(BUTTON) Back to Top [1]Skip to main content

   U.S. flag

   An official website of the United States government
   (BUTTON) Here's how you know
   Dot gov

   The .gov means it's official.
   Federal government websites often end in .gov or .mil. Before sharing sensitive
   information, make sure you're on a federal government site.
   Https

   The site is secure.
   The https:// ensures that you are connecting to the official website and that any
   information you provide is encrypted and transmitted securely.

   [2]NIH NLM Logo
   [3]Log in (BUTTON) Show account info
   (BUTTON) Close

Account

   Logged in as:
   username
     * [4]Dashboard
     * [5]Publications
     * [6]Account settings
     * [7]Log out

   [8]Access keys [9]NCBI Homepage [10]MyNCBI Homepage [11]Main Content [12]Main
   Navigation

   Preview improvements coming to the PMC website in October 2024. [13]Learn More or
   [14]Try it out now.

   (BUTTON)
   (BUTTON)
   Search PMC Full-Text Archive ____________________ (BUTTON) Search in PMC
     * [15]Advanced Search
     * [16]User Guide

   (BUTTON)

     * [17]Journal List
     * [18]Evol Appl
     * [19]v.5(1); 2012 Jan
     * PMC3353335

Other Formats

     * [20]PDF (491K)

Actions

     * (BUTTON) Cite
     *

   (BUTTON) Collections
   Add to Collections
     * ( ) Create a new collection
     * (*) Add to an existing collection

   Name your collection: ____________________
   Name must be less than characters
   Choose a collection:
   Unable to load your collection due to an error
   [21]Please try again
   (BUTTON) Add (BUTTON) Cancel

        Share

     *
     *
     * (BUTTON)
       Permalink
       https://www.ncbi.nlm (BUTTON) Copy

        RESOURCES

     * (BUTTON) Similar articles
     * (BUTTON) Cited by other articles
     * (BUTTON) Links to NCBI Databases

     * [22]Journal List
     * [23]Evol Appl
     * [24]v.5(1); 2012 Jan
     * PMC3353335

   As a library, NLM provides access to scientific literature. Inclusion in an NLM
   database does not imply endorsement of, or agreement with, the contents by NLM or
   the National Institutes of Health.
   Learn more: [25]PMC Disclaimer | [26]PMC Copyright Notice

   [27]Logo of eva

   Evol Appl. 2012 Jan; 5(1): 2-16.
   Published online 2011 Sep 7. doi: [28]10.1111/j.1752-4571.2011.00202.x
   PMCID: PMC3353335
   PMID: [29]25568025

Evolution of plant-pollinator mutualisms in response to climate change

   [30]R Tucker Gilman, [31]Nicholas S Fabina, [32]Karen C Abbott, and [33]Nicole E
   Rafferty
   [34]Author information [35]Article notes [36]Copyright and License information
   [37]PMC Disclaimer
   Department of Zoology, University of Wisconsin, Madison, Wisconsin
   Robert T. Gilman, National Institute for Mathematical and Biological Synthesis,
   1534 White Avenue, Suite 400 University of Tennessee Knoxville, TN, 3799617 1527
   USA. Tel.: +1(865)974-4892 Fax: +1(865)974-9300 E-mail: [38]gro.soibmin@namligtr
   Received 2011 Jun 2; Accepted 2011 Jul 20.
   [39]Copyright [copyright] 2011 Blackwell Publishing Ltd. This is an open access
   article under the terms of the Creative Commons Attribution Non Commercial
   License, which permits use, distribution and reproduction in any medium, provided
   the original work is properly cited and is not used for commercial purposes.
   [copyright] 2011 Blackwell Publishing Ltd. This is an open access article under
   the terms of the Creative Commons Attribution Non Commercial License, which
   permits use, distribution and reproduction in any medium, provided the original
   work is properly cited and is not used for commercial purposes.

Associated Data

   [40]Supplementary Materials
          [41]eva0005-0002-SD1.doc (2.2M)

          GUID: 366FDD2B-F360-4BC7-A21F-E92AED8831FC

Abstract

   Climate change has the potential to desynchronize the phenologies of
   interdependent species, with potentially catastrophic effects on mutualist
   populations. Phenologies can evolve, but the role of evolution in the response of
   mutualisms to climate change is poorly understood. We developed a model that
   explicitly considers both the evolution and the population dynamics of a
   plant-pollinator mutualism under climate change. How the populations evolve, and
   thus whether the populations and the mutualism persist, depends not only on the
   rate of climate change but also on the densities and phenologies of other species
   in the community. Abundant alternative mutualist partners with broad temporal
   distributions can make a mutualism more robust to climate change, while abundant
   alternative partners with narrow temporal distributions can make a mutualism less
   robust. How community composition and the rate of climate change affect the
   persistence of mutualisms is mediated by two-species Allee thresholds.
   Understanding these thresholds will help researchers to identify those mutualisms
   at highest risk owing to climate change.
   Keywords: climate change, coevolution, natural selection and contemporary
   evolution, species interactions

Introduction

   Climate change is altering the phenologies of species worldwide ([42]Parmesan and
   Yohe 2003; [43]Root et al. 2003; [44]Bertin 2008). For example, the onset of
   flowering in many Northern Hemisphere temperate plants ([45]Sparks et al. 2000;
   [46]Abu-Asab et al. 2001; [47]Post et al. 2001; [48]Fitter and Fitter 2002;
   [49]Primack et al. 2004; [50]Miller-Rushing and Primack 2008) and the first
   emergence dates of some insects ([51]Roy and Sparks 2000; [52]Gordo and Sanz
   2006; [53]Parmesan 2007) have advanced with earlier warming. Because the
   responses of species to climate change may differ in magnitude and even direction
   ([54]Fitter and Fitter 2002; [55]Sherry et al. 2007), phenological mismatches
   between interdependent species are expected ([56]Harrington et al. 1999;
   [57]Stenseth and Mysterud 2002; [58]Durant et al. 2007; [59]Memmott et al. 2007;
   [60]Hegland et al. 2009). Asynchrony between host plants and their associated
   insects has already been observed in some systems ([61]Visser and Holleman 2001;
   [62]Doi et al. 2008), to the apparent detriment of food-limited herbivores
   ([63]Visser and Holleman 2001) and pollen-limited plants ([64]Schemske et al.
   1978; [65]Kudo et al. 2004). [66]Memmott et al. (2007) argued that such
   asynchrony may become sufficiently severe to cause local extinctions of some
   mutualist populations.

   In many species, phenological events are triggered by environmental cues that
   have historically predicted optimal conditions for ensuing life-history stages
   ([67]Brewer and Platt 1994; [68]Schauber et al. 2002; [69]Harper and Peckarsky
   2006). For example, many plants use photoperiod as a flowering cue because it has
   historically predicted optimal conditions for reproduction ([70]del Pozo et al.
   2000; [71]Keller and Korner 2003; [72]Venn and Morgan 2007). Climate change can
   decouple cues from the conditions that they have historically predicted
   ([73]Visser et al. 1998; [74]Buse et al. 1999; [75]Both and Visser 2001;
   [76]Visser and Holleman 2001; [77]Lawrence and Soame 2004), creating strong
   selection on populations to use different cues or to use the same cues
   differently ([78]Franke et al. 2006; [79]Moller et al. 2008; [80]Munguia-Rosas et
   al. 2011). In many species, there is substantial genetic variability in the use
   of phenological cues ([81]Blanckenhorn and Fairbairn 1995; [82]Vaughton and
   Ramsey 2001; [83]Kelly et al. 2008; [84]Samis et al. 2008), and such species may
   have the potential to evolve rapidly in response to changes in the predictive
   value of their environments ([85]Burgess et al. 2007; [86]Van Dijk and Hautekeete
   2007; [87]Jensen et al. 2008). There is mixed empirical evidence that plant
   phenology can indeed evolve in response to climate change ([88]Kochmer and Handel
   1986; [89]Etterson and Shaw 2001; [90]Burgess et al. 2007; [91]Franks et al.
   2007), and there is some evidence that insects can evolve in response to changes
   in host-plant phenology ([92]van Asch et al. 2007).

   Whether a plant-pollinator mutualism can survive climate change will likely
   depend on how the species' phenologies evolve ([93]Bronstein et al. 2004;
   [94]Elzinga et al. 2007), but the conditions that promote or oppose the
   coevolution of phenologies in complex communities with changing environments have
   received little study ([95]Lavergne et al. 2010). [96]Forrest and Thomson (2009)
   argued that pollen limitation may prevent the evolution of flowering plant
   phenology when pollinator foraging is frequency dependent and pollinator
   phenology is constant, and suggested that this might lead to the extirpation of
   flowering plant populations under strong selection. If both plant and pollinator
   phenologies evolve, the set of potential outcomes may be more complicated.
   Empirical studies of coevolution in plant-pollinator mutualisms require intensive
   long-term sampling and may be slow, costly, and logistically difficult to
   conduct. Mathematical models can offer testable predictions to guide empirical
   research and may help to identify systems of management concern before empirical
   data become available.

   We developed a model that simulates a plant-pollinator mutualism. The phenology
   of each individual in each population is genetically determined, and the optimal
   phenologies depend on climate and on species-species interactions. The
   environment includes alternative hosts available to the focal pollinator and
   alternative pollinators available to the focal plant. We modeled a climate change
   event that moves the climatically determined optimal flowering date of the focal
   plant earlier, and we tracked the evolution of phenology in both the plant and
   pollinator populations. We asked whether the mutualism persists through climate
   change and how the phenologies of the mutualist species after climate change
   depend on the rate of climate change and on the density and temporal distribution
   of nonfocal species in the community.

Methods

Overview of the focal populations

   We modeled a single population of flowering plants and a single population of
   pollinating insects. The focal plant is pollinated by and provides food resources
   to the focal pollinator. The focal plant can also be pollinated by background
   (i.e., nonfocal) pollinators or autogamy, and the focal pollinator can also
   collect food from alternative resources. The rates of background pollination and
   autogamy and the density of alternative resources are set by model parameters
   ([97]Table 1). Depending on the values assigned to these parameters, each focal
   population can be an obligate mutualist (i.e., unable to persist without its
   focal partner) or a facultative mutualist (i.e., able to persist without its
   focal partner) of the other.

Table 1

   Parameter values used in simulations
   Parameter Symbol Default value
   Days modeled per year (i.e., length of the focal plant growing season) d 60
   Date of maximum of flowering rate function before climate change [theta][i] 40
   Date of maximum of flowering rate function after climate change [theta][f] 15
   Standard deviation of flowering rate function (days) [sigma] 15
   Maximum flowering rate of focal plant (flowers/plant) r^^* 4
   Date of peak alternative resource density [mu][a] 40
   Standard deviation of alternative resource density function (days) [sigma][a] ~
   Peak alternative resource density (portion of carrying capacity of focal plant)
   A^^* ~
   Standard deviation in flowering probability function (days) [sigma][pf] 2
   Standard deviation in pollinator foraging function (days) [sigma][pp] 4
   Pollinator search rate (maximum portion of patch searched/unit pollinator/day) s
   5.82
   Handling time per unit of resource visited (days/unit resource/unit pollinator) h
   0.15
   Reward of alternative resource (pollinator offspring/unit resource visited)
   [omega][a] 0.5
   Rate of autogamy in unpollinated flowers (days^+/-1) c[s] 0 or 0.5
   Rate of pollination by nonfocal pollinators (days^+/-1) c[b] 0.05
   Mortality rate of unpollinated focal flowers (days^+/-1) m[f] 1
   Maximum duration of any single flower (days) s[f] 1
   Segregation variance of focal plant (units genetic value^2) An external file that
   holds a picture, illustration, etc. Object name is eva0005-0002-mu8.jpg . 4
   Segregation variance of pollinator (units genetic value^2) An external file that
   holds a picture, illustration, etc. Object name is eva0005-0002-mu9.jpg . 4
   [98]Open in a separate window

   Values of A^* and [sigma][a] are assigned separately to each simulation.

   Focal populations undergo discrete generations that correspond to years.
   Empirical evidence suggests that the phenologies of wild annual plants may be
   more strongly affected by climate change than those of their longer-lived
   congeners ([99]Fitter and Fitter 2002). Univoltine pollinators include some
   dipterans, lepidopterans, and solitary bees ([100]Pellmyr and Thompson 1992;
   [101]Peat et al. 2005; [102]Biesmeijer et al. 2006), and there is evidence that
   univoltine pollinators may be more vulnerable to environmental change than
   multivoltine species ([103]Biesmeijer et al. 2006). Thus, our use of discrete
   generations captures cases in which the effect of climate change on focal species
   is expected to be severe.

   The potential flowering season in each year comprises d nonoverlapping time steps
   that we call 'days.' Each individual focal plant or pollinator is characterized
   by a single genetic value that governs the days on which it flowers or forages in
   each year (i.e., its phenology). We ignored demographic stochasticity and tracked
   the density rather than the number of individuals with each genetic value.

Model environment

   The environment experienced by the focal species is described by two functions: a
   flowering rate function and an alternative resource density function ([104]Fig.
   1). In nature, the day-to-day quality of an environment for plant growth and
   reproduction depends on climatically determined factors such as temperature,
   water availability, photoperiod, interspecific competition or facilitation,
   parasitism, and herbivory rate ([105]Rathcke and Lacey 1985; [106]Jones and
   Sharitz 1989). The flowering rate function describes the quality of the
   environment experienced by a focal plant with a particular flowering phenology.
   Specifically, the flowering rate function governs the expected number of flowers
   that will be produced by a focal plant seedling with a phenology that flowers on
   day [tau] of year t:
   An external file that holds a picture, illustration, etc. Object name is
   eva0005-0002-f1.jpg
   [107]Open in a separate window
   [108]Figure 1

   Functions that define the within-year model environment before (A) and after (B)
   climate change. The flowering rate function (black line) describes the expected
   number of flowers produced by a focal plant flowering on any given day. The peak
   annual per capita flowering rate of the focal plant, r*, occurs on day [theta][t]
   (A: [theta][t] = 40, B: [theta][t] = 15). The alternative resource density
   function (dark gray) represents the density of alternative resource items
   available to the focal pollinator on each day. The peak annual density of the
   alternative resource, A*, occurs on day [mu][a] (A, B: [mu][a] = 40). The
   distributions of focal plants (light gray) and focal pollinators (middle gray)
   are determined by the genetic values of focal plants and pollinators in the
   system. Parameters are as shown in [109]Table 1, with c[s] = 0, A* = 0.095, and
   [sigma][a] = 6.1. The population state shown in B is from year 76 of the process
   shown in [110]Fig. 2 I and J and is not evolutionarily stable.
   equation image
   (1)

   Here, r^* is the maximum flowering rate of the focal plant and [sigma] determines
   how strongly the flowering rate depends on the flowering date. Plants that flower
   before or after the climatically determined optimal flowering date [theta][t]
   achieve lower flowering rates ([111]Moss 1971; [112]Chaikiattiyos et al. 1994;
   [113]Morrison and Stewart 2002). The flowering rate function captures both the
   effect of climate at the time of flowering and the cumulative effect of climate
   on focal plant fitness prior to flowering, including any effect of climate on
   seedling survival. Thus, our model is appropriate if the effect of climate on
   focal plant fitness is mediated by survival (e.g., [114]Espigares and Peco 1993;
   [115]Quintana et al. 2004; [116]Young et al. 2004) or by flowering rate
   ([117]Morrison and Stewart 2002).

   The alternative resource density function describes the density of alternative
   resources available to the focal pollinator on day [tau] of each year:
   equation image
   (2)

   A* represents the maximum density of the alternative resource, achieved on day
   [mu][a], and [sigma][a] describes how strongly alternative resource density
   depends on date. The dynamics of the alternative resource are not affected by the
   dynamics of the focal pollinator population. In nature, this might be true if
   flowering plants in the alternative resource pool are not pollen limited (e.g.,
   some autogamous species ([118]Larson and Barrett 2000) or species with common
   alternative pollinators ([119]Rymer et al. 2005)), if the focal pollinator does
   not efficiently pollinate alternative resource flowers (e.g., [120]Lazri and
   Barrows 1984; [121]Adrienne et al. 1985; [122]Marten-Rodriguez and Fenster 2008),
   or if the alternative resource is a nonflower item (e.g., dung or carrion
   ([123]Meeuse and Hatch 1960)).

Population dynamics

   We let P[i](t) and S[i](t) represent the density of pollinators and of viable
   focal plant seeds, respectively, having genetic value i at the beginning of year
   t. In each year, focal plant seeds germinate and seedlings experience density
   dependence as a result of competition for resources or space ([124]Mazer and
   Schick 1991; [125]Webb and Peart 1999; [126]Lambers et al. 2002). The number of
   focal plant seedlings with genetic value i that survive intraspecific competition
   in year t follows a Beverton-Holt function:
   equation image
   (3)

   Density dependence in the focal pollinator population is due to competition for
   focal plant flowers and alternative resources as described below.

   On each day of each year, a series of biological events occurs in the following
   order: (i) focal plants flower, (ii) pollinators become active, (iii) pollinators
   visit flowers, (iv) pollinated flowers seed, (v) pollinators lay eggs, and (vi)
   flowers die or senesce. We discuss these steps in the order in which they occur.

Focal plants flower

   The probability that a focal plant with genetic value i flowers on day [tau] is
   described by a Gaussian function centered on day i. The standard deviation,
   [sigma][pf], captures the variability in flowering dates for focal plants with a
   given genetic value. We assume that [sigma][pf] is a constant property of the
   focal plant population and that there is no effect of focal plant density on
   flowering date (but see [127]Mazer and Schick 1991). If a focal plant flowers,
   the number of flowers produced is governed by the flowering rate function. Thus,
   the density of focal plant flowers with genetic value i opening for the first
   time on day [tau] of year t is
   equation image
   (4)

   where erf represents the Gauss error function. The total density of flowers with
   genetic value i present on day [tau] of year t is
   equation image
   (5)

   where An external file that holds a picture, illustration, etc. Object name is
   eva0005-0002-mu1.jpg is the density of flowers of genetic value i persisting from
   day [tau]-1 (see [128]eqn 13). This parameterization assumes that density
   dependence acts before climate-driven selection on phenology. We examine the
   opposite case in [129]Appendix S1.

Focal pollinators become active

   The probability that a focal pollinator of genetic value i forages on day [tau]
   is a Gaussian function with a maximum at day i and a standard deviation
   [sigma][pp] that we assume to be an unchanging property of the population. Thus,
   the density of pollinators of genetic value i foraging on day [tau] of year t is
   equation image
   (6)

   Other biologically reasonable foraging probability functions, including
   platykurtic and leptokurtic distributions and Gaussian distributions with maxima