Temporal Patterns in Rates of Community Change during Succession.

Download Temporal Patterns in Rates of Community Change during Succession.

Post on 09-Dec-2016

213 views

Category:

Documents

1 download

TRANSCRIPT

  • The University of Chicago

    Temporal Patterns in Rates of Community Change during Succession.Author(s): KristinaJ.AndersonSource: The American Naturalist, Vol. 169, No. 6 (June 2007), pp. 780-793Published by: The University of Chicago Press for The American Society of NaturalistsStable URL: http://www.jstor.org/stable/10.1086/516653 .Accessed: 13/05/2013 02:31

    Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .http://www.jstor.org/page/info/about/policies/terms.jsp

    .

    JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact support@jstor.org.

    .

    The University of Chicago Press, The American Society of Naturalists, The University of Chicago arecollaborating with JSTOR to digitize, preserve and extend access to The American Naturalist.

    http://www.jstor.org

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • vol. 169, no. 6 the american naturalist june 2007

    Temporal Patterns in Rates of Community

    Change during Succession

    Kristina J. Anderson*

    Biology Department, University of New Mexico, Albuquerque,New Mexico 87131

    Submitted May 22, 2006; Accepted December 13, 2006;Electronically published April 6, 2007

    Online enhancements: appendix, data files.

    abstract: While ecological dogma holds that rates of communitychange decrease over the course of succession, this idea has yet tobe tested systematically across a wide variety of successional se-quences. Here, I review and define several measures of communitychange rates for species presence-absence data and test for temporalpatterns therein using data acquired from 16 studies comprising 62successional sequences. Community types include plant secondaryand primary succession as well as succession of arthropods on de-faunated mangrove islands and carcasses. Rates of species gain gen-erally decline through time, whereas rates of species loss display nosystematic temporal trends. As a result, percent community turnovergenerally declines while species richness increasesboth in a decel-erating manner. Although communities with relatively minor abioticand dispersal limitations (e.g., plant secondary successional com-munities) exhibit rapidly declining rates of change, limitations arisingfrom harsh abiotic conditions or spatial isolation of the communityappear to substantially alter temporal patterns in rates of successionalchange.

    Keywords: colonization, extinction, turnover, primary succession,secondary succession, arthropods.

    Successioncommunity development following a distur-bance or formation of a new habitatis traditionallythought to embody increasing community stabilitythrough time (e.g., Odum 1969; Whittaker 1975); that is,rates of community change often decrease through timeduring succession (e.g., Drury and Nisbet 1973; Jassby andGoldman 1974; Bornkamm 1981; Schoenly and Reid 1987;Prach et al. 1993; Myster and Pickett 1994; Foster and

    * E-mail: kristaa@unm.edu.

    Am. Nat. 2007. Vol. 169, pp. 780793. 2007 by The University of Chicago.0003-0147/2007/16906-41849$15.00. All rights reserved.

    Tilman 2000; Sheil et al. 2000). Meanwhile, species rich-ness usually increases initially (e.g., Odum 1969; Swaineand Hall 1983; Saldarriaga et al. 1988; Whittaker et al.1989) but then often declines (e.g., AuClair and Goff 1971;Schoenly and Reid 1987; Lichter 1998). However, a clearsynthesis regarding temporal patterns in rates of com-munity change during succession is currently lacking.Here, I (1) describe measures of species gain and loss ratesand how these combine to determine turnover rates andspecies richness, (2) examine the temporal patterns incommunity change rates during succession across a varietyof community types, and (3) discuss the mechanisms thatmay underlie predominant temporal patterns in speciescolonization rates and richness.

    Species gain (colonization) rate. Gain rate (G; time1) isthe rate at which previously absent species appear in thecommunity. In order to measure the magnitude of gainrelative to the existing community, gain rate may be ex-pressed as a proportion of the average number of speciespresent during the measurement period (Gp):

    GG p . (1)p [ ](1/2) S(t ) S(t )1 2

    Here, S(t1) and S(t2) are species richness at the beginningand end of the sampling interval, respectively. The reap-pearance of previously present species that had disap-peared may be included (G and Gp) or excluded ( and

    G); exclusion assumes that absences are an artifact ofGp

    sampling and/or population stochasticity rather than a bi-ologically meaningful event.

    Several major mechanisms may be expected to influencetemporal patterns in gain rate. First, gain rate will be con-strained by the number of species that can establish them-selves and simultaneously persist in the community (KS).Early in succession, when S is far below KS, G will belimited primarily by dispersal. As S approaches KS and theintensity of competition increases, gain rate will decrease(e.g., MacArthur and Wilson 1963; Tilman 2004) until, atKS, it is approximately balanced by loss rate (Goheen etal. 2005). Although clearly an oversimplification, this

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 781

    Figure 1: Schematic diagram showing hypothesized effects of the number of species a community can potentially hold (KS; AC) and dispersalrates (A, D, E) on species richness (S) and gain rate (G). AC, Hypothesized effects of KS being constant (A), sigmoidal (B), or peaked (C) overtime. A, D, E, Consequences of dispersal rates being such that each time step witnesses the arrival of 90% (A), 50% (D), and 10% (E) of thepotential colonists that had not yet arrived. An implicit assumption is that when dispersal limitations do not interfere, S tracks KS.

    schema is useful for making first-order predictions re-garding temporal patterns in succession rate. For example,in the simple case where dispersal is not highly limitingand where KS remains relatively constant over the courseof successionas may generally be the case in secondarysuccessiongain rate should start high and rapidly de-crease as S approaches KS and the intensity of competitionincreases (e.g., MacArthur and Wilson 1963; Bazzaz 1979;Walker and Chapin 1987; Tilman 2004). In the more com-plex case where KS changes substantiallyperhaps as aresult of changing resource availabilitygain rate will takethe form of the derivative of KS(t). Thus, for example, asigmoidal increase in KS over timeas may be the case inharsh environments where time and/or facilitation are re-quired to make resources available (e.g., Walker andChapin 1987)would result in a peaked function of G(fig. 1B), whereas a peak in KSas may be the case forsuccession on ephemeral resources such as corpseswould imply a roughly linear decrease in G (fig. 1C). Inboth cases, the maximum G would be lower than that ofa community that does not face such abiotic limitations(cf. fig. 1A1C). Second, G will be controlled in large partby the rate at which propagules of new species arrive at

    the site. If the rate at which propagules arrive remainsconstant through time, the rate at which new species arrivenecessarily decreases simply because many species are nolonger new. If, at each time step, a constant proportionof the species pool that is not yet represented arrives, G(t)will take an exponential form (fig. 1A, 1D, 1E). Sites thatreceive large numbers of propagules (e.g., 90% of unrep-resented species arrive at each time step) will have rapidlydecreasing G(t) and rapidly plateauing S(t) (fig. 1A). Thelower the rate of propagule arrival, the less rapid the de-crease in G(t), the lower the G(0), and the longer the timeuntil S reaches KS (cf. fig. 1A, 1D, 1E). As a result, suc-cessional communities facing strong dispersal limitationwill display relatively nondescript temporal patterns in G(fig. 1E). Note that, under this scenario, the size of theregional species pool should affect gain rate but not tem-poral patterns therein. Thus, gain rates should decreaseless dramatically in isolated locations (MacArthur and Wil-son 1963; Walker and del Moral 2003) and for commu-nities composed of poorly dispersing species than in suc-cessional communities with high dispersal rates. Third, Gmay be affected by herbivory or predation at any stage ofsuccession (e.g., Walker and Chapin 1987; Fraser and

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • 782 The American Naturalist

    Grime 1999; Howe and Brown 1999; Fagan and Bishop2000). Finally, G(t) may be influenced by loss rate (Barthaet al. 2003), especially in the later phases of successionwhen competition is more intense (e.g., MacArthur andWilson 1963; Bazzaz 1979; Lichter 2000). In combination,these four factors may affect G(t) in a variety of ways.Generally, G will decrease at any time that KS is not in-creasing, and the rate of this decrease will depend on dis-persal rates. An increase in KS will counteract this tendencyfor gain to decrease, sometimes causing it to increase.Conversely, a decrease in KS will force G to be less thanthe loss rate.

    Species loss (extinction) rate. Loss rate (L; time1) is therate at which species disappear from the community. Aswith gain rate, this may be expressed as a proportion ofthe species present over the measurement period (Lp):

    LL p . (2)p [ ](1/2) S(t ) S(t )1 2

    This measure represents the probability that any givenspecies will be lost in one unit of time. Again, species thatdisappear and later reappear may be included (L and Lp)or excluded ( and ), depending on whether such tem- L Lpporary absence is deemed to be biologically significant.

    Several mechanisms may act on temporal trends in lossrate. For example, L should increase with the number ofspecies that may potentially be lost (S). Additionally, bothL and Lp may be expected to increase as the intensity ofcompetition increases (e.g., MacArthur and Wilson 1963;Bazzaz 1979; Lichter 2000). On the other hand, this maybe counteracted by a decreasing rate of invading speciesthat could potentially outcompete existing ones. Addi-tionally, if average body size increases significantly overthe course of succession, increasing life spans may resultin decreasing loss rates (Drury and Nisbet 1973). Thus, itis difficult to predict a priori how L and Lp will changeover successional time. The findings of previous studiesare likewise ambivalent, showing no relationship (Fosterand Tilman 2000), a positive relationship (Facelli et al.1987), or a peaked relationship (Lichter 1998) between Lpand time. Species turnover rate and richness can be ex-pressed straightforwardly as functions of G and L.

    Species turnover rate. Turnover rate (T; time1) is theaverage of gains and losses:

    1 ( )Tp G L . (3)2

    Percent turnover has been defined in a variety of ways(Wilson and Shmida 1984; Koleff et al. 2003); I modify acommon measure of community turnover, Srensens co-

    efficient (CS; Srensen 1948; Koleff et al. 2003), to expressthe rate of percent turnover (Tp):

    C 1 {2S /[S(t ) S(t )]}S C 1 2T p pp t t t t2 1 2 1

    G L Tp p . (4)[ ]S(t ) S(t ) (1/2) S(t ) S(t )1 2 1 2

    Here, SC is the number of species present at both thebeginning and the end of the measurement period. Itshould be noted that, as opposed to narrow-sense mea-sures of turnover that focus on changes in species identity(e.g., Routledge 1977), this measure will also be stronglyinfluenced by changes in species richness (Koleff et al.2003). Note also that Tp is the average of Gp and Lp andrelates to T in the same way that G and L relate to Gp andLp (eqq. [1], [2], [4]). Just as with gain and loss, turnovermay include (T and Tp) or exclude ( and ) species

    T Tpthat disappear temporarily.

    Turnover rate, as the average of gain and loss rates, willbe driven by the mechanisms that drive them. As gaingenerally substantially exceeds loss during early succession,it is likely that T and Tp will decrease with time, if sucha trend exists for gain rate. Such a trend may be accen-tuated in communities where increasing size results inlengthening life cycles (Drury and Nisbet 1973; Foster andTilman 2000). As species richness increases, Tp will alsotend to decrease and possibly to increase toward the endof succession in communities using ephemeral resources(e.g., corpses). These patterns have been previously ob-served in both plant and animal communities (e.g., Born-kamm 1981; Schoenly 1992; Myster and Pickett 1994; Fos-ter and Tilman 2000; Chytry et al. 2001). However, itshould be noted that studies reporting a decrease in turn-over rate based on Shugart and Hetts (1973) l do so inerror (Myster and Pickett 1994; Blatt et al. 2003). Thismeasure is flawed in that (1) while purporting to measureturnover, it actually considers only loss and (2) it is definedas the fraction of original species remaining (ln trans-formed) divided by the age of the community, resultingin a mathematically trivial relationship between rate andtime (i.e., vs. x) that is guaranteed to decrease in ay/xdecelerating manner.

    Species richness. Species richness (S) is defined as thenumber of species present in a community and may ormay not exclude species that are temporarily absent (S and

    , respectively); S(t) is the cumulative difference betweenSgains and losses:

    t t

    Sp G(t) L(t). (5) 0 0

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 783

    Thus, elucidation of temporal patterns in G and L willallow description of temporal patterns of S.

    Here, I analyze temporal patterns in rates of species gain(G, Gp, , and ), loss (L, Lp, , and ), and turnover

    G G L Lp p(T, Tp, , and ) over multiple successional sequences

    T Tpin a variety of community types (table 1). Specifically, Iconsider plant secondary succession in worldwide loca-tions; plant primary succession on volcanic substrates, onsand dunes, and following a receding glacier; terrestrialarthropod succession on defaunated mangrove islands;and arthropod succession on corpses. Detailed descrip-tions of these successional seres are given in the appendixin the online edition of the American Naturalist. For eachrate measuresuccessional sequence combination, I con-sider several mathematical forms that may potentially de-scribe the community change rate, Y(t) (i.e., gain, loss, orturnover), as a function of time over the course of suc-cession. First, the null hypothesis is that Y(t) is constant:

    Y(t)p Y . (6)0

    Second, if a rate is driven by a process that changes linearlywith time, it may be described by a linear function:

    Y(t)p Y yt. (7)0

    Third, a community change rate may display a power re-lationship with time:

    aY(t)p Y yt . (8)0

    Fourth, if a change rate depends on the number of speciespresent in the community (e.g., MacArthur and Wilson1963), an exponential form is to be expected:

    atY(t)p Y ye . (9)0

    Finally, in the event that a community becomes more con-ducive to community change as a linear function of time,the exponential form (eq. [9]) may be modified by addinga linear component of time:

    atY(t)p Y yte . (10)0

    In equations (6)(10), Y0 refers to an initial and/or a finalvalue of Y, y characterizes the magnitude of the ratesresponse to time, and a is an exponent characterizing therate at which Y changes over time. While these mathe-matical forms are by no means the only ones that may beuseful in describing temporal patterns of communitychange rates, they are able to describe the range of pre-dicted temporal trends (fig. 1). For example, a negative,

    decelerating function of community change rate with time(fig. 1A, 1D, 1E)as may be expected for gain and turn-over ratescould be described by equations (8), (9), or(10), with a negative a, a positive y, and a Y0 representingbackground community change rates equal to those of anequivalent steady state community. A rate that peaks andsubsequently declines (fig. 1B)as may be expected ifsuccession gets a slow startcan be described by equation(10) under the above conditions; the prominence of theinitial increase before the subsequent decline depends onthe value of a (as increases, the time at which Y0 peaksFaFdecreases).

    It is important to note that temporal patterns in suc-cession rate will be influenced by the temporal and spatialscales of sampling. Regarding timescales, it is to be ex-pected that the relative influence of different mechanismswill change through time; for example, communities ini-tially limited by dispersal or abiotic conditions will almostinvariably eventually become more strongly shaped by bi-otic interactions (e.g., Walker and Chapin 1987; Lichter2000). As a result, temporal patterns in succession ratedepend on the rate of succession relative to the timescaleof measurement, and, therefore, the dynamics of speciesturnover during succession must always be viewed in lightof the frequency of sampling and the duration of the study.In terms of spatial scales, the species-area relationship (e.g.,Arrhenius 1921) implies that KS should scale with area. Incommunities limited by KS, this should result in higherpeak G and/or a more sustained period during which thereis a net accumulation of species in the community (i.e.,

    ). In dispersal-limited communities, an increase inG 1 LKS will result in stronger dispersal limitation, as a smallerproportion of KS would arrive at each time step.

    Methods

    A search of the literature yielded 62 successional sequenceswhose data were published or available from the re-searcher(s) (table 1; appendix). Studies selected used long-term monitoring rather than chronosequences becausestochastic spatial species turnover and failure of chrono-sequences to represent identical environmental conditionsmay result in artificially high community change rates.However, I included eight chronosequences for plant pri-mary succession, as the slow pace of this process precludeseffective long-term monitoring. Data for one primaryplant succession sequence (Surtsey, Iceland) were obtainedfrom long-term monitoring. Presence/absence datathrough time for all species detected (as opposed to onlydominants) were required. Alternatively, when a study re-ported one or more of the variables of interest (G, Gp,

    , , L, Lp, , , T, Tp, , and/or ) without providing G G L L T Tp p p

    a presence-absence matrix, I used the reported values di-

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Tabl

    e1:

    Succ

    essi

    onal

    sequ

    ence

    sco

    nsi

    dere

    din

    this

    stu

    dy

    Hab

    itat

    Loca

    tion

    No.

    succ

    essi

    onal

    sequ

    ence

    sTr

    eatm

    ent

    No.

    obse

    rvat

    ion

    sFi

    nal

    age

    Rat

    esav

    aila

    ble

    Dat

    aso

    urc

    e

    Pla

    nt

    seco

    nda

    rysu

    cces

    sion

    :A

    ban

    don

    edfi

    eld

    New

    Jers

    ey10

    Last

    crop

    ;se

    ason

    and

    mod

    eof

    aban

    don

    men

    t14

    23

    142

    3A

    llB

    uel

    l-Sm

    all

    succ

    essi

    onst

    udy

    Dis

    turb

    edh

    eath

    lan

    dB

    rnen

    sky

    Kra

    j,C

    zech

    Rep

    ubl

    ic9

    Dis

    turb

    ance

    type

    ;pl

    otsi

    ze8

    98

    G

    Ch

    ytry

    etal

    .20

    01

    Aba

    ndo

    ned

    fiel

    dK

    ansa

    s4

    Pat

    chsi

    zeon

    lan

    dsca

    pe5

    6G

    ,G

    p,

    L,L p

    ,T

    ,T

    pH

    olt

    etal

    .19

    95P

    ostfi

    rech

    apar

    ral

    Cal

    ifor

    nia

    2Sl

    ope

    aspe

    ct4

    3.7

    All

    Gu

    o20

    01G

    arde

    nw

    ith

    impo

    rted

    soil

    Ber

    lin2

    Soil

    type

    88

    Tp

    Bor

    nka

    mm

    1981

    Dis

    turb

    edgr

    assl

    and

    Nie

    ders

    ach

    sen

    ,G

    erm

    any

    119

    19T

    pB

    orn

    kam

    m19

    81

    Cu

    tan

    dbu

    rned

    fore

    stA

    maz

    onas

    ,V

    enez

    uel

    a1

    41.

    83G

    U

    hl

    etal

    .19

    81C

    lear

    -cu

    tfo

    rest

    Gh

    ana

    14

    5.2

    All

    Swai

    ne

    and

    Hal

    l19

    83P

    lan

    tpr

    imar

    ysu

    cces

    sion

    :L

    ava

    flow

    son

    Mau

    na

    Loa

    Haw

    aii

    6E

    leva

    tion

    45

    3,40

    0A

    llG

    .H

    .A

    plet

    ,pe

    rson

    alco

    mm

    un

    icat

    ion

    San

    ddu

    nes

    onla

    kesh

    ore

    Mic

    hig

    an1

    142,

    375

    All

    Lich

    ter

    1998

    Rec

    edin

    ggl

    acie

    rA

    lask

    a1

    81,

    500

    All

    Rei

    ner

    set

    al.

    1971

    New

    volc

    anic

    isla

    nd

    Surt

    sey,

    Icel

    and

    139

    40G

    h

    ttp:

    //w

    ww

    .su

    rtse

    y.is

    /pp

    _en

    s/bi

    ola_

    lines

    .htm

    Art

    hro

    pod-

    man

    grov

    esu

    cces

    sion

    :D

    efau

    nat

    edm

    angr

    ove

    isla

    nds

    Flor

    ida

    6Is

    olat

    ion

    ;is

    lan

    dsi

    ze14

    17

    295

    542

    All

    Sim

    berl

    off

    and

    Wils

    on19

    69;

    Wils

    onan

    dSi

    m-

    berl

    off

    1969

    Art

    hro

    pod-

    carr

    ion

    succ

    essi

    on:

    Rat

    carc

    ass

    Par

    ana,

    Bra

    zil

    6Lo

    cati

    on;

    seas

    on16

    37

    163

    7G

    M

    oura

    etal

    .20

    05R

    abbi

    tca

    rcas

    sC

    olor

    ado

    5E

    leva

    tion

    919

    235

    1A

    llD

    eJo

    ng

    and

    Ch

    adw

    ick

    1999

    Pig

    carc

    ass

    Vir

    gin

    ia4

    Seas

    on(r

    eplic

    ated

    )8

    218

    21A

    llTa

    bor

    etal

    .20

    04Fo

    xca

    rcas

    sE

    ngl

    and

    142

    104

    All

    Smit

    h19

    75R

    abbi

    tca

    rcas

    sE

    ngl

    and

    114

    14A

    llC

    hap

    man

    and

    San

    key

    1955

    Not

    e:N

    o.ob

    serv

    atio

    nsp

    nu

    mbe

    rof

    ages

    atw

    hic

    hsp

    ecie

    spr

    esen

    ce-a

    bsen

    ceda

    taw

    ere

    reco

    rded

    ;fi

    nal

    agep

    age

    ofth

    ela

    stob

    serv

    atio

    n.

    For

    plan

    tpr

    imar

    yan

    dse

    con

    dary

    succ

    essi

    on,

    fin

    alag

    esar

    ein

    year

    s;fo

    rar

    thro

    pod-

    man

    grov

    ean

    dar

    thro

    pod-

    carr

    ion

    succ

    essi

    on,

    fin

    alag

    esar

    ein

    days

    .

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 785

    rectly. No studies meeting the above criteria were excludedfrom this analysis.

    For each successional sequence, I defined each time pe-riod (Dt; ) as the time from one survey to the next.t t2 1For each time step, I counted species richness (S) and thenumber of species gained and lost. Gain rate (G and ;Gyear1) and loss rate (L and ; year1) were obtained byLdividing gains and losses by elapsed time (Dt). From thesevalues, I calculated Gp and (year

    1; eq. [1]), Lp andGp

    (year1; eq. [2]), and T, Tp, , and (year1; eqq. L T Tp p

    [3], [4]). Rates were calculated both including (G, Gp, L,Lp, T, and Tp) and excluding ( , , , , , and )

    G G L L T Tp p pspecies that temporarily disappeared.

    Using Matlab 7.0.1, I used least squares regression tofit equations (6)(10) to each community change rate foreach successional sequence. To avoid unreasonable fits tothe data, I constrained a (eqq. [8][10]) between 2 and2 for equation (8) and between and for equa-100/t 5/ttion (9), where t is the time span of the entire successionalsequence. For equation (10), Y0 and y were constrainedto be positive, and a was constrained between and100/t0. Calculated P values for equations (7)(10) reflect theprobability that these explain more variation in the data(i.e., have a smaller standard deviation) than does a con-stant rate (eq. [6]).

    Results

    The summary statistics for all regressions, representing 192mathematical modelrate measurecommunity type com-binations, are given as both a Microsoft Excel file and atab-delimited ASCII file, available in the online edition ofthe American Naturalist. Here, I focus on rates calculatedunder the assumption that temporary absences of speciesfrom the successional community are an artifact of sam-pling (e.g., Whittaker et al. 1989) and/or population sto-chasticity rather than a biologically meaningful event (i.e.,

    , , , , , and ). The results for rates calculated G G L L T Tp p punder the assumption that such disappearances and reap-pearances are biologically meaningful (i.e., G, Gp, L, Lp, T,and Tp) are generally very similar (Excel data, tab-delim-ited ASCII data); I note any important differences. Itshould be noted that equations (8), (9), and sometimes(10) are usually approximately equally successful in de-scribing the observed patterns (table 2); statistically, itwould be unreasonable to favor one of these mathematicalforms over the other(s) (McGill 2003). It must be em-phasized that many of the successional sequences consid-ered here come from the same study (see table 1; appendix)and therefore are not statistically independent. While noneof the results presented here would differ qualitatively inthe absence of this pseudoreplication, it is important tobear in mind that quantitative values are influenced. I note

    any cases in which pseudoreplication affects theconclusions.

    Gain Rates

    Rates of species gain consistently decline (negative slopewhen fitted with a linear function) over the course ofsuccession ( of 55 successional sequences), al-np 54though this decline is sometimes preceded by an initialincrease ( of 55). In plant secondary successionalnp 10seres, is well described ( ) as a decelerating 2G (t) R 40%decrease (eqq. [8], [9], or [10], with a positive y and anegative a; fig. 2Ai) in all but the 1.8-year secondary forestsere in Venezuela (Uhl et al. 1981) and the two 3.7-yearseres in postfire chaparral (Guo 2001), in which cases thedecrease was essentially linear (Excel data, tab-delimitedASCII data). Generally, these fits are significant at ap

    and explain 190% of the variation (table 2). In plant.05primary successional seres, is generally well describedG (t)either as a decelerating decrease ( of 9) or as anp 4peaked function (eq. [10]; of 9; table 2; fig. 2Bi).np 4With the exception of Surtsey Island, which displays nodetectable temporal pattern, at least 70% of the temporalvariation in could be described by equations (8), (9),G (t)or (10). For arthropods on mangrove islands, tendsG (t)to peak (eq. [10]) or decrease in an accelerating manner(eqq. [8], [9], with negative y and positive a; fig. 2Ci),although no fits are statistically significant (table 2) andR2 tends to be low (averaging 15%35%). For arthropodson carcasses, generally decreases in a roughly linearG (t)fashion that is alternately best described as a peak (29%),a decelerating decline (18%), or an accelerating decline(12%; table 2; fig. 2Di). Again, most fits are not statisticallysignificant (table 2), and average R2 is less than 50%.

    When expressed relative to the existing community( ), gain rate is well described as a decelerating decreaseGp(eqq. [8], [9], or [10]) for all plant secondary successionalcommunities, all arthropod-mangrove communities, 72%of arthropod-carrion communities, and 50% of plant pri-mary seres (table 2). The other 50% of plant primaryseresthe four highest-elevation sites on Mauna Loa, Ha-waiiare best described as peaked functions (eq. [10];table 2).

    Loss Rates

    With the exception of plant primary seres, where loss ratesoften peak (table 2; fig. 2Bi), there are no consistent tem-poral patterns in any of the measures of species loss rate(L, Lp, , and ); these measures tend to increase or

    L Lpdecrease with approximately equal frequency and are rarelysignificantly at (table 2); , however, displays anPp .05 Lincreasing trend in all plant secondary and arthropod-

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Tabl

    e2:

    Sum

    mar

    yst

    atis

    tics

    for

    regr

    essi

    ons

    rela

    tin

    gga

    inra

    te(G

    ),pe

    rcen

    tga

    inra

    te(

    ),lo

    ssra

    te(L

    ),pe

    rcen

    tlo

    ssra

    te(

    ),tu

    rnov

    erra

    te(T

    ),an

    dpe

    rcen

    ttu

    rnov

    erra

    te

    G

    Lp

    p

    ()

    toco

    mm

    un

    ity

    age

    T

    p

    nD

    ecre

    asin

    gtr

    enda

    Wel

    lde

    scri

    bedb

    byeq

    q.(7

    ),(8

    ),(9

    ),or

    (10)

    Dec

    eler

    atin

    gde

    crea

    seP

    eake

    d

    Wel

    lde

    scri

    bedb

    asde

    cele

    rati

    ng

    decr

    ease

    Equ

    atio

    n(8

    )E

    quat

    ion

    (9)

    Equ

    atio

    n(1

    0)c

    Equ

    atio

    n(1

    0)c

    P

    .05

    2R

    P

    .05

    2R

    P

    .05

    2R

    Wel

    lde

    scri

    bedb

    aspe

    aked

    2R

    Pla

    nt

    seco

    nda

    rysu

    cces

    sion

    :G

    23

    100

    100

    (87)

    9183

    92.5

    (94.

    2)83

    92.3

    (94.

    0)87

    91.9

    (93.

    8)0

    Gp

    1310

    010

    0(1

    00)

    100

    100

    (9

    8.4)

    100

    (9

    9.1)

    100

    (9

    9.1)

    0

    L10

    080

    (30)

    0

    0

    L p

    1010

    50(1

    0)10

    038

    .2(

    )0

    45.3

    ()

    045

    .3(

    )0

    T

    13

    100

    100

    (77)

    9277

    76.4

    (80.

    6)77

    78.9

    (83.

    7)77

    79.2

    (82.

    8)8

    (0)

    77.3

    ()

    T

    p13

    100

    100

    (100

    )10

    010

    0

    (97.

    7)10

    0

    (98.

    6)10

    0

    (98.

    6)0

    P

    lan

    tpr

    imar

    ysu

    cces

    sion

    :G

    9

    8989

    (44)

    4433

    90.9

    (98.

    7)44

    (9

    1.4)

    1127

    .05

    (86.

    28)

    44(0

    )81

    .0(

    )

    Gp

    810

    010

    0(5

    0)50

    50

    (99.

    4)38

    95.6

    (95.

    9)13

    35.7

    (86.

    2)50

    (0)

    83.2

    ()

    L7

    8610

    0(4

    3)14

    044

    .2(

    )0

    46.1

    ()

    044

    .6(

    )57

    (14)

    75.6

    (71.

    7) L p

    786

    100

    (14)

    290

    69.9

    ()

    068

    .8(

    )0

    69.4

    ()

    57(0

    )70

    .4(

    )T

    8

    100

    100

    (50)

    5038

    87.3

    (94.

    4)50

    (8

    9.5)

    1341

    .4(8

    0.4)

    50(0

    )80

    .4(

    )

    Tp

    810

    010

    0(5

    0)50

    50

    (98.

    8)38

    95.3

    (95.

    4)13

    38.3

    (84.

    7)50

    (0)

    82.9

    ()

    Art

    hro

    pods

    onm

    angr

    ove

    isla

    nds

    :G

    6

    100

    33(0

    )0

    17(0

    )51

    .2(

    )

    Gp

    610

    010

    0(1

    00)

    100

    100

    (9

    1.7)

    100

    (9

    1.6)

    100

    (9

    1.4)

    0

    L6

    050

    (0)

    0

    0

    L p

    617

    0(0

    )0

    0

    T

    650

    0(0

    )0

    0

    T

    p6

    100

    100

    (100

    )10

    010

    0

    (89.

    0)10

    0

    (89.

    7)10

    0

    (90.

    4)0

    A

    rth

    ropo

    dson

    carc

    asse

    s:G

    17

    100

    59(1

    8)18

    667

    .3(8

    3.4)

    665

    .9(8

    2.1)

    1259

    .4(7

    0.0)

    29(1

    8)59

    .2(6

    2.8)

    G

    p7

    100

    86(5

    0)72

    4382

    .3(8

    9.2)

    4379

    .4(8

    5.0)

    4372

    .5(7

    5.2)

    14(0

    )48

    .3(

    )L

    771

    0(0

    )0

    0

    L p7

    570

    (0)

    0

    0

    T

    7

    100

    43(0

    )0

    43(0

    )49

    .4(

    )

    Tp

    710

    086

    (43)

    7243

    71.8

    (77.

    9)43

    70.8

    (74.

    5)29

    64.9

    (82.

    3)14

    (0)

    45.7

    ()

    Not

    e:E

    mph

    asis

    ispl

    aced

    onde

    cele

    rati

    ng

    decr

    ease

    s(e

    qq.[

    8][

    10])

    and

    peak

    edfu

    nct

    ion

    s(e

    q.[1

    0]).

    All

    valu

    esar

    egi

    ven

    aspe

    rcen

    tage

    s,an

    dth

    ose1

    85%

    are

    inbo

    ld.V

    alu

    esin

    pare

    nth

    eses

    refe

    rto

    regr

    essi

    ons

    that

    are

    stat

    isti

    cally

    sign

    ifica

    nt

    at.

    Reg

    ress

    ion

    ssu

    mm

    ariz

    edin

    this

    tabl

    em

    aybe

    fou

    nd

    ina

    Mic

    roso

    ftE

    xcel

    data

    file

    ora

    tab-

    delim

    ited

    ASC

    IIda

    tafi

    le,a

    vaila

    ble

    inth

    eon

    line

    edit

    ion

    ofth

    eA

    mer

    ican

    ap

    .05

    Nat

    ural

    ist.

    aD

    ecre

    asin

    gtr

    end

    refe

    rsto

    an

    egat

    ive

    slop

    ew

    hen

    fitt

    edw

    ith

    alin

    ear

    fun

    ctio

    n(e

    q.[7

    ]).

    bW

    ell

    desc

    ribe

    dre

    fers

    toa

    fit

    wit

    h.

    2R

    40

    %c

    Equ

    atio

    n(1

    0),

    asco

    nst

    rain

    edin

    this

    stu

    dy(s

    eeM

    eth

    ods

    ),al

    way

    sde

    scri

    bes

    ape

    aked

    fun

    ctio

    n;

    how

    ever

    ,th

    epe

    akm

    ayoc

    cur

    befo

    reth

    eti

    me

    ofth

    eea

    rlie

    stda

    tare

    cord

    (t1),

    inw

    hic

    hca

    seth

    efu

    nct

    ion

    effe

    ctiv

    ely

    desc

    ribe

    sa

    dece

    lera

    tin

    gde

    crea

    se.

    Ifth

    epr

    edic

    ted

    valu

    eof

    t 1is

    grea

    ter

    than

    oreq

    ual

    toth

    atof

    t 2(t

    he

    seco

    nd

    data

    reco

    rd),

    the

    rela

    tion

    ship

    isco

    un

    ted

    asa

    dece

    lera

    tin

    gde

    crea

    se.

    Ifth

    epr

    edic

    ted

    valu

    eof

    t 1is

    less

    than

    oreq

    ual

    toth

    atof

    t 2an

    dth

    atof

    the

    seco

    nd-

    to-l

    ast

    datu

    mgr

    eate

    rth

    anor

    equ

    alto

    that

    ofth

    ela

    st(s

    uch

    that

    ape

    akoc

    curs

    wit

    hin

    the

    ran

    geof

    data

    valu

    es),

    and

    ifth

    eR

    2is

    grea

    ter

    than

    that

    obta

    ined

    for

    anex

    pon

    enti

    alfi

    t(e

    q.[9

    ]),

    the

    rela

    tion

    ship

    isco

    nsi

    dere

    dpe

    aked

    .

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 787

    Figure 2: Representative temporal patterns in gain ( ; Ai, Bi, Ci, Di; black symbols, black lines), loss ( ; Ai, Bi, Ci, Di; gray symbols, gray lines), G Lpercent turnover ( ; Aii, Bii, Cii, Dii), and species richness ( ; Aii, Bii, Cii, Dii) for (A) plant secondary succession on an abandoned field in New T SpJersey (Buell-Small succession study, field 4), (B) plant primary succession on Michigan sand dunes, (C) arthropod succession on mangrove islandE2 in Florida, and (D) arthropod succession on a rabbit carcass in Colorado (elevation 2,786 m). Temporal patterns in , , and are fitted with G L Tppower (eq. [8]; solid lines), exponential (eq. [9]; dashed lines), and (eq. [10]; dash-dotted lines) functions (statistics given in alinear# exponentialMicrosoft Excel data file or a tab-delimited ASCII data file, available in the online edition of the American Naturalist).

    mangrove sequences ( , three significant atnp 16 Pp), partially because this value artificially increases at the.05

    end of the monitoring period when species that disappearhave decreasing time in which to reappear. Similar trends

    are displayed by , with a slightly greater tendency towardLpdecreasing; L and Lp, which do not suffer from artificialincreases at the end of the sequence, increase or decreasewith approximately equal frequency.

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • 788 The American Naturalist

    Turnover Rates

    Species turnover rates, as the average of gain and loss rates(eqq. [3], [4]), generally display temporal patterns similarto those of species gain rates (table 2) because gain tendsto dominate early successional change (fig. 2). Rates ofspecies turnover consistently decline (negative slope whenfitted with a linear function) over the course of succession( of 34 successional sequences), although this de-np 31cline is sometimes best described as a peaked rate (np

    of 35). In plant secondary seres, is always well8 T (t)described ( ) as a decelerating decrease (eqq. [8],2R 40%[9], or [10], with a positive y and a negative a; table 2)in all but one of the 3.7-year seres in postfire chaparral(Guo 2001), in which case the decrease is essentially linear(Excel data, tab-delimited ASCII data). These fits are sig-nificant at more than 75% of the time and explainPp .05more than 90% of the variation (table 2). In all plantprimary seres, more than 65% of the temporal variationin can be explained either as a decelerating decreaseT (t)(eqq. [8] or [9]; of 8) or as a peaked function (eq.np 4[10]; of 8; table 2). For arthropods on mangrovenp 4islands, increases and decreases with equal frequencyT (t)and is never well described by any of the mathematicalfunctions (eqq. [7][10]). For arthropods on carcasses,

    is sometimes well described as a peaked functionT (t)( of 7); the remainder, which all decrease when fittednp 3with a linear function, never have more than 40% of theirvariation explained by equations (7)(10).

    When expressed relative to the existing community( ), turnover rate is well described as a decelerating de-Tpcrease (eqq. [8], [9], or [10]) for all plant secondary seres,all arthropod-mangrove seres, 72% of arthropod-carrionseres, and 50% of plant primary seres (table 2). The other50% of plant primary seresthe four highest-elevationsites on Mauna Loa, Hawaiiare best described as apeaked function (eq. [10]; table 2).

    Discussion

    Despite fundamental differences in the successional com-munities considered, some general trends emerge. First,the rate of species gain generally declines over the courseof succession (fig. 2; table 2; Swaine and Hall 1983; Facelliet al. 1987; Lichter 1998; Foster and Tilman 2000; Barthaet al. 2003). This is not surprising, given increasing com-petitive pressure and a decreasing pool of potential newcolonists (fig. 1; e.g., MacArthur and Wilson 1963). Mean-while, relative to gain rates, loss rates are generally lowand not strongly temporally patterned (fig. 2). Becausecolonization generally dominates early successional change(fig. 2; Sheil et al. 2000), turnover rates decline in a mannersimilar to gain rates (table 2). Generally, percent turnover

    rate can be very well described as a decelerating decreaseover time (fig. 2; table 2), indicating that by far the greatestamount of relative change occurs early in succession. Ad-ditionally, species richness generally increases rapidly dur-ing early succession, plateaus when gain and loss ratesconverge, and subsequently decreases when (and if) gainrate drops below extinction rate (fig. 2). Thus, the resultsgenerally support the idea that community stability in-creases over the course of succession (e.g., Odum 1969).

    These results can be used to address a couple of long-standing hypotheses regarding the nature of successionalcommunity assembly. First, my results universally contra-dict a strictly Clementsian notion of discrete communitiessequentially replacing one another during succession(Clements 1916), which would predict low turnover ratesinterspersed with spikes at each community transition.They are far more consistent with Gleasons (1917) notionthat species appear and disappear as relatively independentunits. Second, gain is generally highest early in succes-sionespecially in plant communities (fig. 2), therebylending some support to the initial floristics hypothesisthat a large proportion of a communitys taxa arrives earlyin succession (Egler 1954; Drury and Nisbet 1973). How-ever, a strict interpretation of the initial floristics hypoth-esis would require that G be virtually absent after theearliest stages of succession, that the majority of subse-quent community change be in the form of species loss,and therefore that species richness start high and decline.My results are not consistent with these predictions (fig.2), indicating that the initial floristics hypothesis is notrealistic in any strict sense.

    Differences in temporal patterns of community changerates between and within community types point to themechanisms that may underlie these patterns (fig. 1). Spe-cifically, my results suggest that three major factors drivingtemporal patterns in succession rate are competition, abi-otic limitations to the number of species a habitat cansupport, and dispersal limitations.

    Competition

    The majority of plant successional sequences display dra-matically decelerating decreases in colonization rates (eqq.[8], [9], or [10]; fig. 2Ai, 2Bi; table 2). Specifically, all butthe three shortest (!4 years) plant secondary successionalsequences display this pattern (fig. 2Ai; table 2; Swaineand Hall 1983). Somewhat surprisingly, some plant pri-mary seres exhibit the same patterns (table 2); specifically,rates of community change decrease in a decelerating man-ner for succession on Michigan sand dunes (fig. 2Bi, 2Bii),following a receding glacier, and at low elevations onMauna Loa (fig. 3A). These patterns match those predictedfor successional communities that are shaped largely by

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 789

    Figure 3: Rates of primary succession, and temporal patterns therein, on the aa lava flows of Mauna Loa, Hawaii, differ with elevation (A), temporalpatterns in the rate of new species gain ( ) at six elevations. Note that open and filled symbols are plotted on separate Y-axes that differ by anGorder of magnitude. B, Average rates of species gain ( ), loss ( ), and turnover ( ) over the first 3,400 years of succession at six elevations. All G L Trates decrease significantly ( ) with increasing altitude.P ! .008

    competition early in succession (fig. 1A). While this studydoes not directly test the idea that competition causes thisdecline in G(t), such a mechanism is likely in light of therecurring finding that G depends on the number of speciesalready present (e.g., MacArthur and Wilson 1963; Tilman2004; Fargione and Tilman 2005) or, in a broader sense,on the number of individuals and/or total biomass (e.g.Peart 1989; Bartha et al. 2003). Competitions role in de-termining G is likewise supported by the fact that maturecommunities maintain relatively constant S through com-pensatory colonization and extinction (Goheen et al.2005). The suggestion that G(t) in successional plant com-munities is shaped primarily by competition does not con-flict with previous observations that early successionespecially in primary seresis limited by factors such asdispersal, harsh abiotic conditions, and herbivory (e.g.,McClanahan 1986; Wood and del Moral 1987; Tsuyuzaki1991; Chapin et al. 1994; Fagan and Bishop 2000; Frid-riksson 2000; Lichter 2000). Rather, my model (fig. 1A)assumes that factors other than competition will determineinitial gain rates and that competitions role in shapingG(t) will appear when S nears KS. While it may be sur-prising that secondary and primary seres display similarpatterns in G(t), it must be recalled that the timescales

    differ dramatically (table 1). Thus, my findings do notimply that primary succession progresses at the same rateas secondary successiononly that relatively early primaryseres are often habitable to many species (e.g., Walker andChapin 1986; Chapin et al. 1994; Lichter 2000) and ac-commodate far higher rates of community change thando the later seres (fig. 2Bi, 2Bii). Thus, while direct testswill be required to decisively prove the role of competitionin shaping G(t), my results suggest that competition be-comes a strong influence on the assembly of plant com-munities at relatively early stages of succession.

    Abiotic Limitation

    Some successional communities appear to be abioticallyconstrained by the development of favorable conditionsand/or accumulation of resources, which affects KS. Spe-cifically, many species may be unable to establish them-selves in a harsh environment before its modification bytime and/or facilitating species (e.g., Connell and Slatyer1977; McAuliffe 1988). These communities, which are ex-emplified by primary succession at Mauna Loas higherelevations, display temporally peaked rates of species gain(fig. 3A), turnover, and even loss (eq. [10]; table 2), as

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • 790 The American Naturalist

    predicted for successional communities whose capacity tosupport species (KS) develops relatively slowly (fig. 1B). Inthese seres, the resources necessary to support a full setof species are initially unavailable and appear with timeand/or facilitation, resulting in a temporally peaked rateof community change (eq. [10]; fig. 1B). Here, coloniza-tion rates appear to be inhibited during early succession,peak when KS is growing most rapidly, and decline againas competition becomes limiting. The idea that abioticconditions affect temporal patterns in succession rate inthis manner is supported by the fact that peak colonizationrates occur progressively later ( ) and have pro-Pp .06gressively lower values ( ) at higher elevations onPp .02Mauna Loa (fig. 3A). Note that the average rate of suc-cession also decreases with increasing elevation (Aplet andVitousek 1994; Aplet et al. 1998), such that succession ratesat the highest elevation (2,434 m) are approximately anorder of magnitude less than those at the lowest elevation(1,219 m; fig. 3B). This gradient is likely related to cor-responding decreases in rates of biomass, nutrient, andsoil accumulation resulting from the colder and drier con-ditions at higher elevations (Vitousek et al. 1992; Apletand Vitousek 1994; Aplet et al. 1998). The Mauna Loagradient clearly exemplifies the principle that the temporalpattern observed is influenced by the rate of successionrelative to the timescale of measurement and hints thatsuccession rates at low elevations might likewise show apeak if measured on a finer timescale. Thus, in additionto slowing the overall rate of succession (e.g., Walker anddel Moral 2003), harsh abiotic conditions appear to delaypeak rates of community change (fig. 3).

    Dispersal Limitation

    A number of the successional seres considered here displaytemporal patterns expected for dispersal-limited com-munities (fig. 1D, 1E). First, in the secondary seres withthe shortest records (i.e., the secondary forest in Venezuelaand the postfire chaparral in California; table 1), de-Gcreases in an approximately linear fashion (Excel data, tab-delimited ASCII data), indicating that the limiting effectsof KS are not yet strongly inhibiting the colonization ofnew species, as appears to be occurring in other plantsecondary successional seres. Another example of a suc-cession that is likely dispersal limited is that of SurtseyIsland (e.g., Fridriksson 2000), which is located 33 km offthe coast of Iceland. There, displays no significant tem-Gporal patterns (Excel data, tab-delimited ASCII data), in-dicating that dispersal limitations may obscure temporalpatterns resulting from abiotic and/or competition-driventrends. As this successional sequence describes only thefirst 3 decades of primary succession, the fact that it failsto display temporal patterns similar to those of other pri-

    mary seres is in line with the prediction that observedtemporal patterns and their underlying mechanisms willdepend on the timescale of measurement. Additionally,most insect successional sequences display relatively non-descript (high variance; Simberloff and Wilson 1969),roughly linear decreases in colonization rate over thecourse of succession (fig. 2Ci, 2Di; table 2) that may beindicative of dispersal limitation (fig. 1D, 1E). Such lim-itation is probable, as both mangrove islands and decom-posing carcasses are islands from the perspective of theirinhabitant species, and succession rates may therefore bedispersal limited. This possibility is supported by the factthat arthropod colonization rates on mangrove islandsclose to colonization sources tend to decline more rapidlythan do those of distant islands ( , ). Ad-np 6 Pp .20ditionally, gain rate decreases more slowly on large islands( , ), supporting the prediction that dispersalnp 6 Pp .06limitation should be more pronounced on large islands(data from Simberloff and Wilson 1969; Wilson and Sim-berloff 1969; Excel data, tab-delimited ASCII data). In thecase of arthropod succession on carcasses, the oftenroughly linear decreases in gain rate may be explainedalternatively by dispersal limitation (fig. 1E) and/or as aresult of a temporally peaked pattern in resource avail-ability (KS; fig. 1C); a combination of the two is not un-likely. Thus, spatially isolated successional communitiesand/or those measured over a short timescale may displayless dramatic temporal patterns as a result of dispersallimitation. The idea that dispersal limits the rate of com-munity assembly agrees well with previous theory indi-cating (1) that succession rate depends on distance fromseed sources (e.g., McClanahan 1986) and (2) that an-thropogenic increases in propagule arrival in unsaturatedcommunities result in increased species diversity (e.g., Fos-ter 2001; Sax et al. 2002).

    Thus, it appears that competition, abiotic limitation,and dispersal are three major processes influencing tem-poral patterns in succession rate. No successional com-munity should be assumed to be free of the influence ofany of these; rather, it is likely that all influence successionand that their relative importance differs through time andwith community type. I propose that successional se-quences may be classified according to the relative influ-ence of these three processes and that temporal patternsin succession rate indicate which of these processes havethe greatest influence over the timescale of interest (fig.4). My classification of the successional communities an-alyzed in this study (fig. 4) is done according to the timeperiod for which data were available; it is to be expectedthat consideration of other timescales would indicate thatother mechanisms dominate over longer or shorter timeperiods; for example, all communities may be expected tomove toward competition limitation as S approaches KS

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 791

    Figure 4: Schematic diagram outlining three main controls on temporal patterns in succession rate: competition, dispersal, and abiotic conditions.Successional sequences considered in this study are placed in hypothesized locations according to their temporal patterns in succession rate, abioticconditions, and isolation. Note that these classifications are largely dependent on the timescale of interest.

    as appears to be occurring with the plant secondary seres.While the triangular framework (fig. 4) is suggested by theresults of this study, further research will be necessary torigorously test it.

    Two additional factors that probably affect temporalpatterns in succession rate cannot be addressed using thisdata set but may form additional axes of variation (fig.4). First, increasing body sizes may result in decreasingsuccession rates (e.g., Drury and Nisbet 1973) such thatthe magnitude of change in average body size may affectthe rapidity with which rates of community change declineduring succession. Second, trophic interactions undoubt-edly affect successional communities. The rate of speciesgain early in succession may be significantly reduced byprimary consumers (Howe and Brown 1999; Fagan andBishop 2000), and there is evidence that herbivores pref-erence for early successional plants (e.g., Coley 1983; God-fray 1985; Fagan et al. 2004) may accelerate the replace-ment of early successional species by later ones (e.g., Fraserand Grime 1999). Likewise, ant predation on sarcosa-

    prophagous arthropods slows the rate at which carcassesdecay (Early and Goff 1986), thereby inevitably affectingsuccession. Thus, trophic interactions have the potentialto either hinder or accelerate community change at a va-riety of successional stages; accordingly, their impact ontemporal patterns in succession rate cannot be easily gen-eralized without further research.

    It must be borne in mind that the appearance and dis-appearance of species from successional communities isonly one aspect of successional change. The numbers andsizes of individuals representing each species may poten-tially change dramatically, with little concurrent change inspecies occurrence. Thus, temporal patterns in rates ofcommunity change that consider relative abundance (e.g.,percent similarity/dissimilarity, detrended correspondenceanalysis) may differ from those observed here; a compar-ative analysis of temporal patterns in these rates would beinstructive.

    In conclusion, there is a general tendency for rates ofcommunity change to decline in a decelerating manner

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • 792 The American Naturalist

    over the course of succession (fig. 2; table 2), a trend thatis consistent with the idea that the size of the existingcommunity affects species gain rates (e.g., MacArthur andWilson 1963; Bazzaz 1979; Peart 1989; Van der Putten etal. 2000; Bartha et al. 2003; Tilman 2004). However, thistendency may be modified by abiotic or dispersal limi-tations (fig. 4) such that temporal patterns in communitychange rates peak (figs. 1B, 3A) or decline in a more linearfashion (fig. 1E; fig. 2Ci, 2Di), respectively. While thisanalysis identifies some general trends in temporal patternsof succession rate and identifies some of the potentialmechanisms that may shape them, its more importantcontribution may be to provide baseline data and quan-titative methods for comparing successional communitiesthat differ in species composition, isolation, trophic struc-ture, and abiotic setting.

    Acknowledgments

    Special thanks to J. G. Anderson for Matlab programmingassistance and to J. H. Brown for helpful comments. I amgrateful to the Buell-Small succession study (National Sci-ence Foundation [NSF] Long-Term Research in Environ-mental Biology grant DEB-9726992) and to G. H. Apletfor providing data and to all researchers whose publisheddata were included in this analysis. Thanks also to S. Baez,S. L. Collins, J. P. DeLong, E. P. White, the Brown andMilne labs, and two anonymous reviewers for helpful com-ments. I was funded by an NSF biocomplexity grant (DEB-0083422).

    Literature Cited

    Aplet, G. H., and P. M. Vitousek. 1994. An age-altitude matrix anal-ysis of Hawaiian rain-forest succession. Journal of Ecology 82:137147.

    Aplet, G. H., H. R. Flint, and P. M. Vitousek. 1998. Ecosystem de-velopment on Hawaiian lava flows: biomass and species compo-sition. Journal of Vegetation Science 9:1726.

    Arrhenius, O. 1921. Species and area. Journal of Ecology 4:6873.AuClair, A., and F. Goff. 1971. Diversity relations of upland forests

    in the western Great Lakes area. American Naturalist 105:499527.Bartha, S., S. J. Meiners, S. T. A. Pickett, and M. L. Cadenasso. 2003.

    Plant colonization windows in a mesic old field succession. AppliedVegetation Science 6:205212.

    Bazzaz, F. 1979. Physiological ecology of plant succession. AnnualReview of Ecology and Systematics 10:351371.

    Blatt, S., J. Janmaat, and R. Harmsen. 2003. Quantifying secondarysuccession: a method for all sites? Community Ecology 4:141156.

    Bornkamm, R. 1981. Rates of change in vegetation during secondarysuccession. Vegetatio 46:213220.

    Chapin, F. S., L. R. Walker, C. L. Fastie, and L. C. Sharman. 1994.Mechanisms of primary succession following deglaciation at Gla-cier Bay, Alaska. Ecological Monographs 64:149175.

    Chapman, R., and J. Sankey. 1955. The larger invertebrate fauna ofthree rabbit carcasses. Journal of Animal Ecology 24:395402.

    Chytry, M., I. Sedlakova, and L. Tichy. 2001. Species richness and

    species turnover in a successional heathland. Applied VegetationScience 4:8996.

    Clements, F. 1916. Plant succession: an analysis of the developmentof vegetation. Carnegie Institution of Washington, Washington,DC.

    Coley, P. D. 1983. Herbivory and defensive characteristics of treespecies in a lowland tropical forest. Ecological Monographs 53:209233.

    Connell, J., and R. Slatyer. 1977. Mechanisms of succession in naturalcommunities and their role in community stability and organi-zation. American Naturalist 111:11191144.

    De Jong, G. D., and J. W. Chadwick. 1999. Decomposition and ar-thropod succession on exposed rabbit carrion during summer athigh altitudes in Colorado, USA. Journal of Medical Entomology36:833845.

    Drury, W., and I. Nisbet. 1973. Succession. Journal of the ArnoldArboretum Harvard University 54:331368.

    Early, M., and M. L. Goff. 1986. Arthropod succession patterns inexposed carrion on the Island of Oahu, Hawaiian Islands, USA.Journal of Medical Entomology 23:520531.

    Egler, F. 1954. Vegetation science concepts. I. Initial floristic com-position, a factor in old-field vegetation development. Vegetatio4:412417.

    Facelli, J. M., E. Dangela, and R. J. C. Leon. 1987. Diversity changesduring pioneer stages in a subhumid pampean grassland succes-sion. American Midland Naturalist 117:1725.

    Fagan, W. F., and J. G. Bishop. 2000. Trophic interactions duringprimary succession: herbivores slow a plant reinvasion at MountSt. Helens. American Naturalist 155:238251.

    Fagan, W. F., J. G. Bishop, and J. D. Schade. 2004. Spatially structuredherbivory and primary succession at Mount St. Helens: field sur-veys and experimental growth studies suggest a role for nutrients.Ecological Entomology 29:398409.

    Fargione, J. E., and D. Tilman. 2005. Diversity decreases invasion viaboth sampling and complementarity effects. Ecology Letters 8:604611.

    Foster, B. L. 2001. Constraints on colonization and species richnessalong a grassland productivity gradient: the role of propagule avail-ability. Ecology Letters 4:530535.

    Foster, B. L., and D. Tilman. 2000. Dynamic and static views ofsuccession: testing the descriptive power of the chronosequenceapproach. Plant Ecology 146:110.

    Fraser, L. H., and J. P. Grime. 1999. Interacting effects of herbivoryand fertility on a synthesized plant community. Journal of Ecology87:514525.

    Fridriksson, S. 2000. Vascular plants on Surtsey 19911998. SurtseyResearch 11:2128.

    Gleason, H. 1917. The structure and development of the plant as-sociation. Bulletin of the Torrey Botanical Club 44:463481.

    Godfray, H. C. J. 1985. The absolute abundance of leaf miners onplants of different successional stages. Oikos 45:1725.

    Goheen, J. R., E. P. White, S. K. M. Ernest, and J. H. Brown. 2005.Intra-guild compensation regulates species richness in desert ro-dents. Ecology 86:567573.

    Guo, Q. F. 2001. Early post-fire succession in California chaparral:changes in diversity, density, cover and biomass. Ecological Re-search 16:471485.

    Holt, R. D., G. R. Robinson, and M. S. Gaines. 1995. Vegetationdynamics in an experimentally fragmented landscape. Ecology 76:16101624.

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

  • Temporal Patterns in Succession Rate 793

    Howe, H. F., and J. S. Brown. 1999. Effects of birds and rodents onsynthetic tallgrass communities. Ecology 80:17761781.

    Jassby, A., and C. Goldman. 1974. A quantitative measure of suc-cession rate and its application to the phytoplankton of lakes.American Naturalist 108:688693.

    Koleff, P., K. J. Gaston, and J. J. Lennon. 2003. Measuring betadiversity for presence-absence data. Journal of Animal Ecology 72:367382.

    Lichter, J. 1998. Primary succession and forest development oncoastal Lake Michigan sand dunes. Ecological Monographs 68:487510.

    . 2000. Colonization constraints during primary successionon coastal Lake Michigan sand dunes. Journal of Ecology 88:825839.

    MacArthur, R., and E. Wilson. 1963. An equilibrium theory of insularzoogeography. Evolution 17:373387.

    McAuliffe, J. R. 1988. Markovian dynamics of simple and complexdesert plant communities. American Naturalist 131:459490.

    McClanahan, T. R. 1986. The effect of a seed source on primarysuccession in a forest ecosystem. Vegetatio 65:175178.

    McGill, B. 2003. Strong and weak tests of macroecological theory.Oikos 102:679685.

    Moura, A. O., E. L. D. Monteiro-Filho, and C. J. B. de Carvalho.2005. Heterotrophic succession in carrion arthropod assemblages.Brazilian Archives of Biology and Technology 48:477486.

    Myster, R. W., and S. T. A. Pickett. 1994. A comparison of rate ofsuccession over 18 yr in 10 contrasting old fields. Ecology 75:387392.

    Odum, E. 1969. The strategy of ecosystem development. Science 164:262270.

    Peart, D. R. 1989. Species interactions in a successional grassland.II. Colonization of vegetated sites. Journal of Ecology 77:252266.

    Prach, K., P. Pysek, and P. Smilauer. 1993. On the rate of succession.Oikos 66:343346.

    Reiners, W. A., I. A. Worley, and D. B. Lawrence. 1971. Plant diversityin a chronosequence at Glacier Bay, Alaska. Ecology 52:5569.

    Routledge, R. 1977. On Whittakers components of diversity. Ecology58:11201127.

    Saldarriaga, J. G., D. C. West, M. L. Tharp, and C. Uhl. 1988. Long-term chronosequence of forest succession in the upper Rio Negroof Colombia and Venezuela. Journal of Ecology 76:938958.

    Sax, D., S. Gaines, and J. Brown. 2002. Species invasions exceedextinctions on islands worldwide: a comparative study of plantsand birds. American Naturalist 160:766783.

    Schoenly, K. 1992. A statistical analysis of successional patterns incarrion-arthropod assemblages: implications for forensic ento-mology and determination of the postmortem interval. Journal ofForensic Sciences 37:14891513.

    Schoenly, K., and W. Reid. 1987. Dynamics of heterotrophic succes-sion in carrion arthropod assemblages: discrete seres or a contin-uum of change? Oecologia (Berlin) 73:192202.

    Sheil, D., S. Jennings, and P. Savill. 2000. Long-term permanent plotobservations of vegetation dynamics in Budongo, a Ugandan rainforest. Journal of Tropical Ecology 16:765800.

    Shugart, H., and J. Hett. 1973. Succession: similarities of speciesturnover rates. Science 180:13791381.

    Simberloff, D., and E. Wilson. 1969. Experimental zoogeography ofislands: the colonization of empty islands. Ecology 50:278296.

    Smith, K. 1975. The faunal succession of insects and other inver-tebrates on a dead fox. Entomologists Gazette 26:277287.

    Srensen, T. 1948. A method of establishing groups of equal ampli-tude in plant sociology based on similarity of species content, andits application to analyses of the vegetation on Danish commons.Kongelige Danske Videnskabernes Selskabs Biologiske Skrifter 5:134.

    Swaine, M., and J. Hall. 1983. Early succession on cleared forest landin Ghana. Journal of Ecology 71:601627.

    Tabor, K. L., C. C. Brewster, and R. D. Fell. 2004. Analysis of thesuccessional patterns of insects on carrion in southwest Virginia.Journal of Medical Entomology 41:785795.

    Tilman, D. 2004. Niche trade-offs, neutrality, and community struc-ture: a stochastic theory of resource competition, invasion, andcommunity assembly. Proceedings of the National Academy ofSciences of the USA 101:1085410861.

    Tsuyuzaki, S. 1991. Species turnover and diversity during early stagesof vegetation recovery on the volcano Usu, northern Japan. Journalof Vegetation Science 2:301306.

    Uhl, C., K. Clark, H. Clark, and P. Murphy. 1981. Early plant suc-cession after cutting and burning in the upper Rio Negro regionof the Amazon basin. Journal of Ecology 69:631649.

    Van der Putten, W. H., S. R. Mortimer, K. Hedlund, C. Van Dijk,V. K. Brown, J. Leps, C. Rodriguez-Barrueco, et al. 2000. Plantspecies diversity as a driver of early succession in abandoned fields:a multi-site approach. Oecologia (Berlin) 124:9199.

    Vitousek, P., G. Aplet, D. Turner, and J. Lockwood. 1992. The MaunaLoa environmental matrix: foliar and soil nutrients. Oecologia(Berlin) 89:372382.

    Walker, L. R., and F. S. Chapin. 1986. Physiological controls overseedling growth in primary succession on an Alaskan floodplain.Ecology 67:15081523.

    . 1987. Interactions among processes controlling successionalchange. Oikos 50:131135.

    Walker, L. R., and R. del Moral. 2003. Primary succession and eco-system rehabilitation. Cambridge University Press, Cambridge.

    Whittaker, R. 1975. Communities and ecosystems. Macmillan, NewYork.

    Whittaker, R., M. Bush, and K. Richards. 1989. Plant recolonizationand vegetation succession on the Krakatau islands, Indonesia. Eco-logical Monographs 59:59123.

    Wilson, E., and D. Simberloff. 1969. Experimental zoogeography ofislands: defaunation and monitoring techniques. Ecology 50:267278.

    Wilson, M. V., and A. Shmida. 1984. Measuring beta diversity withpresence absence data. Journal of Ecology 72:10551064.

    Wood, D. M., and R. del Moral. 1987. Mechanisms of early primarysuccession in subalpine habitats on Mount St. Helens. Ecology 68:780790.

    Associate Editor: Catherine A. PfisterEditor: Monica A. Geber

    This content downloaded from 128.119.168.112 on Mon, 13 May 2013 02:31:46 AMAll use subject to JSTOR Terms and Conditions

Recommended

View more >