18.1 Introduction

Ecological thresholds have long intrigued scientists, dating from the study of threshold effects for age-specific human mortality (Gompertz, 1825) to present-day investigations for biodiversity conservation and environmental management (Roe & van Eeten, 2001; Folke et al., 2005; Huggett, 2005; Groffman et al., 2006). Defined as a sudden change from one ecological condition to another, ecological thresholds are considered synonymous with discontinuities in any property of a system that occurs in nonlinear response to smooth and continuous change in an independent variable. Understanding ecological thresholds and incorporating them into ecological and socio-ecological systems is seen as a major advance in our ability to forecast and thus properly cope with environmental change (Carpenter, 2002; Rial et al., 2004; Gordon et al., 2008). Consequently, ecologists and economists continue to be extremely attracted to the idea that ecological thresholds may exist and can be used in a management context (Muradian, 2001).

Empirical studies of ecological thresholds are diverse and have grown in number in recent years (Walker & Meyers, 2004). In landscape ecology, a working hypothesis is the existence of critical threshold levels of habitat loss and fragmentation that result in sudden reductions in species' occupancy (Gardner et al., 1987; Andrén, 1994; With & Crist, 1995). As the landscape becomes dissected into smaller and smaller parcels, landscape connectivity – referring to the spatial contagion of habitat – may suddenly become disrupted (With & Crist, 1995).

11.1 Introduction

The existence of sympatric speciation has been a contentious issue because empirical support was scarce and the underlying theoretical mechanisms were not as fully understood as we might like (e.g. Futuyma & Mayer, 1980; Rundle & Nosil, 2005). The view on sympatric speciation is currently changing, however. Recent theories demonstrate how ecological adaptations can drive speciation (Dieckmann et al., 2004; Doebeli et al., 2005). In concert with theoretical development, empirical evidence corroborating this view is accumulating (Barluenga et al., 2006; Panova et al., 2006; Savolainen et al., 2006). An obstacle for sympatric speciation is the exchange of alleles between lineages and the homogenising effect of recombination in sexual reproduction (Felsenstein, 1981; Rice & Salt, 1988). The current view on sympatric speciation is therefore that disruptive selection for evolutionary divergence has to be correlated with assortative mating and reproductive isolation (Felsenstein, 1981; Rundle & Nosil, 2005). This can be through linkage between ecological genes and mating genes, or a pleiotropic effect of ecological genes on mating behaviour. Orr & Smith (1998) make the distinction between extrinsic and intrinsic barriers to gene flow. Extrinsic factors are physical barriers in the environment that prevent encounters between individuals. Intrinsic factors are genetic traits that increase pre- or post-zygotic reproductive isolation. They define sympatric speciation as ‘the evolution of intrinsic barriers to gene flow in the absence of extrinsic barriers’.

Introduction

When species with similar sexual signals co-occur, selection may favour divergence of these signals to minimise either their interference or the risk of mis-mating between species, a process termed reproductive character displacement (Howard, 1993; Andersson, 1994; Servedio & Noor, 2003; Coyne & Orr, 2004; Pfennig & Pfennig, 2009). This selective process potentially results in mating behaviours that are not only divergent between species that co-occur but that are also divergent among conspecific populations that do and do not occur with heterospecifics or that co-occur with different heterospecifics (reviewed in Howard, 1993; Andersson, 1994; Gerhardt & Huber, 2002; Coyne & Orr, 2004; e.g., Noor, 1995; Saetre et al., 1997; Pfennig, 2000; Gabor & Ryan, 2001; Höbel & Gerhardt, 2003).

An oft-used approach to assessing whether reproductive character displacement has occurred between species relies on behavioural experiments that evaluate mate preferences from populations that do and do not occur with heterospecifics (sympatry and allopatry, respectively). In such experiments, individuals are presented the signals of heterospecifics and/or conspecifics to assess whether allopatric individuals are more likely to mistakenly prefer heterospecifics than are sympatric individuals (reviewed in Howard, 1993). The expectation is that individuals from sympatry should preferentially avoid heterospecifics, whereas those in allopatry should fail to distinguish heterospecifics from conspecifics (presumably because, unlike sympatric individuals, they have not been under selection to do so). Such patterns of discrimination have been observed, and they provide some of the strongest examples of reproductive character displacement (reviewed in Howard, 1993).

19.1 Introduction

Artificial neural networks (ANNs), inspired by the processing systems present in simple nervous systems, are now widely used for the extraction of patterns or meaning from complicated or imprecise data sets (Arbib, 2003; Enquist & Ghirlanda, 2005). Although modern ANNs have progressed considerably from the early, basic feedforward models to systems of significant sophistication, some with varying levels of feedback, modulation, adaptation, learning, etc. (Minsky & Papert, 1969; Gurney, 1997; Vogels et al., 2005), they rarely contain the full processing capabilities or adaptive power of real assemblies of nerve cells. Part of the problem in modelling such capabilities is that the detailed mechanisms underlying the operation of biological neural networks are not themselves fully identified or well understood, for there is a dearth of good biological model systems that possess a wide range of processing mechanisms but whose physiological processes and cellular interconnections can be fully investigated and characterised. One of the best biological model systems available is the vertebrate visual system, but even here the full range of cellular connections and interactions have not yet been characterised and hence cannot be developed into equivalent models (van Hemmen et al., 2001; Wassle, 2004).

Introduction

In general terms, modular systems are systems that can be decomposed in functional and/or structural independent parts. In cognitive science, modularity of mind (Fodor, 1983) is the controversial cognitivist view according to which human mind is made up of specialised innate modules. In contrast, the connectionist view tends to conceive of mind as a more homogeneous system that results from development and learning during life (see Karmiloff-Smith, 2000).

What is a module? Are modules innate? What is the relationship between modularity, robustness and evolvability? And what is the role of nonmodularity? These are only a few of the open central questions in the nature–nurture debate.

In a paper published in the journal Cognition, Gary Marcus (2006) deals with the vexata quaestio of modularity of mind from an enlightening point of view. He does not offer a detailed definition of modularity, nor an answer to the controversial issue of what is innate and what is learned during life. More simply, he identifies and contrasts two competing ‘hypothetical conceptions of modularity’, which would represent distinct perspectives about modularity of mind, implicitly present in the scientific literature and different in their implications: a ‘sui generis modularity’ and a ‘descent with modification modularity’. According to the former conception, ‘each cognitive (or neural) domain would be an entity entirely unto itself’; according to the latter conception, ‘current cognitive (or neural) modules are to be understood as being the product of evolutionary changes from ancestral cognitive (or neural) modules’.

14.1 Introduction

This paper deals with a general issue in the study of animal behaviour that we call path dependence. The expression refers to the fact that different histories of experiences (paths) may at first seem to produce the same behavioural effects yet reveal important differences when further examined. For instance, two training procedures may establish the same discrimination between two stimuli yet produce different responding to other stimuli, because the two paths have produced different internal states within the animal. There are several reasons why path dependence is an important issue. First, it comprises many phenomena that can provide stringent tests for theories of behaviour. Second, path dependence is at the root of several controversies, for instance whether animals encode absolute or relative characteristics of stimuli (Spence, 1936; Helson, 1964; Thomas, 1993) or whether learning phenomena such as backward blocking and un-overshadowing imply, in addition to basic associative learning, stimulus–stimulus associations or changes in stimulus associability (Wasserman & Berglan, 1998; Le Pelley & McLaren, 2003; Ghirlanda, 2005).

In this paper we use a simple neural network model of basic associative learning (Blough, 1975; Enquist & Ghirlanda, 2005) to show how path dependence can arise from fundamental properties of associative memory. The model has two core components: (1) distributed representations of stimuli based on knowledge of sensory processes and (2) a simple learning mechanism that can associate stimulus representations with responses. We consider examples of path dependence in experiments on generalisation (or ‘stimulus control’).

Introduction

The flexibility and capacity of adaptation of organisms greatly depends on their learning capabilities. For this reason, animal psychology has devoted great efforts to the study of learning processes. In particular, in the last century a huge body of empirical data has been collected around the two main experimental paradigms of ‘classical conditioning’ (Pavlov, 1927; Lieberman, 1993) and ‘instrumental conditioning’ (Thorndike, 1911; Skinner, 1938; Balleine et al., 2003; Domjan, 2006).

Classical conditioning (also called ‘Pavlovian conditioning’) refers to an experimental paradigm in which a certain basic behaviour such as salivation or approaching (the ‘unconditioned response’ – UR), which is linked to a biologically salient stimulus such as food ingestion (the ‘unconditioned stimulus’ – US), becomes associated to a neutral stimulus like the sound of a bell (the ‘conditioned stimulus’ – CS), after the neutral stimulus is repeatedly presented before the appearance of the salient stimulus. Such acquired associations are referred to as ‘CS-US’ or ‘CS-UR’ associations (Pavlov, 1927; Lieberman, 1993).

Instrumental conditioning (also called ‘operant conditioning’) refers to an experimental paradigm in which an animal, given a certain stimulus/context such as a lever in a cage (the ‘stimulus’ – S), learns to produce a particular action such as pressing the lever (the ‘response’ – R), which produces a certain outcome such as the opening of the cage (the ‘action outcome’ – O), if this outcome is consistently accompanied by a reward such as the access to food.

Our aim in this book is to introduce university students to research on nervous systems that is directly relevant to animal behaviour, and to do so without assuming a detailed knowledge of neurophysiology. We concentrate on examples of studies in neuroethology that illustrate clearly how the activity of nerve cells is linked with animal behaviour. The level of the book is for advanced undergraduate students, particularly those studying zoology, biology or psychology, but we hope it will also be useful to students in other disciplines and to postgraduates.

Each chapter is given a title in two parts: a description of its general area and then usually the specific topics to be described. We begin with a consideration of how animal behaviour and brains are organised. Chapter 2 is an introduction to the nuts and bolts of how nerve cells work, and we approach this by referring to specific examples that illustrate concepts without delving into detailed cellular physiology. The next two chapters describe some clear examples where the roles of particular neurons in predator–prey interactions have been established. We then describe two different types of sensory systems in which roles of specific neurons in behaviour have been recognised – vision in insects and hearing in owls and bats – followed by a chapter on the control of rhythmical movements. Chapter 8 describes research on changes in behaviour, including learning.

Diptera (the true, or two-winged, flies) clearly rely on their eyes. They usually escape capture with apparent ease, and during fast flight they turn often, rarely colliding with their surroundings. Good eyesight goes along with aerobatic manoeuvrability: some dipteran behaviours include the most rapid reactions made by animals. An airborne hover fly, blow fly or fruit fly can turn a right angle in less than 50 ms, and can make 10 turns per second (Schilstra and van Hateren,1999; Fry et al., 2003). Flies rarely crash into other objects while airborne, and are able to land precisely on appropriate perches, including cup edges. The spectacle of house flies chasing each other is familiar, and high-speed filming reveals that the chasing fly, usually a male, tracks every turn made by the target fly, often a female but sometimes a rival male (Fig. 5.1a). Robber flies also chase house flies, in this case to prey on them. Although the neuronal pathways involved in visual behaviours of flies and other insects are more complex and involve much larger numbers of neurons than the startle responses of crayfish or fish described in the previous chapter, several visual interneurons of insects have been studied in some detail. Like the T5(2) neurons of toads (Chapter 3), the interneurons respond to particular defined stimulus configurations and so act as feature detectors, and they have been shown to play specific roles in guiding behaviour.

When an animal is startled by a sudden attack from a predator, it must respond with great urgency if it is to escape, and neuronal pathways that initiate such an escape response must be both straightforward and reliable in order to fulfil their biological function. Straightforward pathways are essential to ensure speed in initiating the escape and they must be reliable not only to make sure the response occurs when needed but also to avoid false alarms. These qualities of simplicity and reliability, which are of great survival value to the animal, are also of service to the neuroethologist exploring the roles that nerve cells play in behaviour. Consequently, several startle responses have been studied in detail and they provide valuable insight into the flow of information through the nervous system from sensory inputs to muscular output.

As we have already shown in our description of cockroach escape behaviour in the previous chapter, the neuronal pathways responsible for startle responses often involve neurons that are exceptionally large and so are called giant neurons. The unusual width of the axon of a giant neuron enables it to conduct spikes rapidly along the animal's body. For an experimenter, the size of giant neurons also makes it relatively easy to insert microelectrodes into them, although because any small movements will dislodge an intracellular microelectrode, it is not possible to make intracellular recordings from neurons in freely moving animals.

The interaction between a predator and its prey represents a dramatic example of animal behaviour, in which an evolutionary arms race has greatly stretched the capabilities of nervous systems. This can be seen clearly in the battle for survival that takes place when a toad attempts to catch a cockroach (Chapter 3). In such an encounter, the hunting animal faces the fundamental problems of detecting and localising the prey, and it must solve them on the basis of purely passive information given out inadvertently by the prey. This is a formidable task and it has led to the evolution of some remarkably sophisticated neuronal systems in species that are adapted for hunting.

Predatory birds and mammals do, indeed, possess central nervous systems with the necessary sophistication to handle the complex task of tracking prey, but this sophistication makes most of them unsuitable as subjects for neuroethological research. However, the difficulty can be overcome by looking at species with a highly specialised method of hunting, based on a sensory system that is dedicated to the specialised method of prey detection and localisation. It then becomes easier to correlate the properties of particular neurons in that system with the particular behavioural task, as has been achieved to great effect with the specialised nose of the star-nosed mole (Chapter 1).

Such dedicated systems are found in two groups of animals – owls and bats – that use specialised auditory systems to hunt at night when visually guided predators are at a disadvantage.

What is special about animal behaviour? Many people like watching animals behave, and an understanding of animal behaviour has been vital throughout human history, enabling people to hunt, to farm, and to understand something about themselves. More recently, understanding how the brains of animals work has given important information about how the human brain works, and why it sometimes malfunctions. But although animal behaviour can be complex and even sometimes seems mysterious, it can be understood and appreciated by the same scientific approaches that are used to study other aspects of the structure and function of living organisms. It is shaped by evolution in the same way as anatomical characters, and natural selection acts on animal behaviour by shaping the ways in which nervous systems work.

The ways in which the internal workings of brains control behaviour is the subject of this book and we shall illustrate them by using examples drawn from many different animal groups. There are several reasons for this catholic approach, but two are particularly important. First, some animals have nervous systems that are especially favourable for study. For example, nervous systems of invertebrates usually contain smaller numbers of nerve cells than those of mammals, so it is much more possible to trace the flow of signals from cell to cell within these simpler nervous systems. In some cases, there are particularly large nerve cells that are especially easy to study, such as the giant neurons involved in escape responses described in Chapters 3 and 4.

Sequences of muscle activity in locomotion are basic building blocks for much of an animal's behavioural repertoire, so understanding the mechanisms which generate and control them is fundamental to a knowledge of the neuronal control of behaviour. Many movements used for locomotion are rhythmically repeating, and there are three basic questions about the control of movements such as walking, flying or swimming. First, what mechanisms ensure that muscles contract in the appropriate sequence? In walking, for example, the basic pattern is repeated flexion and then extension of each leg, with flexion of the left leg coinciding with extension of the right. Second, how does a nervous system select, start and end a particular type of movement? For example, what initiates the pattern of walking; and how is walking rather than running or swimming selected? Third, how is the basic pattern for movement modulated appropriately? Stride pattern changes, for example, when a person walks up a flight of steps or turns a corner.

Experimental approaches to these questions have often involved work on lower vertebrates and invertebrates, animals in which the parts of the nervous system that generate programmes for movement contain a limited number of neurons. This offers the opportunity to identify and characterise the individual components involved in generating a particular movement. A question that has occupied many investigators, and the one on which this chapter focuses, is to determine the source of the repetition that underlies rhythmic movements.