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Translating nutritional ecology from the laboratory to the fi eld: milestones in linking plant chemistry to population regulation in mammalian browsers

Jane L. DeGabriel , Ben D. Moore , Annika M. Felton , J ö rg U. Ganzhorn , Caroline Stolter , Ian R. Wallis , Christopher N. Johnson and William J. Foley

J. L. DeGabriel (j.degabriel@uws.edu.au), I. R. Wallis and W. J. Foley, Research School of Biology, Th e Australian National Univ., Canberra, ACT, 0200, Australia. Present address for JLD: Hawkesbury Inst. for the Environment, Univ. of Western Sydney, Locked Bag 1797, Penrith, NSW, 2751, Australia. – B. D. Moore, Hawkesbury Inst. for the Environment, Univ. of Western Sydney, Hawkesbury Campus, Richmond, NSW, 2753, Australia. – A. M. Felton, Southern Swedish Forest Research Center, Swedish Univ. of Agricultural Sciences, SE-230 53 Alnarp, Sweden. – J. U. Ganzhorn and C. Stolter, Dept of Biology, Univ. of Hamburg, Martin-Luther-King Platz 3, DE-20146 Hamburg, Germany. – C. N. Johnson, School of Zoology, Univ. of Tasmania, Private Bag 5, Hobart, Tasmania, 7001, Australia.

A central goal of nutritional ecology is to understand how variation in food quality limits the persistence of wild animal populations. Habitat suitability for browsing mammals is strongly aff ected by concentrations of nutrients and plant sec-ondary metabolites (PSMs), but our understanding of this is based mostly on short-term experiments of diet selection involving captive animals. In the wild, browsers forage in biologically, chemically and spatially-complex environments, and foraging decisions in response to varying food quality will be correspondingly complicated. We have identifi ed four steps that must be achieved in order to translate our understanding from laboratory experiments to populations of mammalian browsers: 1) knowing what foods and how much of these wild browsers eat, as well as what they avoid eating; 2) knowing the relevant aspects of plant nutritional and defensive chemistry to measure in a given system and how to measure them; 3) understanding the spatial distribution of nutrients and PSMs in plant communities, the costs they impose on foraging and the eff ects on animals ’ distributions; and 4) having appropriate statistical tools to analyse the data. We discuss prospects for each of these prerequisites for extending laboratory studies of nutritional quality, and review recent developments that may off er solutions for fi eld studies. We also provide a synthesis of how to use this nutritional knowledge to link food quality to population regulation in wild mammals and describe examples that have successfully achieved this aim.

Nutrition underpins the fi tness and reproduction of individ-ual herbivores, limiting the potential for population growth (Maklakov et al. 2008). Wild mammalian browsers make complex foraging decisions, trading off nutrient acquisition with regulation or avoidance of plant secondary metabolites (PSMs; Robbins 1993). Advances have been made in estab-lishing relationships between plant quality and population growth rates in insects (Agrawal 2004, Maklakov et al. 2008) and other taxa, such as birds (Bairlein 1996). However, we still have a relatively poor understanding of the conditions under which plant nutrients and PSMs can limit popula-tions of mammalian browsers. Reasons for this include: longer generation times, spatial and temporal variability in diets and the problem of integrating multiple aspects of plant chemistry. PSMs are likely to aff ect herbivore popula-tions from the ‘ bottom – up ’ , by limiting the net nutritional gain that animals can derive from foods, either by restricting the number of plants or the amount of a single plant they can eat, or by reducing nutrient absorption (Iason 2005).

Studies with captive and domestic mammals have revealed strong eff ects of nutrients and PSMs on diet selection, diges-tive physiology and reproductive success (Dearing et al. 2005, Estell 2010). Th us, there are good reasons to expect food quality to regulate browser populations, but there are few examples linking PSMs, nutrients and population regulation in wild mammals.

In most systems, linking food quality to population regulation depends on extending our understanding of the processes that drive food choice from the laboratory to the fi eld. Th en we can test predictions about how nutrition will aff ect demography and ultimately, the persistence of popula-tions. However, we should not expect such simple relation-ships between food quality and foraging by wild mammals as demonstrated in captivity or agriculture (Foley et al. 2007). Th is is because animal behaviour may be infl uenced by other factors such as predation risk, the availability of shelter, the abiotic environment, intra-specifi c competition and social constraints (Johnson et al. 2002). Similarly, we

Oikos 123: 298–308, 2014

doi: 10.1111/j.1600-0706.2013.00727.x

© 2013 Th e Authors. Oikos © 2013 Nordic Society Oikos

Subject Editor: Regino Zamora. Accepted 2 July 2013

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must make realistic judgements about what can be achieved in the fi eld and what can be done only in the laboratory. We have identifi ed four steps that must be achieved in order to translate our understanding from laboratory experiments to populations of mammalian browsers: 1) knowing what foods and how much of these wild brows-ers eat, as well as what they avoid eating; 2) knowing the relevant aspects of plant nutritional and defensive chemis-try to measure in a given system and how to measure them; 3) understanding the spatial distribution of nutrients and PSMs in plant communities, the costs they impose on foraging and the eff ects on animals ’ distributions; and 4) having appropriate statistical tools to analyse the data. We provide a synthesis of how to use this nutritional knowledge to link food quality to population regulation in wild mam-mals and describe examples that have successfully achieved this aim. We review recent progress towards these steps in mammalian browsers and discuss the limitations faced by ecologists. We also highlight some new approaches that may off er solutions. Our primary focus is on browsers, but many of the concepts discussed are equally pertinent to grazers or frugivores.

Step 1. Knowing what foods and how much of these browsers eat in the wild

Quantifying the composition of wild browser diets is a major stumbling block in nutritional ecology and vari-ous methods have been reviewed and compared (Chivers 1998, Mayes and Dove 2000, Ortmann et al. 2006, Jones and Krockenberger 2007). Direct feeding observations are inherently diffi cult (Rothman et al. 2012), but some recent technological advances are promising, such as global positioning system (GPS) loggers (Hulbert and French 2001), audio-telemetry (Logan and Sanson 2002) and video-tracking (Bluff and Rutz 2008). Similarly, whereas faecal cuticle analysis is limited by issues such as underestimation of highly digestible fragments and lack of precision (Jones and Krockenberger 2007), plant cuticular wax markers such as n -alkanes, alcohols and long-chain fatty acids (Dove and Mayes 2006), sometimes combined with consideration of carbon isotope ratios (Bezabih et al. 2011), off er the possibility of reconstructing diets from faeces or gut con-tents. Genetic markers are also increasingly being used to determine herbivore diets from faeces (Bradley et al. 2007, Valentini et al. 2009), although this is currently restricted to qualitative analysis.

Th ere is probably no single solution for determin-ing browser diets, because we need to determine both the relative composition and amounts of what is eaten. Th e use and limitations of methods for quantifying intake, such as isotope dilution and doubly labelled water are reviewed elsewhere (Mayes and Dove 2000). Other problems still per-sist, such as the presence of unidentifi ed dietary items and dealing with the complex diets of generalists. Furthermore, some widespread browser species may feed on diff erent foods in diff erent habitats or bioregions, making analysis of the link between food quality and population regulation across the species ’ range even more diffi cult.

We know a lot more about what browsers do not eat than what they do eat

Th e realisation that PSMs could constrain herbivore diets was a breakthrough in ecology (Freeland and Janzen 1974). Although there have been many exemplary laboratory stud-ies, comparatively few have demonstrated eff ects of PSMs on diet selection in the wild. Th e exceptions (Bryant et al. 1983, Vourc’h et al. 2002, Moore and Foley 2005, Stolter et al. 2005), demonstrated that wild browsers avoided plants with high concentrations of PSMs. Th ese studies focussed on tightly co-evolved systems with potent PSMs, often with highly specialised browsers. A complication is that the diets of wild animals may vary with the nutritional requirements of individuals (e.g. due to sex, age, or reproductive status; Krockenberger and Hume 2007, Rothman et al. 2008, and the spatial and temporal availability and quality of food; Ganzhorn 2002, Owen-Smith 1994). Furthermore, although specialist browsers can tolerate higher intakes of some PSMs (Shipley et al. 2009), or tolerate some PSMs but not others (Ganzhorn 1988), generalists can cope with a greater vari-ety of compounds by spreading detoxifi cation over multiple pathways (Ginane et al. 2005, Marsh et al. 2006).

Th e specialised knowledge needed to develop appropri-ate chemical assays for PSMs has inevitably narrowed the scope of the fi eld, but studies of plants that animals avoided (Bryant 1991) prompted the discovery of many new deter-rents with novel chemical structures (e.g. formylated phlo-roglucinol compounds (FPCs); Pass et al. 1998). Although many areas of uncertainty remain, we know relatively more about the basis on which folivores reject some plants (Dearing et al. 2005, Lawler et al. 1998) than the nutritional qualities driving their dietary choices. In contrast, more is known about the physiological basis of diet selection in domestic grazing ruminants (Van Soest 1982). Shifting the focus to quantifying the availability of macronutrients in the diets of wild browsers will off er fresh insight into their foraging environments. Many PSMs are inextricably linked to macronutrient availability. Th ey can reduce the digest-ibility or metabolism of food (McArthur and Sanson 1993, Robbins et al. 1987a, b), while detoxifi cation and excre-tion require nutrients and water (Guglielmo et al. 1996, Mangione et al. 2004). Because digestibility is the main determinant of nutrient gain, we need integrated measures that account for digestibility reducers, as well as any toxins (DeGabriel et al. 2008, Makkar and Singh 1995) in order to link food quality with the performance of individual mammals (Ginane et al. 2005).

Step 2. Knowing what aspects of chemistry to measure and how to measure them

Ecologists frequently analyse plants for a suite of standard measures, e.g. crude protein, total phenolics and fi bre, and attempt to correlate these with frequency of use or indices of animal performance, i.e. growth, or reproduction (Danell et al. 1994, Yamashita 2008). In some cases, this process reveals relationships between plant chemistry and animal foraging (Hj ä lt é n and Palo 1992), but countless studies have

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failed to fi nd chemical bases to habitat use, and very few have linked these measures to browser demography. In most cases, null results are likely to be due to inappropriate measures of plant chemistry. Crude measures of PSMs, such as ‘ total phenolics ’ (e.g. by the use of the Folin – Ciocalteau, or Folin – Denis reagents) do not account for diff erences in the chemi-cal structures of diff erent compounds that may have strong eff ects on their biological activity and hence animal prefer-ences (Lawler et al. 1998). Similarly, many measures, such as protein, tannins and fi bre are inter-related, so relation-ships to diet choice may not be apparent (Makkar and Singh 1995, Rothman et al. 2012). Finally, observed food choice may refl ect other behaviours, such as sampling (Provenza et al. 1992). Clearly, the further one gets from the ideal of actually feeding a food to an animal, the weaker these rela-tionships become (Spalinger et al. 2010). Th us, experiments with captive animals can provide essential guidance to the design of fi eld studies.

A major diffi culty in relating browser nutrition to population regulation is the lack of a common currency in which to measure food quality (Berteaux et al. 1998, Chivers 1998). Aside from energy (Schoener 1971), nutri-tional ecologists have fl agged the importance of total N (crude protein; Mattson 1980, White 1993, Klaassen and Nolet 2008), rapidly-metabolisable energy sources such as soluble sugars (Verheyden-Tixier et al. 2008) and spe-cifi c minerals (Choquenot 1991, Rode et al. 2006). Th is complexity explains the appeal of optimal foraging theory, which seemingly off ers a unifying principle. Unfortunately, most applications of optimal foraging consider only the optimisation of energy intake, at the expense of other nutri-ents and PSMs, and their interactions, leading some to aban-don it in favour of mechanistic foraging models (Bozinovic and Martinez Del Rio 1996).

Integrating the effects of multiple aspects of plant chemistry – the geometric framework

To link food quality to the population dynamics of free-living browsers we need to interpret the interactions between the nutrients and PSMs that an animal ingests (Raubenheimer and Simpson 2004, Robbins et al. 2007). Th e ingestion of one dietary item can aff ect the ingestion or digestion of another, so the eff ects of PSMs on an ani-mal ’ s physiology may be intricately linked to the relative balance of nutrients in the food (Au et al. 2013, Simpson and Raubenheimer 2001, Villalba and Provenza 2005). Th e geometric framework of nutrition (GF) is a multidimensional approach for assessing nutritional priorities (Raubenheimer and Simpson 2004, Raubenheimer et al. 2009). It unifi es several nutritional measures using simple geometrical mod-els, enabling us to compare observed and predicted patterns of nutrient intake. Th e focus of the GF is on observing free choices that individuals make when faced with diverse dietary options scattered throughout a macro-nutritional, multi-dimensional space, and then assessing their rule of compromise when they are inhibited from reaching their preferred target. Th is enables the identifi cation of patterns of diet selection without a priori assumptions as to what may be guiding it.

Most applications of the GF have involved experiments using captive animals. Extending such studies to the fi eld poses additional challenges: the spatial distributions of PSMs; diffi culties in determining which nutritional factors an animal prioritises; identifying the type and the amount of food eaten; and assessing the infl uence of constraints to food intake, such as gut capacity limitations, time spent feeding traded off against predation risk, detoxifi cation time and the intake of indigestible cell wall material (Felton et al. 2009a, b).

As yet, the GF has been applied only to one browsing mammal, the mountain gorilla Gorilla beringei beringei , whose diet is seasonally dominated by leaves (Rothman et al. 2011). However, interesting lessons can be learned from recent stud-ies of wild omnivores and frugivores. Many primates have traditionally been classifi ed as ‘ energy maximisers ’ (Strier 1992), due to their short food-retention times (Milton 1981) and preferences for sugar- and lipid-rich fruit (Dew 2005). However, by analysing the daily nutrient intake of indi-viduals, within the GF, Felton et al . (2009b) demonstrated that the nutritional goal of wild black spider monkeys Ateles chamek was to ingest a certain amount of protein each day, not to maximise daily energy intake. Th ey did this, regard-less of season, relative food availability or PSMs. Hence, what appears to be a strategy to maximise energy may be an artefact of sampling and analytical methodologies that are insensitive to a species ’ underlying nutritional goal (Felton et al. 2009b). In comparison, Rothman et al. (2011) used the GF to demonstrate that mountain gorillas prioritise non-protein energy, and to achieve a stable intake of this, they over-eat protein in seasons when their diet is primarily foliv-orous. Th us, we consider the GF to be the best available tool for identifying the limiting nutrients in each animal ’ s diet.

Protein and digestibility reducing compounds

Protein is often considered to be limiting for browsers, mean-ing that they may struggle to meet their minimum N require-ments for growth, maintenance and reproduction (Barboza and Parker 2008, Robbins 1993). Consequently, many ecologists have attempted to correlate concentrations of total N with browser abundance (Mattson 1980). Whereas total N may be an appropriate currency in studies of some herbivores, such as ruminants, macropods and colobine primates, hindgut fermenters require specifi c amino acids (DeGabriel et al. 2002). Tannins and fi bre can reduce the amount of N or essential amino acids available to hindgut-fermenters (Robbins 1993), unless they practise caecotrophy, thus absorbing essential amino acids synthesised from gut microfl ora by re-ingesting faecal pellets. If hindgut microbes are defi cient in N, less digestion of plant fi bre occurs. Th us, the suggestion that most plants are suffi ciently N-rich to meet the requirements of mammalian herbivores (Klaassen and Nolet 2008) ignores the reality that much of this protein may be indigestible.

Th e limitations of focussing singly on protein have been widely recognised in studies of primates. A popular approach has been to seek correlations between folivore abundance and the protein:fi bre ratio of preferred foods (Oates et al. 1990). Indeed, a number of studies of African primates have found

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chemically-based tannin assays was quickly superseded by novel gravimetric assays that did not depend on exter-nal standards (Silanikove et al. 2001). Now the dominant approach is the use of tannin-blocking agents such as poly-ethylene glycol (PEG) and polyvinyl pyrrolidine (PVP) and we advocate their use in ecology. Blocking the eff ects of tannins with PEG has resulted in marked improvements in protein digestion in vivo (Decandia et al. 2000, Marsh et al. 2003). Detailed understanding of tannin and other phenolic structures is yielding new insights in studies of herbivorous insects, including a better grasp of the eff ects of ellagitannins in the guts of insects (Barbehenn et al. 2006) and their post-absorption oxidative eff ects (Salminen and Karonen 2011). We believe that future progress in understanding the eff ects of tannins on browsing mammals will likewise depend on a better understanding of the eff ects of tannins in the gut.

Step 3. Recognising the spatial distribution of diet components

How browser populations interact with plant nutrients and PSMs depends upon how these are distributed spatially in plant communities (Owen-Smith 2005). Th is aff ects the choice of scale for fi eld studies. Types and concentrations of nutrients and PSMs may vary within and between plant species, at a fi ne scale i.e. amongst neighbouring plants, and across landscapes (Lawler et al. 2000, Stolter et al. 2005), but spatial patterns may be hard to detect. However, evolution-ary ‘ arms races ’ mean that many browsers live close to the limits of their minimum nutritional requirements, and subtle changes in plant chemistry may have large consequences for their foraging behaviour and demography (DeGabriel et al. 2009). Th is may be due to multiplier eff ects, where small diff erences in nutrient or energy intake signifi cantly aff ect the availability of resources for reproduction (White 1983).

In eucalypt forests, concentrations of FPCs are highly heritable, which combined with limited seed dispersal leads to spatial genetic and phenotypic auto-correlation (O’Reilly-Wapstra et al. 2004, Moore and Foley 2005, Andrew et al. 2007). Moore et al. (2010) mapped the palatability of eucalypt trees to visualise spatial variation in the nutritional value of a forest from a koala Phascolarctos cinereus perspec-tive, and compared this to observed patterns of tree use. In another study, DeGabriel et al. (2009) demonstrated that variation in the average concentrations of available N in eucalypt foliage in individual possums ’ home ranges was correlated with large diff erences in their reproductive fi tness. Figure 1 demonstrates how habitat carrying capac-ity is infl uenced by spatial variability in plant chemistry and how ignoring this variability can result in overestimation of habitat quality.

In order to characterise the distribution of PSMs and nutrients in forests, we require methods to analyse suffi cient samples to detect variation and to describe the foraging environments of individuals. One well-established solution is near infrared refl ectance spectroscopy (NIRS; Foley et al. 1998), which enables prediction of concentrations of a range of chemical constituents, and even the standardised intake ( ‘ palatability ’ ) of foliage, both rapidly and cheaply (Stolter et al. 2006, Rothman et al. 2009, Moore et al. 2010). Th ere

positive relationships between the foliar protein:fi bre ratio and primate biomass (Oates et al. 1990, Ganzhorn 1992, Chapman et al. 2002). However, there are many unresolved issues with this approach (Felton et al. 2009a), including statistical problems with the use of ratios and a failure to consider the potentially confounding eff ects of tannins (Wallis et al. 2012). Furthermore, a recent study found no correlation between the ratio and reproductive success or group size in red colobine monkeys (Gogarten et al. 2012).

Measuring the in vivo or in vitro digestibility of foods is superior to measuring the crude concentration of protein, fi bre and energy to estimate nutrient intake (Robbins et al. 1987a, b). Spalinger et al. (2010) and DeGabriel et al. (2008) used in vivo and in vitro assays, respectively, to quantify the combined eff ects of tannins and fi bre on protein availability. Th ese approaches clearly demonstrated that tannins can have variable, but often large, negative eff ects on protein digestibil-ity. In addition, Windels and Hewitt (2011) showed that the negative eff ects of tannins on digestible protein and energy may reduce the carrying capacity of habitats for white-tailed deer Odocoileus virginianus eating high-tannin diets.

An in vitro approach to measuring digestibility using fungal cellulases is widely used in foraging studies, with the intention of ranking the digestibility of plants, rather than perfectly mimicking in vivo processes. Th e main advantage is that it estimates the net nutritional benefi t of eating plants, circumventing the need to calculate it from combined direct measurements of tannins and fi bre. Furthermore, it can be applied at fi ne spatial resolution as well as across broad scales relevant to fi eld studies.

Tannins are ubiquitous in browse and we might expect them to impose strong costs on reproductive fi tness, by limiting protein availability. However, condensed and hydrolysable tannins have either no eff ect, or small positive eff ects, on diet selection in some species, including: roe deer Capreolus capreolus (Verheyden-Tixier and Duncan 2000), white-tailed deer (Jones et al. 2010), moose Alces alces (Stolter et al. 2005) and common ringtail possums Pseudocheirus peregrinus (Marsh et al. 2003). Th is is pre-sumably because these and other species have physiological adaptations to counter tannins, such as tannin-binding salivary proteins (McArthur et al. 1995) and/or caecotrophy. Tannins can also positively aff ect herbivore nutrition and reproduction (Forbey and Foley 2009) and they can be anthelminthic (Hutchings et al. 2003). Th is highlights the importance of a thorough understanding of animal physi-ology and selecting appropriate measures when translating nutritional studies to populations. It also suggests directions for future research, as high parasite loads have been shown to reduce fecundity in browsers, such as mountain hares Lepus timidus (Newey and Th irgood 2004), yet multi-trophic inter-actions between parasites, tannin-rich plants and browsing mammals are not well understood.

Much time and eff ort has been spent investigating extraction methods, reaction conditions and standards for measuring plant tannins to predict their interactions with browsers. With few exceptions, this work has yielded lit-tle ecological insight, and there are few convincing cases where tannins have been unequivocally linked to diet selec-tion in wild mammals. Th e situation in ecology is in stark contrast to that of agricultural science, where the focus on

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unlikely to include the high and low concentrations required to drive animal choice, particularly when these are non-linear (Fig. 2). Linear regression or generalised linear models assuming a Poisson distribution are often used to model the number of feeding events on a plant as a function of PSM or nutrient concentration. However, the number of times a plant is used may be infl uenced by other unmeasured or immeasurable factors, such as plant size, or foraging risk, so instead of the line of points usually observed in captivity, it is common to observe a wedge-shaped scatter (Fig. 3a). Th us, modelling the upper limits of use reveals more about the foraging constraints imposed by nutrients and PSMs (Fig. 3b).

Several methods have been proposed to model the upper limits of polygonal distributions, including regression along the upper bounds (Blackburn et al. 1992; Fig. 3c). Weaknesses of this approach include arbitrary decisions about the number of concentration classes and the method of data partitioning, leading to highly variable results (Scharf et al. 1998). Due to their frequently normal distributions, most sampled PSM concentrations are close to the mean, resulting in a pyramidal, rather than a wedge-shaped scatter plot. Scrivener et al. (2004) dealt with this in a novel way by partitioning the dependent, rather than the independent variable, thus characterising only the right-hand edge of the

is also scope to reconstruct diet composition, food qual-ity and the nutritional status of free-living herbivores from faeces using NIRS (Stuth et al. 2003, Tolleson et al. 2005, Windley et al. 2013). Recent advances include the develop-ment of portable devices to record spectra in the fi eld (Foley et al. 2007) and success in mapping PSMs at larger scales with the use of airborne hyperspectral sensing techniques (e.g. HYMAP). Th is involves capturing the spectra from whole tracts of forest and plotting concentrations of PSMs and nutrients in individual trees using GIS (Skidmore et al. 2010, Youngentob et al. 2012), although fi ne-scale resolu-tion is diminished.

Step 4. Choosing statistical tools to deal with the complexity of nutritional data

Although strong relationships between nutrient or PSM concentrations and food intake are often detected in the laboratory, it is much harder to fi nd similar relationships in the fi eld. Instead of off ering captive animals a random selec-tion of plants, researchers commonly, and sensibly, choose samples that maximise the range of variation in concentra-tions. PSM concentrations are often normally distributed in plant populations (Wallis et al. 2002), so a random sample is

Figure 1 . Spatial structure of a plant population at a scale similar to herbivore home ranges can reduce carrying capacity. Th e top panels show two hypothetical landscapes, containing 1000 trees with identical distributions of available N (availN; x̄ � 0.4, SD � 0.12), diff ering only in spatial location (shading represents mean availN). On the left, availN concentrations show strong spatial autocorrelation, producing large diff erences in mean values for each of nine square ‘ home ranges ’ . Th e right panel has no spatial autocorrelation and mean availN is similar in each home range. In the lower panels, the mean home range availN concentrations are plotted (open circles) on a model of breeding success as a function of mean availN (using data on common brushtail possums feeding on Eucalyptus from DeGabriel et al . 2009). When availN concentrations are spatially structured, some animals breed less successfully, reducing the average (fi lled circle, dashed line) below that predicted from the grand mean availN concentration (dotted lines). Th is eff ect is not apparent without spatial structure.

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scatter. Th ey then used a bootstrap-generated null distribu-tion to test the signifi cance of the Spearman ’ s rank-sum cor-relation between the upper bounds of PSM concentrations and tree use.

A more appropriate approach is quantile regression (VanDerWal et al. 2009), which uses least absolute devia-tion regression to estimate quantiles of regression para-meters, such as the eff ect of PSM concentrations on plant use. Higher quantiles of regression coeffi cients approximate the upper bounds of the scatter and estimate stronger eff ects than if eff ects are estimated by changes in mean responses by standard linear regression (Cade et al. 1999). However, confi dence intervals surrounding higher regression quantiles are often large. Multiple quantile regression is a useful tech-nique when more than one measured factor is believed to infl uence plant use, and is not possible with upper-bounds regression (Cade et al. 1999). Th is approach is relatively new to ecology, but several excellent studies (reviewed by Cade et al. 1999) and the availability of tools (e.g. the package ‘ quantreg ’ in R) mean that it should become more widely used. Quantile regression methods are now available for parametric, non-linear models and even for non-parametric, non-linear smoothers (Cade et al. 1999), allowing the detection of non-linear relationships between PSM concen-trations and plant use. Th ese might occur, for example, when animals are unaff ected by PSMs below a certain threshold concentration, but where their food intake declines with increasing concentrations of the PSM above this level (Moore and DeGabriel 2012).

Alternatively, Moore et al. (2010) used generalised additive modelling (GAM) and generalised additive mixed modelling (GAMM) to model the eff ects of tree size, species, nutrients and PSMs, as well as the quality of neighbouring

Figure 2. (a) A demonstration of the relationship between intake of Eucalyptus melliodora by captive brushtail possums and concentra-tions of a formylated phloroglucinol compound (FPC), and (b) the distribution of FPC concentrations in a natural population of trees (re-drawn from data in Wallis et al. 2002). Th e highlighted area in diagram (a) emphasises that a small random sample of trees from the population would be unlikely to include the extreme FPC concentrations required to detect the relationship with feeding.

Figure 3. (a) Top left – a typical wedge-shaped scatter plot of plant use in the wild against PSM concentrations. Solid line represents the relationship detected in captivity by least squares regression. (b) Top right – bins characterising the upper bounds of this distribution. Full circles represent the largest usage value in each concentration class. (c) Bottom left – least squares regression of upper bounds of distribution, using the data points represented by full circles.

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species distributions. We suggest that these models could be extended further to include the requirements for, and avail-ability of, individual nutrients and PSMs, and even the for-aging risks and tradeoff s associated with assembling suitable diets, such as increased predation risk.

Appropriate demographic data is essential to parameterise population models

To understand how variation in food quality infl uences folivore population regulation, we need to test how specifi c nutrients or PSMs, or combinations of these, aff ect demo-graphic parameters. However, collecting life history data, such as litter size, off spring growth rates and survivorship may not be possible in the fi eld, particularly for species that have large home ranges, or that produce precocious young. In lieu of these data, proxies, such as estimates of female body condition using techniques including isotopi-cally-labelled water (Nagy 1980), nitrogen isotope signatures (Vogel et al. 2012), bioelectrical impedance analysis (Parker and Krockenberger 2002) and residuals from regressions of body mass on measurements of skeletal size (Green 2001), are often used to indicate nutritional status and reproductive potential. Measuring protein stores may also be relevant, as protein intake has a non-linear eff ect on mass gain (Green 2001, Felicetti et al. 2003, Robbins et al. 2004, 2007) and lactation needs are also met by protein catabolism (Parker et al. 1999). Dual energy X-ray absorptiometry (DEXA) has been used to estimate body composition of grizzly bears Ursus arctos horribilis and to compare diff erences between individuals fed diff erent diets (Felicetti et al. 2003, Robbins et al. 2004). Th us, it provides a promising method to link browser nutrition to reproductive performance under some circumstances.

Despite considerable advances in the four steps described above, as well as the availability of methods to approximate reproductive fi tness, there are few studies showing that nutrition infl uences demography in wild browsers and even fewer positing a specifi c role for PSMs. Over the past 25 years, many have attempted to link nutrition, mainly with a focus on N and phenolic compounds, with regu-lation of browser populations, particularly in boreal eco-systems. However, nutritional limitation hypotheses were rejected in snowshoe hares Lepus americanus (Sinclair et al. 1988), red-backed voles Clethrionomys spp. (Boonstra and Krebs 2006) and cotton rats Sigmodon hispidus (Schetter et al. 1998). A retrospective analysis of these studies sug-gests that potential relationships may have been obscured by the use of crude measures of plant chemistry, e.g. total phenolics, which were not relevant to the system, and the lack of integration of PSMs and nutritional components. Rather, a focus on the eff ects of tannins on protein availability may have been more ecologically meaningful. Furthermore, these studies tended to focus on food avail-ability, rather than spatial variation in quality. In contrast, Bryant (2003) provided an excellent example of the eff ects of plant chemistry in Alaskan feltleaf willow on browsing by snowshoe hares, but did not experimentally test the eff ects on relevant demographic parameters.

trees on habitat use by koalas. GAMs enable multivariate models to be constructed and because they use splines to model individual terms, non-linear relationships can be tested, e.g. between intake and a threshold concentration of a PSM. Th e extension of GAMs to GAMMs is valuable where all plants can be mapped in a landscape and rates of herbivory recorded, as it allows the fi tting of spatially-explicit models, reducing the incidence of type 1 statistical errors (Lennon 2000). Although we have described some solutions for dealing with the complexity of fi eld data, complications arise when we attempt to model multiple aspects of plant chemistry simultaneously. Th is poses the next statistical challenge for ecologists attempting to link food quality to browser population regulation.

Linking nutritional understanding to population regulation

Even if the four steps above are accomplished, signifi cant work is still required to link that nutritional knowledge to population regulation in mammalian browsers. Here, we briefl y discuss advances in theoretical modelling, recommend suitable demographic parameters to measure and provide a synthesis by describing examples of how plant chemistry has been successfully linked to mammal population regulation, using our four-step model.

Linking plant chemistry to herbivore population dynamics requires the development of suitable population models

Translating ecological studies from the laboratory to the fi eld requires the formulation, testing and application of mechanistic response functions to describe interactions across spatial scales (Denny and Benedetti-Cecchi 2012). Considerable progress has been made in insect studies, although the majority have focussed on induced plant defences (Haukioja 1980, Underwood 1999). Recently, Reynolds et al. (2012) developed mechanistic models to test the hypothesis that delayed density-dependence in silica induction in grasses drives population cycles in voles. But, in order to link plant chemistry to population dynamics of many browsers, we require appropriate models for systems involving constitutive defences (Feng et al. 2009). Liu et al. (2012) provided an elegant solution, developing age-structured toxicity models to demonstrate that age-dependence in plant defences is suffi cient to produce popu-lation cycles in hares feeding on woody vegetation in boreal forests. Th e challenge is now to test model predictions under fi eld conditions, using the four steps described above.

Recently, Kearney et al. (2010b) developed mechanis-tic models to predict changes in species distribution based on habitat and population parameters, such as food and water. Th ey also modelled species niches through integrating the geometric framework, dynamic energy budgets and bio-physical ecology (Kearney et al. 2010a). Th is established a link between the climatic and nutritional niches of organ-isms and habitat characteristics, and thus has important applications for determining population growth rates and

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of how PSMs and nutrients interact to infl uence demo-graphic parameters and population regulation in mammalian browsers. Th e need for such studies has increasing urgency in the face of global environmental change, which may have cascading eff ects through trophic levels, through infl uencing the availability and nutritional quality of food available to browsers, and consequently the fi tness of their populations (Dearing et al. 2005).

Acknowledgements – We thank Lisa Shipley for helpful discussions and Pete Goddard for comments on an earlier draft. Funding was provided by Australian Research Council grants to CNJ and WJF.

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Choquenot (1991) provided a convincing example of how manipulating the density of animal populations allowed the test of a nutritional hypothesis in a predominantly graz-ing herbivore. By culling individuals, he demonstrated that female donkeys Equus asinus in a growing population had higher levels of essential minerals in their diets, resulting in increased fecundity and off spring fi tness compared to a population at carrying capacity. Two recent examples have also demonstrated that the interaction between nutrients and PSMs can restrict reproduction in wild browsers. DeGabriel et al. (2009) found that their measure of ‘ available N ’ intake (DeGabriel et al. 2008), was positively correlated with both fecundity and off spring growth rates in a popu-lation of common brushtail possums Trichosurus vulpecula . Similarly, Windley et al. (2013) found a positive correlation between their indirect measure of ‘ faecal available N ’ (the proportion of faecal N not bound to tannins) and breeding success in the same population of possums. Marsupials proved an ideal model system, as the relationship between maternal nutrition and off spring growth could be tracked from birth in an essentially foetal state (after only 17 days of gestation) to weaning. In another example, McArt et al. (2009) used a protein-precipitation assay to integrate the eff ects of tannins on N availability, and predicted the eff ects on reproductive output in two populations of Alaskan moose.

Each of these studies achieved the four steps described in this paper: they knew what to measure and how to measure it, and did so at appropriate spatial scales. Furthermore, they used well-studied systems in which diet selection had been characterised from laboratory and fi eld trials. By focussing on an invasive pest species, Choquenot (1991) could ethi-cally manipulate population densities to experimentally test density-dependence. Although similar approaches may be possible for species that are hunted, culling is not possible or desirable in many cases, particularly when the aim is conser-vation. Th e three latter studies all considered some measure of digestible protein, recognising the importance of integrat-ing nutrients and PSMs that aff ect digestion or net nutri-tional gain. Th ese measures of protein availability are closer to an ideal approximation of food quality, refl ecting the outcomes of experimentally feeding plants to animals when N is limiting, and it integrates multiple relevant aspects of plant chemistry. More importantly, the ability of these studies to predict rates of reproduction in populations of wild mammalian browsers highlights their biological meaningfulness.

Conclusions

We have emphasised the importance of using lessons from laboratory experiments and agricultural science to guide investigations into the role of food quality in the regulation of wild populations of mammalian browsers. Similarly, we have highlighted a range of tools and statistical approaches available to ecologists to conduct experiments to test nutri-tional hypotheses in the fi eld. Th us, an aim of nutritional ecologists should be to use these to pursue an understanding

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