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  • C H A P T E R T H R E E

    Tipping Points, Thresholds and theKeystone Role of Physiology inMarine Climate Change Research

    Cristian J. Monaco1 and Brian Helmuth

    Contents1. Introduction 124

    1.1. Non-Linearities, tipping points and concepts of scale 128

    2. Weather, Climate and Climate Change from the Viewpoint of a Non-

    Human Organism 130

    3. Physiological Response Curves 137

    3.1. Thermal physiology of marine organisms 139

    4. Indirect Effects of Climate Change: Species Interactions and Tipping

    Points 144

    5. Putting the Pieces Together: Where Do We Go from Here? 146

    Acknowledgements 150

    References 151


    The ongoing and future effects of global climate change on natural and

    human-managed ecosystems have led to a renewed interest in the concept of

    ecological thresholds or tipping points. While generalizations such as pole-

    ward range shifts serve as a useful heuristic framework to understand the

    overall ecological impacts of climate change, sophisticated approaches to

    management require spatially and temporally explicit predictions that move

    beyond these oversimplified models. Most approaches to studying ecological

    thresholds in marine ecosystems tend to focus on populations, or on non-line-

    arities in physical drivers. Here we argue that many of the observed thresholds

    observed at community and ecosystem levels can potentially be explained

    as the product of non-linearities that occur at three scales: (a) the mechanisms

    by which individual organisms interact with their ambient habitat, (b) the

    non-linear relationship between organismal physiological performance and

    Department of Biological Sciences and Environment and Sustainability Program, University of SouthCarolina, Columbia, SC, USA1 Corresponding author: Email: monacocj@email.sc.edu

    Advances in Marine Biology, Volume 60 2011 Elsevier LtdISSN: 0065-2881, DOI: 10.1016/B978-0-12-385529-9.00003-2 All rights reserved.


  • variables such as body temperature and (c) the indirect effects of physiological

    stress on species interactions such as competition and predation. We explore

    examples at each of these scales in detail and explain why a failure to consider

    these non-linearities many of which can be counterintuitive can lead toType II errors (a failure to predict significant ecological responses to climate

    change). Specifically, we examine why ecological thresholds can occur well

    before concomitant thresholds in physical drivers are observed, i.e. how even

    small linear changes in the physical environment can lead to ecological tipping

    points. We advocate for an integrated framework that combines biophysical,

    ecological and physiological methods to generate hypotheses that can be

    tested using experimental manipulation as well as hindcasting and nowcasting

    of observed change, on a spatially and temporally explicit basis.

    1. Introduction

    Anthropogenic climate change is among the most critical threats fac-ing the worlds natural and human-managed ecosystems (Rockstrom et al.,2009). Numerous studies have documented geographic shifts in speciesrange boundaries (Beaumont and Hughes, 2002; Parmesan and Yohe,2003; Zacherl et al., 2003), alterations in phenology (Parmesan, 2006;Mitchell et al., 2008) and episodes of mass mortality (Harley, 2008; Harleyand Paine, 2009) related to climate change. While temperature is one ofthe more obvious drivers of these patterns (Tomanek, 2008; Portner, 2010;Somero, 2010), stressors such as ocean acidification (OA) (Fabry, 2008;Hoegh-Guldberg and Bruno, 2010) have also been shown to have signifi-cant ecological impacts. The biological effects of climate change in turnhave enormous economic and societal implications (Climate ChangeScience Program, CCSP, 2008; Millennium Ecosystem Assessment, 2005).Subsequently, and as highlighted recently by Hoegh-Guldberg and Bruno(2010), there has been an increase in the number of peer-reviewed papersexamining climate change and its consequences to the natural world andto human society (Fig. 3.1).

    While generalizations such as poleward migrations of species rangeboundaries or upward shifts in altitudinal distribution in mountain envir-onments have served as a useful heuristic framework for exploring theecological impacts of climate change, recent evidence suggests that thesegeneralizations may be violated in nature more often than has previouslybeen appreciated. For example, Crimmins et al. (2011) found that despiteincreases in ambient air temperature, the distribution of 64 plant speciesshifted downward in elevation during the last ca. 75 years. This patternwas explainable because the negative physiological impacts of increasedtemperature were overridden by the positive impacts of increases in pre-cipitation, which had also occurred during this time period. Similarly,

    124 Monaco and Helmuth

  • numerous studies have now shown that geographic patterns of physiologi-cal stress do not always follow simple latitudinal gradients, but ratherexhibit mosaic patterns over geographic scales (Helmuth et al., 2002,2006; Holtmeier and Broll, 2005; Finke et al., 2007; Place et al., 2008;

    Figure 3.1 Number of peer-reviewed articles related to climate change and tipping pointsthat have been indexed by the ISI Web http://apps.webofknowledge.com/WOS_

    GeneralSearch_input.do?SID5S2P9GLPhpF6fKNgfIEp&product5WOS&search_mode5GeneralSearch of Science between 1970 and 2010. (A) Annual number of papers indexed

    with the topic climate change or global warming, restricted to all fields related to biology as

    well as those restricted to aquatic biology. As a control for both the increase in journals as well

    as bias due to the database itself, the number of citations for the term Drosophila is also pre-

    sented. Note that while there is a large overall increase in publications after 1990 (as indicated

    by the control), the increase in the number of these publications appears to be slowing; in

    contrast, the increase in the rate of publication of climate-related papers began much later,

    and is increasing rapidly, as shown by Hoegh-Guldberg and Bruno (2010). (B) General biol-

    ogy and aquatic biology articles that have utilized the concepts tipping points, ecological

    thresholds or stable states independently, or in combination with climate change or global

    warming in papers restricted to the ecological and environmental literature.

    125Tipping Points and the Keystone Role of Physiology

  • Mislan et al., 2009; Pearson et al., 2009), suggesting that our expectationof what to expect in coming decades may not always be poleward shiftsin species range boundaries, but at least in some cases may be localizedextinctions even well within range boundaries. Moreover, recent studieshave documented geographic variability in physiological tolerance(Pearson et al., 2009) and have experimentally shown evidence of localadaptation (Kuo and Sanford, 2009). All of these studies suggest that adetailed understanding of the mechanisms underlying the complex inter-action between changes in the physical environment and organismal andecological responses is vital if we are to predict future patterns of biodi-versity, distribution and abundance, and that simple generalizations arenot always effective as working null hypotheses.

    Moreover, increasingly sophisticated adaptation planning by a widearray of decision makers demands quantitative predictions of ecologicalimpacts of climate change, often at fine spatial and temporal resolutions,along with associated estimates of uncertainty. For example, the emplace-ment of protected areas (Hoffman, 2003), predictions of fishery, cropand livestock productivity (CCSP, 2008) and estimates of the spread ofdisease and invasive species (Chown and Gaston, 2008; Kearney et al.,2008; US EPA, 2008) all demand quantitative, spatially and temporallyexplicit predictions of how climate change is likely to impact organismsand ecosystems (Ludwig et al., 2001). Predictions that are based on simplegeneralizations are highly unlikely to be able to capture the spatial andtemporal complexity of the real world in order to effectively plan andprepare for ongoing and future climate change impacts.

    Furthermore, the publics perception of climate change, and their trustin the scientific communitys understanding of climate change, is signifi-cantly affected by the frequency by which scientific predictions are borneout. Simple generalizations, while logically tractable, are not always theexpected outcome, as shown by Crimmins et al. (2011). Nevertheless,when patterns contrary to sweeping generalizations are reported, they areoften viewed by climate skeptics as evidence of the falsehood of climatechange or, worse, of the duplicity of scientists. In a recent opinion piece,Parmesan et al. (2011) stated that any attempts to attribute individualresponses to climate change were ill advised, suggesting that becausesuch cause and effect linkages were too complex, the scientific communityshould instead focus on overarching trends. In stark contrast, however, datacollected by communication experts have shown that when scientists makeexplicit predictions that can then be tested in a transparent manner, even atthe risk of failure, this builds trust between the public and the scientificcommunity (Goodwin and Dahlstrom, 2011). Thus, explicit, testable pre-dictions made using mechanistic approaches (Helmuth et al., 2005) notonly provide useful information to decision makers, and potentially eluci-date important principles by which weather and climate affect organisms

    126 Monaco and Helmuth

  • and ecosystems, but may also play an important role in