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 ecosystems.

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 significantly, 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 zooplankton 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 of 13 layers in the upper 100 m. To Eastern Bering Sea and also the contrasting life cycle strategies uti- calculate physical advection and life cycle progression of individual lized by coexisting C. finmarchicus, C. glacialis, and C. hyperboreus in copepods in the ArcIBM, ocean temperature, meridional (or north- Disko Bay, West Greenland. ward) and zonal (or eastward) current velocities, and phytoplankton The present study had two overarching questions: (1) how does biomass concentration are daily- and depth-averaged from the ocean C. glacialis biogeographic distribution respond to multiple environ- surface to 60 m or to the bottom, whichever is shallower. Here, the mental drivers (i.e., sea ice, ocean temperature, and phytoplankton phytoplankton (food or prey) in the water column included both prey) that vary on interannual to interdecadal time scales? and (2) released sea ice algae and pelagic phytoplankton, and is a FENG ET AL. | 3 combination of flagellates and diatoms (two basic phytoplankton algal food (Plourde, Campbell, Ashjian, & Stockwell, 2005) that pre- components in the biogeochemical model of BIOMAS; see Zhang cedes the pelagic phytoplankton bloom (Figure 1). et al., 2010). In the simulations, all C. glacialis individuals were advected by The ArcIBM simulates both Lagrangian tracking (i.e., ocean-cur- upper 60 m depth-averaged ocean currents while developing rent-induced drifting) and life cycle development of individual cope- through their life stages until Day-360 (Figure 1). Note that the pods. Single individual copepods are released uniformly across the model does not simulate abundances but rather follows single indi- domain at the start of each year of the simulation; that copepod is viduals to understand how environmental conditions interact with then advected and develops according to the temperature and food individual copepod development. A successful C. glacialis diapauser condition encountered along its track. The Lagrangian tracking mod- was defined as an individual that developed beyond the mid-way ule solves the advection equation using a fourth-order Runge–Kutta point of the first diapausing stage C4 before the end of its growth (RK-4) scheme. Temperature- and food-dependent copepod aging season (determined by food availability). This assumption allows and subsequent life stage progression are represented by a combina- C. glacialis to accumulate sufficient lipids for overwintering survival, tion of Belehr adek (1935) and Ivlev (1955) functions with coeffi- consistent with our previous modeling studies (Feng et al., 2016; Ji cients fitted to the laboratory data of Campbell, Wagner, Teegarden, et al., 2012). During the post-processing of model results, the year- Boudreau, and Durbin (2001) based on the intergeneric equipropor- day when an individual becomes a diapauser (or reaches the age of tional rule of Calanus copepod development (Hart, 1990; see Ji et al., mid-way through C4) was identified and its corresponding geo- 2012 for details). The Belehr adek function describes that under food graphic location was designated as a putative diapause entry site. To saturation the development time within each life stage exponentially depict the annual northern boundary of C. glacialis diapausers, the decreases with rising temperature (McLaren, 1963; Corkett, McLa- northernmost latitude of the diapause entry sites within each degree ren, & Sevigny, 1986; Campbell et al., 2001). The Ivlev function longitudinal range was determined for each calendar year (see blue serves as a nondimensional coefficient to prolong the development curves in Fig. S3). To quantify the boundary shifts, the latitudinal dif- time when food becomes limited. In the ArcIBM, the upper 60 m ferences were calculated by subtracting the 5-year mean northern- depth-averaged BIOMAS variables (current velocity, ocean tempera- most latitudes of diapause entry in 1980–1984 from the mean ture, and phytoplankton concentration) are temporally and spatially northernmost latitudes in 2010–2014. The 5-year means were interpolated at each individual copepod’s present time and location applied to smooth fluctuations associated with annually resolved to update its new location (advection), age (maturation), and life northern boundaries. stage (molting). Two C. glacialis life history traits were derived from model runs. The critical development time (CDT) is the duration required for C. glacialis to develop from the first feeding stage N3 to mid-way C4 2.2 | Numerical experiments during which time sufficient phytoplankton food is required, while The ArcIBM simulations were conducted annually for 35 calendar the growth season length (GSL) quantifies the total duration of food years from 1980 to 2014. All simulations were run from Day 1 to supply (Figure 1). In order for a modeled individual to successfully 360 of each year. In each simulation, a total of 17,134 C. glacialis develop to diapause, the growth season length it experiences must individuals starting from Stage-1 (i.e., egg) were uniformly released in exceed its critical development time. space north of 65°N at the onset of the ocean phytoplankton bloom, For individuals released at identical geographic locations in each identified as the year-day when cumulative biomass concentration year between 1980 and 2014, their success in developing to dia- exceeds 2.5% of the annual total biomass (see Fig. S1). The bloom pause depended on the relative length of the growth season vs. the onset timing was spatially variable but in general occurred earlier in development time. For each release location, the number of years lower latitudes and less ice-covered regions than further to the that C. glacialis individuals successfully developed to diapause was north (Fig. S1). In areas with ice cover, this timing was largely syn- counted. For some locations, C. glacialis developed to diapause in all chronized with ice retreat timing (Fig. S2). This implementation rep- or most of the years, while at others C. glacialis never or rarely resented the primary phenological strategy (or a timing match developed to diapause. A transition zone was defined as the release scenario) for C. glacialis in which adult reproduction and subsequent locations where C. glacialis individuals can develop to diapause in development of the progeny occur concurrently with the availability 20%–80% of the 35 modeled years (i.e., 7–28 years). This zone can of released ice algae and/or pelagic phytoplankton food (income be regarded as a geographic envelope where individuals are most breeding strategy; Varpe, Jørgensen, Tarling, & Fiksen, 2007). The sensitive to the interannual variations in developmental conditions reliance on the food supply period is supported by the observation so that interannual fluctuations and inter-decadal changes of that diapausing C. glacialis can quickly respond to light and food C. glacialis biogeography are likely to occur. Transition-zone individu- emergence by significantly increasing their respiration rates and car- als (n = 2933) accounted for about 17.1% of the total individuals in bon demands (Morata & Søreide, 2013). Note that released sea ice each run. Although perturbing the threshold years would result in algae constitute most of the phytoplankton biomass at the bloom slightly expanded or compressed transition zones, the center of dis- onset in the ice-covered zone, implying that reproduction of tribution remained almost the same (Fig. S4). In the transition zone, C. glacialis underneath the ice cover is dependent on released ice the number of successful individuals (i.e., diapausers) in each 4 | FENG ET AL.

Copepod growth season

Snow Sea Ice

Ice algae N1 – N6 Eggs N3 Lipids C1 C2 C3 C4 Diapause Diapause entry Adult exit female

C4/C5 Critical development time

FIGURE 1 Annual life cycle of the copepod C. glacialis in the Arctic/subarctic environment. Idealized single-peaked phytoplankton concentration and temperature annual time series are shown in green and red curves to exemplify environmental conditions that control C. glacialis life cycle development. In the springtime, C4/C5 individuals become active and ascend from the deep water. Individuals that overwintered in stage C5 mature to adult, while those that overwintered in stage C4 likely require another year to mature. Growth season length is likely the main determinant for which populations have 1- or 2-year life cycle. Populations in relatively lower latitudes tend to have a 1-year life cycle, while a 2-year life cycle is more likely in higher latitudes. Adult females produce eggs by utilizing released sea ice algae (i.e., income breeding). During spring/summer periods, the offspring feed on phytoplankton prey (food) in the upper euphotic zone (illustrated by the green shade) and develop through egg, naupliar (N1 to N6), and copepodid (C1 to C4) stages, and accumulate body lipids (denoted by red- shaded ellipsoids). After maturing to stage C4 and gaining sufficient lipids, C. glacialis descends to the deep water and enters dormancy (diapause) to survive the long starvation period with reduced metabolic rates. The copepod growth season is the time period in which phytoplankton prey is sufficient for C. glacialis reproduction, development, and growth. The critical development time is defined as the period required for C. glacialis to develop from the first feeding stage N3 to mid-way into the first diapausing stage C4. Some concepts were adapted from Kosobokova (1999), Søreide et al. (2010), and Varpe (2012)

calendar year was normalized by the total number of individuals ana- linear trends. Nonparametric Spearman’s rank correlation coefficients lyzed to obtain an annual success rate. A higher annual success rate (r) between annual success rates and annual mean life history traits indicated more transition-zone regions were suitable for C. glacialis were calculated. All statistical analyses were conducted using Matlab development in that year. The annual mean (Æ1 SD) growth season Statistics Toolbox (Mathworks, Natick, MA, USA). The statistical length, critical development time, temperature, and food of the tran- results were deemed significant when p values were < 0.01. sition-zone individuals also were calculated. To further explore regional similarities and differences, the circumpolar shelf/slope seas were partitioned into nine geographic sec- 3 | RESULTS tors (Figure 2b): Beaufort (125°–160°W), Chukchi (160°–180°W), Siberian (180°W–150°E), Laptev (150°–105°E), Kara (105°–55°E), The modeled spatial distributions of successful C. glacialis diapausers, Barents (55°–15°E), Greenland-Iceland-Norwegian (GIN, 15°E– as revealed by the 35 model runs for years of 1980 to 2014, were 45°W), Baffin (45°–80°W), and Canadian Arctic Archipelago (CAA, consistent with the established understanding of this endemic spe- 80°–125°W) sectors. To a large degree, the longitude-based parti- cies with high abundances in the Arctic shelf/slope seas and subarc- tioning followed the geographic convention of the Arctic seas (Inter- tic seas, but much lower abundances (and less likely to sustain viable national Hydrographic Organization, 1953). populations) in the central Arctic basins (e.g., Ashjian, Campbell, One-way analysis of variance (ANOVA) tests were performed to Welch, Butler, & Van Keuren, 2003; Conover, 1988; Hopcroft, examine whether the 5-year mean northern boundary shifts from Clarke, Nelson, & Raskoff, 2005; Kosobokova & Hirche, 2009; Rut- 1980–1985 to 2010–2014 were significant. Linear regression models zen, 2017). In general, the shelf/slope spawned C. glacialis individuals of the annual time series were fitted against calendar years to obtain could succeed in developing to diapause in all or most of the years FENG ET AL. | 5

FIGURE 2 Number of years that C. glacialis individuals develop to diapause before the end of the growth season: (a) in the Pan-Arctic and (b) in the transition zone, and (c) 5-year mean northern boundaries of diapausers in the years of 1980–1984, 1990–1994, 2000–2004, and 2010–2014. In Panel 2a, the 1000 m isobaths (white lines) are illustrated to separate shelf/slope seas from central basins. All C. glacialis individuals are positioned at their initial release locations. In Panel 2b, the transition zone is defined as the release location where C. glacialis individuals can successfully develop to diapause in 20%–80% of the 35 modeled years (7–28 years). The names and longitudinal ranges of all nine geographic sectors are shown. In Panel 2c, the northern boundaries of each 5-year period are the mean annual northernmost latitudes of all simulated diapausers in that period

(red dots in Figure 2a), whereas high Arctic basin spawned individu- Despite the substantial interannual variability, the success rates of als never or rarely succeeded (blue dots in Figure 2a). The transition C. glacialis diapausers in nine circumpolar geographic sectors all zone, in which individuals succeed in 7–28 of the modeled years, exhibited upward trends (Fig. S5). All sectors, except Barents and included the northern parts of the shelf/slope seas along Beaufort, GIN, had statistically significant linear trends in the annual success Chukchi, East Siberian, Laptev, and Kara Seas, as well as mid- and rates (Table S1). eastern Barents Sea, Fram Strait, East Greenland Shelf, Baffin Bay, The changing biogeographic distribution of C. glacialis can be and southern CAA (Figure 2b). Overall, the northern boundaries of linked to contemporaneous changes in the Arctic physical and biologic the successful C. glacialis diapausers expanded poleward from early environments (sea ice, upper ocean temperature, and phytoplankton 1980s to recent years with large regional variability (Figure 2c). The prey; Table 2). The annual success rates of transition-zone individuals poleward shifts between 1980–1984 and 2010–2014 were substan- negatively correlated with modeled September-mean sea ice extents tial, indicating that marked environmental changes significantly (r = À.94, p < .01; Figure 3a) as well as March-mean sea ice extents shifted the biogeography of this critical species (Table 1). Large lati- (r = À.74, p < .01; not shown), implying that environmental condi- tudinal shifts of more than 2.5° were found in the CAA, Baffin, and tions in years with less ice cover were more favorable for C. glacialis Chukchi sectors, whereas no shift occurred in the GIN sector development than those with more ice cover. The highest success (Table 1). One-way analysis of variance showed that the boundary rate (85.3%) occurred in 2012, coincident with the year of the lowest shifts were significant in all sectors except the GIN sector (Table 1). sea ice extent among the 35-year study period. Transition-zone The annual success rates of C. glacialis individuals in the transi- annual mean temperature was negatively correlated with September- tion zone increased significantly over the 35 years with a linear mean sea ice extent (r = À.92, p < .01), as was food availability trend of +1.9%/year (R2 = 0.87, p < .01; Figure 3a and Table S1). (r = À.96, p < .01). Annual mean temperature and food for the transi- This suggested that modeled C. glacialis habitats expanded during tion-zone individuals also showed significant upward trends although the past 35 years, as more and more C. glacialis individuals in the temperature appeared to have much higher variability than food (Fig- transition zone successfully developed to diapause over the modeled ure 3c). All nine sectors had significant upward trends in mean tem- period. Before 1990, only 20%–30% of the transition-zone individu- perature, while all sectors except the Barents and GIN had significant als succeeded while success rates were always >50% after 2000. upward trends in mean food (Table S1 and Fig. S7).

TABLE 1 Latitudinal shifts of C. glacialis diapauser northern boundaries in all and each of the nine sectors, represented by the changes of mean northernmost latitudes in a recent 5-year period of 2010–2014 compared to an earlier period of 1980–1984. Positive values indicate northward (or poleward) boundary shifts, and vice versa. Bold numbers denote statistically significant latitudinal shifts using a one-way ANOVA test (p < .01)

Sector All Beaufort Chukchi Siberian Laptev Kara Barents GIN Baffin CAA Shift (°) 1.68 0.96 2.53 2.12 1.50 2.01 1.15 À0.02 3.55 3.13 6 | FENG ET AL.

FIGURE 3 Annual success rates (ASR; 3a), mean growth season length (GSL; 3b), critical development time (CDT; 3b), temperature (T; 3c), and food (F; 3c) of transition-zone C. glacialis individuals for the modeled years, September mean sea ice extent (3a), and fitted linear trends for all parameters (black dotted lines). Error bars show standard deviations

TABLE 2 Spearman’s rank correlation coefficients between each although the slopes varied among sectors (Fig. S6 and Table S1). In two of the following metrics: modeled September mean sea ice the Barents and GIN sectors that had only weak (not statistically sig- extent (Ice), temperature (T), food (F), growth season length (GSL), nificant) trends in annual success rates, significant trends were critical development time (CDT), and annual success rate (ASR) in detected in critical development time but not in growth season length the circumpolar transition zone. Bold numbers denote statistically significant correlations (p < .01) (Table S1). The annual success rates in all nine sectors were positively correlated with mean growth season length, but negatively correlated Ice T F GSL CDT ASR with mean critical development time (Table S2). Ice 1 À0.92 À0.96 À0.90 0.94 À0.94 T 1 0.93 0.85 À0.95 0.92 F 1 0.93 À0.96 0.97 4 | DISCUSSION GSL 1 À0.90 0.97

CDT 1 À0.95 Based on multi-decadal C. glacialis life cycle development simula- ASR 1 tions, marked poleward range shifts of this critical copepod species in the Arctic Ocean from 1980 to 2014 were demonstrated (Table 1 Overall, the poleward expansion of C. glacialis northern bound- and Figure 2c). A circumpolar transition zone in which C. glacialis aries and the upward trends in its success rate could be directly individuals were susceptible to interannual environmental changes explained by the concurrent changes in both growth season length was identified (Figure 2b). A significant upward trend in the number and critical development time. Temperature and food availability of individuals developing to diapause was detected in this transition together defined the critical life history periods (growth season length zone over the 35-year study period (Figure 3a). Also, during the and critical development time) that drove whether C. glacialis could copepod growth season, both upper ocean temperatures and phyto- achieve the diapausing stage. During the 35-year modeled period, the plankton concentrations trended higher in the transition zone (Fig- annual mean growth season lengths of all transition-zone individuals ure 3c), which further led to lengthening growth seasons and increased with a linear trend of +0.61 day/year (R2 = 0.78, p < .01), shortening critical development times for the copepods (Figure 3b) whereas mean critical development times decreased with a linear that in turn were correlated with sea ice extent (Table 2). trend of À0.59 day/year (R2 = 0.89, p < .01; Figure 3b and Table S1). Moreover, the annual success rates were positively corre- 4.1 | Copepod boundary shifts in response to lated with mean growth season lengths (r = .97, p < .01), but nega- environmental changes tively correlated with mean critical development times (r = À.95, p < .01). Regionally, the seven sectors that showed significant It is well known that the Arctic sea ice has been rapidly diminishing upward trends in annual success rates all had significant linear trends (Perovich & Richter-Menge, 2009; Stroeve et al., 2012), especially in in both mean growth season length and critical development time the marginal ice zone (Strong & Rigor, 2013). Many physical, FENG ET AL. | 7 biogeochemical, and biologic components have been concurrently transition-zone individuals had a significant upward trend (Figure 3c). changing (see review papers of Barber et al., 2015 and Meier et al., The increase in temperature reduces stage durations and shortens 2015). Modeled sea ice extent, ocean temperature, and phytoplank- critical development times, as indicated by the significant downward ton biomass were consistent with observational studies documenting trend of À0.59 days per year in that parameter (Figure 3b). On aver- temporal changes in those attributes. Through analysis of ocean tem- age, mean critical development time decreased by about 3 weeks perature profiles and satellite-retrieved sea surface temperatures in over the 35-year period. Observational evidence from Arctic/subarc- the summer months of July–September, Steele, Ermold, and Zhang tic seas that revealed responses of copepod cohort development (2008) found that the general long-term warming trends on the Arc- indirectly supported our model results. In the White Sea (Russia), tic shelf seas started in 1965 but were especially pronounced since analyses of 50-year zooplankton survey data revealed that in warmer 1995. The increasing food availability and lengthening growth sea- years, the biomass of C. glacialis populations increased earlier in sons corroborate the findings of Arrigo et al. (2008), Arrigo and van spring and consisted mostly of younger stages, while the biomass of Dijken (2015), and Kahru, Lee, Mitchell, and Nevison (2017) based fall populations declined earlier due to the reductions of older stages on satellite-retrieved chlorophyll and sea ice data, which suggested (Persson, Stige, Stenseth, Usov, & Martynova, 2012), probably that the significant increase in annual total net primary production because warmer temperatures accelerated growth and development over much of the Arctic Ocean was mainly due to increases in both with the resultant early descent into diapause. In the Chukchi Sea, open water area and the length of phytoplankton growing season. significant increases in the biomass and abundance of zooplankton Poleward expansions of the Arctic endemic copepod C. glacialis (particularly C. glacialis) were found in recent warm years as com- in response to changing sea ice, upper ocean temperature, and pro- pared to the earlier cold years (Ershova et al., 2015). The strong pos- duction regimes since 1980s were revealed by our multi-decadal itive correlation between mean developmental stage of C. glacialis modeling to occur in all shelf/slope seas except the GIN seas and sea surface temperature from that study agreed with our model- (Table 1 and Figure 2c). Climate-induced biogeographic shifts of phy- ing results, both suggesting that warmer temperatures sped up toplankton and zooplankton have been documented in the North C. glacialis cohort developmental process. It should be noted that Atlantic based on the long-term Continuous Plankton Recorder Chukchi Sea zooplankton surveys inherently may have included both (CPR) surveys (e.g., Barton, Irwin, Finkel, & Stock, 2016; Beaugrand C. glacialis and C. marshallae (a congener that is difficult to distin- & Reid, 2003; Beaugrand, Reid, Ibanez,~ Lindley, & Edwards, 2002; guish taxonomically) populations advected from the Bering Sea, Villarino et al., 2015) and IBMs (e.g., Speirs et al., 2006; Wilson, whereas the model experiments only considered the C. glacialis indi- Banas, Heath, & Speirs, 2016). However, because survey data in the viduals spawned within the Chukchi Sea (i.e., local populations). Arctic Ocean are rather sparse and seasonally and geographically The effects of food on C. glacialis developmental success are biased with most field observations done during summer-early fall twofold, depending both on food quantity and the duration of food and in the marginal seas and fjords, pan-Arctic studies of species dis- supply. In the ArcIBM, the coefficient of the Ivlev function that pro- tribution and their responses to the fast-changing Arctic climate and longs stage durations under food-limited conditions was derived marine environments are rare. This modeling study highlights the from a fit to the experimental data of Campbell et al. (2001). Similar pressing needs of establishing long-term observational programs in effects were observed in other laboratory experiments (Cook, Bun- the critical regions, such as the identified transition zone for ker, Hay, Hirst, & Speirs, 2007; Daase, Søreide, & Martynova, 2011; C. glacialis (Figure 2b), where major ecosystem shifts are likely occur- Jung-Madsen & Nielsen, 2015). The annual mean food concentration ring. Such ecosystem changes ultimately may have impacts for of the transition-zone individuals in 1980–2014 varied between 0.23 indigenous people who rely heavily on ecosystem services. and 0.72 mmol-N/m3. This range is an order of magnitude lower than the saturation concentration (2.3 mmol-N/m3) that derived from the Ivlev function and Campbell et al. (2001) experiment. This 4.2 | Effects of environmental drivers: temperature further suggests that, in the transition zone, food availability con- and food strained C. glacialis development over a substantial period of its The modeling results indicate that increasing temperature and food growth season. The increase in mean food concentration was mod- availability together may cause northward expansion of C. glacialis est, with a linear trend of +0.012 mmol-N/m3 per year (Figure 3c). diapausers (Figure 2c) and greater success rates in life cycle develop- The annual duration of food supply, which is equivalent to the total ment (Figure 3a). However, these two factors strongly correlate and growth season length, appeared to be more important than the interact with each other (Table 2 and Table S2) and their relation- mean food concentration to the developmental success of those ships with C. glacialis development times are highly nonlinear. It is transition-zone individuals. A significant trend of +0.61 days per year difficult to disentangle the relative importance of temperature vs. in the growth season length was detected (Figure 3b) so that the food in driving C. glacialis distributional changes. average growth season was lengthened by about 3 weeks over a The direct physiological effect of temperature on Calanus cope- total of 35 years. In the southeastern Bering Sea, lower abundance pod development has been modeled in the ArcIBM and permits and biomass of Calanus spp. were found in warm and low-ice years exploration of changes in this interaction. Despite large interannual with delayed spring blooms and higher predations by larval and juve- and regional variability, the mean water temperature experienced by nile fish (Eisner, Napp, Mier, Pinchuk, & Andrews, 2014; Hunt et al., 8 | FENG ET AL.

2002), suggesting that timing and duration of food supply and also transport of Pacific water through the Bering Strait (Ershova et al., predation pressure, rather than physiologic effect of temperature, 2015; Woodgate, Stafford, & Prahl, 2015). Regarding changes in control copepod population dynamics in that region. Another model- zooplankton prey quantity and quality, climate change may drive ing study similarly concluded that the viability of Calanus copepods shifts in the phytoplankton abundance and species composition, species near the high-latitude boundaries is most sensitive to the which likely influence fecundity, feeding, development, and survival duration of food availability (Banas et al., 2016) although the struc- of zooplankton, consequently shaping the population dynamics tures and physiologic mechanisms of that model are very different (Cook et al., 2007; Daase et al., 2011; Leu, Wiktor, Søreide, Berge, from ours. & Falk-Petersen, 2010). Additionally, in the shallow inflow shelf seas By dividing the circumpolar transition zone into nine geographic (such as northern Bering/Chukchi Seas) with characteristically high sectors, we showed that all sectors except the Barents and GIN benthic biomass and production, enhanced grazing pressure from exhibited significant upward trends in C. glacialis annual success zooplankton in warmer waters with less sea ice may weaken rates, associated with upward trends in its growth season length and pelagic–benthic coupling and induce a regime shift from benthic- downward trends in its critical development time (Table S1; Figs S5 dominated to pelagic-dominated ecosystems (Grebmeier, 2012; and S6). In those seven sectors, both mean temperature and food Grebmeier et al., 2006). Finally, lightscape changes likely lead to an showed significant upward trends that correlated with the copepod intensified top-down control through vision-based fish foraging as a life history traits. In the Barents and GIN sectors, the annual mean quadrupling of its visual search range following the loss of sea ice growth season length and food concentration were generally stable over last 3–4 decades would increase its clearance rate by 16 times throughout the years without significant trends (Figs S6f–g and S7f– (Langbehn & Varpe, 2017). g). This suggests that temperature was the predominant driver that induced the interannual fluctuations and weakly upward trends in 4.4 | Model caveats and future research directions C. glacialis developmental success in those two sectors (Fig. S5f–g). The results agree with the findings of Kvile et al. (2014) from the Individual-based models have considerable skill in studies of zoo- Norwegian and southwest Barents Seas, which found positive asso- plankton biogeography and its responses to environmental drivers ciations between seasonal mean temperature and C. finmarchicus (a despite huge gaps in observational data. This study gained new congener to C. glacialis) nauplii and copepodite abundances in the insights into C. glacialis biogeographic distribution shifts and relevant spring but negative associations in the summer, and suggested that environmental drivers and life history traits over multiple decades. ocean warming triggered an earlier abundance peak of C. finmarchi- However, the present modeling approach has the following limita- cus copepodites. tions that could be addressed in future research. First, C. glacialis prey selection, particularly use of microzooplankton as prey, as well as diel vertical migration (DVM), was not 4.3 | Copepods in changing Arctic marine included. Grazing experiments suggest that C. glacialis is an omnivore ecosystems rather than an herbivore, and its prey preference for phytoplankton Rapid climate change in the Arctic undoubtedly drives species-level vs. microzooplankton depends on life stages, seasons, and also prey variability in development and survivorship that will ultimately result species and sizes (Campbell et al., 2009, 2016). Adding prey prefer- in biogeographic range shifts. Marine plankton may be regarded as ence in the ArcIBM to explore how prey selection might affect cope- sentinels for ecosystem changes given their paramount importance pod distribution and population variability is a future research topic. in the food web (Hays, Richardson, & Robinson, 2005). Numerous In addition, in situ observations suggested that Arctic copepods dis- studies have provided evidence of large-scale biogeographic changes play DVM patterns during summer/autumn (Falk-Petersen et al., in key copepod species in the North Atlantic Ocean, especially the 2008; Rabindranath et al., 2011) and even during winter polar night dominant C. finmarchicus (e.g. Beaugrand et al., 2002, 2009; Rey- (Berge et al., 2009, 2014). A proper parametrization of DVM gondeau & Beaugrand, 2011). Climate change may alter food web requires a more complete understanding of the seasonally varying interactions between copepods and other critical ecological compo- zooplankton behaviors and their relationships with environmental nents of the Arctic system from both bottom-up and top-down per- cues (e.g., light, temperature, and salinity), as well as predator and spectives and ultimately shift the ecosystem as a whole. prey distributions. Overall, the temperature and food fields from the Furthermore, the Arctic Ocean is tightly linked with the North Paci- ice-ocean-biogeochemical model were capable of capturing the sea- fic and North Atlantic Oceans and its ecosystems are thought to be sonality, interannual variations, and decadal trends of the Arctic mar- net biomass beneficiaries through large-scale ocean circulation (i.e., ine environment (Jin et al., 2016; Zhang et al., 2010, 2015). the total amount of biomass transported into the Arctic Ocean far Therefore, the results of the present study that relied on the depth- exceeds that exported; Wassmann et al., 2015). A direct linkage averaged environmental conditions from the typical depth range of between hydrography and zooplankton population and phenological active C. glacialis cohorts should provide reasonable estimates of shifts has been identified in the Chukchi Sea, suggesting that higher C. glacialis diapauser distributions. abundance and biomass of copepods (C. glacialis/marshallae, Metridia Additionally, food web interactions were not considered when pacifica, and Neocalanus spp.) were driven by higher northward modeling life cycle development and biogeographic distribution for FENG ET AL. | 9

C. glacialis (e.g., predation, interspecific competition, cannibalism, and Lastly, with limited knowledge on individual-level species adapta- hybridization). The modeling experiments were only intended to tion to the new normal Arctic (e.g., warming temperature, shrinking explore whether C. glacialis individuals could potentially reach suc- and thinning sea ice, earlier ice retreat and later ice advance), it is diffi- cessful diapausing stage based on a combination of temperature and cult to project future population distribution and persistence of the food conditions along their advective paths. The modeled C. glacialis Arctic copepods. From metabolic theory, Alcaraz, Felipe, Grote, diapausers and corresponding pathways may be interpreted as fun- Arashkevich, and Nikishina (2014) determined that 6°C is an upper damental ecological niches (Hutchinson, 1957) in which this species temperature threshold for C. glacialis and further speculated that can potentially survive and tolerate the harsh Arctic environment. exceeding this bio-energetic limit or tipping point might lead to an Resolving the realized niches in which Calanus populations actually ecosystem-level disruption. Søreide, Leu, Berge, Graeve, and Falk- exist and thrive would require additional equations and parameters Petersen (2010) hypothesized a timing mismatch scenario that, in a to represent species interactions, such as egg reproduction rate and future Arctic shelf regime, a shorter growth season for ice algae (due mortality rate (Banas et al., 2016; Helaouet€ & Beaugrand, 2009), and to much earlier ice retreat) would result in a shorter time difference so was beyond the scope of present work. A prior study hypothe- between the ice algal and phytoplankton bloom peaks and subse- sized that predatory mortality is a controlling factor in C. glacialis dis- quently lead to a timing mismatch for the development of C. glacialis tribution and prevalence near its southern boundary (Kaartvedt, offspring. More studies are needed to better understand the adapta- 2008). This was based on observational evidence from four Norwe- tion of these copepods to the changes in primary production regimes gian fjords with different predator regimes, suggesting that and ambient environment in their entire life cycles (both during the C. glacialis, rather than C. finmarchicus, prevails in the relatively warm growth season and diapause period). This would require long-term and Lurefjorden, due to a lack of planktivorous fish and a low overwinter ideally year-round field observations on the abundance, stage compo- mortality in this fjord (Bagøien, Kaartvedt, Aksnes, & Eiane, 2001; sition, and biomass of the zooplankton communities and application of Kaartvedt, 2008). emerging tools such as acoustics and genetics, to uncover changing/ For simplicity, the whole suite of life history strategies and adap- evolving life strategies of critical species, including C. glacialis. More tation was not included, but rather a phenological strategy was laboratory studies on the body energetics during both active and dor- implemented to optimize C. glacialis adult reproduction and progeny mant periods are needed to better quantify energy gain and loss, and development with upper ocean food availability, and assumed that diapause duration in relation to lipid content and surrounding environ- C. glacialis enters diapause when attaining mid-way through the first ment so that copepod population plasticity and persistency can be diapausing stage C4. In recent years with reduced sea ice cover and better understood, modeled, and predicted. warming temperature, the earlier modeled dates for C. glacialis individuals to reach the diapausing stage (embodied in the shorter criti- ACKNOWLEDGEMENTS cal development times) may imply an earlier diapause entry. Earlier diapause could potentially reduce predation risk (Banas et al., 2016; We thank the following colleagues for insightful discussions in the Varpe et al., 2007), but also may jeopardize overwintering survivor- experimental designs and manuscript writing: Øystein Varpe, Ann ship by depleting lipid reserves before the next spring/summer Tarrant, Neil Banas, Yun Li, Stephen Elliott, Kristina Kvile, and Kate bloom if coupled with warm winter temperatures (Coyle & Gibson, Lowry. The WHOI High-Performance Computing facility provided 2017; Maps et al., 2012). Alternatively, a fitness-maximization strat- computational resources and troubleshooting services for the numer- egy (Ji, 2011) may allow C4 individuals to continue developing to ical experiments. Benjamin Jones assisted with initial model coding C5, and even adulthood, if food is still adequate later in the growth and setup. We greatly appreciate constructive comments from three season so that a 1-year life cycle for C. glacialis may become more anonymous reviewers. This study was funded by National Science prevalent in a warmer Arctic. Additionally, the modeling experiments Foundation Arctic System Science (ARCSS) Program (PLR-1417677, only investigated C. glacialis life cycle development during the first PLR-1417339, and PLR-1416920). growth season for newly spawned egg individuals, but did not take into account the potential effects of diapause (particularly timing, ORCID depth, and duration) on its survival through inactive diapause period and continued development during successive growth season. Cala- Zhixuan Feng http://orcid.org/0000-0002-4774-7027 noid copepods in polar and subpolar regions demonstrate remarkable plasticity in diapause, with little knowledge on the cues that may trigger diapause entry and exit (Baumgartner & Tarrant, 2017), mak- REFERENCES ing it difficult to parameterize diapause-related processes in present Alcaraz, M., Felipe, J., Grote, U., Arashkevich, E., & Nikishina, A. (2014). model. One possibility is to adopt the lipid-accumulation-window Life in a warming ocean: Thermal thresholds and metabolic balance hypothesis by dynamically calculating lipid accumulation and metabo- of arctic zooplankton. Journal of Plankton Research, 36,3–10. lism and further determining diapause timing and duration based on Arctic Climate Impact Assessment. (2004). Impacts of a Warming Climate: Arctic Climate Impact Assessment (p. 144). Cambridge, UK: Cambridge lipid content (Maps, Plourde, & Zakardjian, 2010 and Maps et al., University Press. 2012). 10 | FENG ET AL.

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