Elsevier

Ecological Modelling

Volume 430, 15 August 2020, 109088
Ecological Modelling

Can DEB models infer metabolic differences between intertidal and subtidal morphotypes of the Antarctic limpet Nacella concinna (Strebel, 1908)?

https://doi.org/10.1016/j.ecolmodel.2020.109088Get rights and content

Highlights

  • We tested the performance of DEB models applied to an Antarctic case study.

  • DEB models can be easily calibrated from literature and experimental data.

  • Our model could not differenciate morphotypes, suggesting environmental explanations.

  • Temperature and food resources likely underpin the physiological differences between morphotypes.

  • Results encourage further research in data poor regions.

Abstract

Studying the influence of changing environmental conditions on Antarctic marine benthic invertebrates is strongly constrained by limited access to the region, which poses difficulties to performing long-term experimental studies. Ecological modelling has been increasingly used as a potential alternative to assess the impact of such changes on species distribution or physiological performance.

Among ecological models, the Dynamic Energy Budget (DEB) approach represents each individual through four energetic compartments (i.e. reserve, structure, maturation and reproduction) from which energy is allocated in contrasting proportions according to different life stages and to two forcing environmental factors (food resources and temperature).

In this study, the example of an abundant coastal limpet, Nacella concinna (Strebel 1908), was studied. The species is known to have intertidal and subtidal morphotypes, genetically similar but physiologically and morphologically contrasting.

The objectives of this paper are (1) to evaluate the potential of the DEB approach, and assess whether a DEB model can be separately built for the intertidal and subtidal morphotypes, based on a field experiment and data from literature and (2) to analyse whether models are contrasting enough to reflect the known physiological and morphological differences between the morphotypes.

We found only minor differences in temperature-corrected parameter values between both populations, meaning that the observed differences can be only explained by differences in environmental conditions (i.e. DEB considered variables, food resources and temperature, but also other variables not considered by DEB). Despite the known morphological difference between the populations, the difference in shape coefficients was small.

This study shows that even with the amount of data so far available in the literature, DEB models can already be applied to some Southern Ocean case studies, but, more data are required to accurately model the physiological and morphological differences between individuals.

Introduction

Antarctic regions have faced strong environmental change since the twentieth century (recently reviewed in Henley et al. 2019), with a strong warming in some regions, such as in the Western Antarctic Peninsula (King et al. 2003, Vaughan et al. 2003, Meredith and King 2005), leading to important shifts in sea ice regimes and seasonality, including the duration and extent of sea ice cover (Stammerjohn et al. 2012, Turner et al. 2016, Schofield et al. 2017). The increase in the rate of glacier melting has been reported as a cause of important disturbance of the physical (currents, salinities) and biological environment (phytoplankton blooms, communities) (Meredith and King 2005, Schloss et al. 2012, Bers et al. 2013). Such changes have a direct impact on marine communities and particularly in coastal marine areas (both intertidal and subtidal)(Barnes and Peck 2008, Smale and Barnes 2008, Barnes and Souster 2011, Waller et al. 2017, Stenni et al. 2017, Gutt et al. 2018), which are places of complex land-sea interface and ecological processes. The multiple effects of ice retreat and meltwater on nearshore marine habitats have contributed to the expansion of intertidal zones and habitat alteration due to seawater freshening and stratification, shifting near-shore sedimentation, changes in water properties and current dynamics.

However, studying Antarctic marine life is challenging. Not only do the environmental conditions make the region difficult to access and work in, but substantial financial and technical constraints make field sampling and experiments difficult to organise (e.g. cold, ice, duration of daylight; Kaiser et al. 2013, Kennicutt et al. 2014, 2015, Xavier et al. 2016, Gutt et al. 2018). However, conducting physiological studies of Antarctic marine organisms has become urgent as we aim to assess their sensitivity and potential response (resilience, distribution shift or local extinction) to environmental change, a key issue for the conservation of marine life and special protected areas (Kennicutt et al. 2014, 2015, 2019 https://www.ccamlr.org/en/organisation/home-page).

An alternative to completing studies in these environments is the use of modelling approaches. Data needs interpretation to test hypotheses, which involves assumptions, that need to be explicit. Using a modelling approach is therefore a good strategy. Ecological modelling is used to describe species distribution and assess their climate envelopes (Elith et al. 2006, Peterson et al. 2011), study species tolerances to toxicants and to environmental change (Jager et al. 2014, Petter et al. 2014, Baas and Kooijman 2015) and model species energetic performance (Serpa et al. 2013, Thomas et al. 2016). Among these ecological models, the Dynamic Energy Budget (DEB) theory (Kooijman, 2010) has become increasingly popular. DEB parameters have been so far estimated for more than 2,000 animal species and collected in the ‘Add-my-Pet’ (AmP) collection (http://www.bio.vu.nl/thb/deb/deblab/add_my_pet/). It constitutes one of the most powerful approaches to characterize individual metabolic performances (Nisbet et al. 2012, Kearney et al. 2015, Jusup et al. 2017) and can be calibrated for data-poor animals (Mariño et al. 2019). DEB models rely on thermodynamic concepts (Jusup et al. 2017) and study how energy flows are driven within individuals during their entire life cycle (Kooijman 2010). Each individual is divided into four energetic compartments: reserve E, structure V, maturation EH and reproduction ER from which the energy is allocated in contrasting proportions according to the different life stages and two forcing environmental factors (i.e. food resources and temperature).

DEB models can be built with data coming from experiments and/or literature, to quantify age, length, weight of the different life stages and provide information on reproduction, growth and metabolic rates to calibrate the model (van der Meer 2006, Marques et al. 2014).

Application of DEB models to Antarctic species is increasing. They can be easily extracted from the AmP collection, using the software AmPtool. The Matlab command “select_eco(‘ecozone’, {‘MS’})” presently gives a list of 37 species, where MS stands for “Marine, Southern Ocean”. Command “select_eco(‘ecozone’, {‘TS’})” gives another 3 species for the terrestrial Antarctic environment, among which the mite Alaskozetes antarcticus. Among the most common and well studied Southern Ocean benthic invertebrates are the sea star Odontaster validus (Agüera et al. 2015), the bivalve Laternula elliptica (Agüera et al. 2017), the bivalve Adamussium colbecki (Guillaumot 2019a) and the sea urchins Sterechinus neumayeri (Stainthorp and Kooijman 2017) and Abatus cordatus (Arnould-Pétré et al. this issue). DEB models have also been developed for some pelagic species such as the Antarctic krill Euphausia superba, the salp Salpa thompsoni (Jager and Ravagnan 2015, Henschke et al. 2018) and are under development for marine mammals such as the elephant seal Mirounga leonina (Goedegebuure et al. 2018). Antarctic species have a range of notable physiological traits when compared to their temperate counterparts. Among others, they are physiologically adapted to constant cold temperatures (Peck et al. 2009, Morley et al. 2009, 2014), shifting day length also imposes a marked seasonal feeding behaviour (McClintock 1994, Clarke et al. 2008, Halanych and Mahon 2018), and they exhibit slow metabolic and growth rates, explaining their longer lifespans and higher longevities compared to species in other regions (Peck and Brey 1996, Peck 2002).

The limpet Nacella concinna (Strebel, 1908) (Mollusca: Patellogastropoda) is a common and abundant gastropod of shallow marine benthic communities. Distributed all along the Western Antarctic Peninsula (González-Wevar et al. 2011, phylogeny recently reviewed in González-Wevar et al. 2018), it has widely been studied for decades (Shabica 1971, 1976, Walker 1972, Hargens and Shabica 1973, Houlihan and Allan 1982, Peck 1989, Clarke 1989, Cadée 1999, Ansaldo et al. 2007, Fraser et al. 2007, Markowska and Kidawa 2007, Morley et al. 2011, 2014, Suda et al. 2015, Souster et al. 2018). The limpet is found from intertidal rocky shores down to over 100 meters depth (Powell 1951, Walker 1972). It has a 2-5 cm long shell (Fig. 1), that grows only a few millimeters a year with a seasonal pattern. It is sexually mature after four to six years and has a life span of up to 10 years (Shabica 1976, Picken 1980, Brêthes et al. 1994). The limpet mainly feeds on microphytobenthos and microalgae (Shabica 1976, Brêthes et al. 1994). It spawns free-swimming planktonic larvae once a year, when water temperature rises in the austral summer (Shabica 1971, Picken 1980, Picken and Allan 1983). Larvae drift in the water column and metamorphose after more than two months (Stanwell-Smith and Clarke 1998).

N. concinna does not have a homing behaviour (Stanwell-Smith and Clarke 1998, Weihe and Abele 2008, Suda et al. 2015) and intertidal individuals can either migrate to subtidal areas in winter to escape freezing air temperatures that may drop below -20°C (Walker 1972, Branch 1981, Brêthes et al. 1994) or shelter in rock cracks and crevices in the intertidal area. In the latter case, they do not become dormant but have a limited access to microphytobenthos, as recently observed around Adelaide Island (Obermüller et al. 2011).

Two morphotypes of N. concinna have been distinguished, an intertidal and a subtidal type, with the intertidal type having a taller, heavier and thicker shell compared to the subtidal one that is characterized by a lighter and flatter shell (Beaumont and Wei 1991, Hoffman et al. 2010). Initially, Strebel (1908) and Powell (1951) referred to these two morphotypes as the ‘polaris’ (intertidal) and ‘concinna’ types (below 4m depth). From that point, the potential genetic differentiation between the two morphotypes has been investigated, some of the studies concluding an absence of genetic distinction (Wei 1988, Beaumont and Wei 1991, Nolan 1991) while contrarily, de Aranzamendi et al. (2008) reported significant differences based on inter-simple sequence repeat (ISSR) markers. More recently, this last method was questioned (Hoffman et al. 2010) and several studies using different markers and populations (Chwedorzewska et al. 2010, Hoffman et al. 2010, González-Wevar et al. 2011) have concluded an absence of genetic differentiation between the two morphotypes.

Apart from the absence of genetic differences, intertidal and subtidal populations strongly contrast in morphology and physiology, which has been explained by the prevalence of habitat heterogeneity and strong environmental gradients along rocky shore habitats, a common feature also observed in other gastropods (Johannesson 2003, Butlin et al. 2008, Hoffman et al. 2010). For instance, in N. concinna, the higher shell thickness observed in the shallow morphotype was hypothesised to play a role in resistance against crushing pack ice (Shabica 1971, Morley et al. 2010). Intertidal morphotypes are further resistant to air exposure thanks to higher shells, bigger inner volumes relative to their shell circumference, a combination that makes them more efficient than subtidal individuals, able to store more water and oxygen, reducing desiccation risks and delaying the metabolic switch to anaerobic fermentation (Nolan 1991, Weihe and Abele 2008). The subtidal morphotype has also proved to be less resistant to cold than the intertidal population (Waller et al. 2006), due to extra production of mucus and stress proteins in intertidal morphotypes (Clark et al. 2008, Clark and Peck 2009, Obermüller et al. 2011) and due to diverse metabolic processes that contrast between both populations (reviewed in Suda et al. 2015).

The development of ecological models enables precise models to be built, that highlight subtle differences in parameters between ecologically similar or closely related species (Freitas et al. 2010, Holsman et al. 2016, Marn et al. 2019, Lika et al. 2020). The idea of building individual-specific models for understanding of physiological processes is not new (Bevelhimer et al. 1985, DeAngelis et al. 1994) and grew from the development of computational ecology that resulted in the possibility of generating “individual-oriented” models (IOM's) (Hogeweg and Hesper 1990, DeAngelis et al. 1994). The IOM theory relies on the principle that “no two biological organisms are exactly alike, even when they have identical genes”. A group of organisms within a population can have contrasting size or physiological performances according to, for example, food conditions or competition. Modelling each individual, separately, therefore constitutes a powerful approach to enhance the understanding of the entire community (DeAngelis et al. 1994).

In this study, due to the known morphological and physiological differences between the morphotypes, we first separately built independent DEB models for the intertidal and subtidal morphotypes of the limpet N. concinna, based on field experiment and literature data, to assess the potential differences between the models. Secondly, we analyse whether the two model outputs suggest contrasting physiologies between the morphotypes, using a method recently developed in DEB theory, that tries to reduce differences in parameter values that are still consistent with the data (Lika et al. 2020). Using this method -the augmented loss function- we try to merge the information of the two species models into a single one. If DEB parameters of the two species can be merged, it means that the physiological differences between these two species are not strongly different.

These results finally help assess DEB model accuracy giving the amount of data available to build the models in the context of Antarctic case studies and help evaluate which type of information is necessary to gather in order to fill model gaps. Finally, the study evaluates if such models are valuable for studying Southern Ocean organisms in the context of altered environments.

Section snippets

DEB Model description

DEB models are based on an ensemble of rules that allocate energy flows to four main compartments (reserve E, structure V, maturity EH, reproduction ER) according to a set of priorities and the level of complexity (i.e. maturity) gained by the organism through time (Fig. 2, Kooijman 2010). Maturity is treated as information, having mass nor energy. Food is first of all ingested and assimilated (p˙A) and energetically stored into a reserve compartment (E). A fraction of the energy that is

Parameters of DEB models

DEB predictions for the separate intertidal and subtidal models are accurate, with MRE values lower than 0.2 (Table 3). Average MRE value of the AmP collection is close to 0.06. Relative Errors are quite low, with the highest values obtained for length~GSI data (RE= 0.6089 and 0.8702 for intertidal and subtidal models respectively) and time~length relationships, obtained from the sclerochronology measurements, that are highly variable between each measured shell (respectively RE= 0.3645 and

DEB models relevance

DEB models are powerful tools enabling predictions of the individuals energetic scope for survival, growth and reproduction, given the considered environmental conditions (Kooijman 2010, Jusup et al. 2017). These mechanistic approaches have been of interest for several years to the marine Antarctic community (Gutt et al. 2012, Constable et al. 2014, Gutt et al. 2018), and have been increasingly developed during recent years (e.g. Agüera et al. 2015, Agë 2017, Goedegebuure et al. 2018,

Authors’ contribution

C. Guillaumot: Conceptualization, Methodology, Writing

T. Saucède: Supervision, Validation, review & editing

S.A. Morley: Data acquisition, Validation, review & editing

S. Augustine: Methodology, Validation, review & editing

B. Danis: Supervision, Validation, review & editing

S.A.L.M. Kooijman: Data curation, Methodology, Validation, review & editing

Acknowledgements

We are thankful to Philippe Pernet for N. concinna pictures, and Jonathan Flye-Sainte-Marie, Jean-François Cudennec for sclerochronology protocol advices, Eric Dabas, Rémi Laffont for sclerochronology lab trials.

This work was supported by a “Fonds pour la formation à la Recherche dans l'Industrie et l'Agriculture” (FRIA) and “Bourse fondation de la mer” grants to C. Guillaumot. SMOR was supported by Natural Environment Research Council core funding to the British Antarctic Survey.

This is

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