Testing Paradigms of Ecosystem Change under Climate Warming in Antarctica

<div><p>Antarctic marine ecosystems have undergone significant changes as a result of human activities in the past and are now responding in varied and often complicated ways to climate change impacts. Recent years have seen the emergence of large-scale mechanistic explanations–or “paradigms of change”–that attempt to synthesize our understanding of past and current changes. In many cases, these paradigms are based on observations that are spatially and temporally patchy. The West Antarctic Peninsula (WAP), one of Earth’s most rapidly changing regions, has been an area of particular research focus. A recently proposed mechanistic explanation for observed changes in the WAP region relates changes in penguin populations to variability in krill biomass and regional warming. While this scheme is attractive for its simplicity and chronology, it may not account for complex spatio-temporal processes that drive ecosystem dynamics in the region. It might also be difficult to apply to other Antarctic regions that are experiencing some, though not all, of the changes documented for the WAP. We use qualitative network models of differing levels of complexity to test paradigms of change for the WAP ecosystem. Importantly, our approach captures the emergent effects of feedback processes in complex ecological networks and provides a means to identify and incorporate uncertain linkages between network elements. Our findings highlight key areas of uncertainty in the drivers of documented trends, and suggest that a greater level of model complexity is needed in devising explanations for ecosystem change in the Southern Ocean. We suggest that our network approach to evaluating a recent and widely cited paradigm of change for the Antarctic region could be broadly applied in hypothesis testing for other regions and research fields.</p> </div

Qualitative network models for the West Antarctic Peninsula (WAP) ecosystem.

<p>Arrows and filled circles represent positive and negative effects, respectively, exerted by one model component on another. Dashed lines represent uncertain interactions. (A) Prey-limitation model as described by Trivelpiece et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093-Trivelpiece1" target="_blank">[11]</a>. (B). Extended network model with additional planktonic groups of particular ecological significance for the region <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093-Moline1" target="_blank">[13]</a>. All model components have a limiting (negative) self-interaction, but for clarity these are not shown. Detailed descriptions of linkages in the extended model are provided in the supporting information.</p

Proportional outcomes (expressed as percentages) from 10³ simulations for the prey-limitation model (Fig. 1A) and the extended model (Fig. 1B) under a scenario of increased regional warming and an increase in the krill fishery.

<p>Results are summarized as the percentage of simulations under which a subset of modeled groups (Adélie and chinstrap penguins, krill and trophic competitors) underwent negative or positive change. Cases of clear model support for an increase or decrease under the perturbation scenario (i.e. >70% of predictions) are asterisked. The full set of outcomes from these analyses is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093.s001" target="_blank">Figs. S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093.s002" target="_blank">S2</a>.</p

Proportional outcomes from 10³ simulations for the spatial WAP model under a scenario of increased regional warming and an increase in the krill fishery.

<p>Blue represents a negative change, grey is no change, and orange is a positive change. Results indicate a higher propensity for negative change in the northern subregion (N) as compared to middle (M) and southern (S) subregions across a range of taxa.</p

Principal components ordination of model predictions (proportion of simulations indicating a negative change for each variable in the spatial model) from sensitivity analyses.

<p>Eigenvectors (with magnitudes >0.2) are represented as a bioplot. 96% of the total variance is captured by PC1 (88%) and PC2 (8%). Results from varying the number of simulations used in qualitative network analyses (case (i) scenarios) are shown in black and grey; the filled grey circle represents the main perturbation scenario examined (concurrent increases in the krill fishery and warming) and default number of simulations (10⁴). Pink squares represent scenarios where the krill fishery and warming were perturbed separately (case (ii) scenarios), while blue squares represent scenarios where models were constrained to meet specific criteria (case (iii) scenarios, as described in the main text).</p

Spatial implementation of the extended network model for the WAP ecosystem (Fig. 1B).

<p>Northern (N), middle (M) and southern (S) subregions correspond approximately with the South Shetland Islands, the Palmer Long-Term Ecological Research Program area and Marguerite Bay, respectively. The model includes south to north transport of larval krill <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093-Thorpe1" target="_blank">[14]</a>, and pelagic foragers as trophic competitors with Adélie and chinstrap penguins. Chinstrap penguins are restricted to the northern subregion <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093-Fraser1" target="_blank">[30]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055093#pone.0055093-Lynch1" target="_blank">[31]</a>. All model components have a limiting (negative) self-interaction, but for clarity these are not shown.</p

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RPA-derived imagery of bird colonies

Compressed folder containing photographs of each colony (n=10) at each sample height (n=4)

R script for analyses

Script used to analyse ground counts, RPA-derived manual counts and RPA-derived semi-automated counts

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Results of mixed models with age as a categorical predictor and log-insulin as a continuous predictor: LS means contrasts (No adult T2DM vs. adult T2DM) and significance at each age averaged over- (Table S1.) or adjusted for the levels of sex (Table S2.) and pairwise comparisons of Least-square means of BMI and 95% CIs at each age in each T2DM status group averaged over levels of sex (Figure S1.) at each age in each T2DM status group and sex group combination (M = males, F-females, 1 = No adult T2DM, 2 = adult T2DM) (Figure S2.) and adjusted for log(insulin). (DOCX 549 kb