Indicators and Surrogates of Biodiversity and Environmental Change

By CSIRO PUBLISHING

Ecological indicators and surrogates are used widely by resource managers to monitor and understand complex biota and ecosystem processes. Their potential to guide complex resource management has meant they have been proposed for use in all ecosystems worldwide. Despite extensive research into indicators and surrogates, there remains much controversy about their use, in addition to major issues and knowledge gaps associated with their identification, testing and application.

Indicators and Surrogates of Biodiversity and Environmental Change provides insights into the use of indicators and surrogates in natural resource management and conservation – where to use them, where not to use them, and how to use them. Using an ecological approach, the chapters explore the development, application and efficacy of indicators and surrogates in terrestrial, aquatic, marine and atmospheric environments. The authors identify current gaps in knowledge and articulate the future directions for research needed to close those gaps.

This book is written by the world’s leading thinkers in the area of indicators and surrogates. It is the first major synthesis of learnings about indicators and surrogates and will be a critical resource for the vast number of people developing and applying them in ecosystems around the world. It will be an essential resource for scientists, policy makers and students with interests in surrogates and indicators.

• Language: English
• Publisher: CSIRO PUBLISHING
• Release date: Nov 2, 2015
• ISBN: 9781486304110

Book preview

1

Introduction – disciplinary and multi-disciplinary perspectives on ecological indicators and surrogates

David Lindenmayer, Jennifer Pierson and Philip Barton

Introduction

The term surrogate can be broadly defined as a proxy for something else. The scientific study of ecological surrogacy, and its application to biodiversity and resource management, is widespread – more than 5000 scientific articles have been published on the topic (Westgate et al. 2014). The reason for this vast literature is the natural environment is so complex that it is neither logistically practical nor financially possible to directly measure and monitor all components of the environment in all locations and at all times. Surrogates or proxies are therefore needed to represent other attributes of the environment that are not able to be directly measured (Caro 2010; Collen and Nicholson 2014). Indeed, the application of ecological surrogates lies at the heart of many key areas of environmental science, ranging from the selection and monitoring of reserves, monitoring of pollutants in the atmosphere or drinking water, the effectiveness of vegetation restoration, and estimates of the number of the total number of plant and animal species in a given location.

The primary focus of this edited volume is on ecological surrogates – although, as we discuss below, our book also includes a chapter on the use of surrogates in medicine and insights on surrogacy from statistical science. However, before we outline some of the content of this book, the reason why it was written and the structure used to guide each chapter, it is important to first define at the outset what we mean by the term ‘surrogacy’, and other terms that fall within its broader remit (in particular the term ‘indicator’). We also highlight the important distinction between surrogacy and direct measurement (sensu Lindenmayer and Likens 2011).

For the purposes of this book, we deem the term surrogate to mean a proxy for some other entity. The primary focus of this book is on ecological surrogates; that is, the use of proxies in the environment, ecology and conservation. Direct measurement in the context of this book means measuring a given entity without implying that it is a proxy for some other entity (Lindenmayer and Likens 2011). For example, the concentration of E. coli in an aquatic ecosystem might be measured simply to determine how much of this microorganism occurs in a given water body. Similarly, the concentration of carbon dioxide in the atmosphere (e.g. the Keeling curve) may simply be a direct measurement of how much of it now occurs in the air above a given area. These direct measurements of E. coli and carbon dioxide do not have to imply anything about the condition of the airshed or of the water. However, if the amount of E. coli is considered to a proxy for the quality of water fit for human consumption or the concentration of carbon dioxide as a proxy for future increases in temperature, then they become ecological surrogates – that is, a proxy for some other entity (water quality or future temperature).

Why is there a need to define these terms? The reason is that many terms in the relevant literature (including the term surrogate itself) have been used in quite different ways and to mean quite different things. This is evident even in this book. Hence, we have asked the author/s of each chapter to specifically define key terms within the text to ensure that readers understand the particular usage.

The aim of this book

There have been many detailed reviews of ecological surrogacy, and the narrower field of species-based indicators, in recent years (e.g. McGeoch 1998; Niemi et al. 1997; Rodrigues and Brooks 2007; Caro 2010). The purpose of this book was not to cover the same ground as these previous reviews. Rather, it was to highlight the diverse ways that surrogacy is studied and applied in the broader environmental sciences, including terrestrial, marine, aquatic, atmospheric and policy realms. Our aim was then to draw together some of the key learnings from these different disciplines and compare and contrast the different ways the study and application of surrogates have evolved within them. In addition, we sought to explore the overlap and divergences between disciplines in the use of ecological surrogates with an explicit aim of trying to uncover ways to improve the application of surrogates in the future.

The structure of this book and the chapters

As part of compiling this book, we asked chapter authors not to re-hash what they have written previously, but rather to expound their personal views on their important area of expertise. In particular, authors we asked to arrange their chapters around 10 points that cover things they have learnt or are known, and remaining challenges or unknowns in their field. The focus was largely on practical (realistic) applications of indicators and surrogates in environmental management and/or conservation management. Each chapter covers discipline-specific areas, and many use terminology developed and used by researchers in that field. Acronyms have been avoided where possible, and definitions provided where necessary.

Each chapter in this book has been kept deliberately short and to the point. This was done for good reason – the information ‘super-glut’ of this era means that there is an overload of information that few people have time to touch on, evenly briefly. Our aim was therefore to request chapter authors to produce short pithy chapters that were readily accessible and with the key points summarised in a way that could be quickly comprehended. The challenge to all authors was to summarise their ideas in a few thousand words – and shorter if possible. To this end, each chapter is structured in a similar way. That is:

• A box with the 10 dot points – one for each key issue that they considered.

• A few short introductory paragraphs with limited background on their topic.

• Approximately five things that have been learnt or are known, and five remaining challenges or unknowns in their field, with one or two paragraphs on each of the 10 key issues.

• A paragraph on each issue laying out why it is essential that it must be tackled.

• A small set of references directing readers to additional literature that further explores the topic.

This book is broken into six main themes. Theme 1 contains four chapters and is a general overview of ecological indicators and surrogates, including global perspectives on their use. Three chapters comprise theme 2, which is focused on ecological indicators and surrogates in the terrestrial environment. Theme 3 also comprises two chapters and both are associated with the application of indicators and surrogates in the measurement and management of the atmosphere. Aquatic ecosystems are the core of the theme 4, with two chapters featured. Theme 5 comprises three chapters on marine ecosystems. Theme 6 is particularly fascinating as it contains two quite different kinds of chapters not normally associated with the field of ecological indicators and surrogates. The first chapter in this theme concerns the increasing use of genetic metrics and explores the exciting potential of this relatively new area in the ecological surrogate domain. The second chapter in Theme 6 pivots around discussions of the learnings for the use of ecological indicators and surrogates in ecology that can be drawn from medical science and statistical science. Theme 7 comprises two chapters about indicators and surrogates in policy. The final theme 8 contains just one chapter – an overview of learnings that come from the collected insights of the chapter authors. In particular, what works well and what doesn’t, and what common ideas can be used to guide future research on the selection and application of ecological surrogates. This chapter was developed from the diversity of author experience and chapter content, especially through a workshop held in south-east Queensland (Australia) in October 2014.

Caveats and notes

As with any book comprising many topics written by different authors, there are some inconsistencies in the approach and flavour of each chapter and the volume editors decided that these could remain. The authors of each chapter were responsible for their own contribution and only limited content editing was conducted. Of course we were acutely aware that there are sometimes more than 10 key issues that could be considered in each sector, ecosystem and cross-cutting theme. However, we felt that asking each author team to highlight their ‘top 10’ was a good way to focus their writing around important insights and key knowledge gaps that need to be filled.

A key concern we had at the commencement of this book writing project was the risk of extensive overlap between chapters. In reality, there proved to be an impressive diversity of topics, important synergies and limited overlap between chapters. We considered the little overlap that did occur was tolerable because each chapter needed to be a stand-alone contribution given the way many readers might ‘dip into and out of’ the book.

Ecological surrogates are likely to become even more important in guiding environmental policy and management in future. Our sincere hope is that this book represents a leap forward in the understanding and application of ecological surrogacy in terms of breadth of expertise and examples, as well as the identification of core learnings that transcend individual disciplines.

References

Caro T (2010) Conservation by Proxy: Indicator, Umbrella, Keystone, Flagship, and Other Surrogate Species. Island Press, Washington DC.

Collen B, Nicholson E (2014) Taking the measure of change. Science 346, 166–167.

Lindenmayer DB, Likens GE (2011) Direct measurement versus surrogate indicator species for evaluating environmental change and biodiversity loss. Ecosystems 14, 47–59. doi:10.1007/s10021-010-9394-6.

McGeoch MA (1998) The selection, testing and application of terrestrial insects as bioindicators. Biological Reviews of the Cambridge Philosophical Society 73, 181–201. doi:10.1017/S000632319700515X.

Niemi GJ, Hanowski JM, Lima AR, Nicholls T, Weiland N (1997) A critical analysis on the use of indicator species in management. The Journal of Wildlife Management 61, 1240–1252. doi:10.2307/3802123.

Rodrigues AS, Brooks TM (2007) Shortcuts for biodiversity conservation planning: the effectiveness of surrogates. Annual Review of Ecology Evolution and Systematics 38, 713–737. doi:10.1146/annurev.ecolsys.38.091206.095737.

Westgate MJ, Barton PS, Lane PW, Lindenmayer DB (2014) Global meta-analysis reveals low consistency of biodiversity congruence relationships. Nature Communications 5, 3899. doi:10.1038/ncomms4899.

2

Surrogates for the distribution and trajectory of biodiversity

Martin Westgate

Things we know

1Most taxa show congruent distribution patterns.

2Processes that influence congruence are scale-dependent.

3Surrogate research is shifting towards species-level inference.

4Functional ecology can help to identify biodiversity surrogates.

5Surrogates should measure progress towards management goals.

Knowledge gaps

6A long-term view of biodiversity surrogates.

7Greater integration is needed across taxonomic boundaries.

8Research on the mechanisms underpinning surrogate relationships is not always worthwhile.

9Complementary surrogates.

10 Adaptive surrogates.

Introduction

Biodiversity is a broad concept that encompasses several real and perceived values of ecosystems. However, the idea that science can help to conserve biodiversity relies on the assumption that biodiversity can be measured. Unfortunately, ecosystems are too complex to allow comprehensive mapping of their specific or genetic diversity: only a small proportion of all species have been taxonomically described, while data on species’ distributions or risks of extinction are rarer still (Scheffers et al. 2012). Consequently, our ability to measure ‘total’ biodiversity is dependent on surrogates: practical metrics that allow us to identify locations or times where we find high numbers and/or distinct combinations of species. In practice, most assessments of the distribution or trajectory of biodiversity rely on information from a small number of frequently studied taxa, typically with an emphasis on vertebrate animals and vascular plants (Westgate et al. 2014). In this chapter, I will refer to such metrics (i.e. those whose goal is the measurement of biodiversity) under the umbrella term ‘biodiversity surrogates’. My aim is to explore the current state of the science of biodiversity measurement, discuss how a reliance on surrogates influences conservation decisions, and suggest future directions for this important area of ecological research.

Things we know

1. Most taxa show congruent distribution patterns

A major component of biodiversity surrogate research involves the assessment of ‘congruence’, which measures the extent to which information on one group of species provides information on a second group. Perhaps the most common application of this approach is to test the relationship between the spatial distributions of two distinct taxonomic groups (known as ‘cross-taxon’ congruence). The success of this approach, and its utility for informing conservation, varies strongly depending on a range of factors. In particular, differences in how target and surrogate groups are defined and measured can strongly influence patterns of congruence (Rodrigues and Brooks 2007), as can attributes of study design such as spatial scale or grain size (Westgate et al. 2014). Further, there is debate regarding the extent to which different metrics for quantifying and comparing the diversity of two groups provide useful information (see point 3 below). Therefore, caution must be taken when using congruence-based approaches to infer patterns of biodiversity.

Despite these caveats, it is encouraging that the degree of congruence between taxa is typically greater than zero, irrespective of the metric under investigation (Rodrigues and Brooks 2007; Westgate et al. 2014). Pointing out that congruence is a common attribute in nature is important because the academic literature is often highly critical of the surrogate concept. Positive correlations occur between taxa because all life responds to similar processes, particularly at large spatial and temporal scales. For example, studies of the influence of biogeographic parameters such temperature, latitude or elevation on taxonomic diversity have a long history in ecology, and their respective influence on different taxa is relatively well understood (e.g. Qian and Kissling 2010). This suggests that progress can be made towards understanding patterns of congruence between different sets of species or locations.

2. Processes that influence congruence are scale-dependent

Understanding the circumstances where we would expect to observe biodiversity congruence would greatly assist conservation decision making. However, the number or diversity of species at a given location is influenced by many processes, each acting at particular spatial and temporal scales. For example, we still lack an overarching theory that describes how evolution, species traits and species interactions combine to determine biodiversity at macro scales (Nuismer and Harmon 2014). Moreover, much research on surrogates in ecology and conservation focuses on how biodiversity responds to landscape-scale processes such as disturbance or fragmentation, which are areas where cross-taxon synthesis has traditionally been difficult (Ewers et al. 2010). These difficulties may explain why many applications of the surrogacy concept lack a theoretical foundation, or are inconsistent with known theoretical concepts (Sætersdal and Gjerde 2011).

Some authors have attempted to identify processes that influence cross-taxon congruence across a range of spatial scales. In particular, groups of species that display strong interactions such as predator–prey dynamics or mutualisms should be highly congruent, but this does not always occur (Dehling et al. 2014). Alternatively, species may share physiological attributes that ensure they display similar responses to climate or elevation, leading to high congruence (Hawkins and Porter 2003). Without a better knowledge of how these processes act and interact, however, we remain unable to predict when we should expect sets of taxa to display high congruence.

3. Surrogate research is shifting towards species-level inference

Early attempts to identify biodiversity surrogates typically focused on correlations in species richness and/or diversity across taxa, but prioritising locations with high species richness for conservation ignored areas with highest threat, or those with a high proportion of endemic species (Orme et al. 2005). Further, many taxa display changes in species composition across ecological gradients, but not species richness (Supp and Ernest 2014). Consequently, methods that use diversity indices to assess congruence have been largely superseded by optimisation of species occurrences as the standard method for spatial prioritisation (Williams et al. 2006). Similarly, hierarchical models are now capable of assessing change in assemblage structure via logistic regression of individual species occurrences (Wang et al. 2012), rather than relying on aggregate metrics. These tools have provided a range of new insights into the distribution and trajectory of biodiversity.

This increased focus on species-level information, while encouraging, brings new challenges. First, species occurrence data are highly scale-dependent, with issues resulting from spatial variation in species occupancy, abundance and detectability increasing at fine scales (Hurlbert and Jetz 2007). Second, different software algorithms, or changes to the underlying assumptions of the analysis, can lead to enormous differences in the ranking of locations or actions for conservation (Grantham et al. 2010). Finally, applications of spatial prioritisation can be more sensitive to the economic value of study units than to their biological composition (Bode et al. 2008): a property that is useful for efficient conservation, but risks promoting the lesson that biodiversity itself is of secondary concern. Consequently, care is needed when relying intensively on species-level datasets for conservation decision-making.

4. Functional ecology can help to identify biodiversity surrogates

The functional approach to ecological research is based on the premise that knowledge of species traits can be used to understand ecosystem structure. This concept is widely accepted and studied, both because of its intuitive appeal (species differ from each other in meaningful ways, and traits reflect that), and its usefulness for describing patterns in biological communities (models that ignore differences between species often perform worse than models that account for those differences). Functional ecology is relevant to the surrogate concept because it provides a robust mechanism that can explain patterns of co-occurrence, and can therefore be used to identify reliable surrogates (Dehling et al. 2014).

While useful, however, several conceptual and practical issues remain in how species traits are applied to biodiversity monitoring and management. In particular, the idea that species traits mediate interactions between species, or between a single species and its environment, is well accepted. Precisely which traits are used, however, and how they are defined and measured, remains challenging. Unfortunately, learning about traits that influence the distribution of plant species can rarely be transferred to understand patterns in animals, and vice versa. Of those traits that can be compared across dissimilar taxa (such as body mass, or related indices such as metabolic rates), controversy remains regarding their mechanistic basis and degree of explanatory power (Isaac and Carbone 2010). Further, work on animal traits remains largely descriptive, despite advances in predictive science based on plant traits. For example, Lindenmayer and colleagues (2014) showed that species selected for monitoring according to their traits did not occupy sites with high numbers of other bird species, negating their usefulness for conservation decision making. While potentially useful, therefore, functional approaches need to be carefully evaluated before being adopted as standard methods in biodiversity monitoring.

5. Surrogates should measure progress towards management goals

Biodiversity surrogates are a tool to provide reliable, up-to-date and cost-effective information on ecosystem state. Much research in the surrogate ecology literature has focused on the efficacy (Rodrigues and Brooks 2007) or efficiency (Kessler et al. 2011) of proposed surrogates, but this ignores a pivotal question: why monitor at all? Decision theory tells us that we should only collect data when: (1) we need that data to answer a question, and (2) the answer to that question will help us manage the system better, and so meet our management goals (McDonald-Madden et al. 2010). Therefore, the attributes of the system that you should monitor (i.e. your choice of surrogate) depend on what your goal is and how the system is changing in relation to your goal.

In this context, it becomes clear that there is no single ‘best’ indicator that should always be selected regardless of context. Instead, the surrogate that should be chosen in a given situation is highly dependent on the question that needs to be investigated. Indeed, for simple or well-designed questions, it may be that no surrogate is needed at all, and that direct measurement is more efficient (Lindenmayer and Likens 2011). Despite this, few ecologists state their reasons for monitoring, the expected value of the resulting information for improving management, or even their overall management goals (Westgate et al. 2013). Without a management target or goal, the proximate problem considered by many articles on surrogates and indicators (i.e. which taxa are effective surrogates for each other) risks perpetuating the misleading suggestion that there exists a single surrogate that is valuable in all instances.

Knowledge gaps

6. A long-term view of ecological surrogates

Surrogate relationships can result from several processes, but the most reliable associations are likely to be those that have a known ecological or evolutionary basis. It follows, therefore, that a long-term view of how surrogate interactions form and change would aid understanding in this field. For example, processes such as fragmentation or disturbance can take decades to influence species distributions (Wearn et al. 2012). Even more broadly, the imprint of co-evolution (Jetz et al. 2004) or geological processes such as glaciation (Essl et al. 2011) can further influence species distributions and patterns of co-occurrence. Despite the strong influence of long-term processes on biodiversity, most assessments of surrogate relationships consist of ‘snapshot’ studies, while long-term monitoring programs of value for understanding consistency in surrogate relationships are similarly rare (Westgate et al. 2013). This longer view of consistency and change in surrogate relationships is developing (Barton et al. 2014), but needs further work.

One way that the short-term view of surrogate relationships could be improved is by greater use of genetic data. The full range of applications for genetics data in surrogate ecology will be discussed in a later chapter (see Chapter 15), but two valuable research directions are particularly relevant here. First, several new methods, including genetic bar-coding and environmental DNA, have enormous potential to reduce the cost of biodiversity monitoring (e.g. Thomsen et al. 2012). This is important because cost is a major factor influencing the longevity and effectiveness of ecological monitoring programs (Lindenmayer and Likens 2009). Second, genetic analysis allows assessment of evolutionary rates, which over long periods may fundamentally alter species responses to each other or to their environment (Nuismer and Harmon 2014). These advances have enormous potential to improve ecologists’ ability to detect and understand long-term trends in biodiversity.

7. Greater integration is needed across taxonomic boundaries

Most research on biodiversity surrogates compares multiple species in one taxon against multiple species in a second taxon. While conceptually straightforward, this approach places primacy on taxonomic categories of biodiversity, despite evidence that phylogenetic relatedness has little influence on patterns of cross-taxon congruence (Westgate et al. 2014). With increases in our ability to monitor several taxonomic groups simultaneously (Kessler et al. 2011) comes greater potential to test more complex cross-taxon interactions in detail.

Several authors have investigated methods for incorporating information from several dissimilar taxa in biodiversity surrogate research. One useful approach is to monitor species that have disproportionately large impacts on the number or diversity of species in an ecosystem, such as keystone species or ecosystem engineers (Caro 2010). Alternatively, maintaining functional diversity (rather than taxonomic or phylogenetic diversity) can be a valuable approach to biodiversity conservation, particularly where ecosystems include large numbers of functionally similar species (Gerisch et al. 2012). Finally, recognition of ecosystem change resulting from changes to species interactions (such as trophic cascades) has prompted some authors to consider monitoring and management of species interaction networks, though this remains an emerging field of research (Tylianakis et al. 2010). Each of these approaches has enormous potential for informing ecosystem-wide conservation via surrogates from a range of taxa.

8. Research on the mechanisms underpinning surrogate relationships is not always worthwhile

I have argued above that the mechanisms that underpin biodiversity congruence relationships are poorly understood, and that this represents a hindrance to the use of biodiversity surrogates (e.g. Barton et al. 2014). However, it does not follow that the need for reliable surrogates justifies any level of investment in research on ecological processes. This is because information on biodiversity should be collected only if it will help to make management decisions that, in turn, lead to better outcomes for biodiversity than would be possible in the absence of that information (McDonald-Madden et al. 2010). Surrogates that have been more thoroughly evaluated are less likely to generate surprising and deleterious results (e.g. falsely believing there is no decline in biodiversity), but also cost more to validate. Further, management experiments can become prohibitively large (and therefore expensive) as the number of processes being investigated increases, and so successful examples are rare (Westgate et al. 2013). Consequently, it is possible for the process of testing biodiversity surrogates to become more expensive than direct measurement, removing the economic justification for using surrogates at all (Lindenmayer and Likens 2011).

The argument that we don’t always need a detailed understanding of ecological mechanisms can also be justified from a purely scientific perspective (i.e. without discussion of cost). In particular, several types of model exist that make useful predictions about the distribution of biodiversity from a limited amount of data. For example, approaches based on the maximum entropy formalism can be used to estimate how the abundance distribution of a group of species will change given certain constraints, without specifying the mechanisms by which those changes occur (e.g. see Dewar and Porté 2008). Similarly, autocorrelation analysis can give valuable information on the rate at which system state changes over space or time (Soininen et al. 2007), allowing scientifically informed but mechanism-free assessment of when and where monitoring should occur. Finally, there are significant difficulties when inferring ecological processes from pattern, as many kinds of processes generate similar patterns (McGill and Nekola 2010). Consequently, there is a growing argument that process-based approaches should be considered to be one option for understanding ecological systems, rather than a necessity in all cases.

9. Complementary surrogates

The principle of complementarity is most perhaps best known in ecology for its application to spatial prioritisation, where it is used to identify sets of sites that maximise the number of previously unprotected species that are included in future zoning arrangements. However, this principle can be applied more broadly, such as where alternate data sources provide different but complementary information on ecosystem state. A particularly useful advance for biodiversity surrogates would be to identify sets of indicators that – when studied or monitored in combination – best describe the total range of ecological responses within that ecosystem (Larsen et al. 2012).

Despite growing awareness that complementary forms of information can help managers to make better conservation decisions, many articles on biodiversity surrogates seek only to identify sets of taxa that display strong, positive congruence relationships. This is somewhat ironic, as optimal ‘surrogate sets’ are likely to be those groups of species that display incongruent distributions, either over space or ecological gradients. Predicting sets of taxa that will provide complementary information is difficult, but is likely to include groups that use different ecological domains (Abell et al. 2011) or display markedly different functional traits such as body size (Velghe and Gregory-Eaves 2013). Such an approach would reduce the risk that some sections of the ecosystem could undergo drastic loss of biodiversity or function without that loss being observed by more taxonomically restricted monitoring programs.

10. Adaptive surrogates

I have argued above that different problems require different surrogates (point 5), while still suggesting that some surrogate relationships may be applicable across a range of locations, times, or taxa (points 6–8). How might ecologists reconcile the need for detailed, location-specific information with the desire to draw on existing knowledge? This is a challenge to applied ecological research and monitoring, where all decisions occur with some degree of uncertainty. However, a useful advance would be to better understand the circumstances where monitoring programs should shift between alternative biodiversity surrogates.

An adaptive monitoring approach (Lindenmayer and Likens 2009)