interactions, which can also compromise the existence/ absence of the species. Unfortunately, there were no available projections for algae distribution as well as other biological stressors. The projections from our model showed some worrying evidences of a decrease of the suitability of Portuguese coastal habitats for the sea urchin populations. In addition, contrary to what was expected based on temperature preferences, the species is not expected to move northwards, but to East instead, probably as a result of a combination of the other environmental factors considered in the model such as a predicted reduction of salinity in the North Atlantic due to the Arctic ice melting. Obviously, uncertainty of these models is high but the results are consistent with similar predictions for other coastal species (Martinez et al. 2015; Assis et al. 2016). We anticipate though a reduction on the area of habitat suitability for the species due to climate change. In particular for the Portuguese population, the reduced suitability might highly compromise the commercial exploitation which emphasises the need for a proper stock management, based on scientific monitoring, to assure a future sustainable harvesting of sea urchins. Table 1 - Percent contribution, permutation importance (MaxEnt) and variable importance (GLM Biomod2) of the predictors used in the models. SSTmin, max and range correspond to sea surface temperatures’ minimum, maximum and the range between them respectively. Variables with * were selected for the final model. MaxEnt Biomod2 Percent contribution Permutation importance Variable importance *SST (min) 8.2 44.7 0.2316 *SST (max) 55.5 20.2 0.2805 *Salinity 31.4 30.7 0.2837 SST (range) 1.1 0 0.1333 pH 3.8 4.5 0.0596 List of figures captions Figure 1 - Functional responses estimated from the different runs of the three predictors used in the species distribution model. a) seawater minimum temperature; b) seawater maximum temperature and c) salinity. Figure 2 - MaxEnt projections of habitat suitability for Paracentrotus lividus. a) habitat suitability for the current conditions (year 2002-2009) where blue dots represent current records of the geographic distribution of the sea urchin Paracentrotus lividus used in this model; b) predicted habitat suitability in 2100 with the worst RCP scenario (8.5). Figure 1 Figure 2 Figure 3 Acknowledgements The present study was supported by the Northern Regional Operational Program (NORTE2020), through the European Regional Development Fund (ERDF), within the Research Line INSEAFOOD Innovation and valorization of seafood products: meeting local challenges and opportunities and through the Strategic Funding UID/Multi/04423/2019. References Angilletta MJ (2009) Thermal adaptation : a theoretical and empirical synthesis. Oxford University Press, Oxford ; New York Assis J, Lucas AV, Barbara I, Serrao EA (2016) Future climate change is predicted to shift long-term persistence zones in the cold-temperate kelp Laminaria hyperborea. Marine Environmental Research 113: 174-182 Bertocci I, Maggi E, Vaselli S, Benedetti-Cecchi L (2010) Resistance of rocky shore assemblages of algae and invertebrates to changes in intensity and temporal variability of aerial exposure. Marine Ecology Progress Series 400: 75-86 Byrne M, Ho M, Selvakumaraswamy P, Nguyen HD, Dworjanyn SA, Davis AR (2009) Temperature, but not pH, compromises sea urchin fertilization and early development under near-future climate change scenarios. Proceedings of the Royal Society of London B: Biological Sciences 276: 1883-1888 Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution 1: 330-42 Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17: 43-57 Gago J, Range P, Luis O (2001) Growth, reproductive biology and habitat selection of the sea urchin Paracentrotus lividus in the coastal waters of Cascais, Portugal. Echinoderm Research: 269-76 Gardiner NM, Munday PL, Nilsson GE (2010) Counter-gradient variation in respiratory performance of coral reef fishes at elevated temperatures. PLoS ONE 5: e13299 Guisan A, Thuiller W, Zimmermann NE (2017) Habitat suitability and distribution models: with applications in R. Cambridge University Press INE Instituto Nacional de Estatística (2011) Estatísticas da pesca 2010. Lisboa, 104pp INE Instituto Nacional de Estatística (2018) Estatísticas da pesca 2017. Lisboa, 150pp IPCC Intergovernmental Panel on Climate Change (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessement Report of the Intergovernmental Panel on Climate Change. Geneve, 151pp Martinez B, Arenas F, Trilla A, Viejo RM, Carreno F (2015) Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae. Global Change Biology 21: 1422-33 Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye T, Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A (2007) Global climate projections. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, 100 pp Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36: 1058-69 Miller DD, Ota Y, Sumaila UR, Cisneros‐Montemayor AM, Cheung WW (2018) Adaptation strategies to climate change in marine systems. Global Change Biology 24: e1-14 Ouréns R, Naya I, Freire J (2015) Mismatch between biological, exploitation, and governance scales and ineffective management of sea urchin (Paracentrotus lividus) fisheries in Galicia. Marine Policy 51: 13-20 Phillips SJ (2005) A brief tutorial on Maxent. AT&T Research Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259 Schulte PM (2015) The effects of temperature on aerobic metabolism: towards a mechanistic understanding of the responses of ectotherms to a changing environment. Journal of Experimental Biology 218: 1856-66 Shpigel M, McBride SC, Marciano S, Lupatsch I (2004) The effect of photoperiod and temperature on the reproduction of European sea urchin Paracentrotus lividus. Aquaculture 232: 343-55 Keywords: Purple sea urchin, Climate Change, Species distribution model (SDMs), habitat suitability, temperature, Salinity Conference: XX Iberian Symposium on Marine Biology Studies (SIEBM XX) , Braga, Portugal, 9 Sep - 12 Sep, 2019. Presentation Type: Poster Presentation Topic: Global Change, Invasive Species and Conservation Citation: Costa A, Freitas V, Campos J and Arenas F (2019). A species distribution model for Paracentrotus lividus: predicted projections of habitat suitability. Front. Mar. Sci. Conference Abstract: XX Iberian Symposium on Marine Biology Studies (SIEBM XX) . doi: 10.3389/conf.fmars.2019.08.00074 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Apr 2019; Published Online: 27 Sep 2019. * Correspondence: Mx. Ana Filipa Costa, Interdisciplinary Center for Marine and Environmental Research, Abel Salazar Institute of Biomedical Sciences, University of Porto, Matosinhos, Portugal, ana.costaciimar@ciimar.up.pt Mx. Joana Campos, Interdisciplinary Center for Marine and Environmental Research, Abel Salazar Institute of Biomedical Sciences, University of Porto, Matosinhos, Portugal, jcampos@ciimar.up.pt Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ana Filipa Costa Vania Freitas Joana Campos Francisco Arenas Google Ana Filipa Costa Vania Freitas Joana Campos Francisco Arenas Google Scholar Ana Filipa Costa Vania Freitas Joana Campos Francisco Arenas PubMed Ana Filipa Costa Vania Freitas Joana Campos Francisco Arenas Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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