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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).

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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

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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

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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