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Biogeographic Responses of the Copepod Calanus Glacialis to a Changing Arctic Marine Environment

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Biogeographic Responses of the Copepod Calanus Glacialis to a Changing Arctic Marine Environment Received: 20 April 2017 | Accepted: 22 August 2017 DOI: 10.1111/gcb.13890 PRIMARY RESEARCH ARTICLE Biogeographic responses of the copepod Calanus glacialis to a changing Arctic marine environment Zhixuan Feng1 | Rubao Ji1 | Carin Ashjian1 | Robert Campbell2 | Jinlun Zhang3 1Department of Biology, Woods Hole Oceanographic Institution, Woods Hole, Abstract MA, USA Dramatic changes have occurred in the Arctic Ocean over the past few decades, 2Graduate School of Oceanography, especially in terms of sea ice loss and ocean warming. Those environmental changes University of Rhode Island, Narragansett, RI, USA may modify the planktonic ecosystem with changes from lower to upper trophic 3Applied Physics Laboratory, University of levels. This study aimed to understand how the biogeographic distribution of a cru- Washington, Seattle, WA, USA cial endemic copepod species, Calanus glacialis, may respond to both abiotic (ocean Correspondence temperature) and biotic (phytoplankton prey) drivers. A copepod individual-based Zhixuan Feng, Department of Biology, Woods Hole Oceanographic Institution, model coupled to an ice-ocean-biogeochemical model was utilized to simulate tem- Woods Hole, MA, USA. perature- and food-dependent life cycle development of C. glacialis annually from Email: [email protected] 1980 to 2014. Over the 35-year study period, the northern boundaries of modeled Funding information diapausing C. glacialis expanded poleward and the annual success rates of C. glacialis Division of Polar Programs, Grant/Award Number: PLR-1416920, PLR-1417339, PLR- individuals attaining diapause in a circumpolar transition zone increased substan- 1417677 tially. Those patterns could be explained by a lengthening growth season (during which time food is ample) and shortening critical development time (the period from the first feeding stage N3 to the diapausing stage C4). The biogeographic changes were further linked to large-scale oceanic processes, particularly diminishing sea ice cover, upper ocean warming, and increasing and prolonging food availability, which could have potential consequences to the entire Arctic shelf/slope marine ecosys- tems. KEYWORDS Arctic Ocean, biogeography, climate change, copepod, individual-based model, marine ecosystem, ocean warming, poleward range shift 1 | INTRODUCTION 2011). Total integrated primary production has increased signifi- cantly, probably due to longer open water periods and associated Climate change is thought to drive shifts in marine ecosystems (e.g., longer growing seasons (Arrigo & van Dijken, 2015; Arrigo, van Dij- Grebmeier et al., 2006; Greene & Pershing, 2007; Perry, Low, Ellis, ken, & Pabi, 2008). Earlier ice retreat and later ice advance (Stam- & Reynolds, 2005) as planktonic communities respond and adapt to merjohn, Massom, Rind, & Martinson, 2012) may also influence the climatic variability (e.g., Beaugrand, Luczak, & Edwards, 2009; phytoplankton phenology (timing). In some areas, significant trends Beaugrand & Reid, 2003). In the Arctic Ocean, unprecedented sea toward earlier phytoplankton blooms were detected in 1997–2009 ice loss and ocean warming have occurred over the past few dec- (Kahru, Brotas, Manzano-Sarabia, & Mitchell, 2011). Phytoplankton ades (Arctic Climate Impact Assessment, 2004; Carmack et al., bloom timing was strongly related to ice retreat timing (Ji, Jin, & 2015), with evidence suggesting that the Arctic marine ecosystem is Varpe, 2013), and shifts from a single bloom polar mode to a double changing simultaneously (e.g., Barber et al., 2015; Carmack et al., bloom temperate mode have been observed (Ardyna et al., 2014). 2006; Loeng & Drinkwater, 2007; Meier et al., 2015; Wassmann, These changes to primary production phenology and magnitude | Glob Change Biol. 2017;1–12. wileyonlinelibrary.com/journal/gcb © 2017 John Wiley & Sons Ltd 1 2 | FENG ET AL. should in turn impact the secondary producers that rely on that pro- what are the driving mechanisms for potential range shifts of this duction. critical species at both pan-Arctic and regional scales? Here, the Four copepod species of the genus Calanus (C. finmarchicus, focus was on how these drivers and distributions have changed over C. glacialis, C. hyperboreus, and C. marshallae) are fundamental com- the last four decades, a period of unprecedented rapid environmen- ponents of the Arctic/subarctic pelagic ecosystem, rely on primary tal changes. This study expanded on the prior work of Feng et al. production, and are prey for large predators (Falk-Petersen, May- (2016), which modeled C. glacialis individuals in two contrasting zaud, Kattner, & Sargent, 2009). Understanding the biogeographic years of the 2000s, and demonstrated a poleward expansion of this distributions and range shifts of these copepods is a key prerequisite species’ distribution in a relatively warm and low sea ice year when for predicting ecosystem shifts and community reorganization under temperature and food conditions were favorable for its life cycle the rapid changes of the Arctic system. Calanus glacialis, an endemic development. In the present work, IBM experiments were conducted Arctic species, is widely distributed throughout the Arctic shelf/slope for a total of 35 years annually from 1980 to 2014, thereby allowing seas and subarctic seas (Conover, 1988; Falk-Petersen et al., 2009). a better characterization of the interannual variations and detection The circumpolar distributions of C. glacialis imply that this species of decadal trends in C. glacialis distribution and its drivers. A circum- can adapt to a wide range of environmental conditions and may uti- polar transition zone in which the success of C. glacialis life cycle lize various life history strategies (Banas, Møller, Nielsen, & Eisner, development is sensitive to environmental conditions during the 2016; Daase et al., 2013). Given this plasticity and adaptation to the growth season was identified from the model runs. Two life history Arctic environment, the response of this species to interannual fluc- traits (including critical development time and growth season length) tuations in environmental conditions is still largely unknown. of the transition-zone individuals were derived and analyzed Because long-term time series observations in the extreme Arctic together with temperature and food conditions to reveal the envi- are almost nonexistent, it is difficult to systematically examine the ronmental drivers that ultimately define C. glacialis geographic distri- distributions of C. glacialis throughout the years from a pan-Arctic butions and range shifts of its northern boundaries. perspective. Only recently have climatological mean abundances of C. glacialis and other important copepod species been generated for 2 | MATERIALS AND METHODS the entire Arctic Ocean based on data synthesis of historical zoo- plankton sampling; nevertheless, spatiotemporal scarcity of the data 2.1 | Coupled individual-based and ice-ocean- made it impossible to determine interannual variability and long-term biogeochemical models trends in zooplankton distribution (Rutzen, 2017). Various types of models have been developed and applied to The approach was to offline couple an Arctic Copepod Individual- investigate changing Arctic/subarctic zooplankton dynamics and the Based Model (ArcIBM) with a pan-Arctic ice-ocean-biogeochemical potential drivers of those changes, from fully coupled ice-ocean-bio- model (i.e., Biology/Ice/Ocean Modeling and Assimilation System or geochemical models (Slagstad, Ellingsen, & Wassmann, 2011; Wass- BIOMAS hereafter) to simulate temperature- and food-dependent mann et al., 2015), to data-driven statistical models (Kvile, life cycle development for the endemic copepod species, C. glacialis. Dalpadado, Orlova, Stenseth, & Stige, 2014; Questel, Blanco-Bercial, Here, the two models and their coupling are briefly described. More Hopcroft, & Bucklin, 2016), to trait-based (Banas et al., 2016) and details are found in Zhang et al. (2010, 2014, 2015), Ji et al. (2012), individual-based models (IBMs; Elliott et al., 2016; Coyle & Gibson, and Feng et al. (2016). 2017; Feng, Ji, Campbell, Ashjian, & Zhang, 2016; Ji et al., 2012). In The BIOMAS has four sub-models, one each for ocean circula- the Arctic Ocean, IBMs are particularly useful for overcoming the tion, sea ice, pelagic ecosystem, and sea ice algae. It assimilates tremendous data gaps in zooplankton studies because of their capa- satellite-derived sea ice concentration (Lindsay & Zhang, 2006) and bility to resolve both physical and biological processes at the individ- sea surface temperature (Manda, Hirose, & Yanagi, 2005). The mod- ual level. For example, Ji et al. (2012) examined the roles of copepod el’s generalized orthogonal curvilinear mesh covers the Northern life cycles and environmental seasonality in shaping the biogeo- Hemisphere from 39°N to the geographical North Pole, with the graphic patterns of two endemic and two expatriate Calanus species. “north pole” of the mesh displaced over Alaska (Zhang et al., 2015). Banas et al. (2016) developed a unified modeling framework that The model employs a Z-coordinate in the vertical direction with a resolves relative developmental stage, structure and reserve biomass, maximum of 30 layers of varying thicknesses. The ocean mixed layer and survivorship, and they successfully replicated the varying abun- and euphotic zone are configured with six vertical layers of 5 m each dances of C. glacialis/marshallae between warm and cold years in the in the upper 30 m and a total
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