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Page 1: Thesis for the degree of Doctor of Philosophy · Thesis for the degree of Doctor of Philosophy Ecological disturbances: The Good, the Bad and the Ugly J. Robin Svensson 2010 Department
Page 2: Thesis for the degree of Doctor of Philosophy · Thesis for the degree of Doctor of Philosophy Ecological disturbances: The Good, the Bad and the Ugly J. Robin Svensson 2010 Department

Thesis for the degree of Doctor of Philosophy

Ecological disturbances:

The Good, the Bad and the Ugly

J. Robin Svensson

2010

Department of Marine Ecology - Tjärnö University of Gothenburg

45296 Strömstad SWEDEN

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© J. Robin Svensson 2010All rights reserved. No part of this publication may be reproduced ortransmitted, in any form or by any means, without written permission.

ISBN 978-91-628-8200-6

Printed by Geson Hylte Tryck, Göteborg, Sweden 2010

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Ecological disturbances

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Till Ivar och Kerstin Karlsson,

och Nils-Erik Hjertquist

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J. Robin Svensson

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Svensson, J. Robin 2010. ECOLOGICAL DISTURBANCES: THE GOOD, THE BAD AND THE UGLY. Abstract. This thesis focuses on the definitions, characterizations and quantifications of ecological disturbances, as well as hypotheses on their impacts on biological communities. The most prominent model on effects of disturbance on diversity is the Intermediate Disturbance Hypothesis (IDH), which is utilized in management of national reserves, has received over 3300 citations and has been corroborated by a multitude of studies from terrestrial and aquatic systems. According to the predictions of the IDH, diversity is high at intermediate levels of disturbance due to coexistence of competitors and colonizers. At low levels of disturbance diversity will be low due to competitive exclusion and few species can persist at high levels of disturbance. In an extension of the IDH, the Dynamic Equilibrium Model (DEM) predicts that the effects of disturbance depend on the productivity of communities, because at high growth rates a stronger disturbance is required to counteract increased rates of competitive exclusion. The IDH and the DEM were tested in a field experiment on effects of physical disturbance (scraping) and productivity (nutrient availability) on hard-substratum assemblages in paper I , where the patterns predicted by the IDH, but not the DEM, were observed. This outcome shows the importance of the nature of productivity alterations, as the productivity treatment had a general positive effect on growth rates but only marginal effects on the dominant species, thereby leaving rates of competitive exclusion unaffected.

In paper II I tested another extension of the IDH, which predicts that smaller, more frequent disturbances will have different effects on diversity compared to larger, less frequent disturbances. In this experiment I used two different regimes of disturbance, small and frequent vs. large and infrequent disturbances, while the overall rate (the product of area and frequency) was kept equal for both regimes. At the site where the IDH was supported, the regime with a large proportion of the area disturbed infrequently showed higher richness, due to a stronger decrease of dominants, compared to the regime with a small proportion disturbed frequently. In addition to these significant differences in diversity effects between different disturbance regimes, it may also matter what agent of disturbance that is causing the damage. In paper III I contrasted the effects of a physical disturbance (wave-action) to that of a biological disturbance (grazing), as well as their respective interactions with productivity in a multifactorial design tested on natural epilithic assemblages. The composition of assemblages and the total species richness was significantly affected by physical disturbance and interactively by biological disturbance and productivity. The algal richness was significantly affected by productivity and biological disturbance, whereas the invertebrate richness was affected by physical disturbance. The results show, for the first time, that biological disturbance and physical disturbance interact differently with productivity due to differences in the distribution and selectivity among disturbances.

In paper IV I investigate how the choice of diversity measure may impact the outcomes of tests of the IDH, which, surprisingly, has not previously been discussed. This was done by an extensive literature review and meta-analysis on published papers as well as by two different approaches to mathematical modelling. Both models support the IDH when biodiversity is measured as species richness, but not evenness. The meta-analysis showed that two-thirds of the published studies in the survey present different results for different diversity measures. Hence, the choice of diversity measure is vital for the outcome of tests of the IDH and related models. Key words: competitive exclusion; DEM; disturbance; diversity; evenness; IDH; marine assemblages; productivity; rate of disturbance; regime; species richness; Tjärnö, Sweden.

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ISBN: 978-91-628-8200-6 Populärvetenskaplig sammanfattning Som den skamlöst fyndiga titeln syftar till så kan ekologiska störningar se väldigt olika ut och ha helt olika effekter på den biologiska mångfalden. Men innan vi ger oss i kast med en djupare tolkning av detta, bör vi bena ut vad en störning egentligen är. Exempel på vanliga störningar i naturen är skogsbränder, stormar, översvämningar, vågor, trålning, föroreningar, uttorkning samt istäcken och drivved som skrapar bort arter på hårda bottnar. Lite ibland räknas även biologiska störningar, d.v.s. djur som tuggar i sig andra djur och växter, eller djur som i ren illvilja eller okunskap trampar ihjäl levande varelser i sin väg. För att krångla till detta en smula så får inte allting som kan ge upphov till skada kallas för en störning, utan i likhet med samhället i stort finns även här vissa som är mer jämlika än andra. Definitioner på vad som får räknas som en faktisk störning finns det lika många som antalet GAIS supportrar; ungefär nio. Enligt den mest konkreta och lätthanterliga definitionen ska en störning döda eller avlägsna organismer i ett samhälle (område med samexisterande arter), och därigenom underlätta för nya arter att etablera sig. Den till synes harmlösa bisatsen om etableringsmöjligheter får oanat stor betydelse när man testar ekologiska förklaringsmodeller om störning och biodiversitet. Överlag sunda läsare undrar nu förmodligen vad i hela Hisingen en ekologisk förklaringsmodell är. Dessvärre kan jag inte skryta med att detta är lika komplicerat som det låter. En förklaringsmodell, eller hypotes, inom ekologi går helt sonika ut på att förklara ett fenomen eller samspel i naturen. I merparten av mina många experiment (tre) har jag undersökt om ’the Intermediate Disturbance Hypothesis’ (IDH) verkligen stämmer. Denna hypotes går i princip ut på att ’Lagom är bäst’ och passar därför väl in i den svenska kulturen. Anledning till att just lagom störning är bäst är att då finns flest antal arter, eftersom alla arter dör ut om det blir för mycket störning och att bara en art kommer ta över hela samhället om det inte finns någon störning alls. Det sistnämnda kallas ’konkurrensuteslutning’ och innebär, kanske inte helt otippat, att en art kan konkurera så effektivt att den utesluter alla andra arter ur ett område om ingenting stoppar den. Exempel på när detta sker i naturen är barrskogar och musselbankar, där en eller ett fåtal arter helt egoistiskt kan ta upp väldigt stora områden. Om en störning kommer in och dödar ett antal individer i dessa områden kan andra, nya, arter etablera sig på den nyligen frigjorda ytan eller marken. Antalet arter i området ökar då alltså, och är man lite fin i kanten kan man istället uttrycka detta som att den biologiska mångfalden höjts. En annan väldigt rolig hypotes, som bygger på den ovan nämnda IDH, kallas ’the Dynamic Equilibrium Model’ (DEM). Tillägget i denna hypotes är att mängden störning som är lagom beror på hur fort arterna i ett samhälle växer. Desto fortare arterna växer, desto kraftigare störning krävs för att bryta konkurrensuteslutning av någon självupptagen liten gynnare. Dessa två hypoteser, IDH och DEM, är vad jag, två GAIS:are och ett gäng ohängda tyskar testar på marina hårdbottensamhällen, bestående av anemoner, havsborstmaskar, havstulpaner, hydroider, musslor, mossdjur, svampdjur, sjöpungar samt grön-, brun- och rödalger, i den första artikeln i avhandlingen. De andra nagelbitarna till artiklar handlar även de om hypoteserna IDH och DEM, om än lite mer indirekt och med större fokus på själva störningsmekanismerna. Den näst första artikeln handlar om störningar som är lika stora i total omfattning, men där en störning som sker dubbelt så ofta då påverkar en hälften så stor yta. Skillnaden vi hittade här var att störning med stor yta som skedde mer sällan gav upphov till fler arter, eftersom detta mer effektivt

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kunde bryta de slemmiga sjöpungarnas konkurrensuteslutning. I det tredje experimentet slängde vi ett getöga på skillnaderna mellan samhällen på stenar som skrapar mot varandra i vågrörelser (fysisk störning), jämfört med samhällen på stenar som blir mumsade på av promiskuösa strandsnäckor (biologisk störning), samt vilken effekt dessa olika störningar får i samspel med hur fort samhällen tillväxer (produktivitet). Förutom att de olika typerna av störning interagerade på olika sätt med tillväxthastigheten, hade de även olika stor effekt djuren och växterna (algerna) i samhällena. Den fjärde och sista artikeln är mer lik en debattartikel, fast med stöd av matematisk modellering och en litteraturundersökning, där jag väldigt ödmjukt påstår att alla andra som jobbar med ekologiska störningar och biodiversitet gör fel, medan jag själv tvivelsutan gör allt rätt. Anledningen till felaktigheterna är att en del testar hypoteser om förändring i antal arter med ett mått på hur jämt arter är fördelade istället för hur många de är. Detta är lite som när Kurt Olsson frågade Patrik Sjöberg hur brett han har hoppat, eller som att räkna antalet äpplen i päronträd, makrillar i änglaklacken eller marxister i vita huset. Summan av kardemumman, efter ett halvt decennium på skattepengar och ett ointagligt rekord i spindelharpan, är alltså att effekterna av störning hänger på vilken slags störning som sker, hur man väljer att mäta den, samt vilka arter som finns i samhället där störningen inträffar. Vill man testa hypoteser om biodiversitet och störning lite grann, så spelar det även roll hur stark konkurrensen mellan arter och nyetableringen av arter är, samt vilket mått på biologisk mångfald som används i studien.

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LIST OT PAPERS

This thesis is a summary of the following papers:

Paper I Svensson, J. R., M. Lindegarth, M. Siccha, M. Lenz, M. Molis, M. Wahl, and H.

Pavia. 2007. Maximum species richness at intermediate frequencies of

disturbance: Consistency among levels of productivity. Ecology 88:830-838.

Paper II Svensson, J. R., M. Lindegarth, and H. Pavia. 2009. Equal rates of disturbance

cause different patterns of diversity. Ecology 90:496-505.

Paper III Svensson, J. R., M. Lindegarth, and H. Pavia. 2010b. Physical and biological

disturbances interact differently with productivity: effects on floral and faunal

richness. Ecology 91:3069-3080.

Paper IV Svensson, J. R., M. Lindegarth, P. R. Jonsson, and H. Pavia. The Intermediate

Disturbance Hypothesis predicts different effects on species richness and

evenness. Manuscript.

Papers I , II and III was reprinted with the kind permission of from the Ecological Society of

America.

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What is ecological disturbance, really?.................................................................................. 8

Definitions of disturbance ...................................................................................................... 8

Agents of disturbance............................................................................................................. 9

Components and quantities of disturbance........................................................................... 11

Differences between Disturbance, Perturbation and Stress ................................................. 13

Ecological Theories on Disturbance..................................................................................... 15

The Intermediate Disturbance Hypothesis (IDH) ................................................................ 15

The Dynamic Equilibrium Model (DEM)............................................................................ 17

Additional related models .................................................................................................... 19

Prerequisites for the IDH and the DEM.............................................................................. 21

Aspects of Colonization ....................................................................................................... 21

Aspects of Competition........................................................................................................ 22

Considerations of diversity.................................................................................................... 24

Conclusions............................................................................................................................. 25

References............................................................................................................................... 26

Acknowledgements................................................................................................................. 32

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What is ecological disturbance, really? Since this thesis is entirely devoted to ecological disturbances, we might as well start at the beginning. That is, to elucidate the concept of ‘disturbance’. There are quite a few definitions of disturbance that I will explain and discuss in the first section, whereafter I move on to agents of disturbance, followed by measures and components of disturbance. An agent of disturbance is the instrument that causes the damage, such as an animal, waves or fire. The components of disturbance are the properties of the damaging force of the disturbance agent, i.e. the heat of the fire, the strength of the waves and the extent of borrowing by an animal. The issues regarding agents and components of disturbance are discussed in paper I and specifically tested in papers II and III . Should I not have failed entirely in my attempt at illuminating the audience on the topic of disturbance in these earlier sections, she or he will have an appropriate background for the following sections on ecological theories on disturbance. More specifically, I will sort out the most prominent hypotheses and models on the effects of disturbance on biodiversity, i.e. the Intermediate Disturbance Hypothesis (IDH) and the Dynamic Equilibrium Model (DEM), as well as a few related models on colonization and the specific components of disturbance. The IDH predicts maximum diversity at intermediate levels of disturbance, whereas the DEM predicts that the level of disturbance required to maximize diversity depends on the level of productivity. The IDH is tested by manipulative experimentation in papers I -III and theoretically evaluated in paper IV , and tests of the DEM is incorporated in the experiments in papers I and III . Furthermore, I will present and discuss a number of possible prerequisites, or assumptions, which these models may rely on. In conclusion, readers that have the stamina to go through the entire thesis will be handsomely rewarded by superior knowledge about definitions, agents and components of disturbance as well as of theories on disturbance and their associated predicaments. Hence, they will know what ecological disturbance really is.

Definitions of disturbance There are quite a few definitions of disturbance, which may or may not help the reader depending on their complexity and explicitness. The most straightforward definition is that by Grime (1977), who defines disturbance as partial or total destruction of biomass. Although simplicity is something to strive for, especially to increase the operationalization of a definition for manipulative experiments, a too simple definition can include processes and mechanism that may in fact only have a marginal effect on species assemblages. The definition by Pickett and White (1985) where disturbance is “…any relative discrete event in time that disrupts ecosystems, community, or population structure and changes resources, substrate availability, or the physical environment”, is also very broad. Although this definition is undoubtedly more explicit, it still encompasses many events that occur naturally and frequently without necessarily have any measurable effects on either diversity or density of species. An extension to this definition was added by Pickett et al. (1989), in which “Disturbance is a change in the minimal structure caused by a factor external to the level of interest”. A benefit with this hierarchical view of disturbance is that one must consider the scale at which a certain disturbance operates. For instance, an herbivorous insect can be a disturbance to the leaves of a single tree, whereas if the study site is an entire forest it may be more relevant to consider wind-throws by hurricanes or large scale forest fires. However, this hierarchal view does not compensate for the drawbacks of the broadness of the original definition. Notable distinctions in definitions comes from of Pain and Levin (1981) and Reynolds et al. (1993), who argue that disturbance should be defined exclusively based on its measurable

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effect on ecological communities. In contrast to descriptions encompassing a range of different processes (c.f. Pickett and White 1985). According to Pain and Levin (1981), “Patch birth rate, and mean and maximum size at birth” can be used as “adequate indices of disturbance.” The definition of a ‘patch’ here is the primary substratum, i.e. space, that is affected by the disturbance. Similarly, Reynolds et al. (1993) defines disturbances as ”primarily non-biotic, stochastic events that results in distinct and abrupt changes in the composition and which interfere with internally-driven progress towards self-organisation and ecological equilibrium; such events are understood to operate through the medium of (e.g.) weather and at the frequency scale of algal generation times”. As indicated by the subordinate clause in this definition, it is explicitly intended for studies on phytoplankton, and the definition by Pain and Levin (1981) only holds for communities where primary space is the limiting resource. Hence, while both definitions are useful within their own fields of study, they will not hold for ecological studies on disturbance and diversity in general. The more operational definitions of disturbance include the alterations of resources as a consequence of a disturbing force. For instance, Shea et al. (2004) define disturbance as an event which “alters the niche opportunities available to the species in a system” by removing biomass and “freeing up resources for other organisms to use” or in any other way cause “a direct shift in available nutrients”. Similarly, Mackey and Currie (2000) define disturbance as “a force often abrupt and unpredictable, with a duration shorter than the time between disturbance events, that kills or badly damages organisms and alters the availability of resources”. The inclusion of freeing of resources is important because this is the characteristic of a disturbance which may ultimately lead to a positive effect on diversity, if the availability of resources enables, or maintains, coexistence in a community. According to Sousa (1984), disturbance is defined as “…a discrete, punctuated killing, displacement, or damaging of one or more individuals (or colonies) that directly or indirectly creates an opportunity for new individuals (or colonies) to become established.” Hence, instead of considering availability or resources, which may or may not affect recruitment, this definition goes straight to the core of the potential for a disturbance to mediate coexistence. That is, opportunities for recruitment created, directly or indirectly, by disturbance, because without new species recruiting into the space freed by disturbance diversity cannot increase (Osman 1977, Collins et al. 1995, Huxham et al. 2000). Thus, like many other researchers, I find this definition of disturbance to be the most practical and operational for investigations of patterns between diversity and disturbance. Consequently, the definition of disturbance by Sousa (1984) will be used throughout this thesis, with the addition that the disturbance should be ecologically relevant for the system under study. Similar to the arguments by Pickett et al. (1989), a disturbance should be considered in relation to scale, but also to relevance of agents and components of disturbance for the specific system and/or the phenomena the model or hypothesis is intended to explain.

Agents of disturbance The mechanisms and processes that are inflicting damage upon species assemblages are called agents of disturbance. Commonly, researchers on disturbance distinguish between biological and physical agents of disturbance (McGuinness 1987, Wootton 1998, Sousa 2001), while some authors use more explicit subdivision (Menge and Sutherland 1987). In order to give a clear picture of what these agents are, I will describe some of the more common agents of disturbance used in previous studies. Examples of agents of physical disturbance include anoxia (Diaz and Rosenberg 1995), boat traffic (Willby et al. 2001), desiccation (Lenz et al. 2004), deposition (Miyake and Nakano 2002), drifting logs (Dayton 1971), erosion (Fox

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1981), fire (Eggeling 1947), floods (Lake et al. 1989), ice-scouring (Gutt and Piepenburg 2003), pesticides (Szentkiralyi and Kozar 1991), pollution (Benedetti-Cecchi et al. 2001), sediment movement (Cowie et al. 2000), temperature (Flöder and Sommer 1999), tilling (Wilson and Tilman 2002), trawling (Tuck et al. 1998), tree poisoning (Sheil 2001), tree lopping (Vetaas 1997), wind (Molino and Sabatier 2001), wave action (McGuinness 1987), and even warfare (Rapport et al. 1985). Biological disturbances are mainly predation (Talbot et al. 1978) and grazing (Collins 1987), although some authors add algal whiplash (Dayton 1975), burrowing (Guo 1996), disease (Ayling 1981), parasites (Mouritsen and Poulin 2005) and trampling (Eggeling 1947).

Due to the differences among these agents of disturbance, agents are commonly divided into groups based on their functional or mechanical characterizations. Menge and Sutherland (1987) divide the agents of disturbance into four different groups: physical disturbance, physiological disturbance, biological disturbance and predation/grazing. Physical disturbance is produced by mechanical forces (e.g. movement of air, water, and sediment), whereas physiological disturbance is the lethal effects produced by biochemical reactions (influenced by e.g. temperature, light or salinity). Biological disturbance is the lethal effects of the activities of mobile animals (e.g. trampling, burrowing, and digging), and predation and grazing is defined as mortality resulting from consumption by animals. In a similar fashion, Wootton (1998) suggests that the effects of consumers should be considered separate to the effects from physical disturbance, because “the biota of the community is less likely to directly control the dynamics of the latter”. That is, agents of biological disturbance may be density dependent to a much higher degree than agents of physical disturbance. An even more important distinction between agents, than those given above, is based on their possibility for selectiveness in the damage they exert. Grazing and predation have been argued to be unsuitable agents of disturbance in studies on disturbance-diversity patterns, because consumers, unlike physical agents, may have preferences in prey species (e.g. McGuinness 1987, Sousa 2001). Due to this predicament, Sousa (2001)

Fig. 1 Disturbance treatment in papers I and II . Physical scraping of settling panels removing all organisms from a given percentage (i.e. 20 or 40 %) of the panel at each disturbance event.

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reserves the term disturbance to include “damage, displacement or mortality caused by physical agents or incidentally by biotic agents”, thus, excluding consumption by grazers and predators. Since this possible high degree of selectivity has no comparison in physical disturbances, outcomes of studies on disturbance using biological agents may be confounded and, therefore, not generally applicable. For instance, if a consumer prefers prey species that are inferior competitors, this biological disturbance will increase the rate of competitive exclusion instead of breaking the dominance of competitive superiors. This degree of selectivity may be even more complex in disturbance-diversity models that include productivity, i.e. the DEM, because grazers have been shown to prefer plants with higher nutrient content in both terrestrial (Onuf et al. 1977) and marine systems (Cruz-Rivera and Hay 2000). Accordingly, in paper III I show that a biological disturbance (grazing by periwinkles) and productivity interactively affected the number of macroalgal species, whereas the physical disturbance (wave-action) only affected the number of invertebrate species in natural marine epilithic assemblages. These patterns were, in part, explained by differences in the degree of selectivity between disturbances. Accordingly, the non-selective physical disturbance (scraping) in papers I and II (Fig. 1) affected all groups of species in the hard-substratum assemblages; annelids, barnacles, bryozoans, hydroids, mussels, sea-anemones, sponges and tunicates, as well as green, brown and red macroalgae. Thus, in contrast to the plain distinction between biological and physical agents of disturbance, a more operationally beneficial distinction may be that between selective and non-selective agents of disturbance.

Components and quantities of disturbance In relation to agents of disturbance, i.e. ‘what is disturbing’, there are also components of disturbance, i.e. ‘how is it disturbing’. These components, also called attributes (Shea et al. 2004), commonly differ in the way they are characterized and measured. According to Osman and Whitlach (1978), “a disturbance agent will have two components, frequency and magnitude”, where frequency is how often a patch is disturbed and the magnitude refers to the number of disturbed patches. Wootton (1998) identifies three components of disturbance “increasing average mortality, increasing temporal variability, and increasing spatial heterogeneity”. There are, however, many more components of disturbance. These may be divided into conceptual and operational terms of disturbance. The conceptual terms; level, intensity, severity, magnitude, regime, timing, and shape, are intended to verbally explain or describe aspects of disturbance, whereas the operational; frequency, extent, duration, time, size, rate and predictability, can be measured using their defined quantities (Table 1). The drawback with the inexplicitly defined conceptual terms is that they are not easily generalized among studies. For example, ‘intensity’ has been used to describe a variety of experimental manipulations and variables, such as penetration depth per bite by limpets (Steneck et al. 1991), type of mechanical scrubbing (McCabe and Gotelli 2000) and degree of oscillation in sediment (Garstecki and Wickham 2003). Similarly, ‘magnitude’ can be a general description, occasionally used synonymously to level, intensity and severity. However, magnitude can also be used for more specific measures, such as the number of patches affected by disturbance (Osman and Whitlatch 1978) and the percentage of biomass removed by floods (Kimmerer and Allen 1982). The fact that the units and meaning of disturbance can be unclear, and differ among studies (Pickett and White 1985, Sousa 2001, Shea et al. 2004), may be a consequence of the unclear formulations of the hypotheses the studies aim to test. This is because the most prominent models on patterns between disturbance and diversity (see section: ecological theories on disturbance) are conceptual

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models based on relatively scaled variables (Schoener 1972, Peters 1991). However, in order to evaluate general ecological theories, it is important that concepts are commensurable among studies.

Term Meaning Quantity

Conceptual

‘regime’Generic term for the types and components of disturbance currently acting in a given area

-

‘level’ General description of overall amount of disturbance -

‘severity’General description used synonymously to intensity and magnitude, and/or specific for damage caused

-

‘intensity’General description used synonymously to severity and magnitude, and/or specific for disturbing force

-

‘magnitude’General description, but also used synonymously to severity and intensity

-

‘timing’When a disturbance occurs and influence of the current conditions at that time

-

‘shape’Specific shape (i.e. oval, rectangular, square) of two- or three-dimensional space disturbed

-

Operational

‘frequency’ Number of disturbance events per unit time time-1

‘time’ Period of time since last disturbance event time

‘duration’ The amount of time a disturbance event lasts time

‘phasing’ Temporal pattern of disturbance "S", i.e. time

‘predictability’ Variance in mean time between disturbances variance

‘size’ Size of an individual disturbance events area

‘extent’ Total two- or three-dimensional space disturbed area or volume

‘rate’ Product of area and frequency area x time-1

One effort to increase the commensurability among studies on disturbance is the proposal of the term ‘rate’ of disturbance by Miller (1982), where rate is the sum of the size of all disturbance events in a given area per unit time, i.e. the product of area and frequency of disturbance. This is comparable to the argument of Osman and Whitlach (1978), who suggested that disturbance is composed of the two components frequency and magnitude, although they did not suggest a general joint measure. Similarly, Petraitis et al. (1989) defines ‘intensity’ as the product of area and frequency (not be confused with the common definition of the term intensity; Connell 1978, Sousa 1984, Shea et al. 2004). Taking into account the combined effects of area and frequency is important, because information about one of these components makes little sense without the context of the other. For instance, specifying an experimental manipulation where a community is disturbed once a week is completely

Table 1 Conceptual and operational terms of disturbance commonly used in ecological studies.

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uninformative if we do not know the extent of the damage. Without doubt, the differences in effects on diversity will differ massively if the area disturbed each week is 1% of the total area compared to if it is 99%. However, disturbances composed of area and frequency are not the only ones that would benefit from a measure that combines the quantities of components. For example, in experiments on forest fires the temperature is vital for the effects on communities (e.g. Gignoux et al. 1997), and this can be combined with both the extent and the duration for increased commensurability among studies. Although the combined effects of disturbance components are always implicit in experimental studies, it is necessary to transform the measure of disturbance into a joint measure, i.e. rate, in order to put any experimental result into a wider context, and to allow for direct and meaningful comparisons among studies. The main benefit of careful specifications of the components of disturbance is that they give information of the manner in which a particular disturbance is exerted. Even for joint measures, such as rate, it is important to specify each component clearly. This is important because disturbances that are equal in extent can nonetheless have significantly different effects on diversity, depending on how the disturbance is distributed (Bertocci et al. 2005, papers II and III). In paper II I show that equal rates of disturbance may still give different patterns in diversity depending on the specific combination of area and frequency, i.e. the regime of disturbance. In accordance with the predictions by Miller (1982), the regime with small, frequent disturbances favoured colonizing species, whereas large, less frequent disturbances favoured competitive dominants. On a similar note, Bender et al. (1984) identified two different types of disturbance, pulse and press, defined as instantaneous alteration of species number (pulse) and the sustained alteration of species densities (press). The distinction between two clearly different mechanisms of disturbance, which may nonetheless be equal in total extent, can be useful for predictions of patterns of diversity. In paper III , the biological, continuous small-scale, disturbance (i.e. press) differed in effects on diversity from the physical disturbance, instantaneous removal or damage of individuals (i.e. pulse). This shows that clear specification of components of disturbance is important, because the way the damage of a given disturbance is exerted can be vital for the outcome of studies on disturbance-diversity patterns.

Differences between Disturbance, Perturbation and Stress In ecological studies, the two concepts ‘perturbation’ and ‘stress’ are often used synonymously to disturbance (e.g. Connell 1978, Bender et al. 1984, Rapport et al. 1985). Processes and mechanisms that are generally described as disturbance may instead be classified as either perturbation (Webster and Patten 1979, Lane 1986) or stress (e.g. McGuinness 1987), and the terms perturbation and stress are often used interchangeably with disturbance without explicitly definitions of any of the terms (e.g. Caswell and Real 1987, Davies et al. 1999). Similarly, the term perturbation can be used to refer to the effects of stress on a system (Petraitis et al. 1989) and the term stress can be used to describe a perturbation (Odum et al. 1979). That these three terms are used haphazardly can be problematic, because definitions of ecological phenomena may be vital for experimental design in tests of hypotheses. Especially, since the concept of disturbance is in itself a quagmire, confounding it with stress or perturbation would be severely suboptimal. The most clear distinction among these three terms is that between disturbance and stress, where disturbance is generally considered to cause more severe damage (Grime 1977, Pickett et al. 1989, Wootton 1998). Among the most common mechanisms and processes

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described as stress are desiccation (Dayton 1971), pollutant discharges (Rapport et al. 1985) and fluctuations in temperature (Jackson 1977), nutrients (Menge and Sutherland 1987) and light (Grime 1977). According to Grime (1977) stress in plant communities is defined as “the external constraints which limit the rate of dry-matter production of all or part of the vegetation”, which is clearly distinct from disturbance events that “limit the plant biomass by causing its destruction”. Wootton (1998) makes a similar distinction between stress and disturbance, where the upper limit of what can be defined as stress is mortality. Stress is here defined by “causing changes in performance as opposed to mortality”, and he states that stress can also “reduce conversion efficiency or increase metabolic costs”. This view is also shared by Sousa (2001) who states that the difference between disturbance and stress, although possibly caused by the same agent, is that disturbance only occurs when “an organisms tolerance is exceeded, resulting in its death or sufficient loss of biomass that the recruitment or survival of other individuals is affected”. Pickett et al. (1989) defines stress as a “change in the interaction maintaining a minimal structure”, caused “directly or indirectly by an external factor”. For example, an herbivorous insect can be a disturbance to a leaf by disrupting its physiological integrity, but a stress to the plant because leaf damage may affect the performance and reproduction of the plant. Thus, the same mechanism will be classified as either disturbance or stress depending on the level of interest (Pickett et al. 1989). Rapport et al. (1985) defines stress as “an external force or factor, or stimulus that causes changes in the ecosystem, or causes the ecosystem to respond, or entrains ecosystemic dysfunctions that may exhibit symptoms”. This definition is not among the more operational, since it is only applicable at the ecosystem level and it is not intuitive what a symptom of an ecosystemic dysfunction may be. Another thought-provoking definition of stress is that by Rykiel in which stress is “a physiological or functional effect; the physiological response of an individual, or the functional response of a system caused by disturbance or other ecological process; relative to a specified reference condition; characterized by direction, magnitude, and persistence; a type of perturbation”. Thus, according to this definition, stress is a type of perturbation that is the effect of disturbance. Here, I much prefer the views of Grime (1977), Wootton (1998) and Sousa (2001), where stress is generally distinguished from disturbance as non-lethal effects and responses. Agents of perturbation are commonly similar to those of disturbance and stress, such as flood scouring (Webster and Patten 1979), environmental variation (Lane 1986), alteration of species densities (Bender et al. 1984). Furthermore, this concept is also used for processes and mechanisms that are not easily defined, such as departure from a normal state (Pickett and White 1985), divergence in spatial organization of badger populations due to bovine tuberculosis (Tuyttens et al. 2000) and the falling of leaves on spider webs (Leclerc 1991). Moreover, the term unperturbed is used by Padisak (1993) to describe systems unaffected by either disturbance or stress. Although definitions of perturbation are scarce in the literature, there are a few notable exceptions. Rykiel (1985) defines perturbation as “the response of an ecological component or system to disturbance or other ecological process as indicated by deviations in the values describing the properties of the component or system; relative to a specified reference condition; characterized by direction, magnitude, and persistence”. Hence, according to Rykiel (1985) disturbance is the agent causing damage whereas perturbation, as well as stress, is the effects of a disturbance. Distinguishing between the cause and effect of disturbances is not unimportant, for instance, if a process defined as disturbance does not invoke any measurable response in the recipient community it is questionable whether a disturbance has really occurred. However, this interpretation of the terms has not been widely accepted, which is likely due to the rather counter-intuitive terminology of stress- and

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perturbation-causing disturbances. Another exception is the definition by Picket and White (1985), where perturbation is “a departure (explicitly defined) from a normal state, behaviour, or trajectory (also explicitly defined)”. Although this definition is rather unclear and exceptionally broad, it may in this case be both appropriate and useful. In the sense that Padisak (1993) uses the term, but in contrast to Rykiel (1985), it may be beneficial to reserve a word that describes process and mechanisms that can be either disturbance or stress, or in fact neither.

Ecological Theories on Disturbance Disturbance has been recognized as a structuring force in ecological communities since the beginning of the last century (Cooper 1913). However, it was not until the 1970ies that disturbance was regarded as a key process in general ecological theory (Dayton 1971, Grime 1973, Levin and Paine 1974). Since then, a number of hypotheses have been proposed to address the involvement of disturbance in ecological phenomena. These hypotheses mainly concern succession and biodiversity (Connell 1978, Miller 1982, Dial and Roughgarden 1998), but also on evolutionary processes (Benmayor et al. 2008), biological invasions (Davis et al. 2000) and ecosystem functions (Cardinale and Palmer 2002). More recently, the productivity in natural communities, another key process in ecology (Connell and Orias 1964, Tilman 1980, Abrams 1995), has been suggested to act in concert with disturbance, which may explain more complex patterns in species diversity (Huston 1979, Kondoh 2001, Worm et al. 2002). The following sections will focus on the most common hypotheses and models on effects of disturbance on biological diversity, the interactive effects of disturbance and productivity, as well as possible assumptions or prerequisites that these models may rely on.

The Intermediate Disturbance Hypothesis (IDH) The most prominent theory on disturbance, and possibly ecology in general, is the Intermediate Disturbance Hypothesis (IDH; Connell 1978) (Fig. 2). The original paper by Connell (1978) has been cited over 3300 times and the IDH also represents one of few well established ecological theories with an impact on management of marine and terrestrial national reserves and parks, e.g. Yellowstone National Park, USA (Wootton 1998). The origin of the IDH is, however, debated (Wilkinson 1999). Even though J. H. Connell is commonly credited as the originator of the IDH, his main argumentation relies on the much earlier work of Eggeling (1947) on patterns of diversity in African rain forests (see: Fig. 1 in Connell 1978). In his article, Wilkinson (1999) also identifies three well-known authors who all, prior to the work of Connell, discussed relatively higher diversity at some form of intermediate level of disturbance; E. P. Odum (1963), J. P. Grime (1973), and H. S. Horn (1975). Similarly, Osman (1977) identified “an optimal frequency of disturbance at which diversity is maximized” in his study on marine epifaunal communities, which he argues is caused by reductions at high and low levels of disturbance “because of a decrease in the number of species present or an increase in dominance”. Surprisingly, neither of Odum (1963), Grime (1973), Horn (1975) or Osman (1977) is cited in the review article by Connell (1978). The IDH predicts that diversity will reach its maximum at intermediate levels of disturbance, while remaining low at high and low levels of disturbance (Fig. 2). The rationale for this is that at low levels of disturbance strong competitors exclude competitively inferior species and communities are dominated by a few species. Intermediate levels of disturbance, however, disrupt competitive hierarchies by increasing levels of mortality and thus making free space

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available for recruitment of competitively inferior species. At successively higher levels of disturbance, recruitment cannot balance the high levels of mortality and slow recruiting

species disappear from the community. The drawback of this straightforward logic, and hence its conceptual appeal, is that it has received criticism from both empirical and theoretical studies for being too simplistic (Pacala and Rees 1998, Huxham et al. 2000, Shea et al. 2004). Furthermore, a literature review revealed that only 20 % of the studies on effects of disturbance on diversity showed the unimodal pattern predicted by the IDH (Mackey and Currie 2001). Nevertheless, the IDH has been supported in field experiments in terrestrial (e.g. Armesto and Pickett 1985, Collins 1987, Molino and Sabatier 2001), freshwater (e.g. Padisak 1993, Reynolds 1995, Flöder and Sommer 1999) and marine communities (e.g. Osman 1977, Sousa 1979a, Valdivia et al. 2005), as well as in laboratory experiments (e.g. Widdicombe and Austen 1999, Buckling et al. 2000, Cowie et al. 2000) and model evaluations (Petraitis et al. 1989, Dial and Roughgarden 1998, Li et al. 2004). In accordance with these studies, the characteristic hump-shape pattern between disturbance and diversity was observed in papers I , II and IV . The apparent simplicity of the IDH may, however, be slightly deceiving. There are, in fact, many aspects of the IDH and the way that disturbance may determine levels of diversity. Although I will spare the reader yet another section on components of disturbance, there are

Fig. 2 The hump-shaped pattern between disturbance and diversity as predicted by the Intermediate Disturbance Hypothesis (IDH). The mechanisms of the IDH are illustrated by settling panels (A, B and C) used in papers I and II . At point A diversity is low due to competitive exclusion, at point B coexistence is enabled by freeing space for new species, and at point C few species survive due to high level of disturbance.

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some fundamental differences among the mechanisms of disturbance in relation to the hypothesis that should be noted. For instance, how often a disturbance occurs (i.e. frequency), how large the disturbance is (i.e. area or extent) and time since the last disturbance (i.e. time). Even though they are all interrelated, through the main rationale of disrupting competitive exclusion, the underlying mechanisms may be different. In the case of frequency, high levels of diversity can be maintained if the disturbance events occur often enough to prevent any one species from achieving dominance, while not occurring so often that only few species can persist. When the extent of disturbance is considered, areas that are too large will eliminate all species, areas that are too small will have little or no impact, whereas intermediate areas may disrupt competitive exclusion and allow establishment of new species in the disturbed patches. In comparison, the time aspect states that high diversity will be observed at some point in time after recolonization of the disturbed area, but before the community returns to its successional climax (i.e. dominance by few species). The main difference here is commonly referred to as the ‘between patch’ vs. ‘within patch’ mechanisms (e.g. Wilson 1990), or sometimes as the resetting of a patch successional clock vs. the creation of a successional mosaic (e.g. Chesson and Huntly 1997). This distinction is articulated in a straightforward way by Wilson (1994): “A single patch does not have a frequency of disturbance, only a time since last disturbance”. Albeit a bit drastic, it has been suggested that the within patch aspect is not a mechanisms of coexistence, as much as a mere observation of succession (Wilson 1990, Wilson 1994, Chesson and Huntly 1997). In contrast, the successional mosaic, or between patch, explanation relies on disturbances occurring in a greater area, where disturbed patches are all in different stages of succession and may, thus, together compose a high regional diversity (Levin and Paine 1974, Chesson and Huntly 1997, Sheil and Burslem 2003). One way to resolve the discussion about the differences between the within-patch and the between-patch mechanisms of the IDH, could be to consider the different components of disturbance, i.e. how the damage from the disturbance is exerted. Bender et al. (1984) distinguishes between ‘pulse disturbance’, i.e. instantaneous alteration of species number, and ‘press disturbance’, i.e. the sustained alteration of species densities (see also section ‘Components and quantities of disturbance’). A press disturbance could unceasingly prevent competitive exclusion of a dominant species, which yields higher within-patch diversity. In contrast, a pulse disturbance would provide patches of different successional stages and ages (younger more r-selected and older more K-selected species), giving rise to the higher between-patch diversity. Hence, this subdivision of disturbance could perhaps be a missing link in the so far unresolved issue (see Sheil and Burslem 2003) of differentiating the within-patch from the between-patch mechanisms of the IDH.

The Dynamic Equilibrium Model (DEM) The Dynamic Equilibrium Model (DEM; Huston 1979, Kondoh 2001) relies on the same general coexistence mechanisms as the IDH (Fig. 3). At low levels of disturbance one, or few, species will dominate and exclude all other species, and at high levels of disturbance very few species can persist, while coexistence is possible at intermediate levels. The addition in the multifactorial model DEM is that the relationship between disturbance and diversity is modified by the level of productivity. Huston (1979) suggested that increased productivity, and thus growth rates of individuals and populations, means that a more severe disturbance is required to prevent competitive exclusion. Consequently, at low productivity, and slow growth rates, maximum diversity is observed already at low levels of disturbance because competitive exclusion occurs at a lower rate. Thus, the shape of the relationship between

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disturbance and diversity is predicted to be of three general types: monotonically decreasing (at low productivity), unimodal (when productivity is intermediate) and monotonically increasing (when productivity is high). Although the DEM has not been experimentally evaluated nearly as much as the IDH, there are corroborating manipulative studies from aquatic as well as terrestrial systems (e.g.Turkington et al. 1993, Worm et al. 2002, Jara et al. 2006). However, in paper I , there was no effect on diversity of the manipulated increase in productivity, whereas maximum species richness was observed at intermediate levels of physical disturbance, in accordance with the IDH. This is likely explained by the productivity treatment, which, despite a general effect on growth rates of algae, did not affect the competitive dominants in the hard substratum assemblages. Thus, the rate of competitive exclusion was not measurably affected and more frequent disturbance was consequently not required to prevent exclusion of inferior competitors at high levels of productivity.

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Similar to the IDH, agents and components of disturbance may influence the outcome of tests on the DEM. For instance, biological and physical agents may differ in selectivity (McGuinness 1987, Wootton 1998, Sousa 2001) and consumers often prefer prey with higher nutrient content (Emlen 1966, Onuf et al. 1977, Pavia and Brock 2000). One indication of a discrepancy between agents of disturbance is that interactive effects between biological disturbance and productivity has been observed in many studies from various environments (see Proulx and Mazumder 1998 and references therein), whereas tests of the DEM using physical disturbance have more variable outcomes (e.g. Turkington et al. 1993, Death and Winterbourn 1995, Death 2002, Jara et al. 2006). In paper III , in order to test for possible differences among agents, I contrasted the effects of a biological to that of a physical disturbance in an experiment on the DEM. Using natural sessile assemblages on boulders (i.e. epilithic communities) composed of invertebrates and macroalgae, I tested for interactive

Fig. 3 The patterns predicted by the Dynamic Equilibrium Model (DEM). At low levels of productivity, maximum diversity is observed already at low levels of disturbance due to low rates of competitive exclusion. At intermediate levels of productivity intermediate levels of disturbance is required, and high levels of productivity high levels of disturbance is required, in order to disrupt competitive exclusion by dominants and free resources for colonizing species.

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effects between productivity (high vs. ambient), physical disturbance (simulated wave-action at five distinct frequencies) and biological disturbance (grazing by periwinkles manipulated as absent or present). The number of algal species was interactively affected by productivity and biological disturbance, whereas the invertebrate richness was affected by physical disturbance only. This may in part be explained by difference in degree of selectivity between agents, but, more interestingly, also in the way the damage is exerted. When biomass is slowly reduced, as exerted by the biological, continuous small-scale disturbance (i.e. press disturbance; Bender et al. 1984), this effect can more easily be counteracted by increased growth of the affected organisms (Huston 1979, Kondoh 2001). In contrast, increased individual growth rate cannot easily compensate for instantaneous loss of individuals, as exerted by the physical disturbance (i.e. pulse disturbance; Bender et al. 1984). In accordance with these arguments and our results, Kneitel and Chase (2004), the only previous study that has tested for interactions of all three factors, also found that biological disturbance (predation), but not physical disturbance (drying), and productivity interactively affected species richness. Thus, agents and components of disturbance may not only influence disturbance-diversity patterns, but also the specific interactive effects between disturbance and productivity on biological diversity of natural communities.

Additional related models The only model on effects of disturbance on diversity that specifically considers the different components of disturbance is that by Miller (1982). In his article, he introduces the term ‘rate’ of disturbance, i.e. the product of area and frequency, which, thus, takes into account the total amount of disturbance inflicted upon a community (see also section ‘Components and quantities of disturbance’). According to Miller (1982), small, frequent disturbances favour species with rapid vegetative growth (i.e. ‘competitors’), whereas large, less frequent disturbances favour species with high capacity for dispersal (i.e. ‘colonizers’) due to the differences in perimeter to area ratios among patches. Although Miller (1982) predominantly focuses on the area of disturbance, the other component of the rate, frequency, is equally important. Similar to variations in area, differences in frequency and timing of disturbance will influence the abundance and composition of natural communities (Sousa 2002). This is because species are likely to increase in abundance when the disturbance regime matches their preferred recruitment time (Underwood and Anderson 1994, Crawley 2004). Furthermore, because of the natural large variation in temporal distribution of propagules among species (Roughgarden et al. 1988, Underwood and Anderson 1994) a single large disturbance can only be colonized by the propagules that are available at the specific time when a limiting resource, i.e. space, is made free. In paper II I tested the model by Miller (1982), or more specifically if the specific combination if area and frequency matters even if the rate is kept constant. In accordance with the predictions by Miller (1982), the regime with small, frequent disturbances favoured colonizing species, whereas large, less frequent disturbances favoured competitive dominants. Thus, as is claimed in the title, equal rates of disturbance did cause different patterns in diversity. In a model on the importance of the timing of disturbance, Abugov (1982) introduces the concept of disturbance ‘phasing’. Abugov (1982) distinguishes between disturbances that are phased compared to those that are unphased. A phased disturbance means that all patches are cleared simultaneously, and the patches are termed to be ‘in phase’. Conversely, during unphased disturbance, the probability of a patch being cleared by disturbance is independent of the disturbance of other patches. Phased disturbances are considered to be more large scale disturbance events such as storms or forest fires, whereas constant predation is given as an

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example of unphased disturbance. The outcome of Abugov’s model showed that highest diversity always occurred at intermediate levels of disturbance, regardless of the degree of phasing, but also that the diversity at any given level of disturbance depend on the degree of phasing. Furthermore, similar to the multifactorial model DEM, high levels of diversity was observed at intermediate degree of phasing at intermediate levels of disturbance. The idea of phasing is similar to that of temporal variability in disturbance, which has been shown to affect the community structure of benthic assemblages on rocky shores (i.e. Bertocci et al. 2005, but see: Sugden et al. 2007). It is also similar to the concepts of ‘Nonadditivity’ (Chesson 2000), ‘Storage Effect’ (Chesson and Huntly 1997) and ‘Spatiotemporal Niche Creation’ (Pacala and Rees 1998). The key argumentation in these concepts is that coexistence is enabled because different species utilize different spatiotemporal niches. The spatiotemporal niches may differ, depending on environmental fluctuations or disturbance, in the amount of available resources, the free space for settling and in their current stage of succession (Amarasekare et al. 2004, Roxburgh et al. 2004, Shea et al. 2004). Due to the suggestions of coexistence mechanisms that are consider to be alternative, the IDH and the DEM have been argued to give “inadequate, inconsistent, or improbable explanations” of species coexistence (see: Chesson and Huntly 1997). However, the main mechanism of coexistence in all these concepts, including phasing and temporal variability, is that different patches are at different successional stages and/or differ in availability of resources. Hence, it could be argued that they are all describing the ‘between-patch’, or ‘successional mosaic’, aspect of the IDH, where coexistence is maintained, or enabled, by disturbance, because patches at different stages in succession differ in species composition. In their investigation of the theoretical validity of the IDH, Dial and Roughgarden (1998) found what they call ‘the intermediate area hypothesis’ and ‘the intermediate recruitment hypothesis’. In contrast to most other models on disturbance (Petraitis et al. 1989, Chesson and Huntly 1997, Kondoh 2001), their mathematical model incorporates the dynamics of pelagic larvae and benthic adults, as well as hierarchal competition for the limiting resource space. The larval-benthic dynamics was purposely considered because the pattern predicted by the IDH is often observed in communities where species have long-lived propagules and space-limited adults, such as marine invertebrates, macroalgae and seed plants (Sousa 1979a, Sousa 1979b, Molino and Sabatier 2001, Jara et al. 2006). More specifically, in these systems the disturbance only affects the sessile adults, while leaving the propagule mortality unaffected (Dial and Roughgarden 1998). The two key points of the outcome of the model was that the IDH is a moderate to high settlement phenomenon, and that a subordinate species must have an adaptation allowing it to survive and/or colonize at levels of disturbance that are lethal to the dominant, if disturbance, area, or settlement is to allow coexistence. According to Dial and Roughgarden (1998), these two key points show that the IDH is not a universal phenomenon, which also leads to the additional outcome of the model, the intermediate area and recruitment hypotheses. If the level of disturbance, at an intermediate value, is kept constant, intermediate levels of recruitment lead to coexistence among species. This is explained by the exclusion of the subordinate species of a dominant superior at high recruitment, and at low recruitment the dominant cannot exist. However, in their model, area is equivalent to settlement, thus, yielding a similar intermediate area effect, where smaller habitats can favour subordinate species’ coexistence with a dominant species. Although it could be argued that the proposed hypotheses are in fact already inherent functions of the IDH, since diversity cannot increase if no new species settle (Osman 1977, Huxham et al. 2000, papers II and III), it may still be noteworthy to point out that disturbance is not the only way exclusion can be prevented and coexistence maintained. Furthermore, it gives important

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insights in the underlying mechanisms of coexistence for the IDH, as well as the possible prerequisites for observing the pattern predicted by the IDH discussed in the next section.

Prerequisites for the IDH and the DEM In response to the inconsistencies in the outcome of manipulative tests of the IDH (reviewed by Mackey and Currie 2001), several authors have suggested that the predictions of the IDH relies on a number of prerequisites. The most common prerequisites, or assumption, are competitive exclusion (Fuentes and Jaksic 1988), large regional species pool (Osman 1977), multiple stages in succession (Collins and Glenn 1997), nonlinear resource use (Chesson and Huntly 1997), availability of spatiotemporal niches (Pacala and Rees 1998) and trade-offs between competition and tolerance (Petraitis et al. 1989) and between competition and colonisation (Dial and Roughgarden 1998). Furthermore, Menge and Sutherland (1987) argued that the effects of disturbance depends on the amount of environmental stress in the system. However, the constructive criticism in the suggestions of the prerequisites primarily concerns aspects of two key processes; competition and colonization.

Aspects of Colonization According to Dial and Roughgarden (1998), the IDH is a ”moderate-to-high settlement phenomenon”, and Collins et al. (1995) pointed out that it is settlement by propagules that may allow for increases in diversity, not disturbance per se. That colonization is important in order for disturbance to have a positive effect on diversity is intuitive and logic. Diversity cannot increase if there are no available propagules to occupy the space, or any other limiting resource, which is freed by disturbance (Sousa 2001). Another suggested prerequisite, that is equally straightforward, but maybe less intuitive, is the importance of a large regional species pool (Osman 1977). This is because diversity cannot increase if the propagules that establish in the cleared space, are the same species that originally inhabit the assemblage. This was clearly shown in a manipulative experiment by Huxham et al. (2000), where the species pool in the intertidal macrofaunal communities was too small to allow for settlement of new species in the assemblages subjected to disturbance. Low rate of colonization is also something that may explain the lack of positive effects of disturbance on diversity in paper III and at one of three sites in paper II . In the experiment on the effects of physical and biological disturbance and productivity on natural epilithic assemblages (paper III ), the recruitment of new species occurred at a rate that was not sufficient to counteract the negative effects of disturbance. Similarly, in paper II , the physical disturbance did not have a significant effect on the richness of the hard substratum assemblages at one site, where richness was generally low and new species did not settle in disturbed patches. In contrast to paper III , this experiment was setup in the waters of the Tjärnö archipelago, where the regional species pool and availability of propagules per definition was natural. However, it has previously been shown that local hydrodynamics in areas near this site may hamper the settling of invertebrate larvae (Berntsson et al. 2004, Jonsson et al. 2004), which also could explain the surprisingly low total cover in the controls assemblages at this site. Thus, local hydrodynamics may be of equal importance to the availability of propagules and the size of the regional species pool, for the outcome of manipulative experiments on the effects of disturbance on diversity.

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Aspects of Competition The other key process in the suggested prerequisites, competition, was mentioned already by Connell (1978), who considered competitive exclusion to be an assumption for the coexistence facilitating mechanism of disturbance. Similar to the arguments for colonization, disturbance cannot increase diversity if there is no exclusion process to interrupt by removing the dominant(s) and allow new species to establish in a community (Huston 1979, Sousa 1984, 2001). This is also linked to the suggested trade-off between competition and colonization. If the inferior species cannot out-compete the dominant at colonizing newly freed substrata, competitive exclusion may not be prevented and diversity will not increase in response to disturbance (Dial and Roughgarden 1998). Similarly, for the trade-off between competition and disturbance tolerance, the inferior species must be better adapted to cope with destructive events, either by physiological tolerance or other means such as fast growth and re-colonization (Petraitis et al. 1989). Thus, in order for a disturbance to facilitate coexistence, the dominant species must be comparatively more susceptible to the damage exerted. Furthermore, the dominant species must also be able to maintain their competitive advantage in the absence of disturbance (Connell 1978). The importance of competition for the outcome of experiments on disturbance is clearly shown in paper II , where the three different responses to disturbance at the three different sites clearly corresponded to the differences in species composition (fig. 4). Competitive exclusion was evident at the site where support for the IDH was found, as also observed in paper I , whereas increasing levels of disturbance only decreased diversity at the site lacking clear dominants in the undisturbed controls. Although assemblages at the third site also lacked dominants, there was no effect of disturbance because the initial diversity was so low that even the limited colonization in this area could counteract the effects of disturbance. Consequently, the same disturbance can give widely different patterns in diversity depending on the composition of species, and the level of competition, in communities. In order to disrupt the competitive advantage of dominants, the destructive event of a disturbance must potentially affect all species in a similar manner, or, conversely, fall heavier on the competitive dominants. The problem with possible selectivity of agents has been discussed for manipulations of disturbance, but not for manipulations of productivity. This lack of considerations of selectivity in agents may severely confound tests of the DEM. The DEM predicts that competitive exclusion will increase with productivity, thus requiring a stronger disturbance to be disrupted, but if the inferior competitors are more strongly affected by the productivity treatment this could instead slow down the rate of exclusion. This would cause diversity to peak at lower, rather than the predicted higher, intensities of disturbance. The issue of the selectivity of agents of productivity was clearly shown in paper I, where the IDH was supported, but the DEM was not. The most likely explanation for this outcome is that the dominant species exerting competitive exclusion, the tunicate Ciona intestinalis, was unlikely to benefit from the manipulation of nutrient availability. Hence, even though the productivity treatment had a general, positive, effect on growth rates in the assemblages, the rate of competitive exclusion did not increase, and higher levels of disturbance was consequently not required to maximize diversity. Even in studies that recognize the issue of selectivity, there is a practical difficulty of designing a non-selective agent of productivity in manipulative experiments. Experimental manipulation of productivity in tests of the DEM is commonly done indirectly, i.e. by adding nutrients or organic matter (Turkington et al. 1993, Widdicombe and Austen 2001, Worm et al. 2002, Kneitel and Chase 2004, Jara et al. 2006, Canning-Clode et al. 2008, Sugden et al. 2008). In such manipulations it is necessary to test independently whether the actual experimental treatment (the adding of nutrients or organic matter) has an effect on productivity. Without evidence for an actual increase in productivity

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Fig 4 Three significantly different communities at sites 1, 2 and 3 which showed three different responses to disturbance in paper II . (a) Species composition, as well as pictures, of the control assemblages at sites 1, 2 and 3 and (b) responses to rates of physical disturbance, significant quadratic and linear quadratic components, respectively, at sites 1 and 2, and no significant pattern at site 3.

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experiments cannot perform an adequate test of the DEM, and without information on the selectivity of the agent of productivity the outcome of tests cannot be adequately interpreted. Unfortunately, this issue is generally overlooked (e.g. Widdicombe and Austen 2001, Scholes et al. 2005, Jara et al. 2006). Nevertheless, if predictions about effects of productivity and disturbance on diversity are to be tested in field experiments, indirect manipulations, such as adding nutrients or organic matter, may be the only conceivable solution.

Considerations of diversity Something that is conspicuously absent in the literature is a discussion on the potentially large variation in outcomes among studies depending on the measure of diversity that is used in tests of the IDH. As discussed in the earlier sections, nearly every aspect of disturbance has been considered, e.g. the definitions, the agents, the components, the quantities, how the damage from disturbance is exerted and a multitude of prerequisites have been suggested to explain inconsistencies in outcomes of the IDH. In addition, many other aspects of the IDH have been discussed, such as alternative mechanisms underlying coexistence (Pacala and Rees 1998), influence of the characteristics of communities (Fuentes and Jaksic 1988), interactive effects of disturbances (Collins 1987), importance of the specific traits of individual species (Haddad et al. 2008) and the context dependence of intermediacy (Shea et al. 2004). Yet, despite over 3300 citations of Connell (1978) and ample attention in the scientific literature, no one has considered the response variable for the conceptual model IDH, i.e. the aspect of diversity. Consequently, in paper IV I investigated how the measure of diversity may affect the outcome of studies on effects of disturbance on diversity. This was done by scrutinizing the original formulations of the models, conducting a meta-analysis of previously published studies and through two different approaches to mathematical modelling. In the formulation of the IDH, Connell (1978) uses the word diversity without any further definition, while Huston (DEM; 1979) rejects all various indices and considers diversity to be solely richness and evenness. In the model presented by Miller (1982) diversity is defined as a measure that includes both “species abundance and number”. However, neither Huston nor Miller makes an effort to explain what kind of effects disturbance would have on species abundances in contrast to the number of species. In the meta-analysis I investigated if all measures of diversity show the same response in studies that use two or measures of diversity within the same experiment. The mathematical modelling was performed using one already established spatially implicit model (Kondoh 2001) and one spatially explicit automation model, in order to specifically contrast the responses to disturbance of the two major components of diversity: richness and evenness. Both models support the IDH when biodiversity is measured as species richness, but, in contrast, predict that evenness increases monotonically with increasing levels of disturbance. The meta-analysis showed that two-thirds of the published studies in the survey present different results for different diversity measures, and the comparisons between richness and evenness showed an even higher degree of dissimilarity. In addition, when the analyses from papers I and II were rerun to include evenness as response variable (these results were not included in any of the papers), the same patterns as in the models emerges. Hence, in accordance with the predictions of the two model, species richness was maximized at intermediate levels of disturbance, and evenness showed linear increases with increasing rates of disturbance (Evenness: linear component MS=0.95, F=28.7, p<0.01; MS=1.75, F=81.8, p<0.01, respectively, quadratic component MS=0.010, F=0.30, p=0.58; MS=0.0052, F=0.24, p=0.63, respectively, Fig. 5). Thus, the meta-analysis, as well as the

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mathematical two models and the re-analysis of previous field experiments clearly show that the measure of diversity is vital for outcomes of tests of the IDH.

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Conclusions In this thesis I have clearly (i.e. hopefully) shown that the definition of disturbance can influence the outcome of studies, depending on which characteristics of disturbances a particular definition encompasses. The type of agent that is causing the disturbance is crucial, because selectivity can differ among disturbance agents and biological agents may choose prey depending on nutritional value. Different components of disturbance can affect communities in different ways, and even the specific proportions of area and frequency within the same rate of disturbance can cause different patterns in diversity. The effects of disturbance will also to a large extent depend on the species composition of the community upon which it is inflicted. In tests of hypotheses on disturbance-diversity pattern, outcomes are generally influenced by the rate of competition, the availability of propagules, the regional species pool and interactions with the abiotic environment. Experimental tests of models that include productivity should also include explicit investigations of whether the manipulative treatment significantly affects the overall productivity, as well as the recognition of the possible selectivity of productivity agents. Furthermore, the measure of diversity used as response variable is vital for the outcome of tests of hypotheses on effects of disturbance on diversity. Clearly, there are many aspects to consider in experimental design and interpretation of results in disturbance-diversity studies. Consequently, in order to increase the generality and commensurability among studies, it will be of great benefit if experimenters (i) define the type of disturbance used in the study, (ii) assign ecologically relevant agents of disturbance and productivity with quantifiable components, (iii) recognize the characteristics of the community the disturbance is inflicted upon, and (iv) specify, and justify, the measure of diversity to be used in tests of hypotheses on effects of disturbance on diversity.

Fig. 5 Hump-shaped patterns between species richness and disturbance, but linear increases in evenness, in the two models from paper IV (a and b) as well as the re-analyzed results from the field experiments in papers I (c) and II (d).

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32

Acknowledgements Egentligen skulle man kanske, nu natten innan tryckning, köra en Kjell Bergquist här; få allt snabbt överstökat genom att tacka sig själv och sen helt sonika dra, men detta skulle givetvis vara särdeles osant. Något som dessutom tar lite emot att erkänna är väl att två stycken ohängda GAIS:are haft en stor del i att allting inte blivit helt värdelöst. Detta trots initierandet av ett mått på en tillsynes kvantifierbar tidsrymd kallad ”nästa vecka”, där saker som utlovas hända inom denna tidsrymd, namnet till trots, aldrig inträffar veckan efter det yttrades. Mina kunskaper inom kvantfysik, parallella universa och strängteori är väldigt begränsade, men jag antar att det är någon ytterligare dimension här som jag ännu inte fått grepp om. Ett annat bra knep, som utövas i den icke-omänskliga tidsrymden, har beskrivits av förbipasserande finlandssvenskar som ”Vad fan gör ni på era möten egentligen? Ni sitter helt tysta och stirrar förvirrat upp i taket varje gång man går förbi!”. Förutom att detta är ett tydligt tecken på total inkompetens från alla inblandade, har det även lett till storslagna vetenskapliga genombrott som av internationellt erkända forskare officiellt benämnts som ”a fundamental flaw in the authors logic” och ”the conclusions of the work are not supported by the data”. Trots att 50 % av handledarna föreslog kollektivt självmord som respons på kritiken, röstades detta förslag (uppenbarligen) ned av hela 67 % av de mer livslustsfyllda medförfattarna. Mer konkreta framsteg är kunskapsöverflyttning från Nycklebybor till Majornabor inom statistikens svårbegripliga värld, där ofattliga akronymer (CAP, SNK, MDS, anova, permanova) lärts ut med ett leende på GAIS-läpparna, citerandes en mer filosofisk approach som i stora drag går ut på att ’tortera datan med olika analyser tills den erkänner’. Det hemliga epitetet ’HH’, som visat sig vara helt oförknippat med 30-talets Centraleuropa, har även lömskt, genom att tvinga fram oändligt många versioner av varje manus, lärt mej tekniken för gränslös episkhet oberoende av data. Lite som när Daniel Larusso vaxade Kesuke Miyagi’s bilar, svärandes samt ovetandes om tragglandets potentiella storhet. Framåt slutet i en tillsynes oändlig GAIS-dimma har det även funnits en Åtvidabergiansk fyr, i vars ljus planer smitts, modeller uppkommit, hypoteser utbenats och elektronisk post besvarats, smått chockerande, redan samma dag. Slutklämligen, utan er hade detta aldrig gått. Flertalet kemiekologiska G-människor har funnits vid min sida där stöttande aktiviteter inte bara inneburit balkongvistande rusdrycksinmundigande, utan även nakenbadande, trolldegsskapande, kemisk analys-assistans, vågmaskinssnickrande, hälsovådliga undervattensaktiviteter, nikotin-snikande, tröstlöst trålletande, fältmässigt Jägermeister shottande och klippstrands överblickande butterkaksätande. Till detta gäng hör även min kontorssambo som inte bara guidat mej genom hela Honshu och Hokkaido, utan även förklarar för mej obegripliga saker som projektuppföljning i datalagret och elektronisk fakturahantering. I Tjärnös begynnelse fanns smålänningar och dalmasar, men även nollåttor, Disney-karaktärer och finnar av båda kön. Skåningar, som det finns alldeles för många av, har lyckligtvis befunnits på behörigt avstånd, men som trots detta, och gärna i kombination med en viss smålänning, ständigt lyckas leta upp en och håna ens fiskekunskaper. There has also been a sensei in the underappreciated art of sandwich making, who relentlessly remind me that things are rarely as good as they seem, and his beloved wife who initially adopted me as a second boyfriend, only to leave me heartbroken for Sverker’s southern regions. Eftersom det finns ett oändligt antal oidentifierbara kräk och slemmiga växter i havsdjupen har jag varit helt beroende de barmhärtiga samariterna Elisabet, Anneli, Fredrik P och Hans-G. I stark kontrast till mentala prövningar finns en slagsmålsklubb som äger rum på Tjärnö skola varje onsdag, där revben brutits, blåa ögon mottagits och utdelats av Per B, Erik B, Swantje, Greg, Fidde, Micke, Lars, Finn, Petri, Tuuli, Ankan, Erkan, Göran, Gunnar, Henke, Mats, Martin G, Martin S, Malin, Piff, Puff, Erika, Andreas, Anders, Geno, Josefin, Carl-Johan, Johanna,

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Johan E, Johan H, Johan R, Johan W, Hanna H, Hanna S, Eva-Lotta, Mia, Filip, Stina, Rickard Lasse Pereyrasson och många, många fler. I slutet på flertalet välbehövliga flyktförsök från en öde ö fanns ett stadsdelsstort högkvarter idogt bevakat av MB, Åsmund, DAF, Pejlert, Mr. Däjvid, Greken samt även Ryssen och Dödskristian, där i rusdryckernas glada brödraskap energi återskapats, smärta och glädje delats och livslånga minnen bildats. Nästgårds har jag även funnit landets vänaste prinsessa som, förutom av mej påtvingad korrekturläsning, håller mej på topp genom överrasknings- brottning likt Kato i Clouseaus kylskåp, och som dragit upp mej ur träsket och visat mej en värld fylld av ’hummer och rosa champagne’. Till sist min familj som funnits där, inte bara under doktorand-perioden, utan alltid.

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Paper I

Paper II

Paper III

Paper IV

Paper V

Paper VI

PAPER I

“I have opinions of my own, strong opinions, but I don't always agree with them.”

- George H. W. Bush

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Ecology, 88(4), 2007, pp. 830–838� 2007 by the Ecological Society of America

MAXIMUM SPECIES RICHNESS AT INTERMEDIATE FREQUENCIES OFDISTURBANCE: CONSISTENCY AMONG LEVELS OF PRODUCTIVITY

J. ROBIN SVENSSON,1,5 MATS LINDEGARTH,1 MICHAEL SICCHA,2 MARK LENZ,3 MARKUS MOLIS,4 MARTIN WAHL,3

AND HENRIK PAVIA1

1Department of Marine Ecology, Goteborg University, Tjarno Marine Biological Laboratory, 452 96 Stromstad, Sweden2Institute for Geological Science, Eberhard Karls University Tubingen Sigwartstr.10, 72076 Tubingen, Germany

3Leibniz-Institute for Marine Science, Dusternbrooker Weg 20, 24105 Kiel, Germany4Biologische Anstalt Helgoland, Alfred Wegener Institute for Polar and Marine Research, Marine Station,

Kurpromenade 201, 27498 Helgoland, Germany

Abstract. Development of a mechanistic understanding and predictions of patterns ofbiodiversity is a central theme in ecology. One of the most influential theories, the intermediatedisturbance hypothesis (IDH), predicts maximum diversity at intermediate levels ofdisturbance frequency. The dynamic equilibrium model (DEM), an extension of the IDH,predicts that the level of productivity determines at what frequency of disturbance maximumdiversity occurs. To test, and contrast, the predictions of these two models, a field experimenton marine hard-substratum assemblages was conducted with seven levels of disturbancefrequency and three levels of nutrient availability. Consistent with the IDH, maximumdiversity, measured as species richness, was observed at an intermediate frequency ofdisturbance. Despite documented effects on productivity, the relationship between disturbanceand diversity was not altered by the nutrient treatments. Thus, in this system the DEM did notimprove the understanding of patterns of diversity compared to the IDH. Furthermore, it issuggested that careful consideration of measurements and practical definitions of productivityin natural assemblages is necessary for a rigorous test of the DEM.

Key words: competitive exclusion; disturbance; productivity; species richness.

INTRODUCTION

Spatial and temporal patterns of diversity in natural

communities are central themes in classical natural

history as well as in contemporary theoretical ecology

(e.g., Huston 1994, Hubbell 2001). Throughout history

the magnitude of existing biological diversity and its

heterogeneous distribution have continuously chal-

lenged ecologists to develop and test models to explain

patterns at a multitude of temporal and spatial scales,

using increasingly more complex models (e.g., Connell

1978, Huston 1994, Hubbell 2001). Some of these

models have been based on biological interactions

(e.g., Miller 1958, Fischer 1960, Paine 1966, Paine and

Vadas 1969, Menge and Sutherland 1987), while others

have primarily focused on abiotic processes (e.g.,

Hutchinson 1961, Levin and Paine 1974, Connell 1978,

Paine and Levin 1981).

Many of these ideas rely on disturbances to disrupt

the effects of biological interactions, such as competitive

exclusion, on diversity. A variety of abiotic (e.g., fire,

wind, wave action, and drifting logs) and biotic factors

(e.g., grazing, predation, and trampling) may act as

agents of disturbance, depending on the specific

properties of the particular ecological system. There is

also a range of definitions of what constitutes an actual

disturbance. Grime (1977) defined disturbance as partial

or total destruction of biomass. Sousa (1984) extended

this definition by adding that disturbance also creates

opportunities for new individuals to become established.

Pickett and White (1985) have a broader definition

where disturbance is ‘‘. . . any relative discrete event in

time that disrupts ecosystems, community, or popula-

tion structure and changes resources, substrate avail-

ability, or the physical environment.’’ Thus, despite

some ambiguity in the definition of the concept of

disturbance, it has direct effects on vital rates and

population dynamics and it is therefore a potentially

useful generalization.

One important conceptual formulation of the effects

of natural disturbances on diversity is the intermediate

disturbance hypothesis, IDH (Connell 1978). The IDH

predicts that diversity will be large at intermediate rates

of disturbance and smaller at higher and lower rates of

disturbance. The rationale for this idea is that at low

rates of disturbance strong competitors exclude com-

petitively inferior species and communities are dominat-

ed by a few species. Intermediate rates of disturbance,

however, disrupt competitive hierarchies by increasing

rates of mortality and thus making free space available

for recruitment of competitively inferior species. At

successively higher rates of disturbance, recruitment

Manuscript received 8 June 2006; revised 25 September 2006;accepted 29 September 2006. Corresponding Editor: S. G.Morgan.

5 E-mail: [email protected]

830

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cannot balance the high rates of mortality, and slow-

recruiting species disappear from the community.

Findings consistent with the predictions of the IDH

have been made in manipulative studies in both

terrestrial (e.g., Molino and Sabatier 2001, Anderson

et al. 2005) and marine (e.g., Osman 1977, Sousa 1979,

Valdivia et al. 2005, Patricio et al. 2006) ecosystems.

However, contradictory observations have also been

made (Lake et al. 1989, Collins et al. 1995, Gutt and

Piepenburg 2003), and due to difficulties of incorporat-

ing all components of natural environments, laboratory

studies are often relatively less supportive (Cowie et al.

2000). In summary, the IDH has been an influential

concept in research and also as a tool in management of

nature reserves (Wootton 1998).

In response to observations that did not appear

consistent with the IDH, Huston (1979) suggested that

the relationship between disturbance and diversity is

modified by the level of productivity. Using a dynamic

equilibrium model (DEM), Huston (1979, later elabo-

rated by Kondoh 2001) suggested that increased

productivity, and thus growth rates of individuals and

populations, means that a more severe disturbance is

required to prevent competitive exclusion. As a conse-

quence, maximum diversity is observed at lower

intensities of disturbance when productivity is low,

compared to when productivity is high. The shape of the

relationship between disturbance and diversity may

therefore be of three general types: monotonically

decreasing (at low productivity), unimodal (when

productivity is intermediate), and monotonically in-

creasing (when productivity is high). These three types

of relationships have been observed in various habitats

(e.g., Mackey and Currie 2001), but evidence from

explicit manipulative studies demonstrating the interac-

tive effects of disturbance and productivity is scarce

(Rashit and Bazin 1987, Widdicombe and Austen 2001).

One pioneering test in marine rocky environments is the

study by Worm et al. (2002), who observed interactive

effects of nutrient enrichment and disturbance (grazing

by mesoherbivores) on algal diversity, which they found

consistent with those predicted by the DEM.

The development from a simple general model

involving only one factor, into a more complex and

detailed model involving multiple factors, may represent

important conceptual progress within a field of research

(e.g., Hilborn and Mangel 1997, Underwood 1997). The

benefit of a more complex model is that it may be used

to accurately predict a more diverse set of conditions

with little discrepancy due to approximation (Zucchini

2000). There are, however, no guarantees that a complex

model is more powerful than a simple one (e.g., Zucchini

2000, Ginzburg and Jensen 2004). This is because a

complex model has a greater uncertainty, as it requires

more parameters to be estimated. Thus, in terms of

predictive power, the utility of a complex model relies on

whether the reduction of error due to approximation is

larger than the increase in error due to estimation.

Indeed, from observational data it appears that the great

range of observed responses of diversity to disturbance(Mackey and Currie 2001) can potentially be more

accurately represented if productivity is included (Hus-ton 1979). Whether this really is the case in a wide range

of ecological systems remains to be tested in manipula-tive experiments.

In this study we contrast predictions from the IDH tothose of the DEM in a marine hard-substratumcommunity. Physical disturbance and nutrient availabil-

ity were manipulated in subtidal communities in thefield, with seven distinct frequencies of disturbance and

three levels of nutrient availability. Manipulative studieson epibenthic assemblages have made important contri-

butions to the development and testing of generalecological models (e.g., Paine 1966, Dayton 1971,

Lubchenco and Menge 1978, Sousa 1979). Due to theirpotential for quick recovery, epibenthic assemblages

have proven particularly useful for investigating distur-bance–diversity patterns over ecologically relevant time

scales in manipulative studies (e.g., Worm et al. 2002,Bertocci et al. 2005, Jara et al. 2006).

MATERIALS AND METHODS

Study site

The field experiment was conducted in the vicinity of

Tjarno Marine Biological Laboratory on the west coastof Sweden. The experimental sites were two bays located

;1 km apart (58852.920 N, 1188.310 E and 58852.170 N;1188.820 E for sites 1 and 2, respectively). Site 1 has an

average depth of 8 m and is surrounded by muddy androcky shores. The surrounding cliffs were covered with

red, green, and brown macroalgae as well as mussels andtunicates. Site 2 has an average depth of 6 m and is

surrounded by sandy beaches and boulder fields. Site 2also has an extensive Zostera meadow and the boulders

were commonly overgrown by fucoids, barnacles, andmussels. The grazers in this system are exclusively so-

called mesoherbivores, such as amphipods, isopods, andlittorinid gastropods (Pavia et al. 1999, Wikstrom et al.2006). Gastropods were effectively excluded from

reaching the panels due to the positioning and construc-tion of the experimental units (see Experimental design),

and because of the low abundance of crustaceanmesoherbivores in the vicinity of the experimental units,

possible effects of grazing are not likely to have affectedthe results of this study. The waters off the Swedish west

coast are generally low in nutrients during the summermonths (Nilsson 1991), and nutrients therefore become

a limiting resource in this system (Soderstrom 1996).

Experimental design

Mooring units, made from 2100 3 250 3 4 mm

polyvinyl chloride (PVC) strips bent into a ring, werehung from a buoy ;0.5 m below the water surface. Inthis way, benthic consumers were excluded from the

setup. The rings were deployed on 1 March to allowsettling and establishment of communities before the

April 2007 831DISTURBANCE, PRODUCTIVITY, AND DIVERSITY

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experimental manipulation started on 12 May. The

experimental manipulation had a duration of 24 weeks

and was terminated on 27 October 2004.

On each ring 10 PVC panels (150 3 150 3 3 mm),

roughened with emery paper, were attached with cable

ties. The panels were randomly allocated to combina-

tions of seven disturbance levels and three nutrient

levels. Disturbance treatments consisted of a manual

removal of biomass from two randomly selected

nonoverlapping areas, each covering 10% of the panel

area, at each disturbance event. The scraping not only

kills or damages individuals, but also facilitates recruit-

ment by the freed substratum, and the disturbance is

therefore coherent with the definition by Sousa (1984).

This disturbance was applied at six different frequencies:

every second, fourth, sixth, eighth, 10th, and 12th week

(treatments D1–D6), or left undisturbed (treatment D0).

Treatments D0–D6 were present in all rings, with two

replicates of D0 on each ring, and the remaining two

panels were randomly assigned disturbance treatments

to allow additional replication within rings.

One of three different levels of nutrient enrichment

was applied to each ring by attaching 10 fertilizer bags

(1-mm mesh) among the panels. For the highest level of

enrichment (Nþþ), bags were filled with 100 g of

fertilizer; for the moderately enriched level (Nþ), bags

were filled with gravel and 50 g fertilizer, and bags with

ambient nutrient concentration (N0) were filled only

with gravel. The slow-release Plantacote Depot 6-M,

(5.7% NO3, 8.3% NH4, 9% P2O5, and 15% K2O;

Aglukon, Dusseldorf, Germany) was used as fertilizer

due to its steady release rate in relation to mass, where a

doubling in mass leads to twice the amount of nutrients

being released (Worm et al. 2000). Each level of nutrient

availability was replicated on four randomly assigned

rings. All bags were placed inside the rings at the start of

the experiment and changed every fourth week in order

to have constant nutrient release throughout the

experiment.

Sampling

Sampling of abundance of each species and compo-

sition of the experimental communities was done before

the start of the manipulation and thereafter every eighth

week until the termination of the experiment. Data on

undisturbed communities obtained from the sampling

after eight weeks were used for testing effects of nutrient

availability on algal cover. The time of sampling was

selected to be early in the growth season to minimize

confounding influences of competition. Data from the

last sampling after 24 weeks were used for the main

analyses, i.e., the tests of the IDH and the DEM, and

data on undisturbed communities from all sampling

events were used for studying changes in the communi-

ties over time. Panels were detached and brought into

the laboratory submerged in seawater, kept under

running seawater in the laboratory during the entire

sampling procedure, and brought back into the field

within 16 hours of each sampling event. Before

sampling, the back side and edges of all panels were

scraped clean and their wet mass was measured. The

percentage cover of bare space and sessile species was

then estimated in 5% intervals using a 153 15 cm plastic

grid (mesh size 5 cm2). A 1-cm margin to all edges of the

panels was not assessed, and the percentage cover of

species with a small holdfast and wide thallus was

estimated from the two-dimensional projection of the

organism on the panel. Sessile epibionts were also

accounted for. Thus, total cover was allowed to exceed

100%.

Statistical analyses

The data on species richness were analyzed with

analysis of variance (ANOVA) using Statistica 6.0

(Statsoft Incorporated, Tulsa, Oklahoma, USA). The

models were tested, with species richness as a measure of

diversity, following the elaboration of the DEM by

Kondoh (2001). Hypotheses about effects of main

factors and interactions were tested using the following

general linear model:

Xijklm ¼ lþ Si þ Nj þ SNij þ Dk þ SDik þ NDjk þ SNDijk

þ RðSNÞlðijÞ þ DRðSNÞklðijÞ þ eijklm

where l is the overall mean, site (Si) is a random factor

with two levels, nutrient enrichment (Nj) and distur-

bance frequency (Dk) are fixed factors with three and

seven levels respectively, ring (R[SN]l(ij)) is a nested

random factor with four levels, and eijklm is a random

deviation. Due to loss of one ring and lack of complete

replication of all levels of disturbance on each ring, type

III sums of squares was used for estimation (Henderson

1953). The residual was estimated from the variability

between undisturbed panels and from the additional

replicated treatments within each ring. To optimize

statistical power of tests, post hoc elimination and

pooling of negligible variance components (i.e., if P .

0.25) were performed (Winer et al. 1991, Underwood

1997).

Support for either of the two models, IDH or DEM, is

provided by two different terms in the linear model. The

IDH is supported if there is a significant effect of

disturbance and if the relationship between richness and

disturbance is unimodal with an optimum at intermedi-

ate levels of disturbance. This is equivalent to the

presence of a significant quadratic component in a

polynomial regression. In contrast the DEM is support-

ed by a significant interaction between disturbance and

nutrient enrichment. The predictions of the DEM then

need to be further evaluated using polynomial regression

within individual levels of nutrient enrichment.

A fundamental premise for any experimental support

for the DEM is that the nutrient treatments actually

cause an increased primary productivity. In order to

detect effects on productivity as a consequence of the

nutrient treatment, differences in cover of macroalgae

J. ROBIN SVENSSON ET AL.832 Ecology, Vol. 88, No. 4

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among levels of enrichment were tested using undis-

turbed panels (D0) after eight weeks. Data were

analyzed using ANOVA:

Xijk ¼ lþ Si þ Nj þ SNij þ RðSNÞkðijÞ þ eijk

RESULTS

General observations

During the experiment a total of 15 species of algae

and 17 species of sessile invertebrates were observed.

The most abundant organisms, occupying large areas of

the panels, were the tunicates Ciona intestinalis and

Ascidiella aspersa and the hydroid Laomedea flexuosa.

At the end of the experiment, ephemeral algae,

bryozoans, and sea anemones were frequent in the

communities, although usually low in cover (Table 1).

Studies of the development of undisturbed communities

showed that richness was highest after 8 weeks at site 1

and after 16 weeks at site 2 (Fig. 1A). The decrease in

richness at later stages suggests that some species were

excluded as a result of competition. This is consistent

with the observation of an earlier peak in richness at site

1, following the establishment of a dense cover of C.

intestinalis at this site (Fig. 1B). The ascidians occupied

.95% of the space on control panels after 24 weeks at

site 1, suggesting that C. intestinalis is a competitive

dominant in this system, capable of excluding both other

invertebrates as well as most species of macroalgae (Fig.

1B).

Assessment of productivity

The analysis of algal cover in undisturbed communi-

ties after 8 weeks showed that there was a statistically

significant response to increased nutrient availability

(F2,44 ¼ 10.74, P , 0.001). Inspection of means (mean

[6SE] cover of algae for N0, Nþ, and Nþþ were 54.5 6

TABLE 1. Abundance (mean percent cover 6 SE) of sessile invertebrate and algal species present in the experimental communitiesfrom both sites after 24 weeks, averaged over nutrient treatment for all levels of disturbance (D0–D6).

Taxon D0 D1 D2 D3 D4 D5 D6

Chlorophyceae

Ulva intestinalis 0.09 6 0.04 0.10 6 0.06 0.10 6 0.06 0.39 6 0.19 0.06 6 0.04 0 0.17 6 0.14Ulva lactuca 0 0 0 0.04 6 0.04 0 0 0

Phaeophyceae

Ectocarpus siliculosus 0.45 6 0.26 0.03 6 0.03 0.59 6 0.29 0.11 6 0.06 0.11 6 0.05 0.18 6 0.16 0.20 6 0.15

Rhodophyceae

Bonnemaisonia hamifera 0 0 0 0 0 0.04 6 0.03 0Ceramium rubrum 1.98 6 0.57 2.62 6 0.53 4.21 6 1.06 3.39 6 1.07 2.23 6 0.55 0.64 6 0.17 2.09 6 1.07Ceramium strictum 0.07 6 0.04 0.07 6 0.05 0.03 6 0.03 0.07 6 0.05 0.03 6 0.03 0.14 6 0.06 0.03 6 0.03Dasya baillouviana 0.04 6 0.03 0.03 6 0.03 0.03 6 0.03 0.04 6 0.04 0.03 6 0.03 0.04 6 0.03 0.17 6 0.14Osmundea truncata 0 0.03 6 0.03 0 0 0.06 6 0.04 0 0.03 6 0.03Polysiphonia fucoides 0.83 6 0.49 0.17 6 0.07 0.38 6 0.18 0.61 6 0.36 1.46 6 1.15 0.21 6 0.07 0.40 6 0.29Polysiphonia urceolata 0 0 0.03 6 0.03 0.21 6 0.18 0 0 0Spermothamnion repens 0.20 6 0.12 0.07 6 0.05 0.03 6 0.03 0.25 6 0.18 0.23 6 0.15 0.38 6 0.17 0.09 6 0.05

Porifera

Leucosolenia botryoides 0.78 6 0.28 1.28 6 0.50 1.10 6 0.43 1.21 6 0.53 1.40 6 0.49 1.36 6 0.43 0.74 6 0.24

Cnidaria

Clytia hemispherica 0 0 0 0.04 6 0.04 0 0 0Laomedea flexuosa 12.8 6 2.30 18.9 6 3.09 19.7 6 3.07 23.9 6 4.30 19.9 6 3.04 30.7 6 3.44 24.4 6 2.44Metridium senile 0.11 6 0.05 0.14 6 0.06 0.31 6 0.18 0.39 6 0.36 0.14 6 0.06 0.14 6 0.06 0.37 6 0.20Sargatiogeton undatus 0.07 6 0.04 0.14 6 0.06 0.03 6 0.03 0.18 6 0.07 0.11 6 0.05 0.14 6 0.06 0.06 6 0.04

Annelida

Pomatoceros triqueter 0.11 6 0.05 0.21 6 0.08 0.38 6 0.09 0.54 6 0.10 0.51 6 0.09 0.61 6 0.08 0.49 6 0.09

Crustacea

Balanus crenatus 0 0.03 6 0.03 0 0 0.06 6 0.04 0 0

Mollusca

Mytilus edulis 0.35 6 0.12 0.52 6 0.18 0.59 6 0.24 0.64 6 0.25 0.63 6 0.24 1.00 6 0.37 0.57 6 0.29Podesmus sp. 0 0.17 6 0.17 0 0 0 0.04 6 0.03 0

Bryozoa

Cryptosula pallasiana 0.39 6 0.19 0.28 6 0.18 0.03 6 0.03 0.04 6 0.04 0.23 6 0.15 0.29 6 0.16 0.06 6 0.04Electra pilosa 0.91 6 0.29 1.34 6 0.46 0.76 6 0.28 0.93 6 0.33 0.46 6 0.20 1.36 6 0.34 0.49 6 0.20Membranipora membranacea 0.33 6 0.19 0.03 6 0.03 0 0.71 6 0.42 0.14 6 0.14 0.18 6 0.16 0

Hemichordata

Ascidiella aspersa 11.9 6 2.38 12.3 6 2.74 12.5 6 2.27 8.32 6 2.13 8.94 6 1.81 8.82 6 1.60 5.03 6 1.17Botryllus schlosseri 0 0 0 0.04 6 0.04 0 0 0Botrylloides leachi 0 0 0.17 6 0.17 0 0 0 0Ciona intestinalis 84.0 6 3.50 75.3 6 5.15 64.3 6 5.17 71.4 6 4.82 68.0 6 4.69 53.6 6 4.04 17.3 6 2.25

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5.13%, 82.5 6 4.41%, and 71.1 6 3.55%, respectively)and the SNK test revealed that there were significant

differences between unfertilized panels (N0) and thosefertilized (Nþ and Nþþ). Furthermore, there was nosignificant interaction term between the two factors, site

(S) and nutrient enrichment (N) (F2,42¼ 1.45, P¼ 0.25),suggesting that nutrient availability had a general effect

on productivity and that useful tests of the DEM were infact possible. However, no significant difference in algalcover was observed between Nþ and Nþþ, which could be

due to a saturation of nutrients already at the Nþ level.

Testing predictions from the IDH and DEM

Analysis of species richness at the end of the

experiment showed that there was a significant effect

of disturbance, but no interactive effect of disturbance

and nutrients (Table 2a). In all levels of nutrients, there

was a tendency for maximum richness at intermediate

levels of disturbance (Fig. 2). Initially it might appear

that maximum diversity occurred at different levels of

disturbance, but the variability among and within levels

of disturbance was large and the predicted shift toward

more frequent disturbances was not observed (maximum

richness was observed at D5, D5, and D2 for N0, Nþ,

and Nþþ, respectively). Considering the fact that the

hypothesis about simple effects of disturbance and that

of interactive effects involving disturbance and nutrients

were both tested using the same pooled mean square as

the error term (with 189 df), conclusions about lack of

interactive effects appear robust and not caused by a

lack of statistical power. This view is supported by

calculation of effect-sizes from estimated mean squares,

which reveal that the effect of disturbance was ;20

times larger than that of the interaction (k2D ¼ 1.82 and

k2N3D ¼ 0.10). There was no significant interaction

involving disturbance and any of the spatial scales, i.e.,

sites and rings (Table 2a). This indicates that the effect

of disturbance was consistent among places. Neverthe-

less, significant variability among rings indicates that

there was small-scale variability in richness within sites.

Further analysis showed that, not only were there

differences among levels of disturbance, but there was

also a significant quadratic component in the polyno-

mial regression (Table 2b), i.e., maximum richness at

intermediate disturbances (Fig. 3A). Consistent with the

IDH, these results suggest that sessile species are

removed at low and high frequencies of disturbance.

Inspection of the mean cover of the most abundant taxa

suggests that they differ in their responses to disturbance

FIG. 1. Temporal patterns of (A) species richness and (B)percent cover of C. intestinalis in fouling communities at sites 1and 2. Data are presented as mean 6 SE.

TABLE 2. (a) ANOVA on species richness at the end of the experiment and (b) regression analysis.

Source df MS F P Error term R2

a) ANOVA on species richness

Site, S 1 7.47 0.94 0.34 R(S 3 N)Nutrients, N 2 2.66 0.69 0.59 S 3 NDisturbance, D 6 7.98 3.16 0.01 pooledS 3 N 2 3.88 0.49 0.62 R(S 3 N)S 3 D 6 3.01 1.10 0.37 D 3 R(S 3 N)N 3 D 12 2.58 1.03 0.43 pooledRing, R(S 3 N) 17 7.90 3.24 0.00 residualS 3 N 3 D 12 0.90 0.33 0.98 D 3 R(S 3 N)D 3 R(S 3 N) 102 2.75 1.13 0.30 residualResidual 69 2.44Pooled 189 2.53

b) Regression analysis�Regression 2 0.44 10.04 0.03 0.83Residual 4 0.04

Notes: (a) Hypotheses about effects of disturbance (consistent with predictions from IDH) and interactions between disturbanceand nutrients (consistent with predictions from DEM) were tested using a pooled error term following nonsignificant tests (P .0.25) of D 3 R(S 3 N), S 3 N 3 D, and S 3 D. (b) Regression analysis for effects of disturbance on species richness.

� Coefficients for the regression analysis are as follows. For the intercept, b¼ 5.25, t¼ 29.12, P¼ 0.00; for D (disturbance), b¼3.73, t ¼ 3.96, P¼ 0.02; for D2, b¼�3.77, t ¼�4.39, P¼ 0.01.

J. ROBIN SVENSSON ET AL.834 Ecology, Vol. 88, No. 4

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(Fig. 3B). Thus there is a strong negative effect on the

cover of the dominant tunicates Ciona intestinalis and

Ascidiella aspersa, while a rapid colonizer such as the

hydroid Laomedea flexuosa is positively affected by

disturbance.

DISCUSSION

In this study we found empirical evidence supporting

the IDH, but not the DEM. Species richness was highest

at an intermediate frequency of disturbance, and this

pattern was not significantly affected by different levels

of nutrient enrichment. This was in spite of the fact that

the nutrient treatment had a significant effect increasing

percentage cover of macroalgae, which is closely linked

to productivity (Death 2002). In contrast to the IDH,

the empirical support for the DEM is scarce. So far

support has come from observational studies of flooding

in riparian wetlands (Pollock et al. 1998), a mesocosm

study of sediment movement and organic enrichment in

deep-sea benthos (Widdicombe and Austen 2001),

laboratory experiments of energy availability and

mortality in microcosms (Rashit and Bazin 1987), and

in the only two experiments that have manipulated

disturbance and productivity simultaneously in the field

(Worm et al. 2002, Jara et al. 2006). The conclusions

from our experiment differ from the few previous studies

testing the DEM. Because productivity was manipulated

using the same procedures as in Worm et al. (2002) and

Jara et al. (2006), the nutrient treatment cannot explain

the different results. Instead, it is more likely that the

divergent outcomes were caused by differences in (1) the

composition of the experimental communities, and/or

(2) the way the communities were disturbed.

The communities in this study were not only rich in

species, but also in terms of higher taxa and functional

groups. During the experiment .30 different species

were observed in the communities, 15 species of macro-

algae and 17 species from such different taxonomic

groups as tunicates, mussels, hydroids, bryozoans,

barnacles, annelids, and sea anemones. Other experi-

ments on the DEM have used more restricted taxon

sampling and studied communities composed mainly of

algae (Worm et al. 2002), polychaetes (Widdicombe and

Austen 2001), protist bacterivores (Scholes et al. 2005),

and bacteria, flagellates, and ciliates (Rashit and Bazin

1987). Experiments conducted in more diverse systems

can be advantageous due to the possibility of recogniz-

ing patterns among more distantly related taxa. In this

experiment tunicates occupied most of the space on

control panels, and were thus capable of excluding a

variety of both invertebrate and macroalgal species. Had

the hypotheses been tested in assemblages of solely

macroalgae or invertebrates, this dominance of one

taxon over several taxa from distant groups might not

have been revealed, and patterns among, for instance,

only macroalgae (cf. Worm et al. 2002) might have been

different and not representative for the natural diversity

of hard-substratum assemblages of temperate marine

waters. Because the DEM and the IDH are general

ecological models intended to explain gradients of

diversity in nature, their generality and explanatory

power should be assessed using natural communities.

The diversity and composition of communities can

influence the outcome of an experiment, because

different species and functional groups respond differ-

ently to experimental treatments. It is therefore impor-

FIG. 2. Effects of disturbance on species richness atdifferent nutrient levels (see Materials and Methods: Experi-mental design). Data are presented as mean 6 SE.

FIG. 3. (A) Species richness on the experimental panels, and(B) percent cover of Ascidiella aspersa, Laomedea flexuosa, andCiona intestinalis, as functions of relative disturbance frequency(see Materials and Methods: Experimental design). Data arepresented as mean 6 SE.

April 2007 835DISTURBANCE, PRODUCTIVITY, AND DIVERSITY

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tant also to consider the composition of communities,

and not only the design of experimental treatments,

when comparing results and conclusions from experi-

ments on effects of disturbance and productivity on

diversity.

Another potential explanation of the difference in

results and conclusions between this study and previous

studies on the DEM is based on the application and the

definition of disturbance. In this study we used

controlled levels of mechanical scraping. This type of

disturbance shares important properties with natural

disturbances, such as ice-scouring (Aberg 1992), drifting

logs (Dayton 1971), and wave action (Dudgeon et al.

1999), in the sense that it makes free space available for

settling (i.e., the limiting resource). This is a central

component in definitions of disturbance (Sousa 1984,

2001), which is not always considered in experimental

manipulations (e.g., Rashit and Bazin 1987, Scholes et

al. 2005). Another potentially complicating issue is the

selectivity of agents of disturbance in manipulative

experiments. Worm et al. (2002) used mesoherbivores

as agents of disturbance in communities composed

largely of macroalgae. In this case it is possible that

interactions, not predicted by the DEM, occurred

between grazing and nutrient enrichment of algae.

Grazers have been shown to prefer plants (Onuf et al.

1977) and macroalgae (Cruz-Rivera and Hay 2000) with

higher nutrient content, whereas physical disturbance

has no such selectivity. Grazing has previously been

argued as an unsuitable agent of disturbance in studies

on the IDH (e.g., McGuinness 1987, Sousa 2001). Due

to selective preference for nutrient-rich individuals,

grazing might be an even less appropriate agent of

disturbance in studies on the DEM.

Despite its conceptual appeal, the scarcity of manip-

ulative studies suggests that empirical testing of the

DEM may not be straightforward. One important issue

that has to be considered in experimental tests of the

DEM is that the extensive discussion about agents and

definitions of disturbance (Grime 1977, Pickett and

White 1985, Sousa 2001) has no equivalence for

productivity. Experimental manipulation of productiv-

ity is often done indirectly, i.e., by adding nutrients. This

has two fundamental implications for the interpretation

of manipulative experiments. First, it becomes necessary

to test not only for effects of the experimental treatment

on diversity, but also to test independently whether the

actual experimental treatment (the adding of nutrients)

has an effect on productivity. Without evidence for an

actual increase in productivity, it is not clear whether the

experiment is testing the DEM or not. Unfortunately,

this is not always made clear (e.g., Widdicombe and

Austen 2001, Scholes et al. 2005, Jara et al. 2006).

Another problematic issue is the fact that productivity

of an assemblage is determined both by external factors

(i.e., light, temperature, energy transport, and nutrients)

and internal processes (i.e., differences in usage of

resources, resource capture ability, and energy conver-

sion ability within and among species [Tilman 1980,

Tilman and Pacala 1993]). In a field experiment on

natural assemblages, energy conversion ability is usually

not amenable to manipulation. One consequence is that

there may be a lack of independence between the

response variable and the levels of the experimental

factor. This is because the productivity of an assemblage

may influence diversity (e.g., Connell and Orias 1964,

Abrams 1995) at the same time as the diversity

influences the productivity (e.g., Tilman et al. 1996).

Therefore, in an experiment where productivity is

manipulated indirectly, the response variable (i.e., some

measure of diversity) may modify the effect of the

experimental treatment. This relationship may lead to

confusion about cause and effect in otherwise carefully

planned experiments. Nevertheless, if predictions about

effects of productivity on diversity are to be tested in

field experiments, indirect manipulations may be the

only conceivable solution. In this system, addition of

nutrients, which are often a limiting resource, is

probably the most effective way to increase productivity

in a field experiment (e.g., Widdicombe and Austen

2001, Worm et al. 2002, Jara et al. 2006).

In a manner similar to manipulations of disturbance,

the experimental manipulations of productivity in

natural communities are often selective. The matter of

selectivity is probably of greater concern in experimental

manipulations of productivity, because designing a

nonselective agent of productivity is more complicated

then designing a nonselective agent of disturbance. If all

organisms are affected equally by the productivity

treatment, or if the dominant organisms are affected

relatively more strongly, it would require a stronger

disturbance to prevent competitive exclusion, as pre-

dicted by the DEM. However, if the inferior competitors

are more strongly affected by the productivity treat-

ment, this could instead slow down the process of

competitive exclusion, which would cause diversity to

peak at lower intensities of disturbance, rather than at

the predicted higher intensities. In this experiment, the

dominant tunicates, unlike the ephemeral macroalgae,

did not noticeably increase their growth rates in

response to the nutrient treatment. This result could

explain why an interaction between disturbance and

productivity was not found. Jara et al. (2006) also

discussed the nutrient treatment as a possible cause for

their weak support for the DEM, because the nutrients

may only have affected the autotrophic part of the

community. Studies that have found the predicted

interaction between disturbance and productivity have

predominantly been made in plant communities (Pol-

lock et al. 1998, Death 2002) or algae (Worm et al.

2002). In such experiments, the species in the commu-

nities would be more equally affected, even though

individual species of plants and algae differ in their

ability to utilize available resources.

In this study, we found maximum richness at

intermediate frequencies of disturbance, which is in

J. ROBIN SVENSSON ET AL.836 Ecology, Vol. 88, No. 4

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accordance with the IDH. A literature review showed

that this is not a universal pattern in experimental tests

of effects of disturbance on diversity (Mackey and

Currie 2001). Less than 20% of the published studies on

disturbance–diversity relations supported the IDH. As

an extended theory, the DEM may explain some of the

results that are inconsistent with the IDH; and it has

therefore been suggested that it is preferable to the IDH

(Stallins 2003). In their review, Mackey and Currie

(2001) found that .50% of all experiments on distur-

bance showed either monotonically positive or negative

patterns with increasing disturbance. These patterns

could in principle be explained by the DEM, if it could

be shown that productivity was high in cases where

diversity increased with disturbance, and low when

diversity decreased with disturbance. The explanatory

power of the DEM is therefore potentially large.

Nevertheless, many alternative explanations may be

proposed for results that are inconsistent with the IDH.

Several authors have suggested that the predictions of

the IDH rely on a number of prerequisites, such as

competitive exclusion (Connell 1978), large regional

species pool (Osman 1977), multiple stages in succession

(Collins and Glenn 1997), and trade-off between

competition and tolerance (Dial and Roughgarden

1998) and between competition and colonization (Pet-

raitis et al. 1989). Menge and Sutherland (1987) argued

that the effects of disturbance depend on the amount of

environmental stress in the system. Accordingly, exper-

iments in systems where these prerequisites are not

fulfilled seldom find support for the IDH. For instance

Cowie et al. (2000) and Huxham et al. (2000) did not

observe maximum diversity at intermediate levels of

disturbance, because settling propagules and a small

regional species pool were lacking. Studies testing the

DEM have also explained lack of support for the model

with the failure of fulfilment of these requirements.

Scholes et al. (2005) suggested that the absence of

recolonization of bacteria and ciliates could explain lack

of support in the closed microcosms, while Death (2002)

found that the DEM could not predict patterns of

diversity in forest streams because such systems are not

driven by competition. These results imply that models

incorporating productivity is only one of several

possibilities for improving our understanding of mech-

anisms behind patterns of diversity. Furthermore, the

predictive power and general applicability of the DEM

needs to be assessed by further experiments in natural

assemblages, where the definition and the ecological

relevance of disturbance and productivity treatments are

explicitly considered.

ACKNOWLEDGMENTS

This work was funded by Formas through contract21.0/2004-0550 to H. Pavia and by Stiftung Mercator througha grant to M. Wahl. Fertilizer was provided by Aglukon,Dusseldorf, Germany. We thank Anneli Lindgren and ElisabetBrock for assistance with identifying species of macroalgae;without their help it would not have been possible to do this

project. We also thank R. T. Paine and one anonymousreviewer, whose comments greatly improved an earlier versionof the manuscript.

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J. ROBIN SVENSSON ET AL.838 Ecology, Vol. 88, No. 4

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Paper I

Paper II

Paper III

Paper IV

Paper V

Paper VI

PAPER II

“You tried your best and you failed miserably. The lesson is 'never try'.“

-Homer J. Simpson

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Ecology, 90(2), 2009, pp. 496–505� 2009 by the Ecological Society of America

Equal rates of disturbance cause different patterns of diversity

J. ROBIN SVENSSON,1 MATS LINDEGARTH, AND HENRIK PAVIA

Department of Marine Ecology–Tjarno, University of Gothenburg, 452 96 Stromstad, Sweden

Abstract. Empirical evidence suggests that disturbance has profound effects on the speciesdiversity of aquatic and terrestrial assemblages. Conceptual ecological theories, such as theintermediate disturbance hypothesis (IDH), predict maximum diversity at intermediate levelsof disturbance. Tests of the predictive power and generality of these models are, however,hampered by the fact that the meaning and units of ‘‘disturbance’’ are not clearly defined. Forexample, it is seldom recognized that the rate of disturbance is the product of both frequencyand extent (e.g., area or volume) of disturbance events. This has important consequences forthe design and interpretation of experiments on disturbance. Here we present, for the firsttime, an experimental design that allows for unconfounded testing of combinations of areaand frequency (i.e., regimes) for a given rate of disturbance. We tested the prediction thatspecies richness responds differently to equal rates of disturbance, depending on the specificcombination of frequency and area, on marine hard-substratum assemblages. Five differentrates of disturbance and two regimes (small frequent or large infrequent disturbances) wereapplied at three sites. The results showed that the effect of a certain rate of disturbance (1)varies strongly among assemblages and (2) also depends on the specific combination offrequency and area of disturbance events. Maximum species richness was observed atintermediate rates of disturbance at site 1 (i.e., support for the IDH), whereas there was amonotonic decline at site 2 and there was no evident pattern at site 3. The variable responsesamong sites were explained by differences in degree of competitive exclusion and rates ofrecruitment. At the site where the IDH was supported, the regime with a large proportion ofthe area disturbed infrequently showed higher richness, compared to the regime with a smallproportion disturbed frequently. This was likely due to a stronger decrease of dominants,which allowed for the recruitment of new colonizing species. In summary, we conclude thattests and general syntheses of models of disturbance–diversity patterns would benefit frommore explicit definitions of the components of disturbance, as well as a stronger focus on theimportance of variation in inherent properties of natural assemblages.

Key words: competitive exclusion; diversity; marine assemblages; rate of disturbance; regime; speciesrichness.

INTRODUCTION

Disturbance is an important factor explaining pat-

terns of biodiversity in many terrestrial (e.g., Eggeling

1947, Grime 1977, Molino and Sabatier 2001) and

aquatic environments (e.g., Sousa 1979, Patricio et al.

2006). One of the most influential formulations of the

effects of disturbance on biological diversity is the

intermediate disturbance hypothesis (IDH; Connell

1978). The IDH predicts low diversity at low levels of

disturbance due to competitive exclusion and also at

high levels as a result of local extinction. At intermediate

levels of disturbance, diversity is higher due to coexis-

tence of rapid colonizers and competitive dominants.

The IDH has been supported in laboratory experiments

(e.g., Widdicombe and Austen 1999, Buckling et al.

2000, Cowie et al. 2000) as well as field experiments in

terrestrial (e.g., Armesto and Pickett 1985, Collins 1987,

Molino and Sabatier 2001), freshwater (e.g., Padisak

1993, Reynolds 1995, Floder and Sommer 1999) and

marine communities (e.g., Osman 1977, Sousa 1979,

Valdivia et al. 2005). Nevertheless, a literature review

revealed that only 20% of the studies on effects of

disturbance on diversity showed the unimodal pattern

predicted by the IDH (Mackey and Currie 2001). Thus,

despite its conceptual appeal, it appears that the

predictive value of the IDH is often limited. This has

led to the development of more complex models, e.g., by

incorporating effects of productivity (the dynamic

equilibrium model [DEM]; Huston 1979, 1994), which

could potentially account for a wider range of patterns.

One important focus for research in general is to

understand the limitations of a theory and to unravel

causes of variable predictive power. A fundamental

difficulty with the IDH and the DEM is that they are

largely conceptual and verbal models based on relatively

scaled variables (Schoener 1972, Peters 1991). Therefore

the units and meaning of disturbance is sometimes

unclear and often different among studies (Pickett and

White 1985, Sousa 2001; Table 1). Conceptual terms, such

Manuscript received 4 October 2007; revised 4 June 2008;accepted 12 June 2008. Corresponding Editor: H. Hillebrand.

1 E-mail: [email protected]

496

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as ‘‘intensity’’ and ‘‘severity,’’ are not explicitly defined,

and are therefore not easily generalized among studies.

For example, ‘‘intensity’’ has been used to describe a

variety of experimentalmanipulations and variables, such

as penetration depth per bite by limpets (Steneck et al.

1991), type ofmechanical scrubbing (McCabe andGotelli

2000), and degree of oscillation in sediment (Garstecki

and Wickham 2003). Clearly, there is a need for system-

specific concepts, but in order to evaluate general

ecological theories, such as the IDH, it is important that

concepts are commensurable among studies.

Disturbance can be operationally defined as a rate,

i.e., the sum of the size of all disturbance events in a

given area per unit time (Miller 1982). The rate is the

only measure, which accounts for the combined effects

of area and frequency, and thus the total amount of

disturbance actually inflicted upon the community under

study. This is important because information about one

of these components makes no sense without the context

of the other. For instance, the information that a defined

biological community was disturbed once a week is

completely uninformative if we do not know the extent

of the disturbance. Surely, we would predict massive

differences in effects on diversity if the area disturbed

each week was 0.5% of the total area compared to if it

was 20%. This is a fundamental fact that is always

considered in the process of defining appropriate

treatment levels in ecological experiments, but it is later

also commonly disregarded in the interpretation and

discussion of the study. Consequently, the combined

effects of frequency and area are always implicit in

experimental studies on the effects of disturbance, but in

order to put any experimental result into a wider

context, and to allow for direct and meaningful

comparisons among studies, it is always necessary to

transform the measure of disturbance into a rate.

From this it does, however, not follow that the rate of

disturbance is always the most accurate predictor of

diversity. Several theoretical arguments can be made,

which suggest that the effect of a given rate of

disturbance may differ depending on the way the specific

rate is obtained, i.e., the regime. For instance, Miller

(1982) suggested that small, frequent disturbances favor

species with rapid vegetative growth (‘‘competitive’’

species), whereas large, less frequent disturbances favor

species with high capacity for dispersal (‘‘colonizers’’)

due to the differences in perimeter to area ratios among

patches. Although the area of disturbance have been the

predominant focus for models of rates of disturbance

(i.e., Miller 1982), the other component of the rate,

frequency, is equally important. Differences in frequen-

cy and timing of disturbance will, similarly to variations

in area, influence the abundance and composition of

natural communities (Sousa 2001), because species are

likely to increase in abundance when the disturbance

regime matches their preferred recruitment time (Un-

derwood and Anderson 1994, Crawley 2004). The large

variation in temporal distribution of propagules among

species (Roughgarden et al. 1988, Underwood and

Anderson 1994) will also have the consequence that a

single large disturbance cannot be colonized by all

species in the regional species pool, but only by the

propagules that are available at the specific time when

space is made free. Thus, it is evident that variations in

both area and frequency of a disturbance rate are likely

to influence the outcome of studies on effects of

disturbance on diversity. However, despite that area

and frequency have well known distinct effects on

diversity, no study, to our knowledge, has performed

unconfounded testing of these factors for a given rate of

disturbance.

Testing of differences among regimes at equal rates of

disturbance is not straightforward because experimental

manipulations of the frequency or the extent of

disturbance are inevitably associated with a change in

the rate of disturbance. Previous experiments, simulta-

neously testing hypotheses about effects of frequency

and extent, have done this using orthogonal designs

(e.g., Collins 1987, McCabe and Gotelli 2000). The

interpretation of such experiments is problematic

because tests of the effects of one factor, within one

level of the other factor, are confounded by changes in

TABLE 1. Conceptual and operational terms used to define the magnitude of disturbance inecological literature.

Term Meaning Quantity

Conceptual

Level general description of overall magnitude of disturbanceSeverity general description often used as ‘‘strength’’ of the

disturbing forceIntensity general description sometimes used synonymously to severityRegime generic term for the types and components of disturbance

currently acting in a given area

Operational

Frequency number of disturbance events per unit time time�1

Time period of time since last disturbance event timeExtent total two- or three-dimensional space disturbed area or volumeSize size of an individual disturbance event areaRate product of area and frequency area/time

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the rate. In order to test for differences among regimes,

specific analytical contrasts need to be extracted and

parts of the experiment become redundant. Further-

more, in a direct analogy to generalizations among

studies, any significant interaction resulting from such

an experiment can only be sensibly interpreted if the rate

of disturbance is considered.

Here we present, for the first time, a field experiment

with an experimental design that allows for uncon-

founded testing of combinations of area and frequency

(i.e., regimes) for a given rate of disturbance. In a

previous manipulative experiment in marine hard-

substratum communities on the west coast of Sweden,

we found that maximum species richness was attained at

intermediate frequencies of disturbance (Svensson et al.

2007). Following from this work, our aim was to use

hard-substratum assemblages to test the hypothesis that,

in these assemblages, the effect of a certain rate of

disturbance depends on the specific combination of area

and frequency. In order to do this mechanical distur-

bance was applied at five distinct rates of disturbance

under two different regimes (disturbing either a small

proportion of the assemblage, frequently, or a large

proportion, infrequently) at three sites.

MATERIALS AND METHODS

Study site

The field experiment was conducted on the west coast

of Sweden in the vicinity of Tjarno Marine Biological

Laboratory. The experimental sites were three bays

located approximately 1 km apart (58852 09200 N,

118803100 E; 5885202000 N, 118807000 E; and 5885207600 N,

118801500 E for sites 1, 2, and 3, respectively). Site 1 had

an average depth of 8 m and was surrounded by muddy

and rocky shores inhabited by red, green and brown

macroalgae as well as mussels and tunicates. Site 2 had

an average depth of 6 m and was surrounded by sandy

beaches and boulder fields. This site also had an

extensive seagrass (Zostera marina) meadow and the

boulders were commonly overgrown by fucoids, barna-

cles, and mussels. Site 3 had an average depth of 10 m

and was surrounded exclusively by rocky shores with a

steep declining sandy bottom. The nearby rocks were

predominantly occupied by breadcrumb sponge (Hal-

ichondria panicea), ephemeral red algae, and fucoids.

Experimental design

Experimental units, made from 2100 3 250 3 4 mm

PVC strips folded into a ring, were placed hanging from

a buoy approximately 0.5 m below the water surface

(Svensson et al. 2007). Ten quadratic PVC panels (1503

150 3 3 mm), roughened with emery paper, were

attached with cable ties on each of the 24 rings. Eight

rings were deployed at each site on 25 April to allow

settling and establishment of communities before the

experimental manipulation started on 25 May. The

experimental manipulation had a duration of 17 weeks

and was terminated 28 September 2005.

The panels were disturbed by either of five different

rates of disturbance (Ra0�Ra4), under two different

disturbance regimes (Re1 and Re2; Table 2). Under the

first regime two randomly selected areas were scraped,

each covering 10% of the panel area, with frequencies

ranging from every week to every eighth week. Under the

second regime, four randomly selected areas, each

covering 10% of the panel area, were scraped at

frequencies ranging from every second week to 16th week

(Table 2). In addition to killing or damaging individuals,

the disturbance treatment also facilitated recruitment by

freeing substratum and is therefore coherent with the

definition of disturbance by Sousa (1984). All rates were

present in all rings with two replicate panels and either of

the two regimes was randomly assigned to rings. Thus,

four rings of regime 1 and four rings of regime 2 was

present at each of the three sites, allowing eight rings with

all five rates replicated per site.

Sampling

The composition and abundance of assemblages were

sampled at the end of the experiment after 17 weeks of

manipulation. Panels were detached and brought into

the laboratory submerged in seawater and then kept

under running seawater in the laboratory during the

entire sampling procedure. Wet weight was measured

and the edges and reverse side of all panels were scraped

clean before sampling. Percent cover of bare space and

sessile species was then estimated in 5% intervals using a

15315 cm plastic grid. A 1-cm margin to all edges of the

panels was not assessed in order to avoid confounding

edge effects. The percentage cover of species with a small

holdfast and wide thallus was estimated from the two

dimensional projection of the organism on the panel.

Cover of epibionts was also estimated, thus, total cover

could exceed 100%.

Statistical analyses

The data obtained from the experiment was analysed

with analysis of variance (ANOVA) using Statistica 6.0

(Statsoft, Tulsa, Oklahoma, USA). Hypotheses about

TABLE 2. Explanation of each combination of experimentalfactors, regime (Re1, small, frequent disturbances; Re2,large, infrequent disturbances), and frequency and rate ofdisturbance.

Regime Area (cm2)Frequency(no./week)

Rate(cm2/week)

Re1 45 0 0Re1 45 2/16 5.63Re1 45 4/16 11.25Re1 45 8/16 22.5Re1 45 16/16 45

Re2 90 0 0Re2 90 1/16 5.63Re2 90 2/16 11.25Re2 90 4/16 22.5Re2 90 8/16 45

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effects of main factors and interactions were tested using

the following general linear model:

Xijklm ¼ lþ Si þ Rej þ SReij þ Rak þ SRaik þ ReRajk

þ SReRaijk þ RiðSReÞlðijÞ þ RaRiðSReÞklðijÞ þ eijklm

where l is the overall mean, site (Si) is a random factor

with three levels, disturbance regime (Rej) is a fixed

factor with two levels, disturbance rate (Rak) is a fixed

factor with five levels, ring (Ri[SRe]l(ij)) is a nested

random factor with four levels and eijklm is a random

deviation.

Support for the hypothesis that equivalent rates of

disturbance may cause different effects depending on the

specific combination of frequency and area of distur-

bance, would be shown by a significant interaction (Ra

3 Re). More specifically the hypothesis is supported if a

difference in species richness is found at equivalent rates

of disturbance for regimes with the larger area (regime

2), compared to regimes with the smaller area (regime 1).

Support for the alternative, i.e., that the effect of rate of

disturbance does not depend on the particular combi-

nation of frequency and area, is obtained from a

significant effect of rate of disturbance (Ra) if there is

a nonsignificant interaction (Ra 3 Re). Furthermore, to

evaluate whether the response of disturbance is consis-

tent with the predictions from the IDH, two additional

tests are necessary. First, if there is a significant negative

quadratic component in a polynomial regression this

indicates that the response is non-linear and that the rate

of increase declines at larger rates of disturbance.

Second, in order to test whether the maximum species

richness occurs at a rate of disturbance that is different

from the two extreme rates of disturbance in our study,

we performed a Mitchell-Olds and Shaw’s test (MOS

test [Mitchell-Olds and Shaw 1987]). This method has

previously been used to investigate patterns of curvilin-

ear relationships between diversity and productivity,

where significant results of the MOS test shows support

for hump-shaped or U-shaped patterns (Mittelbach et

al. 2001, Chase and Leibold 2002, Fukami and Morin

2003).

Initial tests of differences among sites in undisturbed

assemblages (Ra0) were made using analysis of similar-

ity (ANOSIM) using the PRIMER software package

(PRIMER-E, Plymouth, UK). The abundance data was

transformed to the fourth root, in order to avoid bias by

the greater influence of abundant species, following the

recommendations by Clarke and Warwick (1994). These

analyses revealed whether differences were solely related

to the number of species or also dependent on the

particular species present and their relative abundance.

The individual contributions of different species to the

observed differences among sites were evaluated using

SIMPER. A graphical comparison among sites was

obtained using non-metric multidimensional scaling (n-

MDS).

RESULTS

Structure of assemblages

A total of 19 species of algae and 16 species of sessile

invertebrates were observed in the experimental com-

munities at the time of sampling. The most abundant

organisms, occupying large areas of the panels, were the

tunicate Ciona intestinalis, the common blue mussel

Mytilus edulis, the red algae Ceramium rubrum, and the

hydroid Laomedea flexuosa, whereas sea anemones,

bryozoans, barnacles, and most ephemeral algae were

found at low cover (Fig. 1). The composition of species

in undisturbed assemblages differed among all three sites

(ANOSIM; global R . 0.6, P , 0.005 for all pairwise

tests; Fig. 2). Difference in cover of C. intestinalis

explained most of the dissimilarity among all sites

(SIMPER; 77%, 54%, and 35%, sites 1 vs. 2, 1 vs. 3, and

2 vs. 3, respectively). The total area covered of sessile

species in the undisturbed assemblages exceeded 100% at

site 1, whereas assemblages at sites 2 and 3 had less than

full coverage of available space, indicating that the

FIG. 1. Abundance of species in undisturbed communities (rate 0) averaged over regime at sites 1, 2, and 3. Different lettersindicate significant difference in community composition (ANOSIM; R . 0.6, P¼ 0.001).

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strength of competition for space differed substantially

among sites (Fig. 1). The ascidian C. intestinalis covered

over 95% of the space in the undisturbed assemblages at

site 1, and was also the most abundant species at site 2

occupying 33% of the panel. At site 3 no single species

occupied more than 15% of the available space in the

assemblage.

Responses to rates of disturbance

There was no overall effect of the rate of disturbance

on species richness and thus there was no general

support for the IDH (Table 3). Instead, the effects of the

rate of disturbance differed significantly among sites (P

, 0.05 for S 3 Ra; Table 3, Figs. 3 and 4). A graphical

examination of the nature of this interaction suggested

that there was maximum richness at intermediate rates

of disturbance at site 1, a decrease in richness with

increasing rate of disturbance at site 2, while no clear

pattern was distinguishable at site 3 (Fig. 4). Additional

analyses using polynomial regression showed that there

were significant linear and quadratic components at site

1, a significant decreasing linear trend at site 2, and no

significant pattern at site 3 (Table 4). Because significant

quadratic components do not automatically show

evidence for internal maxima in polynomial regressions,

a Mitchell-Olds and Shaw’s test was performed for

assemblages at site 1. This test showed that maximum

richness occurs at an intermediate rate of disturbance

(b1/2b2 ¼ 32.8, P , 0.01 for Mitchell-Olds and Shaw’s

test), and comparisons of adjusted R2 showed that the

hump-shaped model had a better fit to our data

compared to the linear model (adjusted R2 ¼ 0.213 and

0.156, respectively). Thus, although the IDH was not

globally supported, the patterns observed at site 1 were

not only statistically significant but precisely those

predicted by the IDH (Table 4).

Analyses of individual species also showed that these

differed in their response to rate of disturbance (Fig. 5).

Inspection of mean cover showed that the highly

abundant tunicate C. intestinalis was negatively affected

by the disturbance treatment, whereas rapid colonizers,

such as the ephemeral algae Enteromorpha intestinalis

and Ectocarpus siliquosis, increased in cover with

increasing disturbance rate. Although disturbance rate

had a negative effect on richness on many panels it is still

noteworthy that assemblages subjected to the highest

rate, which constituted of scraping 320% of the panel

area over a period of 16 weeks, still had between 5 and

11 species at the termination of the experiment.

Responses to regimes of disturbance

Analysis of species richness at the end of the

experiment also showed that there was a significant

interaction among factors site, regime and disturbance

rate (Table 3). Although calculations of variance

components showed that this component was only half

as large as that of the two-way interaction (for S3Re3

Ra, r2 ¼ 0.46, and for S 3 Ra, r2 ¼ 0.98), this

interaction suggests that the effects of the rate of

disturbance differ between regimes, at least at some

sites. In order to test for differences among regimes in

assemblages where maximum richness was observed at

intermediate rates (Fig. 3), post hoc tests for differences

between regimes within rates were done at site 1. SNK

test showed that regime 2 (large and infrequent

disturbances), had significantly greater species richness

than regime 1 (small and frequent disturbances) at rate

2. In order to further investigate the underlying cause for

this difference in richness we compared the abundance

of species among regimes (Table 5). Assemblages in

regime 2 had more species of ephemeral algae (i.e.,

‘‘colonizers’’) and most algal species had higher cover-

age, whereas the ascidian C. intestinalis and the blue

FIG. 2. Multidimensional scaling (MDS) of species compo-sition for all sites averaged over disturbance regime.

TABLE 3. Analysis of variance on species richness.

Source df MS F P Error term

Site, S 2 89.26 8.34 0.003 Ri(S 3 Re)Regime, Re 1 2.03 0.26 0.660 S 3 ReRate, Ra 4 5.31 0.29 0.876 S 3 RaS 3 Re 2 7.77 0.73 0.499 Ri(S 3 R)S 3 Ra 8 18.26 7.09 0.000 Ra 3 Ri(S 3 Re)Re 3 Ra 4 4.60 0.74 0.593 S 3 Re 3 RaS 3 Re 3 Ra 8 6.26 2.43 0.023 Ra 3 Ri(S 3 Re)Ring, Ri(S 3 Re) 17 10.71 4.95 0.000 residualRa 3 Ri(S 3 Re) 68 2.58 1.19 0.205 residualResidual 115 2.17

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mussel M. edulis (i.e., ‘‘competitors’’) had higher cover

in regime 1. Furthermore, inspection of means showed

that maximum richness was attained at lower rates of

disturbance for regimes involving larger areas at site 1

(Fig. 3). Thus, in addition to significantly affecting the

number of species, the specific combination of area and

frequency of disturbance also determines what kind of

species that will be present in assemblages.

DISCUSSION

This experiment was designed to test the hypothesis

that species richness in a hard-substratum assemblage

respond in specific ways to different rates of disturbance

and that this response depends on the particular

combination of area and frequency of the disturbance

events. We found that the rate of disturbance affects

species richness in a hard-substratum assemblage in

different ways at different sites. At one site richness

responded in accordance with the predictions from the

IDH, while at the other sites there was a monotonic

negative effect on richness or richness was not affected

at all. Furthermore, at the site where patterns were

consistent with the IDH, maximum species richness was

observed at lower rates of disturbance when large areas

were scraped less frequently, compared to when small

areas were scraped more frequently. Because the

experiment allows comparisons among regimes at

comparable rates of disturbance, our study provide the

first unconfounded test and empirical evidence for the

hypothesis that different combinations of area and

frequency at equal rates have different effects on

diversity.

FIG. 3. Effects of rate of disturbance on species richness at different sites and disturbance regimes. Data are presented as meanþ SE. Disturbance rates and regimes are described in Table 2.

FIG. 4. Species richness, averaged over disturbance regime,as a function of the rate of disturbance at sites 1, 2, and 3. Dataare presented as mean 6 SE. See Table 2 for specific details ofhow each rate can be obtained through two different regimes.

TABLE 4. Regressions of species richness on linear andquadratic rates at individual sites.

Source df MS F P

Site 1

Rate 1 61.78 14.74 ,0.001Rate2 1 24.78 5.91 0.018Residual 67 4.19

Site 2

Rate 1 46.76 11.16 0.001Rate2 1 0.32 0.08 0.783Residual 77 2.87

Site 3

Rate 1 2.89 0.69 0.409Rate2 1 1.03 0.25 0.621Residual 77 2.85

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Miller (1982) argued that the underlying cause for

observing higher diversity in assemblages under regimes

composed of larger, compared to smaller, areas at lower

rates, was that larger disturbances would favor specieswith high capacity for dispersal (i.e., ‘‘colonizers’’) due

to the longer persistence of free space. Smaller distur-

bances should benefit ‘‘competitive’’ species because the

larger ratio of perimeter to area allows more rapid

reoccupation of space by vegetative growth. In our

experiment we investigated the effects of regimes with

different area and frequency with similar size and shape

of disturbance events at equivalent rates of disturbance.

This allowed testing for differences among regimes that

are not based on differences in perimeter area ratios.

Nevertheless, similar to the predictions in the model by

Miller (1982), we observed a larger number of species ofephemeral algae (i.e., Ceramium rubrum, Cystoclonium

purpureum, and Ectocarpus siliquosis) in assemblages

under the regime composed of larger area at site 1, for

rates where this regime showed maximum richness. We

also found that the ephemeral algae and the hydroid

Laomedea flexuosa (i.e., colonizers [Svensson et al.

2007]) had higher percent cover in regimes with larger

area, while the ascidian Ciona intestinalis and the blue

mussel Mytilus edulis, which are strong competitors for

space (Lenz et al. 2004, Svensson et al. 2007), were more

abundant in the regime composed of smaller areas at

this site. This means that colonizing species may not

only be facilitated by larger ratios of perimeter to area,as predicted by Miller (1982), but also by removing a

larger part of the assemblage where their propagules

settle. It has previously been shown that post-settlement

survivability of propagules is greatly affected by larval–

adult interactions, such as competition for food (Osman

et al. 1989) and space (Connell 1961), and also that the

survivability of propagules is more sensitive during the

first few days (Gosselin and Qian 1997). A possible

explanation for the difference among regimes in this

study may, therefore, be that post-settlement survivabil-

ity among colonizers is higher where 40% of an

assemblage is removed, compared to 20%, due to lower

levels of larval–adult competition at the time following

the disturbance events. Although the underlying mech-

anisms for this pattern need to be unraveled by further

experiments, our results demonstrate that the distur-

bance regime will affect the outcome of studies

investigating effects of disturbance on diversity.

One striking result of this experiment was that the

assemblages at the three sites all differed substantially in

their response to disturbance. A likely explanation to the

variability among sites in their response to disturbance is

the natural variation in abundance and composition of

assemblages. The total cover in undisturbed assemblages

exceeded 100% at site 1, and the ascidian C. intestinalis

covered more than 95% of the area. At the other sites,

the total cover was less dense and no single taxa covered

more than 35%. The high percentage of total cover and

FIG. 5. Effects of rate of disturbance on cover of C. intestinalis, E. silicuosus, and E. intestinalis at site 1, averaged over regime.Data are presented as mean 6 SE.

TABLE 5. Qualitative differences in cover between regime 1(small area and frequent disturbances) and regime 2 (largearea and infrequent disturbances) at site 1 and rate 2.

Species Difference

Algae

Cystoclonium purpureum þþDasya baillouviana þþCeramium rubrum þCladophora rupestris þEctocarpus siliquosus þEnteromorpha intestinalis þSpermotamnion repens þOsmondea truncata �Polysiphonia fucoides �Ceramium strictum �

Invertebrates

Electra pilosa þþMembranipora membranacea þþAscidiella aspersa þLaomeda flexuosa þCiona intestinalis �Mytilus edulis �Cryptosula pallasiana ��

Note: Symbols are as follows:þ, larger cover in regime 2;�,smaller cover in large areas; ��, species occurs exclusively inregime 1; andþþ, species occurs only in regime 2.

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the dominance of the ascidians at site 1, suggests that C.

intestinalis is a strong competitor for space, excluding

other invertebrates and many species of macroalgae.

Such competitive exclusion is a fundamental premise for

observing higher diversity at intermediate levels of

disturbance in natural communities (e.g., Fuentes and

Jaksic 1988, Collins and Glenn 1997). Accordingly, the

lack of support for the IDH at the other sites is most

likely explained by the absence of clear dominance of

strong competitors. By reducing the cover of the strong

dominant C. intestinalis, and thereby preventing or

disrupting competitive exclusion, disturbance can have a

positive effect on diversity in assemblages with intense

competition. This mechanism has previously been

implied in field experiments in Sweden (Svensson et al.

2007) and Chile (Valdivia et al. 2005), where reduction

in cover of the dominant ascidians C. intestinalis and

Pyura chilensis both resulted an increase in diversity at

intermediate frequencies of disturbance. Additional

evidence for the importance of disrupting competitive

exclusion comes from several studies involving domi-

nant organisms, such as mussels (Paine 1966), bryozoans

(Jara et al. 2006), trees (Molino and Sabatier 2001), and

grasses (Collins 1987).

An important factor with potentially large effects on

the outcome of disturbance experiments is the availabil-

ity of propagules, which is not easily controlled or

measured in field experiments. The availability of

propagules and a large regional species pool, is of great

importance in order for disturbance to have a positive

effect on richness (Osman 1977). In a study on tallgrass

prairie vegetation Collins et al. (1995) pointed out that it

is settlement by propagules, and not disturbance per se,

which increases species richness. If no new species settle

in the cleared space, richness will obviously not increase.

This was clearly shown in an experiment on soft-bottom

intertidal assemblages by Huxham et al. (2000), where

the species settling in areas cleared by disturbance were

the same species that already inhabited the patch. The

different responses to disturbance among experimental

sites in this study, and the large difference in total cover

of undisturbed assemblages among the sites, suggests

that there is large spatial variation in propagule supply.

Jonsson et al. (2004) showed that local hydrodynamics

strongly influenced the settling of planktonic barnacle

larvae and caused highly variable recruitment on panels

at different sites in the archipelago where this study was

conducted. This could possibly explain the surprisingly

low cover (;35%) of substratum in the undisturbed

assemblages at site 3. However, the highly disturbed

panels at this site still did not show a large reduction in

diversity compared to the controls. This suggests that

some propagules were capable of settlement, and that

the amount of propagules that settled successfully could

compensate for the loss in species by the disturbance

treatment.

One important theme of this paper is the distinction

between conceptual and operational terms and its

consequences for the interpretation and synthesis of

empirical results. Comparisons of the result from site 1

in this study and the one by Svensson et al. (2007), which

both provide support for the IDH in the same system,

serves to illustrate some of these consequences (Fig. 6).

If disturbance is not consistently defined (e.g., frequency

the first study and rate in this study) and subsequently

compared on a relative scale, the outcomes of the

experiments appear similar in some respects but

different in others. Maximum diversity was predicted

at similar levels of disturbance (0.5 and 0.7) by both

models. The parameters of the fitted model were

consistent with respect to the intercept, which represents

the diversity in the absence of disturbance, while the

linear (the rate of increase in the absence of disturbance)

and the quadratic components (the curvature) were

FIG. 6. Effects of rate of disturbance on species richness in a study by Svensson et al. 2007 (experiment 1) and this study(experiment 2) expressed as (a) relative disturbance and (b) absolute rate. Data are presented as mean 6 SE.

February 2009 503DISTURBANCE RATE AND COMMUNITY DYNAMICS

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roughly doubled in this study, compared to the previous

(Fig. 6a). When levels of disturbance are transformed

into rates, however, it becomes obvious that maximum

diversity was obtained at rates approximately three

times larger in this compared to the previous study (Fig.

6b). Furthermore, an analysis of the parameters of the

fitted model show that the intercept and initial increase

were consistent between studies, but that the curvature,

i.e., the tendency to decline at higher levels of

disturbance, was less pronounced in this study compared

to the previous (Fig. 6b). These detailed analyses have

important consequences not only for the interpretation

of differences in patterns among studies, but also for the

hypotheses about the processes that were causing these

differences. In a relative perspective it appears that the

effect of competitive exclusion at low levels of distur-

bance was stronger in this compared to the previous

study (Fig. 6a). On an absolute scale, however, the

effects of competitive exclusion were similar in both

experiments, while lack of recruitment of new species in

the first experiment was responsible for the rapid decline

at higher rates of disturbance. Although this analysis

does not provide conclusive evidence about the impor-

tance of different processes it is clear that results may be

interpreted in very different ways depending on how

disturbance is represented.

Conclusions

This study provides the first unconfounded experi-

mental design for testing the hypothesis that equal rates

cause different patterns of diversity. Because the

experiment was designed to test effects of regimes at

equivalent rates of disturbance, we were able to show

that effects on species richness depended on the specific

combination of frequency and area and not only on the

rate. Furthermore, the effects of a certain rate of

disturbance differed substantially among sites. At one

site maximum richness was observed at intermediate

rates (i.e., support for the IDH), at another site richness

of assemblages declined with increasing rates of distur-

bance and at the third site there was no effect of

disturbance. The variable responses among sites were

likely due to differences in degree of competitive

exclusion and rates of recruitment. In summary, the

results suggest that the general understanding of the

magnitude and nature of effects of disturbance would

benefit from more explicit definitions of the components

of disturbance and a stronger focus on the importance of

the inherent properties of natural assemblages.

ACKNOWLEDGMENTS

This study was financially supported by MARICE (aninterdisciplinary research platform at the Faculty of Sciences,University of Gothenburg) and by the Swedish ResearchCouncil through contract no. 621-2004-2658 to H. Pavia, aswell as by Formas through contracts 21.0/2004-0550 to H.Pavia and 217-2006-357 to M. Lindegarth. We thank MalinKarlsson for performing a large part of the fieldwork andElisabet Brock for assistance with identification of macroalgalspecies.

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Paper I

Paper II

Paper III

Paper IV

Paper V

Paper VI

PAPER III

On drawing conclusions from observations:

“Sir Bedevere: What makes you think she's a witch?

Peasant: Well, she turned me into a newt!

Sir Bedevere: A newt?

Peasant: [meekly after a long pause] ... I got better.

Crowd: [shouts] Burn her anyway!”

-the quest for the Holy Grail, Monty Python

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Ecology, 91(10), 2010, pp. 3069–3080� 2010 by the Ecological Society of America

Physical and biological disturbances interact differentlywith productivity: effects on floral and faunal richness

J. ROBIN SVENSSON,1 MATS LINDEGARTH, AND HENRIK PAVIA

Department of Marine Ecology, University of Gothenburg, Tjarno Marine Biological Laboratory, Stromstad 452 96 Sweden

Abstract. Physical and biological disturbances are ecological processes affecting patternsin biodiversity at a range of scales in a variety of terrestrial and aquatic systems. Theoreticaland empirical evidence suggest that effects of disturbance on diversity differ qualitatively andquantitatively, depending on levels of productivity (e.g., the dynamic equilibrium model). Inthis study we contrasted the interactive effects between physical disturbance and productivityto those between biological disturbance and productivity. Furthermore, to evaluate how theseeffects varied among different components of marine hard-substratum assemblages, analyseswere done separately on algal and invertebrate richness, as well as richness of the wholeassemblage. Physical disturbance (wave action) was simulated at five distinct frequencies,while biological disturbance (grazing periwinkles) was manipulated as present or absent, andproductivity was manipulated as high or ambient. Uni- and multivariate analyses both showedsignificant effects of physical disturbance and interactive effects between biologicaldisturbance and productivity on the composition of assemblages and total species richness.Algal richness was significantly affected by productivity and biological disturbance, whereasinvertebrate richness was affected by physical disturbance only. Thus, we show, for the firsttime, that biological disturbance and physical disturbance interact differently withproductivity, because these two types of disturbances affect different components ofassemblages. These patterns might be explained by differences in the distribution (i.e., pressvs. pulse) and degree of selectivity between disturbances. Because different types ofdisturbance can affect different components of assemblages, general ecological models willbenefit from using natural diverse communities, and studies concerned with particular subsetsof assemblages may be misleading. In conclusion, this study shows that the outcome ofexperiments on effects of disturbance and productivity on diversity is greatly influenced by thecomposition of the assemblage under study, as well as on the type of disturbance that is usedas an experimental treatment.

Key words: disturbance; diversity; dynamic equilibrium model (DEM); intermediate disturbancehypothesis (IDH); marine assemblages; species richness; Tjarno, Sweden.

INTRODUCTION

Disturbance has long been recognized as an important

structuring force in ecological communities (Darwin

1859). In the mid-1920s Cooper (1926) initiated a

discussion on possible effects of disturbance on succes-

sion and biodiversity, a discussion that is still ongoing

and has given rise to several hypotheses and models.

Among the most prominent is the intermediate distur-

bance hypothesis (IDH; Connell 1978), which predicts

that diversity will be high at intermediate levels of

disturbance and low at both extremes of a disturbance

continuum. Disturbance does, however, not only have

documented effects on the diversity of biological

communities, but also on evolutionary processes (Ben-

mayor et al. 2008), biological invasions (Davis et al.

2000), and ecosystem functions (Cardinale and Palmer

2002). The ecological literature contains many examples

of agents and definitions of disturbance. One potentially

important distinction is that between agents of physical

and biological disturbance (McGuinness 1987, Sousa

2001, Svensson et al. 2007). Agents of physical

disturbance in previous studies include fire (Eggeling

1947), wind (Molino and Sabatier 2001), wave action

(McGuinness 1987), ice-scouring (Gutt and Piepenburg

2003), drifting logs (Dayton 1971), floods (Lake et al.

1989), sediment movement (Cowie et al. 2000), temper-

ature (Floder and Sommer 1999), desiccation (Lenz et

al. 2004), trawling (Tuck et al. 1998), pollution

(Benedetti-Cecchi et al. 2001), and even warfare

(Rapport et al. 1985). Biological disturbances are mainly

predation (Talbot et al. 1978) and grazing (Collins

1987), although some authors add trampling (Eggeling

1947) and burrowing (Guo 1996).

Because of the rich variety of agents of disturbance, a

number of definitions of disturbance have been pro-

posed to make experiments commensurable (Sousa

2001). The definitions range from Grime’s (1977)

Manuscript received 17 April 2009; revised 27 January 2010;accepted 10 February 2010. Corresponding Editor: B. J.Cardinale.

1 E-mail: [email protected]

3069

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straightforward partial or total destruction of biomass

to the more explicit definition of Pickett and White

(1985:356) in which disturbance is ‘‘. . . any relative

discrete event in time that disrupts ecosystems, commu-

nity, or population structure and changes resources,

substrate availability, or the physical environment.’’

Among the more operational, and therefore more

commonly used definitions, is that of Sousa (1984:7),

in which disturbance not only kills or damages

individuals, but also ‘‘directly or indirectly creates an

opportunity for new individuals (or colonies) to become

established.’’ The recognition that disturbance creates

opportunities for recruitment is crucial, because without

new species recruiting into the freed space, disturbance

cannot increase diversity (Osman 1977, Collins et al.

1995, Huxham et al. 2000).

Disturbance has also been recognized as an important

component in multifactorial models in which interac-

tions among community structuring processes are used

to predict diversity in natural communities. The

dynamic equilibrium model (DEM; Huston 1979,

Kondoh 2001) predicts high diversity at high levels of

disturbance when productivity is high, because a

stronger disturbance is then required to prevent

competitive exclusion. Similarly, diversity will be high

at low levels of disturbance when productivity is low,

because exclusion is then disrupted already by distur-

bances that are less frequent. The DEM has been tested

using either biological or physical agents of disturbance

in several experiments in aquatic as well as terrestrial

systems (e.g.,Turkington et al. 1993, Worm et al. 2002,

Jara et al. 2006, Svensson et al. 2007). The DEM and the

IDH have, however, received criticism from both

empirical and theoretical studies for being too simplistic

and based on weak theoretical grounds (Pacala and Rees

1998, Huxham et al. 2000, Shea et al. 2004). For

example, Chesson and Huntley (1997) showed that

disturbance may not diminish the importance of

competition, as predicted by Huston (1979), and that

indirect benefits of disturbance may fall short of the

direct negative effects. As a consequence of the

elucidation of the models’ weaknesses, their predictions

have been suggested, by empirical studies, to rely on a

number of prerequisites: competitive exclusion (Fuentes

and Jaksic 1988), a large regional species pool (Osman

1977), multiple stages in succession (Collins and Glenn

1997), and trade-offs between competition and coloni-

zation (Wilson 1994). These prerequisites, or assump-

tions, are in essence very similar to the flaws pointed out

in theoretical studies, i.e., that disturbance alone cannot

stabilize coexistence (Chesson and Huntly 1997) and the

underlying mechanisms for coexistence are in fact

nonlinear responses caused by trade-offs in life history

attributes (Amarasekare et al. 2004) and spatiotemporal

niches (Pacala and Rees 1998). In order to benefit from

this critique in a constructive way, Shea et al. (2004)

proposed that combining the suggestions of improve-

ment from both empirical and theoretical studies will

lead beyond mere descriptions of the hump-shaped

pattern. However, despite the many studies suggesting

critique against or improvements of the IDH and DEM,

few studies recognize the potentially large source of

variation caused by differences in the way an assemblage

is disturbed.

The manner in which a disturbance inflicts damage is

important because disturbances that are equal in extent

can nonetheless have significantly different effects on

diversity, depending on how the disturbance is distrib-

uted (Bertocci et al. 2005, Svensson et al. 2009). In a

theoretical study, Bender et al. (1984) identified two

different types of disturbance, pulse and press, and

evaluated their different effects on species’ interactions.

This distinction may also be useful for predictions of

patterns of diversity. Instantaneous alteration of species

number (pulse) and the sustained alteration of species

densities (press) are two clearly different mechanisms

that may still fall under the same general definition of

disturbance. Consequently, inconsistencies in outcomes

may occur if disturbances with dissimilar distributions

are treated without distinction in manipulative experi-

ments (Svensson et al. 2009). Furthermore, it may be

important to make a general distinction between

biological and physical agents, because they commonly

differ in the degree of selectiveness (McGuinness 1987,

Sousa 2001). Such selectivity may be increasingly

complex under interactions with productivity, because

consumers often prefer prey with higher nutrient content

(Emlen 1966, Onuf et al. 1977, Pavia and Brock 2000).

There are many studies from various environments that

show interactive effects between biological disturbance

and productivity (see Proulx and Mazumder 1998),

while tests of the DEM using physical disturbance have

more variable outcomes (e.g., Turkington et al. 1993,

Jara et al. 2006, Svensson et al. 2007). No study, to our

knowledge, has explicitly contrasted differences between

biological and physical disturbances and their interac-

tions with productivity. The only study to apply all three

factors simultaneously found a significant interaction

between biological, but not physical, disturbance and

productivity (Kneitel and Chase 2004). These findings

suggest that the choice of disturbance agent may

influence the outcome of experiments on interactive

effects of disturbance and productivity.

In this study we contrast the interactive effects

between a physical disturbance (i.e., wave action) and

productivity to those between a biological disturbance

(i.e., grazing) and productivity, in natural marine

benthic communities placed in mesocosms. We predict

that: (1) the biological disturbance will have a stronger

impact on macroalgal species and the physical distur-

bance will have a stronger impact on invertebrate species

and (2) the biological and the physical disturbances

therefore will interact differently with productivity.

Furthermore, we attempt to identify underlying mech-

anisms by investigating changes in composition of

assemblages among levels of biological and physical

J. ROBIN SVENSSON ET AL.3070 Ecology, Vol. 91, No. 10

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disturbance and productivity, as well as evaluating

differences in species’ trade-offs in life history attributes(sensu Pacala and Rees 1998, Shea et al. 2004). Hard-

substratum communities, such as the epilithic assem-blages in this study, are generally considered to be

suitable for studies on disturbance, because sessilespecies compete for the limiting resource space (e.g.,Sousa 1984). More specifically, manipulative experi-

ments conducted in this system have previously observedthe pattern predicted by the IDH, as well as strong

competition for space among macroalgae and inverte-brates within one season (Svensson et al. 2007, 2009).

MATERIALS AND METHODS

Experimental assemblages

The marine hard-substratum assemblages that wereused to study the effects of physical and biological

disturbance and productivity were collected from semi-exposed boulder fields in the Tjarno archipelago atdepths of 0.5–1.5 m (58852.170 N, 1188.820 E). The

epilithic assemblages were composed exclusively ofsessile species, and the collected boulders, on which the

assemblages resided, were all of similar size (;20 cmdiameter). Common species in this system include red,

green, and brown macroalgae, as well as mussels,tunicates, bryozoes, and sea anemones (Svensson et al.

2007, 2009; see Plate 1). The 43 different species presentin the experimental assemblages are listed in Table 1.

Associated mobile invertebrates (i.e., amphipods andisopods) were not collected and included in the

experiment, since our aim was to add grazers of similardensity in assemblages subjected to the biological

disturbance treatment. Natural conditions of assem-blages were maintained to a high extent by a constant

supply of unfiltered seawater from the Tjarno bay,allowing natural conditions of salinity, temperature,food for filter feeders, nutrient availability, and propa-

gules for colonization.

Experimental design

The experiment was carried out in the Ecotrone

mesocosm facility at Tjarno Marine Biological Labora-tory (TMBL), Stromstad, Sweden. One hundred boul-

ders with epilithic assemblages were placed separately in10-L plastic containers filled with seawater and covered

with mosquito nets (mesh size ¼ 1 mm) to preventgrazers from escaping and/or entering. Water volume in

the containers was maintained using a constant flowseawater supply, drawn from the bay adjacent to TMBL

at 0.5 m depth. The experimental manipulation startedon 5 June, had a duration of 17 weeks, and was

terminated on 10 October 2005.The physical disturbance treatment was replicated five

times in each treatment combination, and the boulderswere subjected to one of five different frequencies: twiceper week (DP1), once per week (DP2), once every

second (DP3) or every fourth week (DP4), or leftundisturbed (DP0). The physical disturbance was caused

by rolling each boulder by hand for one minute with

equal force in a tub filled with seawater and clean

boulders, in order to mimic effects of wave action in

boulder fields, at each disturbance event. In addition to

killing or damaging individuals, the rolling also facili-

tates recruitment by freeing substratum, and the

disturbance is therefore coherent with the definition by

Sousa (1984).

The productivity treatment consisted of two levels:

ambient (PR0) and increased (PR1). Bags with 100 g

slow-release fertilizer were attached to 50 containers

with boulders subjected to the increased productivity

treatment (PR1), while boulders of the ambient treat-

ment (PR0) were not experiencing increased nutrient

availability. Fertilizer bags were changed every eighth

week in order to have constant nutrient release

throughout the experiment. Plantacote Depot 6-M

(5.7% NO3, 8.3% NH4, 9% P2O5, and 15% K2O;

AGLUKON, Dusseldorf, Germany) was used as

fertilizer due to its steady release rate (Worm et al.

2000).

The common periwinkle, Littorina littorea, is a very

abundant and important grazer in the Tjarno archipel-

ago (Wikstrom et al. 2006, Toth et al. 2007) where the

boulders were collected and was therefore used in the

grazing treatment. Littorina littorea periwinkles were

collected from the same areas as the boulders. In order

to achieve an ecologically relevant grazing pressure we

conducted a pilot study that suggested that ;10

periwinkles would be appropriate for the size of the

boulders in this experiment. Accordingly, 10 L. littorea

of similar size were added to each of the 50 boulders

subjected to the biological disturbance treatment (DB1),

and no periwinkles were added to the remaining 50

boulders, which were not experiencing grazing (DB0).

Sampling

Sampling of abundance and composition of the

communities was done at the end of the experiment

after 17 weeks of manipulation. Boulders were brought

into the laboratory submerged in seawater and also kept

under running seawater in the laboratory during the

entire sampling procedure. An area of 100 cm2 was

randomly chosen on each boulder for sampling, in order

to sample an equal area from all boulders regardless of

differences in actual area and size. Percent cover of bare

space and sessile species was then estimated at 5%intervals using a 10310 cm plastic grid, and a dissecting

microscope (magnification 123) was used for species

identification. Percent cover of species with a small

holdfast and wide thallus was estimated from the two-

dimensional projection of the organism on the panel.

Sessile epibionts were also accounted for. Thus, total

cover was allowed to exceed 100%.

Statistical analyses

The data obtained from the experiment were analyzed

with a three-way factorial ANOVA using Statistica 6.0

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(Statsoft, Tulsa, Oklahoma, USA) and with permuta-

tional multivariate analysis of variance (PERMANO-

VA) using the Permanova software (Anderson 2001).

The multivariate analyses were performed to reveal

whether differences were solely related to the number of

species or also dependent upon the particular species

present and their relative abundance. Hypotheses about

effects of main factors and interactions were tested using

TABLE 1. Abundance (percent cover, mean 6 SE) of sessile invertebrate and algal species present in the experimental communitiesafter 24 weeks, averaged over nutrient treatment for all levels of physical disturbance (DP0–DP4).

Species DP0 DP1 DP2 DP3 DP4

Chlorophyceae

Chaetomorpha melagonium 0.5 6 0.11 0.6 6 0.11 0.85 6 0.24 0.8 6 0.25 1.6 6 0.54Cladophora albido 0 0.1 6 0.07 0.3 6 0.25 0.05 6 0.05 0.25 6 0.10Cladophora rupestris 0.9 6 0.54 1.2 6 0.57 0.55 6 0.26 0.3 6 0.11 0.5 6 0.26Codium fragile 0 0 0.25 6 0.25 0.05 6 0.05 0Enteromorpha prolifera 0.4 6 0.26 0.45 6 0.26 1.75 6 1.25 1.45 6 0.60 0.9 6 0.33Enteromorpha intestinalis 1.4 6 0.67 3.7 6 3.24 2.8 6 1.54 5.4 6 3.18 4.15 6 1.59Ulva lactuca 1.65 6 1.09 1.45 6 0.60 0.75 6 0.34 1.1 6 0.45 1.9 6 0.70

Phaeophyceae

Ahnfeltia plicata 0.4 6 0.26 2.5 6 1.38 0.1 6 0.07 0.8 6 0.55 0.05 6 0.05Dumontia incrassata 1.3 6 1.25 0.05 6 0.05 0.25 6 0.10 0.55 6 0.26 0.05 6 0.05Ectocarpus siliculosus 0 0.05 6 0.05 0.1 6 0.07 0.05 6 0.05 0Fucus serratus 1.0 6 1.0 1.55 6 1.03 0 0.05 6 0.05 0Fucus vesiculosus 21.55 6 7.59 0.75 6 0.55 0 0 0Ralfsia tenuis 0 0.05 6 0.05 0 0 0Sargassum muticum 0.5 6 0.50 0 0 0 0Sphacelaria cirrosa 7.35 6 3.38 5.1 6 2.61 1.75 6 0.71 1.2 6 0.57 0.9 6 0.33Sphacelaria plumosa 0.25 6 0.25 0 0 0 0

Rhodophyceae

Bonnemaisonia hamifera 1.6 6 1.50 0.25 6 0.25 0 0.05 6 0.05 0Ceramium rubrum 0.1 6 0.07 0.1 6 0.07 0 0.1 6 0.07 0.1 6 0.07Ceramium strictum 0.1 6 0.07 0 0 0 0Chondrus crispus 19.6 6 7.04 17.1 6 8.03 7.25 6 3.46 7.35 6 2.94 2.0 6 0.58Corallina officinalis 0.05 6 0.05 0 0 0 0Cystoclonium purpureum 2.75 6 1.56 1.35 6 1.25 0.05 6 0.05 0 0Furcellaria lumbricalis 1.25 6 1.25 0.25 6 0.25 0 0 0Hildenbrandia rubra 4.1 6 2.52 2.6 6 1.11 2.85 6 1.11 4.25 6 1.59 3.05 6 1.04Lithothamnion sp. 15.6 6 3.97 8.15 6 2.25 7.65 6 2.23 8.9 6 3.63 3.7 6 1.18Osmundea truncata 1.5 6 1.50 0 0 0 0Polysiphonia nigrescens 1 6 0.78 0.05 6 0.05 0.05 6 0.05 0 0.1 6 0.07Polysiphonia urceolata 2.45 6 1.99 1.75 6 1.22 0.1 6 0.05 0 0.1 6 0.07Polysiphonia violacea 0.15 6 0.08 0.25 6 0.25 0.05 6 0.05 0 0.05 6 0.05Spermothamnion repens 0 0 0 0.05 6 0.05 0

Porifera

Leucosolenia botryides 0.05 6 0.05 0 0.05 6 0.05 0 0

Cnidaria

Dynamena pumila 0.05 6 0.05 0 0.05 6 0.05 0 0Laomedea flexuosa 0.05 6 0.05 0.05 6 0.05 0.1 6 0.07 0.25 6 0.10 0.65 6 0.34Metridium senile 0.5 6 0.26 0 0 0.05 6 0.05 0

Annelida

Pomatoceros triqueter 0.2 6 0.09 0.4 6 0.26 0.05 6 0.05 0.1 6 0.07 0.35 6 0.25Spirorbis spirorbis 0.75 6 0.50 0.05 6 0.05 0.4 6 0.26 0.15 6 0.08 0.15 6 0.08

Crustacea

Balanus crenatus 0.05 6 0.05 0.1 6 0.07 0.1 6 0.07 0.05 6 0.05 0.15 6 0.08Semibalanus balanoides 0.05 6 0.05 0 0 0 0

Mollusca

Acanthocardia sp. 0.05 6 0.05 0 0 0 0Leptochiton sp. 0.25 6 0.10 0 0 0 0Mytilus edulis 0.4 6 0.26 0.05 6 0.05 0 0 0

Bryozoa

Cryptosula pallasiana 2.4 6 0.75 1.45 6 0.79 0.6 6 0.34 0.45 6 0.26 0.55 6 0.26

Hemichordata

Ciona intestinalis 0.6 6 0.05 0 0 0 0

Total coverage 90.1 6 8.86 49.8 6 9.52 29.2 6 4.98 31.6 6 6.25 21.3 6 3.13

Note: The marine hard-substratum assemblages that were used to study the effects of physical and biological disturbance andproductivity were collected from semi-exposed boulder fields in the Tjarno archipelago, Sweden.

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the following general linear model:

Xijkl ¼ lþ PRi þ DBj þ PRDBij þ DPk þ PRDPik

þDBDPjk þ PRDBDPijk þ eijkl

where l is the overall mean, PRi is the ith level of

productivity, DBj is the jth level of biological distur-bance, DPk is the kth level of physical disturbance, and

eijkl is a random deviation. Productivity, biologicaldisturbance, and physical disturbance are fixed factors

with two, two, and five levels, respectively. For theunivariate analysis post hoc tests of differences among

means were analyzed using the Student-Newman-Keuls

test (SNK), and t tests were used for the multivariateanalysis following the recommendations by Anderson

(2001). For all analyses data were tested for meeting theassumptions of the statistical methods.

The hypothesis that physical and biological distur-bance will interact differently with productivity is

supported if there is an interaction between productivityand either biological disturbance or physical disturbance

exclusively or if a three-way interaction reveals differentpatterns for different factorial combinations. Support

for the hypothesis that physical and biological distur-bance will have different effects on different components

of assemblages is found if algal and invertebrate richnessare affected by either type of disturbance exclusively or

if the effects of the treatments show different patterns(e.g., increase vs. decline) for algal compared to

invertebrate richness. In order to visualize patterns ofthe multivariate tests and identify differences in species’

life history trade-offs, canonical analysis of principalcoordinates (CAP; Anderson and Willis 2003) was used

as a constrained ordination procedure on appropriate

terms found to be significant using PERMANOVA.

RESULTS

General observations

During the experiment a total of 13 sessile inverte-

brates and 31 algal species were observed in theexperimental communities. The macroalgae were not

only numerous in species, they also covered most of thearea in the experimental assemblages, and the most

abundant organisms were the red alga Chondrus crispus,

the brown alga Fucus vesiculosus, and the green algae

Enteromorpha intestinalis and Chaetomorpha melago-

nium. Unlike the algae, the ascidians, bryozoans,

crustaceans, molluscs, and sea anemones were usually

infrequent and had low percent cover in the assemblages

(Table 1).

Efficiency of the productivity treatment

In order to detect effects on productivity as a

consequence of the nutrient addition, differences in

percent cover among levels of nutrient availability were

tested. The ANOVA showed that there was a significant

effect of nutrient availability on total cover (F1,80¼ 26.9,

P , 0.001). Inspection of means (total cover for PR0

and PR1 were 29.4 6 4.0 and 58.7 6 4.0 [mean 6 SE],

respectively) showed that the total percent cover in

assemblages experiencing increased nutrient availability

was significantly higher than total cover under ambient

nutrient availability. This large difference in coverage

was mainly caused by increases in percent cover of algal

species (cover of algae for PR0 and PR1 were 27.9 6 4.0

and 56 6 4.0, respectively).

Effects of frequency of physical disturbance

Analysis of total species richness and community

composition at the end of the experiment showed that

there were significant effects of physical disturbance

(Tables 2 and 3 and Figs. 1a and 2), but no interactive

effects with productivity (Tables 2 and 3). A tendency

TABLE 2. ANOVA on total species richness, algal richness, and invertebrate richness.

Source df

Total richness Algal richness Invertebrate richness

MS P MS P MS P

PR 1 171.28 0.000 132.49 0.000 2.49 0.154DB 1 61.76 0.000 99.34 0.000 4.44 0.058DP 4 16.82 0.003 2.41 0.552 7.30 0.000PR 3 DB 1 16.74 0.040 8.88 0.097 1.24 0.313PR 3 DP 4 0.79 0.935 1.31 0.797 1.94 0.178DB 3 DP 4 8.54 0.073 4.55 0.228 1.07 0.473PR 3 DB 3 DP 4 3.26 0.496 1.44 0.767 1.75 0.223Residual 80 3.83 3.15 1.20

Notes: Values in boldface indicate significance. Abbreviations are: PR, productivity; DB, biological disturbance; DP, physicaldisturbance.

TABLE 3. Permutation multivariate analysis of variance(PERMANOVA) with data transformed to presence/ab-sence.

Source df MS P

PR 1 14 240 0.001DB 1 39 085 0.001DP 4 2899 0.034PR 3 DB 1 4044 0.028PR 3 DP 4 972 0.944DB 3 DP 4 2313 0.159PR 3 DB 3 DP 4 1237 0.829Residual 80 1746

Notes: Values in boldface indicate significance. Abbrevia-tions are: PR, productivity; DB, biological disturbance; DP,physical disturbance.

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for an interaction between physical and biological

disturbance was found in the univariate analysis (P ¼0.07; Table 2), indicating that the effects of biological

disturbance on richness were stronger in the presence of

physical disturbance (i.e., grazers had no impact on

undisturbed assemblages: richness for DP0DB0 and

DP0DB1 were 8.1 6 0.50 and 8.1 6 0.71, respectively).

Post hoc analysis on the multivariate test showed that

undisturbed assemblages were significantly different

from those experiencing higher levels of physical

disturbance (post hoc, DP0 ¼ DP1 6¼ DP2 6¼ DP3 6¼DP4, at a¼ 0.05) and in the univariate test all levels of

disturbance had lower species richness compared to the

undisturbed treatments (post hoc, DP0 . DP1¼DP2¼DP3¼DP4, at a¼ 0.05). Graphical examination of the

multivariate test using CAP revealed that undisturbed

assemblages and assemblages experiencing high levels of

physical disturbance were distributed at opposite sides

along the first axis with overlap around the origo (Fig.

2). Inspection of the mean cover of the most abundant

species suggested that there was a strong negative effect

on the cover of the perennial red alga Chondrus crispus

and the brown alga Fucus vesiculosus, whereas the

ephemeral green alga Enteromorpha intestinalis and

Chaetomorpha melagonium, capable of rapid growth

and colonization, were positively affected by disturbance

(Fig. 1b). Investigation of the effects of physical

disturbance on richness of algal and invertebrate species

showed that only the invertebrate species were signifi-

cantly affected (Table 2). Graphical analysis also

showed that the number of invertebrate species was

higher in assemblages that were not subjected to

physical disturbance compared to assemblages that

experienced physical disturbance, whereas the algal

richness remained fairly constant over the disturbance

continuum (Fig. 1a).

FIG. 1. (a) Effects of frequency of physical disturbance on total species richness, algal richness, and invertebrate richness (mean6 SE). (b) Effects of frequency of physical disturbance on mean cover of Chaetomorpha melagonium, Chondrus crispus,Enteromorpha intestinalis, and Fucus vesiculosus averaged over nutrient and grazing treatments (mean 6 SE). The marine hard-substratum assemblages that were used to study the effects of physical and biological disturbance and productivity were collectedfrom semi-exposed boulder fields in the Tjarno archipelago, Sweden.

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Effects of biological disturbance

There was a significant interaction between biological

disturbance and productivity for total species richness,

whereas algal richness was significantly altered by

biological disturbance and productivity independently

and the invertebrate species was not affected by either

factor (Table 2 and Fig. 3a–c). Inspection of means

showed that the effect of biological disturbance on total

richness was greater in assemblages in which nutrients

were not added (Fig. 3a). Thus richness was significantly

higher in assemblages with both grazers and nutrient

additions than in assemblages with only grazers (post

hoc; PR0, DB0 . DB 1; PR1, DB0¼DB1; DB0, PR0 ,

PR1; DB1, PR0 , PR1; at a ¼ 0.05), which suggested

that the productivity treatment counteracted the effects

of biological disturbance on richness. The interaction

between biological disturbance and productivity was

also significant in the multivariate analysis (Table 3) and

post hoc analysis showed that all factor combinations

differed significantly (post hoc; PR0, DB0 6¼ DB1; PR1,

DB0 6¼ DB1; DB0, PR0 6¼ PR1; DB1, PR0 6¼ PR1; at a¼ 0.05). Further graphical examination using CAP

revealed that, similar to the univariate analysis, there

was a more distinct separation between communities

with high compared to low productivity when grazers

were present (Fig. 4). Inspection of mean cover at the

species level showed that the green alga Chaetomorpha

melagonium was positively affected by biological distur-

bance, whereas Sphacelaria cirrosa and Enteromorpha

intestinalis decreased in the presence of grazers, and that

Hildenbrandia rubra was more frequent at high levels of

productivity (Fig. 4). Thus, in addition to significantly

affecting the number of species, the levels of biological

disturbance and productivity interactively determine

what species will be present in the assemblages.

DISCUSSION

In accordance with the first hypothesis, biological and

physical disturbances had different effects on the

marcoalgal and invertebrate species in the assemblages.

Biological disturbance significantly reduced only macro-

algal richness, whereas physical disturbance exclusively

reduced invertebrate richness. Our second prediction,

that interactive effects of biological disturbance and

productivity on species richness were different from

those of physical disturbance and productivity, was also

supported. Increased productivity had a positive effect

on the number of algal, but not invertebrate, species.

Total species richness was negatively affected by

biological disturbance under ambient productivity, but

richness was generally larger and not affected by

biological disturbance when productivity was increased.

Physical disturbance, on the other hand, had a negative

effect on richness irrespective of whether productivity

was high or ambient. Similarly, the multivariate analyses

showed that increased productivity affected species

composition interactively with biological disturbance

but not with physical disturbance. Furthermore, evalu-

FIG. 2. Constrained canonical analysis of principal coordinates (CAP) plot on Bray-Curtis similarity comparing assemblagesamong levels of physical disturbance (twice per week [DP1], once per week [DP2], once every second [DP3] or every fourth week[DP4], or left undisturbed [DP0]). Values shown for d2 are the squared canonical correlation coefficients.

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ation of responses of individual species to both types of

disturbance and productivity indicate that differences in

assemblage composition may be attributed to differences

in species’ trade-offs in life history characteristics.

The results of this study are consistent with those of

Kneitel and Chase (2004) and Svensson et al. (2007),

who found that the effect of physical disturbance on

richness was not affected by levels of productivity.

Svensson et al. (2007) suggested that their results could

be explained by the lack of a positive effect of the

productivity treatment on the dominant invertebrate

species. However, this cannot explain the lack of an

interactive effect in our experiment, since the dominant

organisms were macroalgae, which benefit directly from

increased nutrient availability (e.g., Worm et al. 2002).

The outcome is instead more likely explained by the

different effects of treatments on different components

of the assemblages. Physical disturbance affected the

number of invertebrate species, but not algal richness.

The invertebrate species were not affected by either

productivity or biological disturbance. Thus, the deci-

mation of invertebrate species by physical disturbance

could not be counteracted by productivity, and,

consequently, there were no interactive effects between

the two treatments. There was, however, a tendency for

interactive effects between physical and biological

disturbance, although physical disturbance did not show

the quadratic function predicted by the IDH, which has

previously been observed in this environment (Svensson

et al. 2007, 2009). The lack of support for the IDH is

likely explained by the low rate of competitive exclusion,

despite the presence of competitive species, and the

tendency for interactive effects is possibly caused by

consumers inhibiting recolonization of free substratum

(e.g., Underwood 1980, Robles 1982). Unlike physical

disturbance, biological disturbance had interactive

effects with productivity on total species richness, which

is in accordance with previous studies from many

different environments (Proulx and Mazumder 1998).

The number of algal species was higher in assemblages

subjected to both grazing and productivity than in

assemblages subjected only to grazing. This indicates

that the positive effect of the productivity treatment

counteracted the negative effects of the biological

disturbance. Thus, it would appear that productivity,

rather than biological or physical disturbance, was the

factor promoting diversity in this experiment, in contrast

to previous manipulative experiments in the same system

(Svensson et al. 2007, 2009).

It has been shown in both theory (Chesson and

Huntly 1997) and practice (Huxham et al. 2000) that

disturbance, by either physical or biological agents, is

not in itself a diversity-promoting mechanism. Advance-

ments have, however, been made in response to this

criticism by the suggestions of specific prerequisites that

are necessary for disturbance-mediated coexistence (e.g.,

Collins and Glenn 1997) and alternative theoretical

models, such as the ‘‘storage effect’’ (Roxburgh et al.

2004) and ‘‘successional niche’’ (Pacala and Rees 1998).

The common ground in both the suggested prerequisites

and the alternative models is that disturbance can

promote coexistence in spatially homogeneous compet-

itive environments with large species pools if the species

show differences in life history trade-offs. Coexistence

may then occur through creation of spatiotemporal

niches by disturbance, which may allow inferior species

competitive advantages over dominants in different

niches. Interpretation of results from multivariate

analyses within this framework may allow identification

of underlying mechanisms of disturbance–diversity

patterns through investigation of changes in community

FIG. 3. Interactive effects of productivity (PR0, PR1) andbiological disturbance, averaged over levels of physicaldisturbance, on (a) total species richness, (b) algal richness,and (c) invertebrate richness (mean 6 SE). Productivity 0 isambient productivity, and PR1 is increased productivity(increased growth rates in the experimental assemblagesthrough the addition of nutrients: 100 g of slow-release fertilizerper experimental assemblage).

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composition and life history trade-offs (Shea et al.2004). In this study, there were significant differences in

composition of species in the assemblages among levelsof physical disturbance. Biological disturbance and

productivity interactively affected the composition ofassemblages to form four distinct groups based on thetreatment combination (PR0DB0, PR0DB1, PR1DB0,

and PR1DB1). Although these four groups were allsignificantly different, there were larger differences

among levels of productivity in the presence of grazers,which is in accordance with the results from theunivariate analyses. Evaluation of responses of individ-

ual species allows for speculations on whether differ-ences in species’ trade-offs in life history characteristics

may be the underlying cause for differences in assem-blage composition. For instance, the green alga Chae-tomorpha melagonium was positively affected by both

physical and biological disturbance, but not by produc-tivity, possibly suggesting life history attributes toward

environmental tolerance rather than competition or fastgrowth (Dial and Roughgarden 1998). Conversely,

Enteromorpha intestinalis, another green alga, waspositively affected by physical disturbance and produc-tivity, but not by biological disturbance, thus indicating

a trade-off for fast growth compared to grazer toleranceor competitive capacity (Petraitis et al. 1989). Two algal

species, Fucus vesiculosus and Chondrus crispus, had

coverage of up to 100% in individual undisturbedassemblages and were negatively affected by physical

disturbance, while not benefiting from either productiv-ity or biological disturbance. This shows that there were

species present in the assemblages that have character-istics of competitive dominants, even though the strongcompetitive exclusion seen in this system in previous

experiments (Svensson et al. 2007, 2009) was notapparent here. In accordance with the theories of Pacala

and Rees (1998), trade-offs in species’ life historyattributes allowed assemblages to differ in composition,depending on the levels and treatment combinations of

biological and physical disturbance and productivity.Thus, it would appear that combinations of different

regimes of disturbances and productivity enable differ-ent species to thrive under different conditions, ulti-mately maintaining regional and/or local coexistence.

The underlying mechanisms of the interaction be-tween biological, but not physical, disturbance and

productivity, is most likely a combination of differencesin growth rates of species among levels of productivity

and mechanical differences in the way the damage isinflicted on the assemblages. In addition, productivityinteracted with biological but not physical disturbance,

reflecting qualitative differences between the damageexerted by the two types of disturbance. It has

previously been shown that similar disturbances can

FIG. 4. Constrained canonical analysis of principal coordinates (CAP) plot on Bray-Curtis similarity comparing assemblagesamong levels of productivity (high [PR1] or ambient [PR0]) and biological disturbance (present [DB1] or absent [DB0]). Treatmentcombinations plotted are: productivity and biological disturbance (PR1DB1), solely biological disturbance (PR0DB1) orproductivity (PR1DB0), and controls (PR0DB0). Values shown for d2 are the squared canonical correlation coefficients.

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have different effects on diversity, depending on the way

the damage is distributed (Bertocci et al. 2005, Svensson

et al. 2009). In a theoretical study, Bender et al. (1984)

have described differences between two kinds of

mechanical disturbances, pulse and press. The herbivo-

rous periwinkles in our experiment could be character-

ized as a press disturbance because they exert a

continuous small-scale reduction of biomass in algal

species. When biomass is slowly reduced, this effect can

more easily be counteracted by increased growth of the

affected organisms (Huston 1979, Kondoh 2001). The

productivity treatment probably had such a positive

effect, since diversity was higher in assemblages that

experienced grazing and nutrient addition compared to

assemblages subjected solely to grazing. The frequency

of physical disturbance shows characteristics similar to

pulse disturbance, which instantaneously kills, or

removes, a fraction of the community (Bender et al.

1984, Sousa 1984). Increased individual growth rate

cannot easily compensate for instantaneous loss of

individuals, which could explain the lack of interactive

effects between productivity and physical disturbance in

this experiment. In accordance with these arguments and

our results, Kneitel and Chase (2004), in the only

previous study that has tested for interactions of all

three factors, also found that biological disturbance

(predation), but not physical disturbance (drying), and

productivity interactively affected species richness.

Although Kneitel and Chase (2004) did not discuss

their treatments in terms of press and pulse disturbances,

the predatory mosquito larvae in their study could be

characterized as a press disturbance, whereas drying

every third or eighth day is similar to a pulse

disturbance. Thus, not only the selectivity, but also the

way that the damage caused by disturbance is applied,

may differ between agents of biological and physical

disturbance and determine the outcome of multifactorial

experiments.

In this study we have shown that the outcome of

experiments on disturbance and productivity is greatly

influenced by the type of disturbance that is used as a

treatment and also on the composition of the assem-

blage upon which the disturbance is inflicted. Previous

studies testing the DEM have commonly looked at

specific parts of natural communities, such as annelids

(Widdicombe and Austen 2001), macroalgae (Worm et

PLATE 1. (Main image) A typical boulder field on the west coast of Sweden. (Inset) Submerged boulders with benthicassemblages composed of green, brown, and red macroalgae, as well as bryozoans, hydroids, mussels, tunicates, polychaetes, andsea anemones. A color version of this plate is available in the Appendix. Photo credits: J. R. Svensson.

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al. 2002), or periphytes (Cardinale et al. 2006), or used

artificial assemblages composed of bacteria and proto-

zoans (Rashit and Bazin 1987), protozoans and rotifers

(Kneitel and Chase 2004), or bacteria and ciliates

(Scholes et al. 2005). The effects of treatments in such

experiments may be overestimated if the specific group

of species in the study are strongly affected or,

conversely, underestimated if species strongly affected

by the process in nature are not present in the

experimental assemblages. We have also shown, for

the first time, that an agent of biological disturbance and

an agent of physical disturbance interacted differently

with productivity due to their different effects on

different components of assemblages. Differences in

community composition and in responses of individual

species also indicate that the underlying mechanism for

the observed effects of both types of disturbance and

productivity may be traced back to species’ trade-offs in

life history attributes. In conclusion, our findings suggest

that experiments testing hypotheses on interactive effects

between disturbance and productivity, such as the

DEM, benefit from working with natural diverse

communities and should consider the ecological rele-

vance of manipulative treatments in relation to both the

explanatory model and the system under study.

ACKNOWLEDGMENTS

This study was financially supported by MARICE (aninterdisciplinary research platform at the Faculty of Sciences,Goteborg University) and by the Swedish Research Councilthrough contract number 621-2007-5779 to H. Pavia, as wellas by Formas through contracts 21.0/2004-0550 to H. Paviaand 217-2006-357 to M. Lindegarth. We thank MalinKarlsson for performing a large part of the fieldworkand Anneli Lindgren for assistance with identification ofmacroalgal species. We also thank two anonymous reviewers,whose comments greatly improved an earlier version of themanuscript.

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APPENDIX

A color version of Plate 1 showing a typical boulder field on the west coast of Sweden (Ecological Archives E091-213-A1).

J. ROBIN SVENSSON ET AL.3080 Ecology, Vol. 91, No. 10

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Paper I

Paper II

Paper III

Paper IV

Paper V

Paper VI

PAPER IV

”Du utför ditt slitgöra, match efter match, och håller käften. Då kommer du dit du vill.”

- Håkan Mild

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The Intermediate Disturbance Hypothesis predicts different

effects on species richness and evenness

J. Robin Svensson1, Mats Lindegarth, Per R. Jonsson, and Henrik Pavia

Department of Marine Ecology - Tjärnö, University of Gothenburg, SE-452 96 Strömstad, Sweden

Abstract. The Intermediate Disturbance Hypothesis (IDH) is among the most influential theories in ecology. Yet, the aspect of biodiversity predicted to peak at intermediate disturbance is not explicitly defined, or even discussed, in the literature. An issue that reaches beyond the scientific community, since the IDH also influences management of national parks and reserves. As a consequence of this apparent lapse in disturbance theory, tests of the IDH and later extensions are often based on unclear hypotheses and ambiguous measures of biodiversity. We used one established model and one new, spatially explicit model to compare the responses to disturbance between the two major aspects of biodiversity: species richness and the evenness of species abundance. Both models support the IDH when biodiversity is measured as species richness, but predict that evenness instead increases monotonically with increasing levels of disturbance. In order to investigate the generality of this discrepancy, we performed an extensive meta-analysis of studies that use more than one measure of diversity and support the IDH. In accordance with the predictions of the models, two-thirds of the published studies in the survey present different results for different diversity measures. More specifically, comparisons between richness and evenness showed an even higher degree of dissimilarity. Hence, based on the logic behind the underlying mechanism of the IDH, the predictions of our two models and the meta-analysis, we argue that species richness is the most straightforward and appropriate response variable in tests of the IDH and its associated models.

Key words: disturbance; diversity indices; evenness; ecological models; IDH; species richness.

INTRODUCTION

Arguably, the most fundamental steps in science are the formulation and testing of hypotheses (Popper 1959, Underwood 1997, Quinn and Keough 2002). This involves the logical linking of results from observations or experiments to the hypothesis under test. Without clear and explicit definitions of response variables, empirical studies cannot unambiguously test the predictions of the model. In ecological sciences, a central concept with such an elusive definition is biodiversity (e.g. CBD Rio 1992). Because of the great inconsistency in the way scientists define and measure biodiversity (e.g. Hurlbert 1971), hypotheses aiming to predict patterns of diversity, and the subsequent tests, can be unclear. The well-known intermediate disturbance hypothesis (IDH; Connell 1978) constitute, together with its related models (Huston 1979, Miller 1982, Kondoh 2001), a keystone in ecological theory, but it is also a case where many tests are based on unclear predictions and ambiguous measures of biodiversity. The original formulation of IDH predicts maximum biodiversity to occur at an intermediate level of disturbance due to coexistence of competitive dominants and rapid colonizers, while diversity will be low at both extremes due to competitive exclusion and local extinction. The IDH is one of few well established ecological theories and has influenced management and conservation of nature reserves (Wootton 1998) as well as grass- and pasture-land (Olff and Ritchie 1998). The original work by Connell has received more than 3000 citations and continues to generate scientific papers at an increasing rate, with over one third of all articles published

the last five years. The IDH, and the related dynamic equilibrium model (DEM; Huston 1979, Kondoh 2001), have received criticism in both empirical and theoretical studies (Pacala and Rees 1998, Huxham et al. 2000, Shea et al. 2004). Variable outcomes of empirical tests have led to the awareness that the models rely on a number of assumptions: competitive exclusion (Fuentes and Jaksic 1988), large regional species pool (Osman 1977), multiple stages in succession (Collins and Glenn 1997) and trade-offs between competition and colonization (Wilson 1994). Similarly, theoretical studies argue that the underlying mechanisms for coexistence are nonlinear responses to competition (Amarasekare et al. 2004) and spatiotemporal differences in niches (Pacala and Rees 1998). This critique has, thus, led to a more thorough understanding of the underlying coexistence mechanisms (Shea et al. 2004). However, regardless of these improvements of the models, tests of the IDH will inevitably also depend on the choice of diversity measure and this has not yet received attention. Considering the large body of literature on the IDH and its later extensions it is surprising that it is still unclear what aspects of species diversity that are predicted or measured. This is even more remarkable, given that many other aspects of the IDH have received ample attention, such as alternative mechanisms underlying coexistence (Pacala and Rees 1998), influence of characteristics of communities (Fuentes and Jaksic 1988), interactive effects of disturbances (Collins 1987), specific traits of individual species (Haddad et al. 2008), temporal variation of disturbance (Bertocci et al. 2005), how disturbance is applied (Svensson et al. 2009) as well as the important

1Corresponding author: J. Robin Svensson Email: [email protected]

An Epic Journal 1(1), 2010, pp. 1-8 © 2010 by the El Grande Society

1

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discussion on definitions of ecological disturbance (Pickett and White 1985). In contrast, explicit discussions of how to measure species diversity for appropriate tests of the IDH is lacking in even the most extensive and influential reviews on disturbance (cf. Sousa 1984, Mackey and Currie 2001, Sousa 2001, Shea et al. 2004). Hence, it is not surprising that there is no consensus on which measure of diversity to use. A consequence of the lack of such a consensus is that the IDH is tested with a plethora of measures and indices of diversity, such as, Margalef’s Richness, Simpson’s D, clonal diversity, functional diversity, 1-lambda, and the more well known Shannon index H’ (a.k.a the Shannon-Wiener or Shannon-Weaver index, eqn 3; Shannon 1948, Shannon and Weaver 1963), Pielou’s Evenness (eqn 4; Pielou 1966), and Species Richness (i.e. number of species). It is likely that much of the confusion about how to measure diversity stems from the lack of clarity in the original formulations of IDH and related models. In the original article by Connell (1978), the word diversity is frequently used without being defined in the text, while species richness is the only specific measure of diversity used in graphs and tables. This indicates that the IDH primarily was intended to predict changes in the number of species. The dynamic equilibrium hypothesis (DEM; (Huston 1979, Kondoh 2001), an extension of the IDH, predicts that the level of disturbance where maximum diversity is observed will depend on the level of productivity. Huston (1979) defines diversity as only richness and evenness, rejecting various diversity indices, but makes no distinction in predictions between effects of disturbance on richness and evenness. Kondoh (2001) discusses only species richness and does not consider specific effects of productivity and disturbance on evenness. In yet another extension of the IDH, on differences in effects depending on distribution of disturbance, Miller (1982) stated that the highest diversity will occur at an intermediate rate of disturbance “…if diversity is a measure of both species abundance and number”. The addition of species abundance to the hypothesis is, however, not explained or motivated in the article. The only articles to our knowledge that discuss the relevance of different diversity measures in tests of the IDH, are those by Sommer (1995) and Weithoff et al. (2001). Both articles mainly concern phytoplankton communities, but while Weithoff et al. (2001) argues that functional diversity, rather than species diversity, is the most suitable response variable, Sommer (1995) maintains that theories about coexistence principally predict changes in species number, not abundances or diversity indices. In their review, Shea et al. (2004) argue that the IDH cannot be tested by studies using only one species, because the IDH does not make predictions about abundances of species. If the IDH does not predict differences in abundance, not only single species studies but also tests using compound diversity indices or the evenness of species distributions as response variables are inappropriate. The lack of an explicit definition of diversity in the original presentation of the models has, together with the variety of response variables that have been used in subsequent experimental tests, made the status of the IDH and related models unclear. This

apparent and unrecognized lapse in the tests of IDH and related hypotheses/models jeopardizes the applicability of disturbance-diversity theory in both basic and applied ecology. The confusion about what aspects of biodiversity that are predicted by IDH led us to explore in detail the logical link between IDH and biodiversity. We first show that models of the IDH generate qualitatively different predictions for different biodiversity measures. Secondly, we apply a meta-analysis of the published tests of IDH to show that support of IDH indeed depends on how diversity is measured. Finally, we discuss the need for hypotheses about mechanisms explaining the relationship between disturbance intensity and specific measures of biodiversity.

METHODS Model predictions of how disturbance affects species richness and evenness The two models, spatially implicit and explicit, both involve one-sided competition, occupancy as a function of colonization ability, competitive strength and local extinction, which increases with disturbance. A pool of 20 species was used in all modeling runs and colonization rates of the ith species, ci, were modeled as ci=0.1/0.9i: (Kondoh 2001). The first model (A) is a spatially implicit patch-occupancy model proposed by Kondoh (2001) and later used by Worm et al. (2002). The model was solved using an ordinary differential equation solver in Matlab® 7.6 (MathWorks Inc) and colonization rates of the ith species, ci, was modeled as ci=0.1/0.9i: (Kondoh 2001). For more details see Kondoh (2001). Since spatial relationships are well-known to affect population- and community dynamics (e.g. Hassell et al. 1991, Molofsky 1994), the second modeling approach (B)

a) Measure of diversity Supported Tested

Species richness (S) 109 123

Shannon index (H') 49 56

Pielous Evenness (J) 16 38

Other measures/indices 27 32

Total number of studies 160

b) Dissimilarity among measures Total Dissimilar

Studies testing >1 diversity measure 60 42 (70%)

Studies testing both richness and evenness 33 25 (76%)

Table 1. a) Number of studies supporting the IDH and the measures used in tests; Species richness (S), Shannon- Wiener index (H') and Pielous Evenness (J). b) Number of studies where different measures of diversity differed in their support of the IDH.

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THE IDH PREDICTS; RICHNESS ≠ EVENNESS

3

was spatially explicit using a cellular automaton model (e.g. Silvertown et al. 1992, Ermentrout and Edelsteinkeshet 1993). The model was set up as a one-dimensional universe with 100 cells. At each time step, a proportion of the cells were subjected to a random, local extinction. Thereafter, transition of each cell was achieved either by competition or by recruitment. In the event of competition, the state (i.e. the occupying species), a, of the jth cell at time t+1, was determined by the state of neighboring cells by:

(1) a jt +1 = max([a j −1

t a jt a j +1

t ])

Recruitment occurred with a probability of 0.1 in unoccupied cells. The probability of recruitment of the ith species was modeled as:

(2) pi = ci

ci

i=1

20

Meta-analysis of diversity measures and support for IDH In the meta-analysis of previous tests of IDH and choice of diversity measure we followed Shea et al. (2004), only included studies reporting support for proceeded from the list of papers provided in Shea et al. (2004) and complemented it by searching in ISI Web of Science for recent articles (2003-2010)citing Connell’s original paper (Connell 1978). Of the over one thousand articles initially reviewed, 143 studies in 132 publication were found which reported support for the IDH (Table 1, Appendix S1). Among these, 60 studies included more than one measure of diversity (Table 1b, Appendix S1), mainly species richness, Shannon’s index H’ (eqn 3; Shannon 1948, Shannon and Weaver 1963), and evenness (eqn 4; Pielou 1966).

(3) ss

HS

i

1ln

1

1

'max ∑

=

−=

(4) 'max

'

H

HE =

For the examination of dissimilarities in outcomes of tests of the IDH using more than one measure of diversity, we specifically contrasted the number of species (i.e. species richness) to the evenness of species distributions (i.e. Pilou’s Evenness). This was done because (i) these two measures are the key components in all indices of diversity, and (ii) they represent two very different components of the concept of diversity. In order to compare differences in outcomes between species richness and evenness we calculated the quadratic coefficient in regression models describing the relationship between disturbance and richness. The quadratic components were calculated through regression analyses after z-transformation of data extracted from publications using the graph digitizer GrabIt!© (Datatrend Software, Raleigh, North Carolina, USA). The z-transformations were done in order to allow comparisons between component values for richness and evenness. Disturbance levels were normalized between 0 and 1. Data extraction was possible in 28 studies from the articles reviewed (Fig. 1, Appendix S1). The strength and polarity of the quadratic coefficient was then plotted with species richness on the x-axis and evenness on the y-axis. A high negative quadratic coefficient indicates a strong hump-shaped relationship between disturbance and diversity, thus supporting the IDH.

RESULTS

Model predictions of how disturbance affects species richness and evenness We applied a modeling approach to explore how disturbance affects different measures of biodiversity. Here we report effects on species richness and Pielou’s evenness since these measures extract the two main components of species-abundance distributions. Other compound indices (e.g. Shannon’s H’) yielded intermediate results. The response of species richness and evenness to different rates of disturbance was explored with two different models, one well-established (Kondoh 2001, Worm et al. 2002) spatially implicit (model A) and one spatially explicit (model B). Both models involve one-sided competition, and occupancy of a particular species is a function of colonization ability, competitive strength and local

Fig. 1 Quadratic components for Species richness and Evenness, calculated through regression analyses after z-transformation of data extracted from studies in the meta-analysis that used both measures (see methods), a) plotted together for each study for comparisons within studies and b) plotted separately for general comparisons among measures. Support for the IDH, i.e. a hump- shaped relationship, is indicated by high negative values of quadratic components for each measure of diversity.

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extinction, which increases with disturbance (see Methods). In both model A and model B richness shows a unimodal hump-shaped pattern, whereas evenness is asymptotically increasing with increasing disturbance levels (Figs. 1a and b). Thus, both mathematical models of the IDH predict qualitatively different effects on species richness and evenness. Meta-analysis of diversity measures and support for IDH Of the over one thousand articles initially reviewed, 143 studies in 132 publications reported support for the IDH and 60 of these studies included more than one measure of diversity (Table 1). In studies including more than one measure of diversity the support for the IDH was often inconsistent between different diversity measures. When outcomes among all measures are compared they show dissimilar support in 70% of the cases (Table 1b). In comparisons specifically contrasting outcomes among tests using both richness and evenness, these two measures differed in their support in over 75 % of the cases. The support for the IDH in 28 previous studies using both species richness and evenness as biodiversity measures is shown in Fig. 2. Negative values of the quadratic component in the statistical model of the effect of disturbance on diversity indicate a hump-shaped (unimodal peak) relationship and thus support for the IDH. Only when diversity is measured as species richness is there a consistent hump-shaped relation supporting IDH (Fig. 2a), and the cumulative distributions in Fig. 2b show that the range of the quadratic coefficients is narrower for the tests using species richness compared to when evenness is used.

Discussion

We here show that an established model as well as a new, spatially explicit model only support IDH when bio-diversity is measured as species richness. Both models predict that evenness instead increases monotonically with increasing levels of disturbance. Our extensive meta- analysis of published empirical tests of the IDH is also

consistent with the model predictions as species richness yielded stronger hump-shaped relationships between disturbance and diversity, than did evenness. This also corresponds with our finding that two-thirds of the published studies supporting the IDH present different results for different diversity measures. Specifically, when both species richness and evenness were used the relationship between disturbance and diversity showed an even higher degree of dissimilarity. It is surprising that the use of different diversity measures and implications for how to interpret tests of the IDH has not received any previous attention. Mackey and Currie (2001) reviewed tests of IDH and they found a hump-shaped relationship for species richness, the Shannon index H’ and evenness with disturbance in 19, 10 and 3 out of 85 analyzed articles, respectively. They did not, however, discuss possible causes of the different outcomes based on the selected measure of diversity. This potentially confounding factor in tests of the IDH is also neglected in the otherwise excellent review by Shea et al. (2004), where they focus on the mechanisms of coexistence underlying the hump-shaped pattern. Why then do different measures of diversity differ in response to disturbance? According to the original formulation of the IDH by Connell (1978), it is the number of species that will increase when disturbance prevents competitive exclusion to occur and allows new species to colonize, up to a certain point when disturbance becomes too severe for species to persist (Eggeling 1947, Osman 1977, Connell 1978). Thus, the prediction that the number of species should show a hump-shaped response to disturbance rests on logic arguments, and the hypothesis is easily tested with species richness as the most evident response variable. It is, however, less logical that this prediction should automatically also apply to various diversity indices, such as Shannon’s H’ and evenness, that also consider how abundance is distributed among species. Species do not need to be more evenly distributed at intermediate disturbance just because the number of species is large. If the predictions are logical for the number of species, but not for species-abundance

Fig. 2 Species richness (solid line) and Evenness (dashed line) as functions of magnitude of disturbance predicted by a) the spatially implicit model A and b) the spatially explicit model B. Parameters in A are: productivity level=2, extinction rate=0.05, threshold for local extinction=0.01, time steps=500. Data are presented as mean ± SE

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distributions (Shea et al. 2004), there is no clear reason for H’ to be a preferable index in disturbance studies, as has previously been suggested (Worm et al. 2002). On a more general level, Stirling and Wilsey (2001) argued that H’ was the best measure of diversity because it considers both the separate effects of richness and evenness and also their interrelations. Although this may be advantageous under certain circumstances, it may be less so in efforts to unravel specific changes in diversity, because the underlying ecological process or mechanism causing changes in H’ can be traced back to effects on either richness or evenness (Hurlbert 1971). Thus, a more interesting and challenging question is why patterns of richness and evenness differ, and if a logical pattern between evenness and disturbance can be conceived within the framework of the IDH. The IDH relies on the assumption that one or a few species will dominate the community in the absence of disturbance (Fuentes and Jaksic 1988, Collins and Glenn 1997, Svensson et al. 2007). An uneven distribution of species is therefore to be expected at low levels of disturbance, which is also commonly observed in marine and terrestrial field experiments (Eggeling 1947, Molis et al. 2003, Lenz et al. 2004b, a). According to the compensatory mortality hypothesis(Janzen 1970), mortality from causes unrelated to the competitive interactions falls heaviest on whichever species that ranks highest in competitive ability. The reduction of a highly abundant basal species (i.e. dominant) by disturbance may lead to colonization of new species in the free space (e.g. Connell 1978). Consequently, both the number of species and the evenness of species distributions are likely to initially increase following a disturbance in an already uneven community. In accordance with this, the presented mathematical models (Figs. 2a and b), as well as previous field experiments from both marine and terrestrial systems (Vujnovic 2002, Kimbro and Grosholz 2006), support these patterns. Following the plausible increase in evenness at low to intermediate disturbance levels, logical predictions and patterns are less clear for high levels of disturbance. Commonly, high disturbance is associated with larger areas of free substratum (e.g. Paine and Levin 1981, Miller 1982, Valdivia et al. 2005). This hinders dominants to achieve large abundances, or even exist, and allows the few rapid colonizers able to withstand the disturbance to settle in the free space. These colonizers are all likely to initially be low in abundance, which might lead to a high level of evenness despite low total coverage in assemblages at high levels of disturbance (Kimbro and Grosholz 2006), which is in accordance with our model predictions. Maximum evenness at intermediate levels of disturbance has, however, been found in a few manipulative experiments (Lenz et al. 2004b, a). Logical arguments explaining the subsequent decrease in evenness are not given in these studies, possibly because clarification of patterns thought to conform to an existing model seemed redundant. One possible explanation for low evenness at intense disturbance is caused by the dominance of a few disturbance specialists, where a well-known example is metal-tolerant grasses on soils

contaminated with mine tailings (Gregory and Bradshaw 1965). However, because of the general lack of discussion of what is predicted about evenness, we recommend that studies choosing evenness as the response variable in tests of the IDH and DEM should present logical arguments, a priori , to why the predicted pattern can be observed in natural communities. In conclusion, we argue that the logic behind the underlying mechanism of the IDH, the predictions of our two models and the meta-analysis, all suggest that species richness is the most straightforward and appropriate response variable in tests of the IDH and its associated models. Furthermore, since the IDH is also utilized in management of marine and terrestrial national reserves and parks (e.g. Yellowstone National Park, USA), a consensus on appropriate response variables will have benefits reaching beyond the scientific community.

Acknowledgements This study was financially supported by MARICE (an interdisciplinary research platform at the Faculty of Sciences, Göteborg University), by the Swedish Research Council through contract no. 621-2007-5779 to HP and 621-2008-5456 to PRJ, and by Formas through contracts 21.0/2004-0550 to HP and 217-2006-357 to ML.

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