**Meet the editors**

Dr. Yueh-Hsin Lo graduated in Forest and Natural Resource Management at the National Taiwan University, and obtained a PhD in Forest Ecology from the University of British Columbia, Canada. She is currently working as Research Associate at the Public University of Navarra, Spain. At present she is involved in several research lines studying the long-term influence of environmental factors

on tree growth and forest development. She is also interested in the practical applications of forest ecology in restoration of altered forest plantations, and has co-edited the book "Forest Ecosystems - More than Just Trees" also published by InTech.

After graduating in Agricultural Engineering, Dr. Juan A. Blanco obtained a PhD in Forest Ecology from the Public University of Navarra, Spain. He is currently working as a Senior Research and Marie Curie Research Fellow at the same university. His work is focused on the development and evaluation of ecological models to simulate the influences of management, climate and other ecological

factors on tree growth. He is currently collaborating with research teams from several countries in using ecological models to explore the effects of climate change and alternative forest practices in natural and planted forest in boreal, temperate and tropical forests. He has also co-authored the first book dedicated exclusively to the use of hybrid ecological models in forest management, "Forecasting Forest Futures" (Earthscan, London), and has co-edited three books on "Climate Change" and one on "Forest Ecosystems - More than Just Trees", also published by InTech.

Dr. Shovonlal Roy completed a Masters in Applied Mathematics, and obtained a PhD in Mathematical Biology with particular emphasis in marine ecosystems. Currently, Dr. Roy is a Lecturer at the University of Reading, UK. As an ecological modeller, his research aims at better understanding of the mechanisms of stability and diversity in ecosystems by combining empirical data and mechanistic models.

His works include application of dynamical systems theory, dynamics and biodiversity of marine plankton, satellite remote sensing of ocean colour, and data assimilation

## **Contents**


## **Section 2 Concepts and Theories 203**


V. Polonio, A. Muñoz, T. Patrocinio, R. Vilela, M. Barba and P. Marín


Víctor L. Finot, Clodomiro Marticorena, Alicia Marticorena, Gloria Rojas and Juan A. Barrera

Chapter 16 **Plant Structure in the Brazilian Neotropical Savannah Species 407** Suzane Margaret Fank-de-Carvalho, Nádia Sílvia Somavilla, Maria Salete Marchioretto and Sônia Nair Báo

Chapter 17 **Social Perception and Supply of Ecosystem Services — A Watershed Approach for Carbon Related Ecosystem Services 443**

**Section 2 Concepts and Theories 203**

Chapter 9 **Hydromorphology and Biodiversity in Headwaters — An Eco-**

**Assessment of the Conservation Status of "Veiga de Ponteliñares", NW Spain (Natura 2000 Network), Using**

Amaia Pérez-Bilbao, Cesar João Benetti and Josefina Garrido

**Over Vulnerable Marine Ecosystems on the High Seas of the**

J. Portela, J. Cristobo, P. Ríos, J. Acosta, S. Parra, J.L. del Río, E. Tel, V. Polonio, A. Muñoz, T. Patrocinio, R. Vilela, M. Barba and P. Marín

**Ecosystem — The Iriomote Cat Changes Feeding Patterns in**

**of the German Subdued Mountains 205**

Chapter 10 **Biodiversity and Conservation of Temporary Ponds —**

Chapter 11 **A First Approach to Assess the Impact of Bottom Trawling**

Chapter 12 **Agrorural Ecosystem Effects on the Macroinvertebrate Assemblages of a Tropical River 299**

Bert Kohlmann, Alejandra Arroyo, Monika Springer

Chapter 13 **Ecohidrology and Nutrient Fluxes in Forest Ecosystems of**

Chapter 15 **Grasses (Poaceae) of Easter Island — Native and Introduced**

Chapter 16 **Plant Structure in the Brazilian Neotropical Savannah**

Maria Salete Marchioretto and Sônia Nair Báo

Víctor L. Finot, Clodomiro Marticorena, Alicia Marticorena,

Suzane Margaret Fank-de-Carvalho, Nádia Sílvia Somavilla,

Carlos E Oyarzún and Pedro Hervé-Fernandez Chapter 14 **Ecological Flexibility of the Top Predator in an Island**

**Relation to Prey Availability 353**

Martin Reiss and Peter Chifflard

**Freshwater Invertebrates 241**

**Southwest Atlantic 271**

and Danny Vásquez

Shinichi Watanabe

**Species 407**

**Species Diversity 383**

Gloria Rojas and Juan A. Barrera

**Southern Chile 335**

**Faunistic Substrate Preference Assessment in Forest Springs**

Antonio J. Castro, Caryn C. Vaughn, Jason P. Julian, Marina García Llorente and Kelsey N. Bowman


André Pelser, Nola Redelinghuys and Anna-Lee Kernan

Chapter 25 **Shortage of Biodiversity in Grassland 627** Ricardo Loiola Edvan, Leilson Rocha Bezerra and Carlo Aldrovandi Torreão Marques

## **Preface**

During the 20th century urban development was extended to all the regions of the world. With a booming human population and the intensification of economic development (first in Europe and North America and lately in the rest of the world) practically all the ecosystems in the world were impacted in one way or another. Therefore, it was just a matter of time that some of the iconic wildlife species of the world started to suffer from fast reductions in their populations, or even facing extinction. The danger of losing species such as whales, lions, tigers, elephants, panda bears, gorillas, brown bears, buffalos, sequoias, etc., was very real [1]. This danger was highlighted by scientists and environmental managers around the world, and the society responded with the creation of environmentalists groups, whose social pressure helped to create lists of endangered animal and plant species needing specific actions for conservation. This was the base to develop programs and activities focused on the protection of individual high-profile species. Many of these campaigns were supported by the public due to the easy sympathy or spiritual connection with some of these majestic species, and as a consequence, natural conservation was seen by the main public as "avoiding things getting worse". Some of these activities have achieved important successes, such as the halt in commercial hunting of whales, the breeding programs of panda bears or the increase in numbers of American buffalos [2]. However, in other cases the protection of the target species was not enough to prevent its decline or extinction (e.g. the Yangtze River dolphin, or the Pyrenean wild goat), or just the species were not interesting enough for the public opinion and therefore not the main focus of protection efforts, such in the case of "ugly" species such as amphibians, reptiles, insects, cacti, etc. Such species-specific conservation programs are becoming less important as the world realizes that biodiversity loss is not a matter of losing a few iconic species, but a large number of all kinds of species of plants, animals, fungi and even microbial organisms.

IX

In the latest decades, there has also been an increase in the understanding on how the presence or absence of species could affect not only ecosystem structure, but ecosystem function as well. The discussion for formal maintenance and conservation of biological diversity (biodiversity) was first organized in a cohesive fashion by the United Nations Environment Programme in 1992 at the Rio Earth Summit. The following year, 168 countries signed the Convention of Biological Diversity (CBD) to protect and ensure conservation and sustainable use of biodiversity [3]. More recently the United Nations Secretary General initiated and completed the Millennium Ecosystem Assessment to assess the consequences of ecosystem change for human wellbeing and the scientific basis for action needed to enhance the conservation and sustainable use of ecosystems [4]. The assessment provided a reaffirmation that sustainable societies

are dependent on the goods and services provided by ecosystems, including clean air and water, productive soils, and the production of food and fiber, and, more importantly, it propagated the ecosystem services paradigm upon which to assess and value biotic resources throughout the world [5]. The latest event at the policy-making level has been the creation of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), a body created by the United Nations following the success of the Intergovernmental Panel on Climate Change, as it is becoming clear that the risk of massive biodiversity loss is at the same level as the risk of massive climate change.

During the first years of the 21st century, human-caused climatic and land use changes have caused that almost all ecosystems on Earth are under different varieties of stresses such as habitat loss and degradation, shortage of water and food supplies, toxic contaminants, and invasive species. These stresses have affected ecosystem structure or functions, altered the viability of species and communities, and therefore changed the quality and forms of ecosystem services [6]. The issue on how biodiversity support ecosystem functions is becoming increasingly important in ecological and biological studies. Up to date, to predict the ecological consequences of biodiversity loss, researchers have spent much time and effort quantifying how biological variation affects the magnitude and stability of ecological processes that underlie the functioning of ecosystems. Some of the most important ecosystem functions (biomass production, nutrient, water, and energy recycling, among others) could be affected by the presence or absence of plant, animal, fungi, and microbial species.

The current state-of-the-art confirms that biodiversity does indeed simultaneously enhance both the production and stability of biomass in experimental systems, and this is broadly true for terrestrial [7] and aquatic primary producers [8]. During the last few years an important number of experiments and field research has generated enough data to allow for extensive synthesis [9-17, among others]. From these synthesis studies, it has become clear that the strength of diversity effects on ecosystem functions such as biomass production or organic matter recycling is independent of diversity effects on temporal stability [17]. From such review, it has become evident that the highest levels of productivity in a diverse community are not associated with the highest levels of stability. Thus, on average, diversity does not maximize the various aspects of ecosystem functioning we might wish to achieve in conservation and management. In addition, the previous reviews have highlighted the issues related to researching the connections between ecosystem biodiversity and function, as knowing how biodiversity affects productivity gives no information about how diversity affects stability (or vice versa). Therefore, to predict the ecological changes that occur in ecosystems after extinction, we will need to develop separate mechanistic models for each independent aspect of ecosystem functioning [17]. Within this book, readers can find some of such models for aquatic (see chapter by Roy) and terrestrial (see chapter by Lo et al.) ecosystems.

Most of the previous studies, however, have been done in terrestrial ecosystems. For example, it is well established the role of non-tree species in the functioning, productivity, and stability of the ecosystem [7]. On the other hand, the aquatic habitats are of global importance due to their large spatial coverage, significant functional role in carbon fixation, oxygen generation, and high biodiversity. However, unlike the terrestrial world, our knowledge of biodiversity patterns across aquatic habitats is limited. Within the aquatic ecosystems, the relationships between ecosystem function and biodiversity are not always similar to that in the terrestrial or benthic ecosystems, and the relationships are often debated (e.g. [8]).

Theoretical as well as empirical studies addressed this question from different perspectives. The aquatic habitats can be divided into three major categories, primarily based on the salinity level of the water medium: freshwater systems, transitional and brackish waters, and marine systems. Although this categorization is rather crude, it helps to understand aquatic biodiversity on regional scales. The biodiversity in marine ecosystems is generally higher than that in freshwater systems, and the transitional waters are generally less diverse than freshwater and marine systems. The extreme diversity in aquatic or marine ecosystems is often puzzling in view of the established ecological theories of species coexistence. Theoretically, several mechanisms have been proposed to explain the biodiversity within aquatic ecosystems, which include temporal effects (e.g., environmental fluctuations, periodic forcing), spatio-temporal effects (spatial heterogeneity), self-sustaining cycles, deterministic chaos, spatio-temporal chaos, self-organized segregation, grazing and chemical signaling (e.g. [18]).

X

are dependent on the goods and services provided by ecosystems, including clean air and water, productive soils, and the production of food and fiber, and, more importantly, it propagated the ecosystem services paradigm upon which to assess and value biotic resources throughout the world [5]. The latest event at the policy-making level has been the creation of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), a body created by the United Nations following the success of the Intergovernmental Panel on Climate Change, as it is becoming clear that the risk of

During the first years of the 21st century, human-caused climatic and land use changes have caused that almost all ecosystems on Earth are under different varieties of stresses such as habitat loss and degradation, shortage of water and food supplies, toxic contaminants, and invasive species. These stresses have affected ecosystem structure or functions, altered the viability of species and communities, and therefore changed the quality and forms of ecosystem services [6]. The issue on how biodiversity support ecosystem functions is becoming increasingly important in ecological and biological studies. Up to date, to predict the ecological consequences of biodiversity loss, researchers have spent much time and effort quantifying how biological variation affects the magnitude and stability of ecological processes that underlie the functioning of ecosystems. Some of the most important ecosystem functions (biomass production, nutrient, water, and energy recycling, among others) could be

The current state-of-the-art confirms that biodiversity does indeed simultaneously enhance both the production and stability of biomass in experimental systems, and this is broadly true for terrestrial [7] and aquatic primary producers [8]. During the last few years an important number of experiments and field research has generated enough data to allow for extensive synthesis [9-17, among others]. From these synthesis studies, it has become clear that the strength of diversity effects on ecosystem functions such as biomass production or organic matter recycling is independent of diversity effects on temporal stability [17]. From such review, it has become evident that the highest levels of productivity in a diverse community are not associated with the highest levels of stability. Thus, on average, diversity does not maximize the various aspects of ecosystem functioning we might wish to achieve in conservation and management. In addition, the previous reviews have highlighted the issues related to researching the connections between ecosystem biodiversity and function, as knowing how biodiversity affects productivity gives no information about how diversity affects stability (or vice versa). Therefore, to predict the ecological changes that occur in ecosystems after extinction, we will need to develop separate mechanistic models for each independent aspect of ecosystem functioning [17]. Within this book, readers can find some of such models for aquatic (see chapter by Roy) and

Most of the previous studies, however, have been done in terrestrial ecosystems. For example, it is well established the role of non-tree species in the functioning, productivity, and stability of the ecosystem [7]. On the other hand, the aquatic habitats are of global importance due to their large spatial coverage, significant functional role in carbon fixation, oxygen generation, and high biodiversity. However, unlike the terrestrial world, our knowledge of biodiversity patterns across aquatic habitats is limited. Within the aquatic ecosystems, the relationships between ecosystem function and biodiversity are not always similar to that in the terrestrial or benthic ecosystems, and the relationships are

massive biodiversity loss is at the same level as the risk of massive climate change.

affected by the presence or absence of plant, animal, fungi, and microbial species.

terrestrial (see chapter by Lo et al.) ecosystems.

often debated (e.g. [8]).

On the other hand, empirical and experimental studies have not only established some of the mechanisms proposed theoretically, but also put forward new scenarios. For example, experiments have shown how food supply may affect the function and dynamics aquatic ecosystems, and identified factors such as the effects of temperature fluctuation, inducible defense, autotoxin and density-dependent effects for the sustainability of simple aquatic ecosystems (e.g. [19]).

The first section of this book provides an overview of different concepts and theories to be taken into account when dealing with links between ecosystem biodiversity and function. Roy theoretically describes a mechanism termed as pseudo-mixotrophy, by which allelopathy among marine phytoplankton mediates nutrient competition and promotes the diversity of the primary producers. Much of his theoretical framework could be translated into terrestrial ecosystems. Jurburg and Falcão-Salles discuss the role of functional redundancy on ecosystem function. Sobrinho et al. explore the implications for ecosystem functioning of biodiversity in tropical forests. Harvey and Malcicka examine the interactions between climate change and modifications of species distribution shifts and their implications in multitrophic networks. Yapp introduces the use of criteria and indicators for managing the change and restoration of biodiversity in vegetated landscapes. Lo et al. review the different theoretical models available to examine the interactions between plant diversity and stand growth in ecological restoration projects. De Souza et al. analyze the implications at ecosystem level of microbial diversity and assembly. To finish this section, Bavec and Bavec review the management options available to increase biodiversity in agroecosystems.

The second section of this book provides a wide range of studies showcasing evidence and facts that support the need and importance of research on links between biodiversity and ecosystem function. A range of studies on different aquatic environments can be found, where the authors described new mechanisms of biodiversity, and patterns of biodiversity distribution across different habitat. Reiss and Chifflard address the issue of the fauna-microhabitat relationship of springheads, and have presented results on quantitative and qualitative assessment on substrate preferences of invertebrates. Pérez-Bilbao et al. study the invertebrate fauna in freshwater ponds, and investigate if those invertebrates could be used as good indicator of the environmental quality of temporary ponds. Acosta et al. study the effects of bottom trawling on benthic vulnerable marine ecosystems on the high seas of the Southwest Atlantic using several research cruises, and present preliminary results on the impacts of fishing activities on sensitive benthic organisms. Kohlmann et al. examine the possible impacts of human activities on the macro-invertebrate biodiversity along the length of a tropical river, and their

influence on the structure and function of agrorural environments. Oyarzun and Hervé-Fernández discuss the implications that forest management has on the eco-hydrology of Chilean rivers.

Moving into terrestrial ecosystems, the next block of studies provides an extended view of the links between ecosystem biodiversity and function at different ecological scales. Watanabe explores the role of niche and prey diversity on the presence of wild cats in Pacific islands. Finot et al. discuss the diversity of grass species on Easter Island, and the implications for ecosystem function of the presence of invasive species. Fank-de-Carvalho et al. also explore the diversity of plant species but in the Brazilian neotropical savannah. The former studies provide facts and evidence for mostly natural ecosystems. The next block of chapters, however, provides evidence on the connections between management, biodiversity and ecosystem functions for a variety of terrestrial ecosystems. Castro et al. explore the connections between social perception of ecosystem values and biodiversity. On a similar line on the links between ecology and society, Oishi and Tabataanalyze discuss the importance of conserving large trees for spiritual purposes with the conservation of epiphytic bryophyte diversity. Ribeiro et al. review the state-of-the-art on research aimed to support the sustainable use of miombo woodlands in southern Africa.

The next three chapters of this block deal with the possibility of carrying ecological restoration to gain some of the biodiversity lost by human actions. In a methodological study, Marcuzzo and Viera introduce the approach to studying and carrying ecosystem restoration projects in ecological units with homogeneous features. González-Izquierdo et al. describe the steps taken and the results obtained in three different restoration projects on tropical Caribbean forests, highlighting the importance of the social component of such projects and the increased success rate when incorporating locals into the process. Roccotiello et al. provide an example of the complexities on restoring biodiversity in highly altered ecosystems such as abandoned mine sites.

The final block of chapters in this book deals with the links between ecosystem diversity, functions and services in agroecosystems, a highly important issue as they are the source of most of food for human societies. Bavec and Bavec review the effects of traditional and organic farming on agroecosystem biodiversity and its links with agricultural productivity. Pelser et al. discuss the possibilities to include local use of agricultural resources inside protected areas to achieve both local development and biodiversity conservation. Edvan et al. examine the consequences on grassland and herbivore production of shortages in grasses biodiversity, and the influence of exotic invasive grasses.

All things considered, these 26 chapters provide a good overview of the links between ecosystem biodiversity and ecosystem functions, which are then translated into ecosystem services useful for all humans. These chapters show the importance of biodiversity on the structure and function of terrestrial and aquatic ecosystems, and in managed or natural conditions, which that can be applied to all the regions of the world. They are an introduction to the research being done around the globe in connection to this topic. We hope the readers from academia, management, conservation, and any other stakeholders will enjoy reading this book and regard it as an initial source of information and study cases on what is the role that biodiversity plays in ecosystems.

The Editors want to finish this preface acknowledging the collaboration and hard work of all the authors. We are also thankful to the Publishing Team of InTech for their continuous support and assistance during the creation of this book. Special thanks are due to Ms. Iva Lipović for inviting us to

lead this exciting project and for coordinating the different editorial tasks. Last but not least, we want to acknowledge InTech´s generosity and social commitment by supporting scientists from developing countries and making their research freely available.

### **Dr. Yueh-Hsin Lo**

**Dr. Juan A. Blanco** Dep. Ciencias del Medio Natural, Universidad Pública de Navarra, Spain

### **Dr. Shovonlal Roy**

Dep. Geography and Environmental Science, University of Reading, UK

#### **References**

XII

woodlands in southern Africa.

altered ecosystems such as abandoned mine sites.

influence on the structure and function of agrorural environments. Oyarzun and Hervé-Fernández

Moving into terrestrial ecosystems, the next block of studies provides an extended view of the links between ecosystem biodiversity and function at different ecological scales. Watanabe explores the role of niche and prey diversity on the presence of wild cats in Pacific islands. Finot et al. discuss the diversity of grass species on Easter Island, and the implications for ecosystem function of the presence of invasive species. Fank-de-Carvalho et al. also explore the diversity of plant species but in the Brazilian neotropical savannah. The former studies provide facts and evidence for mostly natural ecosystems. The next block of chapters, however, provides evidence on the connections between management, biodiversity and ecosystem functions for a variety of terrestrial ecosystems. Castro et al. explore the connections between social perception of ecosystem values and biodiversity. On a similar line on the links between ecology and society, Oishi and Tabataanalyze discuss the importance of conserving large trees for spiritual purposes with the conservation of epiphytic bryophyte diversity. Ribeiro et al. review the state-of-the-art on research aimed to support the sustainable use of miombo

The next three chapters of this block deal with the possibility of carrying ecological restoration to gain some of the biodiversity lost by human actions. In a methodological study, Marcuzzo and Viera introduce the approach to studying and carrying ecosystem restoration projects in ecological units with homogeneous features. González-Izquierdo et al. describe the steps taken and the results obtained in three different restoration projects on tropical Caribbean forests, highlighting the importance of the social component of such projects and the increased success rate when incorporating locals into the process. Roccotiello et al. provide an example of the complexities on restoring biodiversity in highly

The final block of chapters in this book deals with the links between ecosystem diversity, functions and services in agroecosystems, a highly important issue as they are the source of most of food for human societies. Bavec and Bavec review the effects of traditional and organic farming on agroecosystem biodiversity and its links with agricultural productivity. Pelser et al. discuss the possibilities to include local use of agricultural resources inside protected areas to achieve both local development and biodiversity conservation. Edvan et al. examine the consequences on grassland and herbivore

All things considered, these 26 chapters provide a good overview of the links between ecosystem biodiversity and ecosystem functions, which are then translated into ecosystem services useful for all humans. These chapters show the importance of biodiversity on the structure and function of terrestrial and aquatic ecosystems, and in managed or natural conditions, which that can be applied to all the regions of the world. They are an introduction to the research being done around the globe in connection to this topic. We hope the readers from academia, management, conservation, and any other stakeholders will enjoy reading this book and regard it as an initial source of information and

The Editors want to finish this preface acknowledging the collaboration and hard work of all the authors. We are also thankful to the Publishing Team of InTech for their continuous support and assistance during the creation of this book. Special thanks are due to Ms. Iva Lipović for inviting us to

production of shortages in grasses biodiversity, and the influence of exotic invasive grasses.

study cases on what is the role that biodiversity plays in ecosystems.

discuss the implications that forest management has on the eco-hydrology of Chilean rivers.


**Section 1**

## **Concepts and Theories**

XIV

[13] Schmid B et al. Consequences of species loss for ecosystem functioning: a meta-analysis of data from biodiversity experiments. In: Naeem S, Bunker D, Loreau M, Hector A, Perring C (eds). Biodiversity

[14] Srivastava DS, Cardinale BJ, Downing AL, Duffy JE, Jouseau C, Sankaran M, Wright JP. Diversity has stronger top-down than bottom-up effects on decomposition. Ecology 200;90 1073–1083. [15] Quijas S, Schmid B, Balvanera P. Plant diversity enhances provision of ecosystem services: A new

[16] Flynn DFB, Mirotchnick N, Jain M, Palmer MI, Naeem S. Functional and phylogenetic diversity as predictors of biodiversity–ecosystem-function relationships. Ecology 2011;92 1573–1581.

[17] Cardinale BJ et al. Biodiversity simultaneously enhances the production and stability of community

[18] Roy S, Chattopadhyay J. Towards A Resolution of 'The Paradox of The Plankton': A Brief Overview of

[19] Roy S, Chattopadhyay J. The stability of ecosystems: A brief overview of the paradox of enrichment.

and human impacts. Oxford; Oxford University Press; 2009. p14–29.

biomass, but the effects are independent. Ecology 2013;94(8) 1697–1707.

The Proposed Mechanisms. Ecological Complexity 2007;4(1) 26-33.

synthesis. Basic and Applied Ecology 2009; 11: 582–593.

Journal of Biosciences 2007;32(2) 421-428.

**Provisional chapter**

## **Importance of Allelopathy as Peudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton Importance of Allelopathy as Pseudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton**

Shovonlal Roy Shovonlal Roy

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/59055 10.5772/59055

## **1. Introduction**

Phytoplankton are responsible for oceanic primary production and oxygen generation; and essential for regulating the global carbon cycle [1]. The dynamics and diversity of phytoplankton are constrained by several top-down and bottom-up effects. Complexities further arise from inter-species interactions within phytoplankton communities. Resources available for the growth of phytoplankton (e.g., light and dissolved nutrients) are often limited. But, despite the presence of limited variety of resources, phytoplankton are capable of maintaining an extreme level of species diversity [1–3]. This diversity is paradoxical to the theory of competitive exclusion [3], which suggests that in the steady state the number coexisting species cannot exceed the number of limiting resources [4, 5]. The mechanisms proposed to explain phytoplankton diversity include environmental fluctuations, periodic fluctuations, spatial heterogeneity, deterministic chaos, life cycles, grazing, and chemical interactions (detailed in [6]). But, when the top-down effects and external factors are negligible, it is difficult to explain the 'building block' of the extreme diversity of phytoplankton, i.e., the stable coexistence of two phytoplankton on a single limiting resource.

There is a growing body of evidence, both theoretical and experimental, suggesting that allelopathic interactions among phytoplankton species have a major role in shaping phytoplankton-zooplankton dynamics and regulating phytoplankton diversity [6–23]. Some of these studies [20, 21] suggested that 'toxin-allelopathy' can prevent competitive exclusion in Lotkta-Volterra interactions. Further, the allelopathic effect can potentially mediate resource competition in a chemostat. Focusing on simple resource-competition models, Roy [22] proposed that two phytoplankton can stably coexist on a single resource in a homogeneous media without any external factors when allelopathy acts as 'pseudo-mixotrophy'. This chapter elucidates how this mechanism ('if you cannot beat them or eat them, just kill them by chemical weapons' [22]) determines the outcome of resource

<sup>© 2015</sup> The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ©2012 Roy, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

competition between two phytoplankton, and how it potentially contributes to maintaining phytoplankton diversity in natural waters.

### **2. Mixotrophy and allelopathy**

Mixotrophy is known to influence species interactions within a food web [24]. Mixotrophic algae that can combine phototrophy and phagotrophy are an important component of phytoplankton communities (e.g., [25]). Mixotrophy can be an effective strategy for securing essential carbon required for the survival of algae in adverse conditions, such as, low radiation, unfavourable temperature, salinity or pH [26, 27]. Studies further suggested that certain algae (e.g., Prymnesium) can simultaneously be toxin producer and mixotrophic to 'kill and eat' [28]. However, not many species are known to follow this dual strategy that combines allelopathy and mixotrophy. But, several species are known to be allelopathic as they produce toxic or allelopathic chemicals (e.g., [13]). Studies suggested that the dynamics of phytoplankton with competitors and grazers are modulated by the presence of toxic species (e.g., [21, 29–31]). Allelopathy of toxin producers affects the growth and competitive ability of sensitive species. Allelopathy alone can potentially overturn the outcome of interspecific competition by providing 'additional' competitive and growth advantages to the allelopathic species [20, 22]. Roy [22] proposed that theoretically the effect of allelopathy can be viewed as pseudo-mixotrophy for the survival or coexistence of phytoplankton in nutrient competition. In the rest of the chapter, this mechanism will be discussed.

### **3. Allelopathy mediating competition for a single nutrient**

To demonstrate the allelopathic effect on nutrient competition, a standard resource-competition model (presented in Table 1) is considered, which is a generalised version of the model analysed by [22].


**Table 1.** Representation of allelopathy in a nutrient-competition model of two phytoplankton


2

competition between two phytoplankton, and how it potentially contributes to maintaining

Mixotrophy is known to influence species interactions within a food web [24]. Mixotrophic algae that can combine phototrophy and phagotrophy are an important component of phytoplankton communities (e.g., [25]). Mixotrophy can be an effective strategy for securing essential carbon required for the survival of algae in adverse conditions, such as, low radiation, unfavourable temperature, salinity or pH [26, 27]. Studies further suggested that certain algae (e.g., Prymnesium) can simultaneously be toxin producer and mixotrophic to 'kill and eat' [28]. However, not many species are known to follow this dual strategy that combines allelopathy and mixotrophy. But, several species are known to be allelopathic as they produce toxic or allelopathic chemicals (e.g., [13]). Studies suggested that the dynamics of phytoplankton with competitors and grazers are modulated by the presence of toxic species (e.g., [21, 29–31]). Allelopathy of toxin producers affects the growth and competitive ability of sensitive species. Allelopathy alone can potentially overturn the outcome of interspecific competition by providing 'additional' competitive and growth advantages to the allelopathic species [20, 22]. Roy [22] proposed that theoretically the effect of allelopathy can be viewed as pseudo-mixotrophy for the survival or coexistence of phytoplankton in

nutrient competition. In the rest of the chapter, this mechanism will be discussed.

*d t* =

*d t* =

*d t* =

**Table 1.** Representation of allelopathy in a nutrient-competition model of two phytoplankton

To demonstrate the allelopathic effect on nutrient competition, a standard resource-competition model (presented in Table 1) is considered, which is a generalised

+

growth *f*<sup>1</sup> (*N*) *P*<sup>1</sup> −

growth *f*<sup>2</sup> (*N*) *P*<sup>2</sup> −

net nutrient input *d* (*N*<sup>0</sup> − *N*) −

> recycling from *P*<sup>1</sup> *η α*<sup>1</sup> (*m*<sup>1</sup> *P*<sup>1</sup> + *φ*(*P*1, *P*2) *P*1) +

> > loss *m*<sup>1</sup> *P*<sup>1</sup> −

loss *m*<sup>2</sup> *P*<sup>2</sup> uptake by *P*<sup>1</sup> 1 *η*1

*f*<sup>1</sup> (*N*) *P*<sup>1</sup> −

loss by allelopathy *φ*(*P*1, *P*2) *P*<sup>1</sup>

uptake by *P*<sup>2</sup> 1 *η*2

recycling from *P*<sup>2</sup> *η α*<sup>2</sup> (*m*<sup>2</sup> *P*2)

*f*<sup>2</sup> (*N*) *P*<sup>2</sup>

**3. Allelopathy mediating competition for a single nutrient**

phytoplankton diversity in natural waters.

**2. Mixotrophy and allelopathy**

version of the model analysed by [22].

Eq. (1): Nutrient *d N*

Eq. (2): Non-allelopathic species *d P*<sup>1</sup>

Eq. (3): Allelopathic species *d P*<sup>2</sup>

**Table 2.** Functions and parameters with their meanings used in the nutrient competition model with allelopathic effect. The quantities *N*, *P*<sup>1</sup> and *P*<sup>2</sup> are the concentrations of the nutrient, non-allelopathic species and allelopathic species, respectively.

The nutrient (with concentration *N*) uptakes by the non-allelopathic species (with concentration *P*1) and allelopathic species (with concentration *P*2) are described by the functions *f*1(*N*) and *f*2(*N*). In particular, these functions can take the standard Michaelis-Menten forms (Table 3). The parameters of the model are described in Table (2). Allelopathy of species 2 imposes a higher mortality to the non-allelopathic species, which can be described by an 'additional' mortality term in the form of a phenomenological function *φ*(*P*1, *P*2). This function may be a high-order interspecific product of *P*<sup>1</sup> and *P*<sup>2</sup> (see, Table 3) - a particular case of which was considered in [22]. In the absence of allelopathy, the model takes the form of a standard resource-competition model, which predicts the persistence of one of the two species depending on the lowest minimum nutrient requirements (i.e., depending on minimum *<sup>R</sup>*<sup>∗</sup> [5]). So, if *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = 0, and if the non-allelopathic species has a lower minimum nutrient requirement, it will win over the allelopathic species in nutrient competition. However, if *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) �= 0, allelopathy provides advantage to species 2 by imposing a higher mortality to species 1.

### **3.1. Coexistence of two phytoplankton on single nutrient**

As mentioned in the previous section, the loss rate of species 1 due to allelopathy of species 2 can be described by a high-order product of *<sup>P</sup>*<sup>1</sup> and *<sup>P</sup>*2: *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = *<sup>γ</sup> <sup>P</sup>β*<sup>1</sup> <sup>1</sup> *<sup>P</sup>β*<sup>2</sup> <sup>2</sup> . A particular case was analysed in [22], where the exponents were taken as *β*<sup>1</sup> = 1 and *β*<sup>2</sup> = 2. For *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = *<sup>γ</sup> <sup>P</sup>β*<sup>1</sup> <sup>1</sup> *<sup>P</sup>β*<sup>2</sup> <sup>2</sup> , it can be derived (following the analysis of [22]) that there exist a critical value *γ<sup>c</sup>* for the allelopathy parameter *γ*, such that, if *γ* < *γc*, no coexisting steady state is possible. However, if *γ* > *γc*, two alternative steady states are possible, and depending on the initial conditions the system will settle to one of the two steady states (see, Fig. 1). Therefore, for *γ* > *γ<sup>c</sup>* one can find suitable initial concentrations of *P*<sup>1</sup> and *P*<sup>2</sup> for which stable coexistence two phytoplankton on a single nutrient is possible: Fig. 1-(b) shows that in this case the ratio of *P*<sup>2</sup> to *P*<sup>1</sup> is stabilised to a non-zero value.

**Figure 1.** Dynamics two phytoplankton when allelopathy exceeds critical level. (a) Saddle-node bifurcation for the model system with *γ* as the bifurcation parameter. The figure was reproduced from [22] with permission from the publisher. (b) & (c) Ratio of *P*<sup>2</sup> to *P*<sup>1</sup> corresponding to the stable and unstable dynamics presented by (a), respectively. Condition for no recycling was used with other parameters and functions fixed at their default values/forms as in Table (2).

### **3.2. Critical conditions for coexistence**

The critical level of allelopathy *γ<sup>c</sup>* is a crucial quantity, which can be computed from the parameters of the model. Corresponding to *γc*, there exists an unique coexisting steady state *<sup>N</sup>*∗, *<sup>P</sup><sup>c</sup>* <sup>1</sup> , *<sup>P</sup><sup>c</sup>* 2 , where the magnitudes of *P<sup>c</sup>* <sup>1</sup> and *<sup>P</sup><sup>c</sup>* <sup>1</sup> depend on the model parameters. Extending the analysis of [22] , the magnitudes of these quantities can be derived explicitly for all possible forms of the function *φ*(*P*1, *P*2) (Table 3). When *γ* > 0, the critical conditions for the existence of the unique steady state can alternatively be derived with respect to *N*<sup>0</sup> - the input nutrient concentration. The allelopathy parameter *γ* would depend on the inherent biological properties of the allelopathic species, and hence its magnitude cannot normally be altered using experimental conditions. However, the parameter *N*<sup>0</sup> associated with the experimental conditions can very well be controlled. Rearranging the expressions of *γ<sup>c</sup>* (Table 3), one can derive the corresponding threshold magnitudes of the input nutrient concentration, say, *N<sup>c</sup>* 0, so that, for *N*<sup>0</sup> > *N<sup>c</sup>* <sup>0</sup> alternative steady states are possible leading to the stable coexistence of two phytoplankton. The explicit expressions of *N<sup>c</sup>* <sup>0</sup> for different forms of *φ*(*P*1, *P*2) are

<sup>20</sup><sup>4</sup> Importance of Allelopathy as Pseudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton 5 10.5772/59055 Importance of Allelopathy as Peudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton 5 http://dx.doi.org/10.5772/59055 21

`

`

`

`

`

`


4

and depending on the initial conditions the system will settle to one of the two steady states (see, Fig. 1). Therefore, for *γ* > *γ<sup>c</sup>* one can find suitable initial concentrations of *P*<sup>1</sup> and *P*<sup>2</sup> for which stable coexistence two phytoplankton on a single nutrient is possible: Fig. 1-(b)

(b)

<sup>0</sup> <sup>500</sup> <sup>1000</sup> <sup>1500</sup> <sup>2000</sup> <sup>0</sup>

Time

(c)

<sup>0</sup> <sup>500</sup> <sup>1000</sup> <sup>1500</sup> <sup>2000</sup> <sup>0</sup>

`

`

`

`

a

a

Time

<sup>1</sup> depend on the model parameters. Extending

<sup>0</sup> for different forms of *φ*(*P*1, *P*2) are

0,

`

`

0.1 0.2 0.3 0.4 0.5

Ratio of P2 to P1

**Figure 1.** Dynamics two phytoplankton when allelopathy exceeds critical level. (a) Saddle-node bifurcation for the model system with *γ* as the bifurcation parameter. The figure was reproduced from [22] with permission from the publisher. (b) & (c) Ratio of *P*<sup>2</sup> to *P*<sup>1</sup> corresponding to the stable and unstable dynamics presented by (a), respectively. Condition for no recycling

The critical level of allelopathy *γ<sup>c</sup>* is a crucial quantity, which can be computed from the parameters of the model. Corresponding to *γc*, there exists an unique coexisting steady state

the analysis of [22] , the magnitudes of these quantities can be derived explicitly for all possible forms of the function *φ*(*P*1, *P*2) (Table 3). When *γ* > 0, the critical conditions for the existence of the unique steady state can alternatively be derived with respect to *N*<sup>0</sup> - the input nutrient concentration. The allelopathy parameter *γ* would depend on the inherent biological properties of the allelopathic species, and hence its magnitude cannot normally be altered using experimental conditions. However, the parameter *N*<sup>0</sup> associated with the experimental conditions can very well be controlled. Rearranging the expressions of *γ<sup>c</sup>* (Table 3), one can derive the corresponding threshold magnitudes of the input nutrient concentration, say, *N<sup>c</sup>*

<sup>0</sup> alternative steady states are possible leading to the stable coexistence

<sup>1</sup> and *<sup>P</sup><sup>c</sup>*

was used with other parameters and functions fixed at their default values/forms as in Table (2).

**3.2. Critical conditions for coexistence**

, where the magnitudes of *P<sup>c</sup>*

of two phytoplankton. The explicit expressions of *N<sup>c</sup>*

 *<sup>N</sup>*∗, *<sup>P</sup><sup>c</sup>* <sup>1</sup> , *<sup>P</sup><sup>c</sup>* 2 

so that, for *N*<sup>0</sup> > *N<sup>c</sup>*

Ratio of P2 to P1

shows that in this case the ratio of *P*<sup>2</sup> to *P*<sup>1</sup> is stabilised to a non-zero value.

(a)

**Table 3.** Parametric conditions for stable coexistence when allelopathy acts as pseudo-mixotrophy in nutrient competition models. The allelopathic effect is denoted by *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2), the critical steady state by (*N*∗, *<sup>P</sup><sup>c</sup>* <sup>1</sup> , *P<sup>c</sup>* <sup>2</sup> ), the critical level of allelopathy by *γ<sup>c</sup>* , and the threshold level of *N*<sup>0</sup> by *N<sup>c</sup>* <sup>0</sup> . The quantities *c*1, *c*2, *c*<sup>3</sup> and *A* used in the table are defined as: *c*<sup>1</sup> = <sup>1</sup> *<sup>η</sup>*<sup>1</sup> *<sup>f</sup>*1(*N*∗) <sup>−</sup> *η α*<sup>1</sup> (*m*<sup>1</sup> <sup>+</sup> *<sup>A</sup>*), *<sup>c</sup>*<sup>2</sup> <sup>=</sup> <sup>1</sup> *<sup>η</sup>*<sup>2</sup> *<sup>f</sup>*2(*N*∗) <sup>−</sup> *η α*<sup>2</sup> *<sup>m</sup>*2, *<sup>c</sup>*<sup>3</sup> <sup>=</sup> *<sup>d</sup>* (*N*<sup>0</sup> <sup>−</sup> *<sup>N</sup>*∗), with *<sup>A</sup>* <sup>=</sup> *<sup>f</sup>*1(*N*∗) <sup>−</sup> *<sup>m</sup>*1, and *<sup>N</sup>*<sup>∗</sup> is given by *<sup>f</sup>*2(*N*∗) = *<sup>m</sup>*2. In particular, *<sup>f</sup>*1(*N*) = *<sup>µ</sup>*<sup>1</sup> *<sup>N</sup> <sup>K</sup>*1+*<sup>N</sup>* and *<sup>f</sup>*2(*N*) = *<sup>µ</sup>*<sup>2</sup> *<sup>N</sup> <sup>K</sup>*2+*<sup>N</sup>* , *<sup>N</sup>*<sup>∗</sup> <sup>=</sup> *<sup>m</sup>*<sup>2</sup> *<sup>K</sup>*<sup>2</sup> *<sup>µ</sup>*2−*m*<sup>2</sup> .

a

` ` **Figure 2.** The magnitudes of *γ<sup>c</sup>* , *N<sup>c</sup>* <sup>0</sup> , *P<sup>c</sup>* <sup>1</sup> and *P<sup>c</sup>* <sup>2</sup> are computed for a range of values of the exponents *β*<sup>1</sup> and *β*<sup>2</sup> corresponding to the function *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = *<sup>γ</sup> <sup>P</sup>β*<sup>1</sup> <sup>1</sup> *<sup>P</sup>β*<sup>2</sup> <sup>2</sup> . The parameters are fixed at their default values as in Table (2).

presented in Table (3). The results in Table (3) can be used to address how the critical values *γc*, *N<sup>c</sup>* <sup>0</sup>, *<sup>P</sup><sup>c</sup>* <sup>1</sup> and *<sup>P</sup><sup>c</sup>* <sup>2</sup> may change due to uncertainties in describing the allelopathic effect by a phenomenological function. Considering the general form *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = *<sup>γ</sup> <sup>P</sup>β*<sup>1</sup> <sup>1</sup> *<sup>P</sup>β*<sup>2</sup> <sup>2</sup> , the magnitudes of *γc*, *N<sup>c</sup>* <sup>0</sup>, *<sup>P</sup><sup>c</sup>* <sup>1</sup> and *<sup>P</sup><sup>c</sup>* <sup>2</sup> are computed for a range of values of the exponents *β*<sup>1</sup> and *β*<sup>2</sup> (Fig. 2). If the model parameters are fixed, *γ<sup>c</sup>* or *N<sup>c</sup>* <sup>0</sup> would be minimum when *β*<sup>1</sup> is the lowest and *β*<sup>2</sup> is the highest (Fig. 2-a, b). The unique steady states of *P*<sup>1</sup> and *P*<sup>2</sup> depend on both *β*<sup>1</sup> and *β*1: for a given *β*1, *P<sup>c</sup>* <sup>1</sup> decreases but *<sup>P</sup><sup>c</sup>* <sup>2</sup> increases with *β*<sup>2</sup> (Fig. 2-c, d).

#### **3.3. Allelopathy as pseudo-mixotrophy**

The function of allelopathy in mediating the coexistence can be understood from the Figs. (3) & (4). Under nutrient-limiting conditions, allelopathy of the weaker competitor helps increase the availability of nutrient the by killing the stronger competitors: an illustration of this process based on the model of [22] is given in Fig. (3-a). In the simplest scenario,

**Figure 3.** Allelopathy as pseudo-mixotrophy: (a) Enlargement of the available nutrient pool due to killing of competitors by allelopathy; (b) Increased ratio of per capita nutrient uptake by allelopathic species to that by non-allelopathic species. Red and black lines indicate conditions of no allelopathy and allelopathy beyond the critical level, respectively. Condition for no recycling was used with other parameters and functions fixed at their default values/forms as in Table (2).

when recycling of nutrient is 'turned off' in the model, and no killing by allelopathy takes place, the level of available nutrient decreases and stabilises to a low value where the non-allelopathic species alone survives eventually (Fig. 3-a, b). However, the extra (higher) mortality of species 1 (*P*1) due to killing by allelopathy of species 2 (*P*2) leads to elevation of the nutrient concentration (and further prevents it from decreasing gradually) (Fig. (3-a); the nutrient concentration eventually stabilises to a level where both species stably coexist (Fig. 3-a, b). The ratio of per-capita nutrient uptake by *P*<sup>2</sup> to that of *P*<sup>1</sup> decreases to a low value when killing by allelopathy does not take place (Fig. 3-b, Fig. 4-a); however, this ratio stabilises to a considerably higher value when allelopathy kills stronger competitors (Fig. 3-b, Fig. 4-b). When nutrient recycling is incorporated, killing by allelopathy increases the dead cells (Fig. 4-c,d), and the recycling process releases a portion of the nutrient quota of the dead competitors available for uptake (Fig. 4-d). The recycling process coupled with killing by allelopathy thus generates an extra amount of nutrient (Fig. 4-d) available for uptake by the species. Therefore, by imposing higher mortality to stronger competitor, allelopathy provides clear advantage to the weaker competitor. This mode of action of allelopathy <sup>22</sup><sup>6</sup> Importance of Allelopathy as Pseudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton 7 10.5772/59055 Importance of Allelopathy as Peudo-Mixotrophy for the Dynamics and Diversity of Phytoplankton 7 http://dx.doi.org/10.5772/59055 23

6

magnitudes of *γc*, *N<sup>c</sup>*

<sup>0</sup>, *<sup>P</sup><sup>c</sup>*

**3.3. Allelopathy as pseudo-mixotrophy**

(a)

10−1 <sup>100</sup> <sup>101</sup> <sup>102</sup> <sup>103</sup> 0.007

Time (days)

was used with other parameters and functions fixed at their default values/forms as in Table (2).

0.008 0.009 0.01 0.011 0.012 0.013 0.014 0.015

γ=0 γ>γ c

Nutrient concentration

both *β*<sup>1</sup> and *β*1: for a given *β*1, *P<sup>c</sup>*

<sup>1</sup> and *<sup>P</sup><sup>c</sup>*

*β*<sup>2</sup> (Fig. 2). If the model parameters are fixed, *γ<sup>c</sup>* or *N<sup>c</sup>*

by a phenomenological function. Considering the general form *<sup>φ</sup>*(*P*1, *<sup>P</sup>*2) = *<sup>γ</sup> <sup>P</sup>β*<sup>1</sup>

<sup>1</sup> decreases but *<sup>P</sup><sup>c</sup>*

lowest and *β*<sup>2</sup> is the highest (Fig. 2-a, b). The unique steady states of *P*<sup>1</sup> and *P*<sup>2</sup> depend on

The function of allelopathy in mediating the coexistence can be understood from the Figs. (3) & (4). Under nutrient-limiting conditions, allelopathy of the weaker competitor helps increase the availability of nutrient the by killing the stronger competitors: an illustration of this process based on the model of [22] is given in Fig. (3-a). In the simplest scenario,

10−0.353

γ=0 γ>γ c

10−0.352

Ratio of nutrient uptake by P2 to that by P1

**Figure 3.** Allelopathy as pseudo-mixotrophy: (a) Enlargement of the available nutrient pool due to killing of competitors by allelopathy; (b) Increased ratio of per capita nutrient uptake by allelopathic species to that by non-allelopathic species. Red and black lines indicate conditions of no allelopathy and allelopathy beyond the critical level, respectively. Condition for no recycling

when recycling of nutrient is 'turned off' in the model, and no killing by allelopathy takes place, the level of available nutrient decreases and stabilises to a low value where the non-allelopathic species alone survives eventually (Fig. 3-a, b). However, the extra (higher) mortality of species 1 (*P*1) due to killing by allelopathy of species 2 (*P*2) leads to elevation of the nutrient concentration (and further prevents it from decreasing gradually) (Fig. (3-a); the nutrient concentration eventually stabilises to a level where both species stably coexist (Fig. 3-a, b). The ratio of per-capita nutrient uptake by *P*<sup>2</sup> to that of *P*<sup>1</sup> decreases to a low value when killing by allelopathy does not take place (Fig. 3-b, Fig. 4-a); however, this ratio stabilises to a considerably higher value when allelopathy kills stronger competitors (Fig. 3-b, Fig. 4-b). When nutrient recycling is incorporated, killing by allelopathy increases the dead cells (Fig. 4-c,d), and the recycling process releases a portion of the nutrient quota of the dead competitors available for uptake (Fig. 4-d). The recycling process coupled with killing by allelopathy thus generates an extra amount of nutrient (Fig. 4-d) available for uptake by the species. Therefore, by imposing higher mortality to stronger competitor, allelopathy provides clear advantage to the weaker competitor. This mode of action of allelopathy

10−0.351

10−0.35

<sup>2</sup> are computed for a range of values of the exponents *β*<sup>1</sup> and

<sup>2</sup> increases with *β*<sup>2</sup> (Fig. 2-c, d).

<sup>0</sup> would be minimum when *β*<sup>1</sup> is the

(b)

10−1 100 101 102 103

Time (days)

<sup>1</sup> *<sup>P</sup>β*<sup>2</sup> <sup>2</sup> , the

**Figure 4.** Schematic diagram showing the function of allelopathy as pseudo-mixotrophy. Competition between two phytoplankton (*P*1, *P*2) under a single nutrient (*N*) with or without the effect of allelopathy: (a) no nutrient recycling and no allelopathy; (b) no nutrient recycling, but killing by allelopathy; (c) nutrient recycling, but no allelopathy; and (d) nutrient recycling and killing by allelopathy. The thin and thick arrows indicate low and high values for nutrient uptake, respectively; dashed and continuous lines represent low and high level of recycling respectively; and the sizes of the circles and squares represent concentrations of the variables. The orange curved-arrows indicate when killing by allelopathy is incorporated. In (a) and (c), the weak competitor *P*<sup>2</sup> is excluded, whereas, *P*<sup>1</sup> survives. In (b) and (d), *P*<sup>1</sup> and *P*<sup>2</sup> stabilises with concentrations depending on the model parameters (which are not represented by the relative size of the circles).

that provides growth advantage to the allelopathic species, not through direct predation but through killing of the competitors (e.g., Fig. 4-b, Fig. 4-d), was termed as pseudo-mixotrophy [22]. In this process killing by allelopathy provides a positive feed-back by increasing of the growth limiting resource that reduces the competition pressure (e.g., Fig. 4). Clearly, this feedback loop provides crucial benefit to the growth rate of the allelopathic algae, and modulates the dynamics of the resource competition within a common trophic level.

### **4. Relevance to empirical and experimental studies**

It is clear from the previous sections that allelopathy acting as pseudo-mixotrophy can theoretically stabilise nutrient competition of two phytoplankton on a single limiting nutrient. However, the applicability of this mechanism across natural phytoplankton is largely unexplored. An empirical or experimental evidence for pseudo-mixotrophy is still in demand. But, recent studies have shown promise that the role of allelopathy in maintaining biodiversity of natural phytopalnkton may be explored further. For example, chemical warfare has been shown to increases bio-diversity in microbial realm [32]; and [20] showed that allelopathy may be responsible for co-existence of the competing phytoplankton in the Bay of Bengal. The question of how much diversity of phytoplankton can be supported though allelopathy alone was addressed by [23], who derived a deterministic relationship between the abundance of the potential allelopathic species and the diversity of non-allelopathic phytoplankton (see, Fig. 5). The abundance-diversity relationship in Fig.

**Figure 5.** Deterministic relationship between the abundance of toxin-producing phytoplankton (TPP) and the diversity of non-toxic phytoplankton (NTP) in the Bay of Bengal. The figure is reproduced from [23] with permission from the publisher. The Shannon diversity of non-toxic species is plotted as a function of the abundance (nos./l) of toxin-producing phytoplankton defined by [23]. The solid line represents the fitted model of with the data presented in open circles. The dashed lines are the predicted model at 95% confidence level.

(5) shows a unimodal pathway through which the abundance of allelopathic phytoplankton regulates the diversity of non-allelopathic phytoplankton [23].

### **5. Concluding remarks**

This chapter elucidates how phytoplankton allelopathy may function as pseudo-mixotrophy in determining the dynamics of nutrient-phytoplankton models, and how phytoplankton diversity is maintained in those systems. Firstly, the ecological conditions under which allelopathy functioning as pseudo-mixotrophy overturns the outcome of nutrient competition between two phytoplankton (e.g., [22]) is presented explicitly in terms of the model parameters. Secondly, the difficulties in mechanistically describing the allelopathic effect of a phytoplankton on its competitors is addressed by considering a phenomenological function, and the ecological conditions for the coexistence of phytoplankton species and stability of competition dynamics are derived. Thirdly, the competition dynamics is explored under the assumptions of 'no nutrient recycling' and 'continuous nutrient recycling'; and the effects of changing initial nutrient pool in culture media is explored. Therefore, a comprehensive set of constraints is derived under which allelopathy acts as pseudo-mixotrophy in nutrient-phytoplankton models. Finally, the evidences of allelopathic effects in determining the diversity of phytoplankton in natural systems are presented. In particular, how the increasing abundance of allelopathic species may regulate the diversity of phytoplankton (e.g., [23]), is discussed. The mechanism presented here would be useful for better understanding of the biodiversity and function of marine ecosystems. Allelopathy functions as 'pseudo-mixotrophy' in nutrient-phytoplankton models, which are often the basis of marine biogeochemical and ecosystem models. This mechanism has not been explored in relation to ocean biogeochemical models, which are generally used to predict phytoplankton species composition, and estimate the scale of oceanic carbon sink. Given the complexities in representing phytoplankton functional types in global biogeochemical models (e.g., [33]), it would be useful to understand how allelopathy or pseudo-mixotrophy of a phytoplankton type may affect the dynamics of the other types. The ecological conditions derived will be useful for investigating the role of 'pseudo-mixotrophy' in marine ecosystem models. The current challenges in monitoring, controlling and managing harmful algal blooms (HAB) (e.g., [34]), and predicting their consequences in aquatic ecosystems require better understanding of the dynamics of toxic or allelopathic species. Recent studies have also reported other roles of phytoplankton allelochemicals, e.g., defence against predators [35], and 'casual parasitism' that helps supplying organic nutrient to the mixotrophic donors by lysis of prey [36, 37]. It will be worthwhile to further explore the mechanism presented here in relation to the succession of phytoplankton taxa that are known to form HABs. It is noteworthy that currently the mechanism has been explored using simple resource-competition models that can be tested in an experimental (chemostat) set up. Such an experiment will be helpful in formulating and parameterising resource-competition models including allelopathy, and for better understanding of the constraints of phytoplankton diversity.

### **Author details**

Shovonlal Roy

8

0

predicted model at 95% confidence level.

**5. Concluding remarks**

0.5

1

1.5

NTP diversity

2

2.5

3

3.5

still in demand. But, recent studies have shown promise that the role of allelopathy in maintaining biodiversity of natural phytopalnkton may be explored further. For example, chemical warfare has been shown to increases bio-diversity in microbial realm [32]; and [20] showed that allelopathy may be responsible for co-existence of the competing phytoplankton in the Bay of Bengal. The question of how much diversity of phytoplankton can be supported though allelopathy alone was addressed by [23], who derived a deterministic relationship between the abundance of the potential allelopathic species and the diversity of non-allelopathic phytoplankton (see, Fig. 5). The abundance-diversity relationship in Fig.

0 1 2 3 4 5 6 7 8 9 10

TPP biomass (nos./lit)

**Figure 5.** Deterministic relationship between the abundance of toxin-producing phytoplankton (TPP) and the diversity of non-toxic phytoplankton (NTP) in the Bay of Bengal. The figure is reproduced from [23] with permission from the publisher. The Shannon diversity of non-toxic species is plotted as a function of the abundance (nos./l) of toxin-producing phytoplankton defined by [23]. The solid line represents the fitted model of with the data presented in open circles. The dashed lines are the

(5) shows a unimodal pathway through which the abundance of allelopathic phytoplankton

This chapter elucidates how phytoplankton allelopathy may function as pseudo-mixotrophy in determining the dynamics of nutrient-phytoplankton models, and how phytoplankton diversity is maintained in those systems. Firstly, the ecological conditions under which allelopathy functioning as pseudo-mixotrophy overturns the outcome of nutrient competition between two phytoplankton (e.g., [22]) is presented explicitly in terms of the model

regulates the diversity of non-allelopathic phytoplankton [23].

x 104

Address all correspondence to: shovonlal.roy@reading.ac.uk

Department of Geography and Environmental Science, University of Reading, Whiteknights, U.K.

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## **Functional Redundancy and Ecosystem Function — The Soil Microbiota as a Case Study**

Stephanie D. Jurburg and Joana Falcão Salles

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58981

## **1. Introduction**

Understanding ecosystem functioning has been a main focus of ecological studies due to its importance for the maintenance of ecosystem integrity and human livelihood. While identi‐ fying and measuring relevant ecosystem functions may be a seemingly straightforward task, isolating the biota responsible for the provision of a particular function is far more complicated. In this context, understanding how biota influence ecosystem functioning remains a very active area of research in ecology, known as Biodiversity-Ecosystem Function (BEF) [1]. Given the accelerating rates of biodiversity loss [2] and predicted increases in the intensity and duration of extreme climate events [3], understanding how species interact to provide ecosystem functions is crucial for anticipating change as well as for establishing appropriate biodiversity buffers in order to minimize the risk of functional loss and maintain ecosystem integrity.

Functioning can be evaluated in the short-term, in which case the magnitude of the process is of interest, or in the long-term, measured as the probability that this is maintained in the face of environmental change. In both cases, functioning is an emergent property of ecosystems: interactions between the system's members and coevolution result in functioning which deviates from that expected from a system in which functioning was simply additive. In the case of environmental change, redundancy—the phenomenon in which a function is carried out by multiple species in an ecosystem—buffers functioning, as for any given environmental state there will be multiple organisms within a functional group which can perform optimally at a range of environmental conditions.

It has been suggested that concerns for the maintenance of biodiversity cannot be extended to microbes [4]. The implicit assumption is that microbial community composition is not relevant for determining function because microbes are endlessly diverse, so that the only filter

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

determining their function is the environment. Specifically, in microbial systems, where diversity and abundance are extreme and growth rates are rapid, it was formerly assumed that redundancy is so high that diversity and community composition are decoupled from functioning due to the following observations: 1) most microbial species are ubiquitous and present in very low densities, awaiting an opportunity to "bloom"; 2) the rapid adaptability of microbes means that such a system will never be so impoverished as to cease functioning; and 3) the microbial system is so tightly linked to its physical environment that it cannot be studied within the context of cause-effect that is generally necessary for BEF studies. However, recent studies have shown that community composition matters to function [5,6]: in soil, microbial communities exhibit a home-field advantage in decomposing endemic vs. foreign litter [7,8] and different communities do not become more similar when exposed to the same environment [9]. This ongoing discussion has been particularly important in the realm of ecosystem models, where stable physical parameters or very coarse microbial parameters (such as total biomass) are assumed to accurately represent microbial contributions to ecosystem function [10].

Despite the current gaps in knowledge of microbial communities, this is an extremely attractive system for the study of BEF: the ease of manipulation, wide range of metabolic diversities, and availability of direct links between genetic diversity and function (i.e. functional gene analyses) allow for a range of experiments which would not be possible in other ecosystems. Particularly, the high turnover rate and diversity allow for studies which target the effect of redundancy on long-term function. A wide range of studies regarding this relationship are now available (for in-depth reviews, see [11,12]), but the results of microbial BEF studies have often been contradictory. The purpose of this chapter is to provide a comprehensive analysis of redun‐ dancy in microbial communities, paying special attention to the intricacies of these systems, in order to understand why these contradictions arise, and shed light on how redundancy might bolster ecosystem function in these extremely diverse ecosystems.

### **2. Microbial diversity and its contribution to ecosystem function**

Microbial systems are responsible for the provision of a wide range of crucial ecosystem services, but little is known about the role of diversity in maintaining this function. This is mostly due to the overwhelming complexity found in them: the study of microbial commun‐ ities has been likened to the study of solar systems [13]. This diversity is still not properly constrained: the lack of an ecological species definition for prokaryotes [14] has led to the usage of the operational taxonomic unit (OTU), defined as 97% sequence similarity in the 16S rRNA gene, is used as a threshold for prokaryotic species, however this threshold may not be comparable to the eukaryotic definition of species [14]. This means that most prokaryotes can be identified based on their sequences alone, which makes distinguishing rare species from sequencing errors nearly impossible [15], and obscures the definite measurement of prokary‐ otic diversity. Nevertheless, it is agreed that microbial diversity is extremely high: one gram of soil may contain 103 -106 unique taxa [16,17]. Furthermore, the link between phylogeny and function is truncated for prokaryotes, where horizontal gene transfer allows for the acquisition of functions—particularly those associated with adaptability to new environments—further complicates analyses of function through genes [18].

determining their function is the environment. Specifically, in microbial systems, where diversity and abundance are extreme and growth rates are rapid, it was formerly assumed that redundancy is so high that diversity and community composition are decoupled from functioning due to the following observations: 1) most microbial species are ubiquitous and present in very low densities, awaiting an opportunity to "bloom"; 2) the rapid adaptability of microbes means that such a system will never be so impoverished as to cease functioning; and 3) the microbial system is so tightly linked to its physical environment that it cannot be studied within the context of cause-effect that is generally necessary for BEF studies. However, recent studies have shown that community composition matters to function [5,6]: in soil, microbial communities exhibit a home-field advantage in decomposing endemic vs. foreign litter [7,8] and different communities do not become more similar when exposed to the same environment [9]. This ongoing discussion has been particularly important in the realm of ecosystem models, where stable physical parameters or very coarse microbial parameters (such as total biomass) are assumed to accurately represent microbial contributions to

Despite the current gaps in knowledge of microbial communities, this is an extremely attractive system for the study of BEF: the ease of manipulation, wide range of metabolic diversities, and availability of direct links between genetic diversity and function (i.e. functional gene analyses) allow for a range of experiments which would not be possible in other ecosystems. Particularly, the high turnover rate and diversity allow for studies which target the effect of redundancy on long-term function. A wide range of studies regarding this relationship are now available (for in-depth reviews, see [11,12]), but the results of microbial BEF studies have often been contradictory. The purpose of this chapter is to provide a comprehensive analysis of redun‐ dancy in microbial communities, paying special attention to the intricacies of these systems, in order to understand why these contradictions arise, and shed light on how redundancy

might bolster ecosystem function in these extremely diverse ecosystems.

**2. Microbial diversity and its contribution to ecosystem function**

Microbial systems are responsible for the provision of a wide range of crucial ecosystem services, but little is known about the role of diversity in maintaining this function. This is mostly due to the overwhelming complexity found in them: the study of microbial commun‐ ities has been likened to the study of solar systems [13]. This diversity is still not properly constrained: the lack of an ecological species definition for prokaryotes [14] has led to the usage of the operational taxonomic unit (OTU), defined as 97% sequence similarity in the 16S rRNA gene, is used as a threshold for prokaryotic species, however this threshold may not be comparable to the eukaryotic definition of species [14]. This means that most prokaryotes can be identified based on their sequences alone, which makes distinguishing rare species from sequencing errors nearly impossible [15], and obscures the definite measurement of prokary‐ otic diversity. Nevertheless, it is agreed that microbial diversity is extremely high: one gram

function is truncated for prokaryotes, where horizontal gene transfer allows for the acquisition

unique taxa [16,17]. Furthermore, the link between phylogeny and

ecosystem function [10].

302 Biodiversity in Ecosystems - Linking Structure and Function

of soil may contain 103


Despite these obstacles, microbial BEF—particularly for soil microbial communities demands much attention. In addition to serving as repositories of genetic information [19], they provide ecosystem services which are fundamental for human persistence, including the maintenance of agricultural systems and waste recycling [20]. In an assessment of the economic benefits of biodiversity, the soil microbiota was partly or fully responsible for waste recycling, soil formation, nitrogen fixation, bioremediation of chemicals, biotechnology, and biocontrol of pests. These services amounted to an estimated \$1.16 trillion dollars per year globally, which was over a third of the estimated annual contribution of terrestrial ecosystem services to the worldwide economy [21]. This study contrasts sharply with another estimate which, while considering both terrestrial and marine ecosystem services, differed in its estimate of the total annual value of these services by more than an order of magnitude[22]. This discrepancy illustrates the prevailing lack of consensus regarding the economic weight of ecosystem services, which is particularly problematic the face of biodiversity loss [20] because it obscures the value of preserving biodiversity for the sake of the services it provides. It also illustrates how functional classifications may be considered arbitrary: depending on the functions selected, how they are measured, and how they are valued, very different views of the same system can be obtained.

Novel technologies are beginning to open the door for the pursuit of deeper ecological understanding of microbial systems, but these advances are not accompanied by an increase in ecological theory. High-throughput sequencing has greatly accelerated the rate at which new microbial species can be detected, but their ecological properties remain a mystery [19]. Thus, although we know increasingly more about "*who is there?"*, this information is not accompanied by characterization of the new species' niche spaces ("*what are they doing?"*), which precludes the understanding of how additional species affect function at an ecosystem level. Instead, the large majority of BEF studies in microbial ecology tend to focus on a single or few ecologically relevant functions, and often measure the abundance and diversity of functional groups or genes associated with those functions. For example, the soil microbiota play a crucial role in the nitrogen cycle and studies trying to understand the link between N associated functions and soil microbiota use functional genes associated with different steps of the cycle, such as those associated with nitrification and denitrification, as a way to cut through the overwhelming diversity found in soils, and focus on functionally relevant microbial community dynamics, which may scale up and affect functioning at the ecosystem level [23].

## **3. Microbial BEF: A world of contradictions**

Due to their rapid generation times and the large diversity found in small volumes, microbial systems are ideal settings to probe BEF relationships, particularly in controlled laboratory microcosm experiments [19]. Indeed, while much remains unknown about the world's microbiota [24], microbial BEF research has seemingly kept pace with macroecological research [25]. The former, however, has been riddled with contradictory results, and evidence for a positive BEF relationship has not been as strong as for the latter. Some of these discrepancies may arise from the heterogeneity which is unique and inherent to the microbial system. From an environmental perspective, the extremely heterogeneous soil matrix may unevenly buffer the effect of environmental change, reducing the homogeneity of the community's response. It is also important to note that the phenomena occurring in microenvironments within which the soil microbiota exist are of necessity averaged out for measurement, as current methodol‐ ogies require soil to be homogenized before studying [26]. Furthermore, while positive BEF relationships are expected [1], a negative relationship resulting from antagonistic interactions has been documented [27,28].

Many contradictions have been attributed to differences in experimental setup. A recent metaanalysis indicates that most microbial BEF research has relied on comparative approaches, which test the BEF relationship across environmental gradients or treatments, rather than explicitly manipulating biodiversity [25] (Figure 1). The more common, comparative ap‐ proaches are potentially riddled with hidden variables, and thus do not allow for the drawing of a direct link between diversity and function. For this reason, here we focus mainly on experiments which involve direct manipulation of diversity, which tend to find a strong, positive BEF relationship [13].

**Figure 1.** The relationship between diversity and function is asymptotic; different experimental approaches target dif‐ ferent levels of species richness [13]. By greatly reducing diversity and environmental variability, assembly experi‐ ments seek mechanistic insight into the direct effect of diversity on process rates under minimized redundancy, that is, short-term function (a). Dilution-to-extinction and fumigation experiments retain greater species richness, and tend to emphasize the relationship between diversity and stability (i.e. long-term function) under otherwise stable environ‐ mental conditions (b). These experiments focus on systems in which the functioning asymptote is approached, al‐ though some dilution experiments cover broader ranges of diversity, as in [29] (b, dotted line). In observational studies, diversity is not manipulated, and the focus is rather on the effect of environmental change on the community's ability to maintain process rates (c). In this case, the level of redundancy is high enough to ensure no effect of diversity on functioning, although both positive and negative effects (c, dotted lines) have been observed for this type of experi‐ ments [28,30]

The manipulative experiments fit within two categories. In **assembly experiments**, a com‐ munity is experimentally assembled to test the effect of each additional species or community structure on the community [31]. By studying overly-simplified communities, these studies tend to target the ecological functioning that arises from minimally redundant systems—that is, right before functioning begins to 'saturate' (Figure 1a). This approach has been criticized because it can only include culturable bacteria, which may represent less than 1% of soil microbes [32], and because the diversity levels achieved are always unrealistically low, and effects observed at such low diversity levels may not be relevant or applicable to more realistic scenarios and thus is not representative. Furthermore, this setup generally ignores the effect of historical selection patterns on community composition, which seems to be related to functioning as well [7]. Nevertheless, studying only culturable microbes allows for a full functional characterization of each population introduced into the system, and in this way over-yielding of the community as an emergent property of biodiversity can be studied mechanistically. For example, by characterizing 16 species of denitrifying bacteria in terms of their use of 6 carbon resources found in soil, Salles and colleagues created a model to predict CO2 production and denitrification based on the added functioning of each individual in the system. In this way, they were able to detect over-yielding and potential antagonism within their assembled communities [33]. This body of work has found a strong, positive BEF relationship, but has also stressed that it is the diversity of the functional traits in the com‐ munity—not the number of taxa present—which affect functioning: for example, a recent 12 strain assembly experiment found that the best predictor of function was the phylogenetic diversity of each microcosm [34], which agrees with previous findings [35]. The ability to manipulate genotypic and functional diversity as well as the distribution of species in assembled communities has been crucial for this [36,37]. Unfortunately, assembly experiments represent less than 1% of microbial BEF studies, and long-term studies using assembly experiments are non-existent: the lack of further mechanistic insight is one of the greater gaps in microbial BEF research [13,25].

microbiota [24], microbial BEF research has seemingly kept pace with macroecological research [25]. The former, however, has been riddled with contradictory results, and evidence for a positive BEF relationship has not been as strong as for the latter. Some of these discrepancies may arise from the heterogeneity which is unique and inherent to the microbial system. From an environmental perspective, the extremely heterogeneous soil matrix may unevenly buffer the effect of environmental change, reducing the homogeneity of the community's response. It is also important to note that the phenomena occurring in microenvironments within which the soil microbiota exist are of necessity averaged out for measurement, as current methodol‐ ogies require soil to be homogenized before studying [26]. Furthermore, while positive BEF relationships are expected [1], a negative relationship resulting from antagonistic interactions

Many contradictions have been attributed to differences in experimental setup. A recent metaanalysis indicates that most microbial BEF research has relied on comparative approaches, which test the BEF relationship across environmental gradients or treatments, rather than explicitly manipulating biodiversity [25] (Figure 1). The more common, comparative ap‐ proaches are potentially riddled with hidden variables, and thus do not allow for the drawing of a direct link between diversity and function. For this reason, here we focus mainly on experiments which involve direct manipulation of diversity, which tend to find a strong,

**Figure 1.** The relationship between diversity and function is asymptotic; different experimental approaches target dif‐ ferent levels of species richness [13]. By greatly reducing diversity and environmental variability, assembly experi‐ ments seek mechanistic insight into the direct effect of diversity on process rates under minimized redundancy, that is, short-term function (a). Dilution-to-extinction and fumigation experiments retain greater species richness, and tend to emphasize the relationship between diversity and stability (i.e. long-term function) under otherwise stable environ‐ mental conditions (b). These experiments focus on systems in which the functioning asymptote is approached, al‐ though some dilution experiments cover broader ranges of diversity, as in [29] (b, dotted line). In observational studies, diversity is not manipulated, and the focus is rather on the effect of environmental change on the community's ability to maintain process rates (c). In this case, the level of redundancy is high enough to ensure no effect of diversity on functioning, although both positive and negative effects (c, dotted lines) have been observed for this type of experi‐

has been documented [27,28].

324 Biodiversity in Ecosystems - Linking Structure and Function

positive BEF relationship [13].

ments [28,30]

A second approach is to erode a large part of the microbial population selectively (e.g. using heat or chloroform) or randomly (reinocculating sterile soil with serial dilutions of the original community), in the so called **removal experiments**. These systems seem to maintain redun‐ dancy and a large part of their complexity, and much of the extant long-term BEF research has depended on removal microcosms (Figure 2b). The first studies on microbial BEF used these approaches [38], and together with subsequent works have found that broad microbial functions, such as organic matter decomposition, are not affected by large decreases in diversity, but that soils with lowered diversity seem to be less resistant to invasion and less resilient to disturbance [38,39]. Nevertheless, these studies have also yielded contradictory results. For example, in one case, microbial diversity was reduced by inoculating sterile soil with serial dilutions of its original community, but the rate of carbon mineralization, nitrifi‐ cation and denitrification enzyme activity were not related to the diversity treatments, even after diversity reductions of more than 99% of the soil biota, suggesting no BEF relationship [40]. Using the same serial dilution approach, another experiment found that while a 10-5 dilution led to a 75% decrease in estimated richness, the potential denitrification rates of these soils was reduced by about 75% as well, pointing at a strong, positive BEF relationship [29].

Soil microbes are intricately tied to their environment and to each other. The complexity of the system requires that it be simplified for study, but in doing so in ways which maintain an ecosystem which is representative of the natural one has been incredibly challenging [13]. The three approaches discussed here—comparative gradient analysis, assembly, and removal experiments—target the study of the effect of the environment, diversity, and redundancy on functioning, respectively.

## **4. Functional redundancy and diversity**

Redundancy is a characteristic of ecological systems which arises when "different species perform the same functional role in ecosystems so that changes in species diversity do not affect ecosystem functioning", and must be defined relative to the system being studied [41]. The term was first developed in an attempt to optimize conservation efforts and direct them towards the most ecologically relevant species, highlighting the importance of diversity in maintaining functional stability and the integrity of the ecosystem in the face of environmental fluctuation [42], and was later taken up as a way to calculate how much biodiversity could be lost before it affected function [43].

Functional redundancy emerges from the functional classification of its individuals. In contrast to taxonomic classifications, functional ones group organisms based on their contribution to ecosystem functioning rather than phylogeny. This classification paradigm has several advantages: functional diversity is generally a better indicator of ecosystem functioning than the direct measurement of species richness [34,44–46], and functional classifications implicitly account for environmental and biotic interactions by measuring only the outcome of com‐ munity composition, thereby overcoming the oversimplification which stems from studying individual species in a laboratory setting. While this classification scheme is not universally applicable in the sense that functions must be defined relative to the system, it allows for the comparison between ecosystems that contain different species [47].

A major obstacle in applying functional classifications is the different interpretations of what constitutes a functional group, functional guild, or functional type. While functional classifi‐ cations are not new to ecology, they became popular fairly recently, with the definition of the functional guild as a conceptual tool: "…a group of species that exploit the same class of environmental resources in a similar way. This term groups together species, without regard to taxonomic position, that overlap significantly in their niche requirements. (…) A species can be a member of more than one guild" [48]. Since then, new terms (e.g. functional group, strategy, trait, etc.) emerged and were used to define slightly different, yet overlapping concepts (for an in-depth discussion, see [49]). While the concept was rapidly adopted by ecology, it was not applied rigorously during the development of classification schemes, rendering them incomparable in many cases. Perhaps the biggest problem has been differen‐ tiating between *functional response groups* (groups of organisms which respond similarly to changes in environmental factors) and *functional effect groups* (groups of organisms species which contribute in a similar way to ecosystem function) [50]. In order to understand the link between ecosystem functioning and biodiversity, both of these classifications are necessary: under a given environmental condition, knowing which organisms are in their optima and which are out of their functioning range precludes the understanding of how biodiversity affects function, as much of this diversity may be apparent in terms of functioning if the organisms are diverting resources from growth to persistence. Classifying organisms into functional response groups becomes even more important if the functions in question are longterm, and environmental variability is a factor (see section 5.2).

Nowhere is the need for functional effect classifications more important than in the soil microbiome, where it is estimated that 85% of microbial cells and over 50% of microbial OTU's are inactive at any given time [51]. This means that a majority of the soil microbial diversity is only apparent with regards to short term functioning (the long term implications of these 'microbial seed banks' are discussed in a later section). Despite the need, to our knowledge only one experiment has classified a set of microbes based on their response to environmental change [52]. In this study, respiration—which is related to growth—was used both as an indicator of function (functional effect trait) and fitness (functional response trait) for 23 individual strains of bacteria and 22 strains of fungi across a range soil moisture contents. While for some organisms the wettest soil coincided with the highest respiration, many strains exhibited optimal respiration rates at intermediate moisture contents. Different niche breadths —tolerance to a wide range of environmental change—were observed. There was a strong phylogenetic signal associated with moisture tolerance: closely related strains performed more similarly that would be expected if the relationship between phylogeny and functional response were random. Finally, it was observed that biofilm-producing organisms performed better at low moisture content and had a wider tolerance range, but grew more slowly, highlighting the fact that environmental adaptation requires trade-offs [52].

The above study created the first microbe-focused functional response classification, but did not further study whether these strains, when combined, behave similarly, or whether the behavior changes with increasing community diversity. To our knowledge, no such studies exist. The novel practice of seeking the 'core microbiome' of an environment—that is, to distinguish between microbial species which change in response to the environment [53] alludes to the need to group organisms based on their response traits, but it is generally measured in natural environments, and as such is riddled with confounding factors. One factor which distinguishes prokaryotes from other organisms is the ability to acquire mobile genetic elements (i.e. plasmids), which often contain genes that facilitate survival in a wider range of environmental states [18]. The potential change in response trait classification resulting from the acquisition of mobile genetic elements also remains unexplored.

## **5. The additive effect of biodiversity**

Soil microbes are intricately tied to their environment and to each other. The complexity of the system requires that it be simplified for study, but in doing so in ways which maintain an ecosystem which is representative of the natural one has been incredibly challenging [13]. The three approaches discussed here—comparative gradient analysis, assembly, and removal experiments—target the study of the effect of the environment, diversity, and redundancy on

Redundancy is a characteristic of ecological systems which arises when "different species perform the same functional role in ecosystems so that changes in species diversity do not affect ecosystem functioning", and must be defined relative to the system being studied [41]. The term was first developed in an attempt to optimize conservation efforts and direct them towards the most ecologically relevant species, highlighting the importance of diversity in maintaining functional stability and the integrity of the ecosystem in the face of environmental fluctuation [42], and was later taken up as a way to calculate how much biodiversity could be

Functional redundancy emerges from the functional classification of its individuals. In contrast to taxonomic classifications, functional ones group organisms based on their contribution to ecosystem functioning rather than phylogeny. This classification paradigm has several advantages: functional diversity is generally a better indicator of ecosystem functioning than the direct measurement of species richness [34,44–46], and functional classifications implicitly account for environmental and biotic interactions by measuring only the outcome of com‐ munity composition, thereby overcoming the oversimplification which stems from studying individual species in a laboratory setting. While this classification scheme is not universally applicable in the sense that functions must be defined relative to the system, it allows for the

A major obstacle in applying functional classifications is the different interpretations of what constitutes a functional group, functional guild, or functional type. While functional classifi‐ cations are not new to ecology, they became popular fairly recently, with the definition of the functional guild as a conceptual tool: "…a group of species that exploit the same class of environmental resources in a similar way. This term groups together species, without regard to taxonomic position, that overlap significantly in their niche requirements. (…) A species can be a member of more than one guild" [48]. Since then, new terms (e.g. functional group, strategy, trait, etc.) emerged and were used to define slightly different, yet overlapping concepts (for an in-depth discussion, see [49]). While the concept was rapidly adopted by ecology, it was not applied rigorously during the development of classification schemes, rendering them incomparable in many cases. Perhaps the biggest problem has been differen‐ tiating between *functional response groups* (groups of organisms which respond similarly to changes in environmental factors) and *functional effect groups* (groups of organisms species which contribute in a similar way to ecosystem function) [50]. In order to understand the link

comparison between ecosystems that contain different species [47].

functioning, respectively.

lost before it affected function [43].

**4. Functional redundancy and diversity**

346 Biodiversity in Ecosystems - Linking Structure and Function

The primary concern of BEF research is not the individual capacity of an organism to function, but rather the emergent properties that arise from biodiverse communities. This improvement in functioning may be an increase in functional output—known as the short term effects of biodiversity—or an increase in the probability that this level of functioning will be maintained given environmental change, known as the long term effects (Figure 2). These emergent properties are particularly hard to measure in complex systems due to the difficulty of partitioning and attributing changes in community function amongst a plethora of individuals.

**Figure 2.** The short and long-term effects of biodiversity are studied in systems where diversity is simplified to differ‐ ent levels: for the former, the assembly approach discussed in section 3 is generally optimal (a), as simple systems are more tractable and it is easier to link an individual to increases in function. For the study of the long-term effects of biodiversity on ecosystem function—namely stability and adaptive capacity—more diversity is preserved. The empha‐ sis is on monitoring the variability of functional parameters over time, if the goal is to determine intrinsic stability (b); or to measure resistance and resilience of the system to disturbance, if the focus is on functional stability *sensu* Pimm 1984 [54]. The study of alternative stable states and adaptive capacity is in its infancy, and even less is known regard‐ ing the redundancy on these two ecological properties in microbial systems.

#### **5.1. Short term effects: Productivity**

The idea that biodiversity increases ecosystem function was engraved in Darwin's original work "...if a plot of ground be sown with one species of grass, and a similar plot be sown with several distinct genera of grasses, a greater number of plants and dry herbage can be raised in the latter than in the former case (...) the truth of the principle that the greatest amount of life can be supported by the great diversification of life, is seen under many natural circumstances" [55]. At the most basic level, BEF research seeks to understand which characteristics arise from the presence of additional species in an ecosystem before ecosystem function begins to saturate (Figure 2a). These emergent properties—also known as biodiversity effects—are broadly categorized as selection or complementarity [25], and are considered to be the mechanistic processes by which more diverse ecosystems exhibit higher process rates.

**Selection** refers to the phenomenon in which a more diverse community will have a higher probability of containing more productive organisms. The better-performing organism tends to outcompete the rest for resources, returning the system to a monoculture in which its productivity dominates the entire system's productivity; interactions between competing species are not considered to be significant contributors to changes in function. Here, the maximum functioning for the community is determined by the rate of functioning of the most productive species [25,56]. In cases where the most competitive species is the less productive one, selection can lead to a negative BEF relationship.

biodiversity—or an increase in the probability that this level of functioning will be maintained given environmental change, known as the long term effects (Figure 2). These emergent properties are particularly hard to measure in complex systems due to the difficulty of partitioning and attributing changes in community function amongst a plethora of individuals.

**Figure 2.** The short and long-term effects of biodiversity are studied in systems where diversity is simplified to differ‐ ent levels: for the former, the assembly approach discussed in section 3 is generally optimal (a), as simple systems are more tractable and it is easier to link an individual to increases in function. For the study of the long-term effects of biodiversity on ecosystem function—namely stability and adaptive capacity—more diversity is preserved. The empha‐ sis is on monitoring the variability of functional parameters over time, if the goal is to determine intrinsic stability (b); or to measure resistance and resilience of the system to disturbance, if the focus is on functional stability *sensu* Pimm 1984 [54]. The study of alternative stable states and adaptive capacity is in its infancy, and even less is known regard‐

The idea that biodiversity increases ecosystem function was engraved in Darwin's original work "...if a plot of ground be sown with one species of grass, and a similar plot be sown with several distinct genera of grasses, a greater number of plants and dry herbage can be raised in the latter than in the former case (...) the truth of the principle that the greatest amount of life can be supported by the great diversification of life, is seen under many natural circumstances" [55]. At the most basic level, BEF research seeks to understand which characteristics arise from the presence of additional species in an ecosystem before ecosystem function begins to saturate (Figure 2a). These emergent properties—also known as biodiversity effects—are broadly categorized as selection or complementarity [25], and are considered to be the mechanistic

**Selection** refers to the phenomenon in which a more diverse community will have a higher probability of containing more productive organisms. The better-performing organism tends

processes by which more diverse ecosystems exhibit higher process rates.

ing the redundancy on these two ecological properties in microbial systems.

**5.1. Short term effects: Productivity**

368 Biodiversity in Ecosystems - Linking Structure and Function

**Complementarity** on the other hand, results from the competition for resources within a community, which may result in specialization and niche differentiation: as two species compete for a resource, they become specialized in exploiting the resource in different ways or times in order to minimize competitive pressure. In time, a greater efficiency is expected from the system as resources are used more thoroughly. Facilitation is a special case of complementarity, where mutualisms arise among organisms in a community, and result in higher ecosystem productivity [25]. While complementarity also predicts an asymptotic relationship between diversity and function, in this case the maximum productivity of the system may be higher than the productivity of any single member species—a phenomenon termed overyielding. In this scenario, the productivity of the system should be superior from the added productivities of the component species [57,58].

Evidence for resource-use complementarity in the soil microbial system is scant: in one case, microcosms containing up to 8 strains of cellulolytic bacteria were assembled and monitored over 25 days. Greater species richness supported more individuals and faster decomposition rates than any monoculture. Furthermore, the initial frequency distribution of inoculated organisms was maintained in the richest microcosms, suggesting coexistence, but it was not possible to distinguish whether this coexistence was due to niche differentiation or facilitation, although the authors suggest both mechanisms were present [59]. Similarly, in the assemblage experiment with denitrifying bacteria mentioned earlier, the expected function of an assem‐ bled community ('community niche') was calculated by summing the functioning of each of its members, and this was compared to the realized function. The most productive species in terms of CO2 did not coincide with the most productive denitrifiers, illustrating the danger of underestimating relevant species when a single function is used to study the community. In addition, community niche had a much greater explanatory power for the observed functions than species richness alone. The positive relationship between community niche and function suggested that the pattern of resource utilization of the species in a community are a major driver of the increased functioning resulting from higher diversity (i.e. complementarity). The authors also found a minor selection effect, where certain species had a greater effect on community functioning than others, but they argue that in such dynamic communities, teasing out the influence of selection from complementarity is irrelevant, as these are tightly inter‐ twined [33]. In contrast, a study using a similar experimental approach found that respiration in assembled bacterial microcosms was lower in pairwise cultures than expected from the monocultures, and even lower in multispecies cultures, suggesting a predominance of negative interactions in this system [27].

### **5.2. Long-term effects: Stability and resilience**

Ecosystems are dynamic, and communities must maintain ecological processes in the face of environmental change (stability), recover from radical environmental change (resilience), and adapt to constantly changing environments (self-organization) in order to persist. These three properties of diverse systems arise from the interplay between functionally redundant organisms in the community: species within a functional effect group might belong to different functional response groups. When environmental change occurs, it is the presence of organ‐ isms with different response patterns that allows for the maintenance of function, as species with more favorable responses to environmental change can compensate for the loss of function by the more sensitive species. In a similar way, the presence of functionally redundant organisms allows for other, tolerant individuals to maintain function when sensitive ones die or go dormant in response to disturbance.

Redundancymaybeparticularlyimportantforthehighlydynamic soilmicrobial systemwhere, while diversity may be extreme, it may be necessary to buffer environmental change and guarantee the maintenance of function. The most well studied long-term BEF effect is function‐ al stability. The notion that redundancy results in stability is not new, however interest in the development of mathematical models which mechanistically explain *why* this occurs did not become popular until the late 1990's. The importance of redundancy to ecosystem perform‐ ance was initially modeled by applying concepts of reliability engineering to the stability of function[59].Inthismodel,ecosystemfunctioningwasdefinedas"thebiogeochemicalactivities of an ecosystem or the flow of materials and processing of energy", complexity as the number offunctional groups in the system, andreliability as the probability thatthe system willprovide enoughservicestoperpetuatethecycle.Here,diversityincreasesthestabilitywithinafunctional groupthrough**compensatorygrowth**,bywhichone specieswithina functionalgroupincreases when another is reduced. This refers to the difference in environmental tolerances between organisms, which suggests that in redundant systems, there is a higher probability that some organisms will be unaffected by the environmental change, and these will be able to use the resources left behind by the more sensitive species. Interestingly, this model looks at each functional group in the system as a compartment that feeds into the others, and so collapse of the system may come about if a single functional group becomes unstable.

The insurance hypothesis, developed a year later [60], builds on the previous model, and attributes the increase in functioning and decreased variability to the **positive selection** of the more productive species and the **temporal asynchronicity** of species responses to environ‐ mental fluctuation, respectively. Here, stability arises because the dynamics of the diverse systems are less dependent on individual species. This is particularly important in soils, which exhibit a very high species turnover rate: in one case, the bacterial and archaeal ammonia oxidizing communities in a range of Dutch agricultural soils showed above 50% change in community structure between seasons [61,62]. In another, it was shown that when colonizing a novel environment, the microbial community undergoes drastic rearrangement, and draws heavily from members of the 'rare biosphere' [9,63], a strategy which may be crucial for stressresponse [51].

While the intrinsic variability of soils and the mechanisms that support it may be of interest to understanding how redundancy contributes to microbial ecosystem function (figure 2b), soil research rarely focuses on this aspect of stability. Instead soil stability is measured by applying a disturbance to soils with naturally or artificially differing levels of diversity and testing whether the microbiota are able to maintain function in the face of disturbance (resistance), and the time it takes the function to be restored to its pre-disturbance levels (engineering resilience, figure 2c) [54]. Redundancy can be measured as the diversity within a functional group, which is often assessed through functional gene markers that allow for the inclusion of unculturable organisms. As a whole, the results emerging from this area of research are hard to interpret: the usage of disturbances of different identity, duration, and intensity as well as the different time intervals between the measurements of resistance and resilience render these studies incomparable [64].

**5.2. Long-term effects: Stability and resilience**

1038 Biodiversity in Ecosystems - Linking Structure and Function

or go dormant in response to disturbance.

response [51].

Ecosystems are dynamic, and communities must maintain ecological processes in the face of environmental change (stability), recover from radical environmental change (resilience), and adapt to constantly changing environments (self-organization) in order to persist. These three properties of diverse systems arise from the interplay between functionally redundant organisms in the community: species within a functional effect group might belong to different functional response groups. When environmental change occurs, it is the presence of organ‐ isms with different response patterns that allows for the maintenance of function, as species with more favorable responses to environmental change can compensate for the loss of function by the more sensitive species. In a similar way, the presence of functionally redundant organisms allows for other, tolerant individuals to maintain function when sensitive ones die

Redundancymaybeparticularlyimportantforthehighlydynamic soilmicrobial systemwhere, while diversity may be extreme, it may be necessary to buffer environmental change and guarantee the maintenance of function. The most well studied long-term BEF effect is function‐ al stability. The notion that redundancy results in stability is not new, however interest in the development of mathematical models which mechanistically explain *why* this occurs did not become popular until the late 1990's. The importance of redundancy to ecosystem perform‐ ance was initially modeled by applying concepts of reliability engineering to the stability of function[59].Inthismodel,ecosystemfunctioningwasdefinedas"thebiogeochemicalactivities of an ecosystem or the flow of materials and processing of energy", complexity as the number offunctional groups in the system, andreliability as the probability thatthe system willprovide enoughservicestoperpetuatethecycle.Here,diversityincreasesthestabilitywithinafunctional groupthrough**compensatorygrowth**,bywhichone specieswithina functionalgroupincreases when another is reduced. This refers to the difference in environmental tolerances between organisms, which suggests that in redundant systems, there is a higher probability that some organisms will be unaffected by the environmental change, and these will be able to use the resources left behind by the more sensitive species. Interestingly, this model looks at each functional group in the system as a compartment that feeds into the others, and so collapse of

the system may come about if a single functional group becomes unstable.

The insurance hypothesis, developed a year later [60], builds on the previous model, and attributes the increase in functioning and decreased variability to the **positive selection** of the more productive species and the **temporal asynchronicity** of species responses to environ‐ mental fluctuation, respectively. Here, stability arises because the dynamics of the diverse systems are less dependent on individual species. This is particularly important in soils, which exhibit a very high species turnover rate: in one case, the bacterial and archaeal ammonia oxidizing communities in a range of Dutch agricultural soils showed above 50% change in community structure between seasons [61,62]. In another, it was shown that when colonizing a novel environment, the microbial community undergoes drastic rearrangement, and draws heavily from members of the 'rare biosphere' [9,63], a strategy which may be crucial for stressNevertheless, this body of work has yielded important insights into the relationship between diversity and stability. For example, one study found that the diversity of both nitrite oxidizing and denitrifying bacteria in soil was not significant in determining the rate of functional recovery from experimental heating; rather, the main factor affecting this phenomenon was the abundance of the genes responsible for the functions tested [65]. In this case, it was not diversity, but sheer abundance which was responsible for stability. In another case, the recovery rate of two soils with naturally differing levels of diversity was compared: while mineralization of a labile carbon source (14C-labeled wheat shoot) remained unaffected, mineralization of a recalcitrant substrate (14C-labeled 2,4 dichlorophenol) was impaired. The more diverse soil was able to recover within the 9 weeks of the experiment, while the less diverse soil did not [30], suggesting here diversity mattered not only for stability, but also for the decrease in function.

Generally, narrower or less redundant functions have been found to be less stable following disturbance than broad functions[66], supporting the notion that biodiversity acts to buffer the system against fluctuations. In one case, respiration in serially diluted soil microbial micro‐ cosms exhibited no change in basal respiration or decomposition despite the large reductions in diversity, but nitrification was progressively retarded with each dilution [38]. Changes in community composition may affect function when, following disturbance, an abundant and efficient species is replaced by a redundant, but less efficient yet tolerant one. For example, monitoring potential nitrite oxidation (PNO) on soils that were treated with the cessation of tillage on tilled land or the establishment of tillage on untilled land, it was possible to detect a switch from *Nitrobacter*-like nitrite oxidizers to *Nitrospira*-like nitrite oxidizers with tillage, which explained the decrease in PNO [67]. The cessation of tillage did not result in a restoration of the *Nitrobacter* community within the 17 months of study, suggesting that long-term function might have been permanently affected by treatment. In an assembly experiment comparing the recovery from heating or metals in microbial communities of 1-12 bacterial species, biodiversity increased stability, measured as community biomass, but this stability was closely associated to the number of tolerant species in the community, a phenomenon analogous to the selection effect [34]. In a separate experiment, altering the pH in mixed culture fermentation reactors was shown to bring about the dominance of different species of *Clostridium* and elicit slight changes in the reactor's chemical output in accordance with the dominant species' preferences [68].

The distribution of species abundances within a community also affect stability: more evenly distributed communities are generally more stable than communities characterized by one or two dominant species [37,69]. In one case, the effect of selective stress on the stability of assembled denitrifying communities of up to 18 species was highly dependent on initial community evenness [37]. Even excluding the effect of the presence of tolerant species on the community's response, evenness played a significant role in maintaining stability.

Perhaps the clearest results have been obtained from studies looking at invasion resistance as an indicator of functional stability [11]. In general, diversity decreases invasibility in microbial systems [36,39,45]. By using both assembly and dilution in bacterial microcosms, a strong, negative correlation was shown between diversity and invasibility of an invader *E. coli* strain [39]. In a more recent experiment, the authors were able to attribute this decrease in invasibility to a reduction in easily available resources and reduced competitive advantage in the more diverse treatments. This result was confirmed by applying a resource pulse to the community following invasion, which led to an increase in the abundance of the invading species [70]. An analogous result was found in assembled communities consisting of different strains of *Pseudomonas fluorescens*, where genetic dissimilarity within a community increased produc‐ tivity and decreased the success of the invader *Serratia liquefaciens* by decreasing the amount of resources available to the invader [71].

While it seems that theoretical predictions of a positive relationship between diversity and stability are somewhat in agreement, a large gap in the literature arises from differences between the definition of stability employed in these two fields: while experimental microbial ecology uses a functional definition of stability, which depends on resistance and resilience, theory often relies on intrinsic functional stability, which is a stand-alone parameter that measures the reduction of variability when there is no change in environmental parameters (Figure 2b). It is expected that more diverse communities will be more functionally stable and less compositionally stable, yet this has received little attention. The measurement of intrinsic stability requires the repeated measurement of an unperturbed community over time. Instead, a measurement is made immediately before disturbance to determine "normal" levels of functioning, immediately after to evaluate whether the system was resistant, and for a third time after a recovery period has passed. This approach does not consider that the system at equilibrium exhibits a constant variability which is intrinsic to the system, called normal operating range (NOR) [72], and thus cannot distinguish whether the response of a community to disturbance fits within 'normal' ranges of fluctuation or not, or whether a system that is deemed recovered is in a similar equilibrium to its undisturbed state.

### **6. Moving forward: Redundancy and adaptive capacity**

The concept of resilience employed in the measurement of functional stability—engineering resilience—differs from the ecological concept as it was originally proposed [73]—ecological resilience. The former sees ecosystems as simple, rebounding springs, while the latter includes the possibility of the system shifting to alternative states due to perturbation, and thus is much harder to measure [73]. If ecological resilience is considered as a function in the same way as invasion resistance and stability, then it too can be progressively eroded. While research on this topic has been sparse and has not directly manipulated diversity, evidence of this phenomenon exists [74–78]. For example, mercury-contaminated, heat-shocked soils respond‐ ed much more slowly to substrate additions than the transiently tylosin-contaminated or control soils [79]. The authors observed a significant decrease in the microbial diversity of the mercury-contaminated soils, which may explain the reduced response following additional disturbances. Mercury constitutes a long-term stress, so the heat-shocked communities were already coping with the original disturbance; however some studies find that even when the soils are allowed to recover from transient perturbations, their response to further disturbances is slower than that of the control soils: in another case, grassland soils which had experienced various forms of perturbation (reseeding, application of sewage-sludge, biocide/nitrogen and lime additions) recovered their ability to decompose more slowly following both copper and transient heat stresses than the unperturbed controls [78].

The distribution of species abundances within a community also affect stability: more evenly distributed communities are generally more stable than communities characterized by one or two dominant species [37,69]. In one case, the effect of selective stress on the stability of assembled denitrifying communities of up to 18 species was highly dependent on initial community evenness [37]. Even excluding the effect of the presence of tolerant species on the

Perhaps the clearest results have been obtained from studies looking at invasion resistance as an indicator of functional stability [11]. In general, diversity decreases invasibility in microbial systems [36,39,45]. By using both assembly and dilution in bacterial microcosms, a strong, negative correlation was shown between diversity and invasibility of an invader *E. coli* strain [39]. In a more recent experiment, the authors were able to attribute this decrease in invasibility to a reduction in easily available resources and reduced competitive advantage in the more diverse treatments. This result was confirmed by applying a resource pulse to the community following invasion, which led to an increase in the abundance of the invading species [70]. An analogous result was found in assembled communities consisting of different strains of *Pseudomonas fluorescens*, where genetic dissimilarity within a community increased produc‐ tivity and decreased the success of the invader *Serratia liquefaciens* by decreasing the amount

While it seems that theoretical predictions of a positive relationship between diversity and stability are somewhat in agreement, a large gap in the literature arises from differences between the definition of stability employed in these two fields: while experimental microbial ecology uses a functional definition of stability, which depends on resistance and resilience, theory often relies on intrinsic functional stability, which is a stand-alone parameter that measures the reduction of variability when there is no change in environmental parameters (Figure 2b). It is expected that more diverse communities will be more functionally stable and less compositionally stable, yet this has received little attention. The measurement of intrinsic stability requires the repeated measurement of an unperturbed community over time. Instead, a measurement is made immediately before disturbance to determine "normal" levels of functioning, immediately after to evaluate whether the system was resistant, and for a third time after a recovery period has passed. This approach does not consider that the system at equilibrium exhibits a constant variability which is intrinsic to the system, called normal operating range (NOR) [72], and thus cannot distinguish whether the response of a community to disturbance fits within 'normal' ranges of fluctuation or not, or whether a system that is

The concept of resilience employed in the measurement of functional stability—engineering resilience—differs from the ecological concept as it was originally proposed [73]—ecological resilience. The former sees ecosystems as simple, rebounding springs, while the latter includes the possibility of the system shifting to alternative states due to perturbation, and thus is much harder to measure [73]. If ecological resilience is considered as a function in the same way as

deemed recovered is in a similar equilibrium to its undisturbed state.

**6. Moving forward: Redundancy and adaptive capacity**

community's response, evenness played a significant role in maintaining stability.

of resources available to the invader [71].

1240 Biodiversity in Ecosystems - Linking Structure and Function

The concept of ecological resilience can be broken down into three characteristics: 1) the amount of change a system can undergo while retaining the same controls on function and structure; 2) the degree to which the system is capable of self-organization; and 3) the ability to build and increase the capacity for learning and adaptation [80]. Systems in which ecological resilience has been lost are unable to adapt to environmental change beyond a certain thresh‐ old, and in response to change shift to alternative stable states, in which the community is characterized by a different set of interactions (Figure 2d). One question that arises from this is whether microbial systems have stable equilibria to begin with. This is unclear, since the detection of alternative stable states requires the measurement of intrinsic variability which, as mentioned in the previous section, is not common practice in microbial ecology.

Another question is whether these irreversible shifts to alternative stable states have any relevance to ecosystem cycles. By analyzing the available literature, we may find mechanisms by which they do: the previously mentioned experiment in which tillage and no-tillage agricultural lands experienced an exchange in practice and the productivity and structure of the nitrite oxidizing communities underwent a catastrophic shift as, in response to an envi‐ ronmental change (tilling), the dominant members of a functional group (nitrite oxidizing bacteria) fundamentally changed from a those belonging to a more efficient genus (*Nitrobact‐ er*) to a less efficient one (*Nitrospira*), leading to a decrease in function. Furthermore, cessation of tillage did not result in the opposite change in community. This may be an example of hysteresis—the phenomenon in which a system fails to return to its original state once perturbation ceases [81]—which is a property of systems that exhibit alternative stable states. In this case, the system may fail to return to its no-tillage state because the *Nitrobacter* community has been eroded beyond its ability to recover, or because the new dominant, generalist group is well suited for a wide range of environmental states, and it cannot easily be outcompeted when the system returns to its original conformation. Regardless of the underlying mechanism, this study provides evidence that a shift in the *identity* of the dominant organisms in a functional group may have an effect on functioning, and that this change may be irreversible. The implications of applying the ecological resilience concept to BEF studies are unknown but potentially very relevant, however to our knowledge, no work explicitly targets the measurement of ecological resilience in microbial systems, and this represents a serious gap in ecological research.

## **7. Conclusion**

The last decade has seen a shift in focus, from a function-focused to a stability and probabilityfocused perspective. This is to be expected: at a time in which climate is expected to become more unpredictable [3], and biodiversity loss is expected to accelerate [2], it becomes important to be able to guarantee not only that ecosystems will be able to function, but that they will still be able to function in the face of drastic change. As mentioned earlier, the concept of functional redundancy was developed as a way determine which species within a community required the most conservation attention, and was later used to refer to a 'minimum' amount of biodiversity needed to keep the system functioning. As the focus shifts from the from the shortto the long-term effects of redundancy on ecosystem functioning, it becomes clear that the ecological value of redundant species lies in their ability to buffer against environmental change. Microbial communities are excellent model systems to study such buffering, not only due to the extremely high level of functional redundancy found here, but also due to the fact that these systems routinely experience rapid changes which may be catastrophic from a microbial perspective, and yet as a community they are able to maintain function. It seems that, even in the extremely diverse soil microbial system, diversity reductions result in reductions in either long-term or short-term function, or both, although the current gap in knowledge regarding microbial functional responses impairs our ability to understand the mechanics of this reduction as well as our ability to predict when environmental change results in functional change [82].

While the relevance of diversity to resilience and self-organization, and their contribution to the maintenance of function may be elusive and hard to study experimentally, these relation‐ ships warrant our attention. Initial studies have already revealed the importance of rare species in restructuring communities. Given current knowledge, it seems that in changing environ‐ ments, every species matters, even in communities as diverse as the soil microbial community. Future research must delve into whether certain individuals matter more by evaluating the functional response profiles of individuals and communities, and quantifying the effect of changes of community composition on function.

## **Author details**

Stephanie D. Jurburg and Joana Falcão Salles\*

\*Address all correspondence to: falcao.salles@rug.nl

Department of Microbial Ecology, Centre for Life Sciences, University of Groningen, Groningen, The Netherlands

## **References**

targets the measurement of ecological resilience in microbial systems, and this represents a

The last decade has seen a shift in focus, from a function-focused to a stability and probabilityfocused perspective. This is to be expected: at a time in which climate is expected to become more unpredictable [3], and biodiversity loss is expected to accelerate [2], it becomes important to be able to guarantee not only that ecosystems will be able to function, but that they will still be able to function in the face of drastic change. As mentioned earlier, the concept of functional redundancy was developed as a way determine which species within a community required the most conservation attention, and was later used to refer to a 'minimum' amount of biodiversity needed to keep the system functioning. As the focus shifts from the from the shortto the long-term effects of redundancy on ecosystem functioning, it becomes clear that the ecological value of redundant species lies in their ability to buffer against environmental change. Microbial communities are excellent model systems to study such buffering, not only due to the extremely high level of functional redundancy found here, but also due to the fact that these systems routinely experience rapid changes which may be catastrophic from a microbial perspective, and yet as a community they are able to maintain function. It seems that, even in the extremely diverse soil microbial system, diversity reductions result in reductions in either long-term or short-term function, or both, although the current gap in knowledge regarding microbial functional responses impairs our ability to understand the mechanics of this reduction as well as our ability to predict when environmental change results

While the relevance of diversity to resilience and self-organization, and their contribution to the maintenance of function may be elusive and hard to study experimentally, these relation‐ ships warrant our attention. Initial studies have already revealed the importance of rare species in restructuring communities. Given current knowledge, it seems that in changing environ‐ ments, every species matters, even in communities as diverse as the soil microbial community. Future research must delve into whether certain individuals matter more by evaluating the functional response profiles of individuals and communities, and quantifying the effect of

Department of Microbial Ecology, Centre for Life Sciences, University of Groningen,

serious gap in ecological research.

1442 Biodiversity in Ecosystems - Linking Structure and Function

**7. Conclusion**

in functional change [82].

**Author details**

Groningen, The Netherlands

changes of community composition on function.

Stephanie D. Jurburg and Joana Falcão Salles\*

\*Address all correspondence to: falcao.salles@rug.nl


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