1169
Condition indices for conservation: new uses for evolving tools
R. D. Stevenson1,* and William A. Woods, Jr.y
*Department of Biology, University of Massachusetts Boston, 100 Morrissey Blvd., Boston MA 02125-3393, USA;
y
Department of Biology, Tufts University, Medford, MA 02155, USA
Synopsis Biologists have developed a wide range of morphological, biochemical and physiological metrics to assess the
health and, in particular, the energetic status of individual animals. These metrics originated to quantify aspects of human
health, but have also proven useful to address questions in life history, ecology and resource management of game and
commercial animals. We review the application of condition indices (CI) for conservation studies and focus on measures
that quantify fat reserves, known to be critical for energetically challenging activities such as migration, reproduction and
survival during periods of scarcity. Standard methods score fat content, or rely on a ratio of body mass rationalized by some
measure of size, usually a linear dimension such as wing length or total body length. Higher numerical values of these indices
are interpreted to mean an animal has greater energy reserves. Such CIs can provide predictive information about habitat
quality and reproductive output, which in turn can help managers with conservation assessments and policies. We review
the issues about the methods and metrics of measurement and describe the linkage of CIs to measures of body shape.
Debates in the literature about the best statistical methods to use in computing and comparing CIs remain unresolved. Next,
we comment on the diversity of methods used to measure body composition and the diversity of physiological models that
compute body composition and CIs. The underlying physiological regulatory systems that govern the allocation of energy
and nutrients among compartments and processes within the body are poorly understood, especially for field situations, and
await basic data from additional laboratory studies and advanced measurement systems including telemetry. For now,
standard physiological CIs can provide supporting evidence and mechanistic linkages for population studies that have
traditionally been the focus of conservation biology. Physiologists can provide guidance for the field application of
conditions indices with validation studies and development of new instruments.
Introduction
A fundamental goal of conservation biology is to
ensure the long-term survival of species (Meffe
and Carroll 1997; Hunter 2001; Primack 2006).
Traditionally, conservation biologists have approached species health at the population level. They
use tools such as population viability analysis to
ascertain whether populations that make up a species
are increasing, approximately stable or decreasing over
time (Beissinger and McCullough 2002). If the
populations are widely distributed and stable, conservation biologists conclude the species is not at risk,
whereas if there are a few isolated populations or if the
populations are declining, then the risk of extinction is
concluded to be more immediate. Changes in
populations can result from a great many causes. In
the short term, across the range of a species, there are
likely to be locations where populations are increasing,
stable or decreasing. These changes can be driven by
natural factors such as climatic events, population
cycles or interspecific interactions. Over the long term,
centrally located populations are generally more
productive than populations at the edge of a species’
range. The source and sink model (Pullium 1988)
posits a net flow of individuals from higher quality,
centrally located areas to the peripheral areas of the
range. Species may also be impacted by human-related
factors such as over harvesting, habitat loss or toxic
substances (Soulé and Orians 2001). In such cases
populations fail to reproduce, ranges shrink and
populations go extinct. Scientists have now documented extinctions of populations and species on a
global scale (Meffe and Carroll 1997; Hunter 2001;
Primack 2006).
Attempts to dissect population changes depend on
understanding the mechanistic basis of reproduction
and survival. A population flourishes or wanes based
on the health of its individuals. Condition indices
(CIs) are used to quantify individual health. In
addition to ecological studies, conditions indices are
From the symposium “Ecophysiology and Conservation: The Contributions of Energetics” presented at the annual meeting of the Society
for Integrative and Comparative Biology, January 4–8, 2006, at Orlando, Florida.
1 E-mail: [email protected]
Integrative and Comparative Biology, volume 46, number 6, pp. 1169–1190
doi:10.1093/icb/icl052
Advance Access publication October 20, 2006
Ó The Author 2006. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved.
For permissions please email: [email protected].
1170
widely used in the study of human health and, less
often, in animal husbandry.
We begin this paper with a brief historical
perspective of CIs emphasizing the body mass
index and Fulton’s index. We then review the wide
range of other morphological metrics and outline
other metrics that have been used to quantify
body condition. Most often scientists are trying to
measure the amount of energy reserves stored as
fat. The next section discusses the broad uses of
CIs in conservation and field biology. We present
four case studies of the use of CIs for conservation
purposes. Next, we review the methodological
problems associated with CIs. These problems are
based on a general absence of standards, statistical
complications and the lack of an underlying framework to model changes in body condition. We
contrast “cargo” or structural with dynamic models
for studying changes over time. These models can
compute CIs directly. Physiologists can advance the
cause of conservation biology by validating these
indices, building models that compute CIs and
developing instrumentation for field use.
A word of caution is in order for the reader. This
overview draws on literature from an unusually
diverse range of disciplines, including human
health, fisheries and wildlife management, animal
husbandry, behavioral ecology, toxicology, and
environmental physiology. Electronic versions of
journals and tools such as Google Scholar have
greatly enhanced our ability to find and obtain
access to papers and the gray literature; inevitably
there will be novel ideas and important papers
that have been overlooked. Indeed the data presented by Brown (1996) and Garcı́a-Berthou (2001)
support our experience that the literature on CIs is
very large. Despite its limitations, we hope this
paper illustrates how physiological ideas, specifically the concept of body CIs, will further the
development of physiological ecology and conservation biology.
Historical perspective on condition
indices
Body mass index (BMI)
More than 150 years ago, Quételet, a Belgian scientist
and mathematician, proposed the body mass index
(BMI) to quantify the physical condition of humans
(Jelliffe and Jelliffe 1979; Wikipedia 2006). This index
is computed as body mass (kg) divided by the square
of height (meters). The BMI for a normal body
condition ranges between 18.5 and 25 (CDC 2006).
Lower scores indicate a person is underweight (for
R. D. Stevenson and W. A. Woods
example, Ferron and others 1997 for anorexia and
bulimia) and higher scores suggest a person is
overweight. Garrow and Webster (1985) concluded
that BMI is a reliable measure of obesity. It is certainly
much used; a search of NIH’s PubMed database using
the phrase “Body Mass Index” returned almost 50,000
references. However, subsequent studies (Gallagher
and others 1996; Daniels and others 1997; Prentice
and Jebb 2001) have pointed out limitations to the use
of BMI. Specifically, they noted that when body fat is
estimated with more sophisticated techniques (see
Table 4), BMI can vary with age, sex or racial group.
Notice also that the dimensions of BMI are mass
divided by length squared making BMI dependent on
size even for isometric changes in size. Alternatives
for quantifying obesity exist [for example, the waist
to hip ratio, the ponderal or Khosla–Lowe Index
(M H3) or the geometric model of Bagust and Walley
(2000)]. Nonetheless, BMI remains popular for
medical studies. In 2005 alone, over 6700 papers,
about 1% of all the papers listed in NIH’s PubMed
database for the year, reference BMI. While not
precise, the literature suggests that BMI is popular
because it is an easy and inexpensive method to
quantify the amount of body fat and because the data
to calculate it are collected during routine examinations (Gallagher and others 2000). Furthermore, it
was the standard adopted by the World Health
Organization (de Onis and Habicht 1996) and
remains so today (CDC 2006).
Fulton’s condition factor
Fulton’s condition factor (K) for fish is another
example of a popular metric that has been used for a
long period (Fulton 1904; Bolger and Connolly
1989). K is computed as body mass divided by the
cube of body length. A multiplier may be included
depending on the units of measurement (that is, a
multiplier of 1000 is used when mass is measured in
grams and length in mm, which gives K as kg m3).
Another multiplier may be included as a way to
present the index as a whole number rather than a
fraction. The calculation of K assumes isometric
growth because length is raised to the 3rd power.
Unlike BMI that is applied just to one species of
mammal—humans—Fulton’s condition factor has
been applied to many species of fish. The assumption
of isometric growth is a fair approximation for many
species (Jones and others 1999; Bister and others
2000; Kimmer and others 2005), but not all. A variety
of other metrics have been proposed that provide
useful alternatives to the isometric assumption (see
next section) but Fulton’s condition factor continues
1171
Condition indices for conservation
to be widely used because of its simplicity and
historical precedence.
Other morphological condition indices for fish
Le Cren (1951) and Ricker (1973, 1975) expanded on
the notion of Fulton’s condition factor by proposing
the model Mass ¼ a Lengthb, where both a and b
are empirically determined by regression. Relative
condition, Kn, is then computed as the observed mass
of a specific individual divided by the mass predicted
from equations for the population or species under
study. The exponent b can be more or less than 3,
meaning that fish become more or less rotund at
larger sizes.
Wege and Anderson (1978) suggest a third index
called relative mass, Wr (Brown and Murphy 1991;
Murphy and others 1991). Wr is computed by dividing
the measured mass by the computed mass (Ws) of a
species-specific “standard fish.” Bister and others
(2000) described a procedure to establish the relationship of standard mass to body length. It requires
using the regression-line-percentile (RLP) technique
based on a large number of populations (>50 are
recommended), removing individual outliers or
population outliers and any other data suspected to
have resulted from measurement errors, and establishing ranges of lengths for which the standard mass
calculation is statistically valid. Gerow and others
(2005) published a new statistical approach that
overcomes biases of RLP. Hansen and Nate (2005),
using a large dataset based on 640 surveys of walleye
fish in Wisconsin, found a length-related bias for Ws
for fish measured from January to April but not from
other seasons.
More recently, Jones and others (1999) suggested a
more general model in which body mass W ¼ r
LaLbLc. r symbolizes density, and L raised to the a, b
and c powers represents three different linear
dimensions. The exponents a, b and c are suggested
to be independent of fish size. In practice, Jones and
others (1999) used just two independent dimensions,
girth and length. Their model reduced to W ¼ BL2H
where B is a coefficient determined by regression, L is
length and H is height.
Bolger and Connolly (1989), Brown and Murphy
(1991), Cone (1989), Patterson (1992), Jones and
others (1999) and Blackwell and others (2000)
provided useful comments about the three indices
and their computation. Blackwell and others (2000)
comment on two additional methods used in fisheries:
direct comparison of regression lines appropriate for
comparing populations and the use of residual
analysis. Among governmental researchers the use of
relative mass seems to be increasingly favored
(Blackwell and others 2000). Having communitywide standards helps managers (Bonar and Hulbert
2002; Murphy and others 1991). Recently, Hansen and
Nate (2005) suggested calculating body condition as
the product of Ws and a seasonally calculated Kn.
Morphological condition indices for vertebrates
As Hayes and Shonkwiler (2001) noted in their very
useful review of CIs, most published examples other
than those from humans are of studies of fish,
followed by those of birds. Our impression is that fish
are more often studied in an applied context while
birds are more commonly the focus of basic ecological
research. Among vertebrates, however, amphibians are
the most endangered group (Stuart and others 2004),
so we thought it useful to provide examples of specific
metrics that have been used across various vertebrate
taxa (Table 1). Only instances in which we found
examples of the same index being applied to more
than 1 taxon are included. We found the overview
papers listed in the first column in Table 1 to be very
useful as introductions to methods and practices
within each taxon.
Perhaps the most surprising result from our survey
of indices (Table 1) is that BMI, the most commonly
applied metric for humans, is rarely, if ever, applied to
animals. A second noteworthy observation is the
diversity of the measures. They range from categorical
scoring systems to advanced multivariate statistical
techniques. In addition, we found that scientists rarely
cited literature from other taxa but seemed to follow
traditions within their taxonomic discipline or to
invent a new metric that satisfied a current need.
Beyond morphological condition indices
Scientists have expanded the kinds of measurements
they make to quantify condition beyond morphological approaches. In addition to external assessments
based on photographs, physical examinations and
measurements of mass and length, biologists examine
internal organs and measure their dimensions, take
fecal, blood, or tissue samples, or measure body
composition directly (Table 2, Barton and others
2002; Hõrak and others 2002; Kalmbach and others
2004). Fecal, blood or tissue samples are obtained in
the field but analysis is conducted in the laboratory.
These samples are useful for conservation studies
because they provide information about the endocrine
and immune systems as well as about the state of
energy reserves. The RNA/DNA ratio is a particularly
interesting metric because it allows one to make
inferences about rates of growth (changes in body
Bolger and
Connolly
(1989);
Anderson
and
Neuman
(1996)
Brown
(1996)
Fish
Birds
Reptiles
Amphibians
Mammals Krebs and
Singleton
(1993);
Cook and
others
(2005)
Lee and
others
(1981);
Revicki and
Isreal
(1986)
Humans
Suthers and
others (1992);
Edwards and
others (2006)
Mass/length
Chilliard
(1993);
White and
others
(1997);
Vervaecke
and others
(2005);
Windberg
and others
(1991)
/3
L1
Amaden
(1943);
Saino and
Møller
(1996)
Mass
L1X L2XL3
Gendron and
others (2003)
Berger and
others (2005);
Bjorndal and
others (2000);
Guarino and
others (2002)
Rivas
Madsen and
Shine (1999); (2000)
Madsen and
Shine (2002)
Read (1990)
Jönsson and
others (1999);
Møller and
Erritzøe (2003)
Luckenbach and
others (2003)
Pope and
Matthews
(2002)
Bradshaw
and others
(2000);
Berger and
Peacock
(1988)
Le Cren
(1951);
Smolders
and others
(2002)
Benn
(1971);
Flegal
(1990)
Bonnet and
Naulleau
(1995)
Pope and
Matthews
(2002)
Haramis
and others
(1986)
Wege and
Anderson
(1978);
Blackwell
and others
(2000)
Benn (1971);
Flegal (1990)
Relative
condition Relative
factor, a Lb mass, Ws
Nagy and
Shine
others (2002) (1995)
Pehrsson
(1987); Sibley
and others
(1987)
Khosla and Lowe
(1967)
Fulton’s
condition
factor, aL3
Levey (2003);
Bell and
others (2004)
McMurry and Hart and
others
others
(1995);
(1985)
Marker and
Dickman
(2003)
Helms and
Bailey (1979);
Drury (1960); Whyte and
Baily (1979); Bolen (1984)
Kaiser (1993)
Rogers
(2003); de
Goede
and van der
Lingen (2005)
Guthrie (1975)
Fat or
condition
score
Heithaus and
Carter
others (2005)
(1997);
Starch and
Beese (2002)
White and
others
(1989);
Festa-Biachet
(1997)
Amadon
(1943);
Vanderkist
and others
(2000)
Overview
papers
Body size
Table 1 Morphological condition indices applied to vertebrates
Residuals
analysis
Caughley and
others (1988);
Stewart and
others (2005)
Shine and
Madsen (1997);
Blouin-Demers
and others
(2005)
Perez-Orella
and SchulteHostedde
(2005);
Blackwell
(2002)
Pepin and
others (1996);
Read (1990);
Cattet and
others (2002)
Leary and
others
(2004)
Golet and
Irons (1999);
Takekawa
and others
(2002);
Burton and
others (2006)
Battley and
others
(2004);
Velando
and AlonsoAlvarez
(2003)
Multivariate
or other
Heath and
others (2003);
Dubiec and
Cichoñ (2001)
de Silva (1985); Fechhelm and
Meretsky and others (1995);
others (2000) Heath and
others (2003)
Regression
comparison
1172
R. D. Stevenson and W. A. Woods
1173
Condition indices for conservation
Table 2 Overview of methods, data, levels of biological organization, and indices that are used to quantify or correlate with
body condition
Category
Measurement
method
Morphology (whole Photograph
animal)
Anatomy (organs)
Body fluids and
tissues
(biochemistry of
blood, feces, urine,
tissues)
Examples of
Data Obtained
Example
Condition Indices
Literature examples
Size, shape of
animal
Berger (1992); Pettis
and others (2004)
Physical
examination
Skin
characteristics,
fat score
Kaiser (1993);
Vervaecke and others
(2005)
Measure lengths
Body length, bill
length
Weigh
Body mass
Atkinson and Ramsey
(1995)
Examination
Organ
inflammation
Goede and Barton
(1990)
Measure lengths
Spleen size, bone
marrow deposits
Hewison and others
(1996), Cook and
others (2005)
Weigh
Liver mass
Liver somatic index
Anderson (1972);
Hohman (1993); Brown
and Murphy (2004)
Parasite
identification
Parasite counts
Parasite loads
Neff and Cargnelli
(2004)
Blood
composition
Hematocrit,
blood sugar, uric
acid
Ben-David and others
(1999), see Bolger and
Connolly (1989) for
citations
Hormonal
analysis
Testosterone,
corticosterone
Wingfield and others
(1992)
Immunological
Lymphocytes
Cytology
Fraction of water
in fat cells
Johnston (1973)
Heavy metal
analysis
Lead, mercury
Drasch and others
(1987); Burger and
Gochfeld. (1988)
Chemical
compounds
Molecular
analysis
These are nondestructive
methods, very commonly
used, which can often be
made in the field
BMI, Fulton’s Index, Carbonell and others
Standard Weight,
(2003)
Relative fatness
Heterophil to
lymphocyte ratios
RNA to DNA ratio
These techniques, with the
exception of a laparotomy
that measures
reproductive condition,
require dead animals
This group represents a
very wide array of
techniques and data.
Except for feces, most of
the samples require having
the animal in hand. Often,
as a part of marking or radio
tagging, blood samples are
drawn and preserved until
laboratory analyses can be
undertaken
Gleeson and others
(2005); Ots and others
(1998)
Organophosphates
DNA, RNA
Relevance for
Conservation
Alleva and others (2006)
Buehler (2004);
Clemmesen and others
(2003)
1174
Table 2
R. D. Stevenson and W. A. Woods
Continued
Category
Body composition
(see Table 3)
Measurement
method
Examples of
Data Obtained
Example
Condition Indices
Dry and weigh
% body water
% body water
Chemical
analysis
% body fat, %
protein
% body fat, %
protein
Scanning
techniques
% body water,
fat, protein
% body fat
mass over time) rather than just getting a snapshot of
the components of body mass.
Today more efforts are being made to compare CIs
involving different levels of biological organization.
For instance, scientists want to know whether blood
chemistry and hormonal status are correlated with
standard measures of body condition (see Galapagos
marine iguana example below). While cellular and
molecular CI are more expensive to obtain than
are those based on physical measurements, they
provide information about how subsystems of the
body are functioning. Advances in science often
depend on the weight of the evidence, so we think it is
important to encourage complementing traditional
CIs with cellular and molecular CIs when the
appropriate samples can be collected.
Use of condition indices in
conservation
Condition indices have been used in a wide variety of
contexts in conservation and environmental biology
(Table 3). Some studies relate CIs to the primary drivers of
environmental degradation such as habitat loss, pollution, overharvesting and climatic change. Other studies
relate CIs to life history patterns (reproduction, juvenile
survival and migration) and ecological interactions
(parasite load, social dominance, diet and density) of
threatened or endangered species. To illustrate in greater
depth the diversity of uses and indices, we present four
case studies focusing on Galapagos marine iguanas, desert
tortoises, polar bears and caribou. These examples are all
of vertebrates from environments in which there are large
seasonal fluctuations in resources and in which scientists
can detect changes in body condition at the population
level.
Marine iguanas
Galapagos marine iguanas (Amblyrhynchus cristatus)
offer an unusually complete example of an organism
Literature examples
Relevance for
Conservation
Traditional chemical
analysis requires using dead
animals. The other
techniques can be
completed on live animals,
but usually in the lab
Beck and others (2003)
that has been studied under both natural and humaninduced stressors, with both dimensionally based
(Romero and Wikelski 2001) and hormonally based
(Romero and Wikelski 2001; Romero and Wikelski
2002a; Romero and Wikelski 2002b) indicators of
condition linked to fitness as measured by survival.
The presence of marine iguanas on more than one
island in the Galapagos, together with what can be
very different conditions between islands and between
years, have provided both treatments and controls in
these studies.
The iguanas feed on marine algae, which can be in
short supply during El Niño events; during these
events, both dimensional and hormonal CIs are good
predictors of fitness (Romero and Wikelski 2001). La
Niña events are associated with abundant food, and
serve as a control. During an El Niño event, there was
high mortality associated both with a simple dimensional index (body mass/snout-vent length3) and with
corticosterone levels (Romero and Wikelski 2001).
Human activity can cause increases in glucocorticoid concentrations in species of interest to conservation (for example, Wasser and others 1997). In
the case of Galapagos marine iguanas, in which
glucocorticoid levels have been linked directly to
fitness (Romero and Wikelski 2001), such assays have
a particularly strong relevance to conservation. For
example, when an oil tanker broke up and exposed
one island to low-level contamination while not
affecting another island, iguanas on the affected island
had a strong stress response as indicated by elevated
plasma corticosterone levels. Based on stress responses
measured in starving iguanas during an El Nino event,
Romero and Wikelski (2002b) predicted mortality
rates of 40% on the oil-impacted island; by the
following season, actual mortality was 62%. This
result underscores the value of glucocorticoid information, which can be obtained rather quickly after the
onset of a human-induced impact and can serve as a
long-term predictor of the consequences.
1175
Condition indices for conservation
Table 3 Examples of the use of condition indices in conservation biology or environmental research involving threatened or
endangered species and nonthreatened species
Threatened or endangered
Category
Nonthreatened
Species
Reference
Species
Reference
Habitat loss or
quality
Humpback Chub
Caribou Bog Turtle
Rainbow trout
Meretsky and others
(2000); Douglas and
others (2002); Carter
(1997); Ruiz-Campos
and Gomex-Ramirez
(1991)
Blackcaps Redshank
Carbonell and others
(2003); Burton and
others (2006)
Harvesting
Stellar sea lions
Rosen and Trites (2000)
Fish
Kurkilahti and others
(2002)
Climate change
Galapagos penguins
Boersma (1998)
Moose
Solberg and others
(2004)
Pesticides
Amphibians
Levey (2003)
Leopard frog
Gendron and others
(2003)
Invasive species
European pond turtle
Cadi and Joly (2003)
Mink Nile perch
Sidorovich and others
(1999); Ogutu-Ohwayo
(1999)
Road kill
Otter
Kruuk and Conroy
(1991)
Endocrine
disruptors
Guppies
Edwards and others
(2006)
Aspic vipers, Bluefooted booby,
Mosquitofish
Bonnet and Naulleau
(1995); Velando and
Alvarez (2003);
Reznickl and Braun
(1987)
American toad
Howard and Young
(1998)
Vultures Snow geese
Kirk and Gosler (1994);
Bêty and others (2003)
Greater flamingos
Barbraud and others
(2003)
Environmental threats
Life cycle
Reproduction
Rhinoceroses,
Northern Atlantic cod
Brown and others
(2001); Rideout and
Burton (2000)
Calling
Migration
Spectacled eider
Lovvorn and others
(2003)
Natal dispersal
Seasonal cycles
Desert tortoise
Nagy and others (2002)
Canvasbacks Snapper
Walleye
Hohman (1993); Francis
(1997); Hansen and
Nate (2005)
Juvenile survival
Atlantic salmon
Gardiner and Geddes
(1980)
European starlings
Ardia (2005)
European minnow,
Black-capped
chickadees
Metcalfe and Steele
(2001); Karpouzos and
others (2005)
Small mouth bass
Gillooly and Baylis
(1999)
Mammals
Cameron (2004)
Atkinson and Ramsay
(1995)
Long tail duck
Flint and others (2001)
Whiteman and Parker
(2004); Collyer and
Stockwell (2004)
Bluegill sunfish Wild
boar
Neff and Cargnelli
(2004); Wolkers and
others (1994)
Activity cycles
Parental care
Polar bear
Atkinson and Ramsay
(1995)
Sex ratio
Molt
Hibernation
Polar bear
Ecological interactions
Parasite load
Galapagos hawk White
Sands pupfish
1176
R. D. Stevenson and W. A. Woods
Table 3 Continued
Threatened or endangered
Category
Nonthreatened
Species
Reference
Species
Reference
Social dominance
Grey Mouse lemurs
American bison
Genin and others
(2005); Vervaecke and
others (2005)
Sand Martin,
Canvasbacks
Brzek and Konarzewski
(2001); Hohman (1993)
Diet
Northern bald ibis
Sanchez-Guzman and
others (2004)
Ringed seal American
kestral
Harwood and others
(2000); Lavigne and
others (1994)
Density
White-tailed deer
Garroway and Broders
(2005); Sams and others
(1998)
Range
Kangaroos
Caughley and others
(1988)
Climate
White-tailed deer
Garroway and Broders
(2005)
Predation
Wood pigeons
Kenward (1978)
Willow tits
Kullberg (1998)
One potential value of such studies lies in their
ability to provide information about which human
activities do not reduce survival, thereby preventing
misplaced or ineffective conservation practices. It is
welcome news for wildlife managers that individuals
in areas heavily exposed to tourism did not
show chronic stress (Romero and Wikelski 2002a).
This was tempered, however, by the finding that acute
stress was lower than for iguanas in areas not
visited by tourists (Romero and Wikelski 2002a),
thus reducing the potential benefits of this response in
the wild.
Desert tortoises
The desert tortoise (Gopherus agassizii), found in the
western deserts of the United States (Nevada,
California), is an endangered species (see Tracy and
others 2005, Tracy and others this volume). This longlived, slowly reproducing species is sensitive to
disturbance by humans and to habitat destruction.
For about 15 years now, state and federal agencies
have supported research directed toward understanding its biology and to devising management
plans. Like other desert reptiles, these tortoises have
evolved mechanisms allowing them to endure long
periods of time without water and food. They have
the ability to store from 30 to 50% of their body
mass as urine in their bladders. As herbivores they can
also have large amounts of food in their digestive
systems.
As a way to assess conditions in the field, Nagy and
others (2002) devised a CI based on body mass
divided by product of the length, width and height of
the shell. Using grams and centimeters as units of
measure, the index gives a ratio in units of g cm3,
familiar to most because water has a density of 1 g
cm3. The highest values of the index found in the
field averaged 0.64. This fraction is to be expected
because the dome-shape of a tortoise does not fill
the rectangular prism described by the product of
the three linear shell dimensions. Nagy and others
(2002) demonstrated that body condition varies with
season and among populations but is largely
independent of age. They believed the CI to be a
good first approximation of condition and hydration
rates in the field.
Polar bears
Humphries and others (2004) reviewed the
impact of climatic change on arctic mammals. They
predicted the disappearance of current species and the
emergence of new species as the direct result of, and as
indirect effects through, the cascade of interactions in
the trophic structure. Currently, species go through
several seasonal bottlenecks in availability of energy
and have complex seasonal patterns of movement and
feeding related to energy demands.
Polar bears (Ursus maritimus) face significant
challenges to their survival as a species. In addition
to the accumulation of organophosphates in fat
(Anderson and others 2001) that act as endrocrine
disruptors (Braathen and others 2004), climatic
change is affecting their feeding behavior (Derocher
and others 2004). The bear’s vulnerability is tied to its
1177
Condition indices for conservation
role as a top predator and its need to accumulate large
energy stores during relatively brief hunting seasons.
Amstrup and others (2006) reported the first
observations of cannibalism in polar bears, which
they suggest is the result of poor nutritional status. In
late 2005, news reports noted that polar bears, for the
first time, were found dead, floating in the ocean
20–60 km offshore (Carlton 2005). These bears are
strong swimmers but it is speculated that a lack of sea
ice for hunting or resting, perhaps combined with
poor energy reserves, caused them to drown. Petitions
from conservation groups to list U. maritimus as a
threatened species are currently being considered by
the United States government.
Almost 15 years ago, Stirling and Derocher (1993)
predicted that global warming would increase the
duration of ice-free periods and cause a decline in
body condition, reproductive rates and survival of
polar bear cubs. A series of subsequent studies have
documented relationships among feeding biology,
energetics, physiological condition and reproduction.
The ratio between body fat and lean body mass
(Atkinson and Ramsay 1995) and the log of body
mass divided by the log of body length (Cattet and
others 2002) can indicate physiological condition.
During the course of an annual cycle, a polar bear’s
mass can vary from 200 to 400 kg (Atkinson and
Ramsay 1995). Most gains in body mass come in
the form of fat derived from hunting seals on the ice.
During summer the ocean is ice-free and the bears
must fast. During winter hibernation, they fast again.
Female reproductive success is tied to the amount of
fat stores (Atkinson and Ramsay 1995).
Caribou
Caribou (Rangifer tarandus) are not as threatened as
polar bears. They still exist in several large herds across
North America, Europe and Asia. However, the
annual cycle of each herd is complex and success
depends on the timing of resource availability.
Recently, Heuer (2005) wrote a book documenting
the dramatic life cycle of the Porcupine herd. He
undertook this effort because of people’s concerns
about the calving grounds of the Porcupine herd that
are threatened by proposals to develop the Arctic
National Wildlife Refuge and offshore regions for oil
extraction. Radiotelemetric studies have shown that a
typical cow in this herd travels over 28,000 miles per
year, traversing a range of 96,000–125,000 square
miles. Systematic aerial surveys have documented
long-term annual cycles in this population, which
started at a low of 100,000 in 1972, peaked in 1989 at
about 180,000 individuals, and then steadily declined
to a low of 120,000 animals in 2001 (Douglas and
others 2002).
A number of CIs have been used (Huot and
Goudreault 1985; Gerhart and others 1996; Kofinas
and others 2002) and scientists have established
relationships between body condition and reproduction in caribou (Thomas 1982; Cameron and others
1993; Chan-McLeod and others 1999). A particularly
interesting condition index, from a conservation
perspective, is a categorical index of fat reserves.
Because caribou are still hunted widely for subsistence,
scientists (Lyver and Gunn 2004) have developed a
method to enlist the community of Denesoline tribal
hunters to help evaluate the health of local populations based on a score of fat reserves of freshly killed
animals. The body condition index was based on a 1–4
scoring system indicating skinny, not so bad, fat and
really fat. Scientists rated the depth of the fat of the
brisket and back, the amount of stomach fat, the fat
coverage of the kidney and the color of femur marrow.
Lyver and Gunn (2004) compared the hunters’
impression with this categorical index and found
that both indicated that fatter females had a higher
probability of being pregnant than leaner ones.
This approach allows the aboriginal community
and government biologists to jointly manage the
caribou herd.
Evolving approaches to the
measurement and prediction of
condition indices
Measurement techniques
Most morphological CIs are based on some combination of body mass and linear dimensions of the
organism. Each of these measurements can present
problems for interpretation unless investigators are
familiar with the organisms under study and follow
standard procedures. Consider the following three
examples. Among feral mice, Krebs and Singleton
(1993) reported low repeatability in CIs because
techniques for measuring length varied among
observers. Fish “length” could be one of four different
measurements: the distance from the nose (1) to the
start of the tail (standard length), (2) to the notch in
the tail (fork length), (3) to the end of the tail when
the tail fin is in a relaxed position (natural total
length) or (4) to the end of the tail when the tail is
pulled in the lengthwise direction as far as it can go
(maximum total length) (Anderson and Gutreuter
1983). For snakes and lizards, biologists often measure
the snout-vent length from the nose to the cloaca or
vent and the total length from the nose to the tip of
1178
the tail. These measurements are more variable than
would be ideal because the animals resist being
stretched out or because the ends of the tails become
damaged or shed.
Which morphological index?
Morphological CIs (Table 1) fall into three categories.
Some, such as mass/length or BMI, are ratios. Ratio
indices give a relative number but the units are
difficult to interpret. Most have dimensions that are
not independent of mass or length so one assumes the
ratios are likely to be correlated with size. Multipliers
are often introduced to produce indices that vary
between 1 and 100. Another set of CIs, Ws or
residuals, represent differences from a predicted
standard mass computed on the basis of a statistical
formula. These indices have the dimensions of mass.
To make them more meaningful, scientists sometimes
standardize them by the predicted mass, yielding a
number that gives a percentage difference (compare
with coefficient of variation). A third set of indices is
computed so as to produce a non-dimensional
number. Mass is divided by density (assumed to be
1 g cm3) to give the dimensions of volume and that
result is divided by some combination of linear
dimensions to yield a dimensionless number. We
found few discussions of the relative merits of the
biological meaning of these three types of CIs. The
numbers themselves are difficult to interpret without
direct experience with measuring the animals being
studied. The underlying assumption in all these
metrics is that body mass is a good measure of
condition because size (¼ “structural mass”) is
constant or that mass is being scaled by some measure
of size when the individuals vary in size. Normally the
indices are based on data that are easy to measure
rather than on something that has a strong justification from a physical or biological perspective.
Problems arise with the scaling quantity, in part,
because we have no convenient and inexpensive
method of measuring body volume independent of
mass.
Interestingly, the data on mass and length used for
several CIs (Table 1) were also used by Kooijman
(2000, p 23–29) to define shapes of organisms. His
nondimensional shape factor is s ¼ (W/r)1/3 L1.
This is mathematically equivalent to another CI
(Table 1). Fulton’s condition factor (K) is equal to s3
if first one divides K by body density (usually assumed
to be 1 g cm3). So, the question arises whether these
CIs and Kooijman’s shape factor are equivalent. The
answer is “sort of.” The role of CIs is to quantify the
health of individuals in a population or to tell whether
R. D. Stevenson and W. A. Woods
a population is healthy relative to other populations.
Condition factors generally represent differences from
some standardized “shape” that are important.
Kooijman’s s, on the other hand, defines the shape
of a species, with a goal of knowing how surface areato-volume ratios change with body size. This
comparison suggests one can view morphological
CIs as individual deviates from a standardized healthy
“species shape.” It also draws attention to the lack of
established procedure of definition of what is
“healthy.” In the literature on humans and the fish,
large sample sizes have allowed scientists to detect
differences between populations, but clearly differences from a population mean could result from
underlying genetic differences as well as from
physiological conditions.
Statistical considerations
First in the literature on humans (Khosla and Lowe
1967; Benn 1971; Lee 1981; Revicki and Israel 1986;
Flegal 1990; Lazarus and others 1996; Mei 2002) and
subsequently in that from fisheries (Bolger and
Connolly 1989; Anderson and Neuman 1996) and
ecology (Green 2001; Hayes and Shonkwiler 2001),
scientists have raised concerns about statistical issues
associated with CIs. Often they want to know if one
index performs better than others relative to an
independent measure of condition or if there is
inherent bias. As noted previously, several CIs are
ratios. Ratios can complicate analyses and are often
size dependent (Lee and others 1981; Hayes and
Shonkwiler 2001). Green (2001) and Hayes and
Shonkwiler (2001) discussed some of the statistical
errors in untransformed and log-transformed data in
different types of regression models and in the use of
residuals. Jakob and others (1996) suggested that
residuals serve as a useful CI while Garcı́a-Berthou
(2001) described some of the complications introduced by using residuals. Recently, Schulte-Hostedde
and others (2005) empirically tested some of the
concerns raised by Hayes and Shonkwiler (2001) and
Green (2001) and found their concerns unwarranted
for the datasets being analyzed. As discussed above,
some metrics are non-dimensional but because of
allometric growth they are not independent of size.
The relative condition factor aLb is inherently less
biased than is mass/length, BMI or Fulton’s index
(Lee 1981), but Benn (1971) and Flegal (1990) state
that a relative mass index is statistically unbiased
over the entire size range. In the section “Other
morphological CIs for fish” above, there are citations
to literature that discusses statistical techniques for
computing standard mass and relative mass index.
1179
Condition indices for conservation
Cone (1989) advocated use of straight regression
techniques. We conclude that statistical approaches
used for computing CIs, will continue to evolve and
that this development will be supported by more
rigorous experimental work such as that of Cook and
others (2005).
Measurement of body composition
An important aspect of using a CI is to know that it
represents something real within the body, such as a
measure of the fat stores. Therefore, scientists compare
morphological CIs with data on body composition.
The traditional standard used for documenting body
condition has been a four component molecular model
of body composition (Wang and others 1992;
Speakman 2001a), that is, to measure the relative
amounts of water, fat, organic and inorganic matter
(Reynolds and Kutz 2001). Typically, the gut contents,
and often developing embryos, are dissected out and
the carcass dried to a constant mass to determine water
content. After grinding the carcass, fat is extracted
chemically from aliquots. Finally, the remains are
burned to determine the ash (¼ inorganic) content,
with the organic content determined by subtraction.
This traditional method requires that the animal be
sacrificed. As a rule, conservation biologists do not
want to kill their subjects, but sometimes animals can
be analyzed in this way because they are already dead
(for example, after from being hit by a car or killed
accidentally in a trap).
Fortunately, there are many nondestructive alternatives for measuring body composition that have the
inherent advantage of being able to measure the same
individual repeatedly (Lukaski 1987; Davies and Cole
1995; Poskitt 1995; Piersma and Klaassen 1999; Ellis
2001; Speakman 2001b). They include relatively
simple physical techniques (morphometrics, measuring skin-fold thickness with calipers, mass balance,
air-displacement plethysmography and hydrostatic
weighing), chemical dilution techniques (isotope
dilution and gas dilution), electrical techniques
(total body electrical conductivity and bioelectrical
impedance) and scanning techniques (whole body
counting, ultrasound scanning, dual-energy X-ray
absorptiometry, computed tomography, in vivo
neutron activation analysis, magnetic resonance
imaging and infrared interactance) (Table 4).
Unfortunately, the large number of alternatives
reminds us that no one method is ideal. All the usual
variables, the kind of data obtained, cost of the equipment, size of the organism relative to the instrument,
accuracy, repeatability, training of operators, maintenance and operating costs, ethical rating, and
opportunity for access to instruments, help experimentalists determine the best protocols (Garroway and
Webster 1985; Golet and Irons 1999; Ellis 2001).
Body mass is made up of a number of different
constituents depending on the level of biological
organization (Fig. 1, Wang and others 1992). Most of
the nondestructive techniques used to measure body
composition, however, divide the body into just two
components: fat and fat-free (Table 4). Scanning
methods that image the entire body can parameterize
three and four component models at the molecular
level and they have the potential to provide spatial
information at the tissue level. Development of these
methods often depends on advancements in computer
algorithms for interpreting the raw data.
One of the most promising techniques applied
to conservation was total-body electrical conductivity
(TOBEC) because the instrument was portable and
relatively inexpensive. A number of studies published in
the 1990s used the technique, some with success
but others less effectively. The technique appears to have
fallen out of favor, however, because the commercial
instrument is no longer available and the number of
papers using the method declined greatly after 2000.
Modeling body composition change
A central feature of many physiological studies is the
mechanistic approach to understanding biological
processes. A number of different disciplines, including
the agriculture sciences (Keele and others 1992; King
2001; de Lange and others 2003), fisheries (Iwama and
Tautz 1981; Cacho 1990), physiological ecology
(Cryan and Wolf 2003; Pennycuick and Battley 2003;
Polishchuk and Vijverberg 2005) and behavioral
ecology (Bednekoff and Houston 1994; Houston and
others 1997; Brodin 2000; Thomas 2000; Stillman
and others 2001; Bety and others 2003) model the
physiological state of organisms. Body mass and body
mass composition are usually central variables in these
models, which often include specific compartments
for fat, protein and water. Biophysical ecology
(O’Connor and others 2006; Porter and others
2006), dynamic energy budgets (Kooijman 2000),
metabolic theory of ecology (Brown and others 2004)
and ecological stoichiometry (Sterner and Elser 2002;
Vrede and others 2004) offer broad, mechanistic
frameworks for developing models.
Using the simple “body as a machine” metaphor,
biologists compare the functioning of animals with
that of devices built by engineers. For instance,
principles derived from the aerodynamics of airplanes
and helicopters have provided insights about how
birds, bats and insects fly. Models of processing for
1180
R. D. Stevenson and W. A. Woods
Table 4 Nondestructive techniques for measuring body mass composition
Technique
Physical Principle
References
Morphometrics
Indirect volume mass relationship
Cattet and Obbard (2005)
Skinfold Caliper
Indirect volume mass relationship
Lukaski (1987); Ostojic (2003)
Mass balance
Mass change, diet
Piersma and Klaassen (1999)
Air-displacement plethysmograph
Displacement
Fields and others (2002); Ma and others
(2004)
Hydrostatic weighing
Displacement
Biuw and others (2003)
Isotope dilution
Change in concentration of isotopes of
hydrogen and/or oxygen in body water
Speakman and others (2001c)
Gas dilution
Change in concentration of lipid soluble gas
Henen (2001)
Total body electrical conductivity
(TOBEC)
Electrical conductivity of entire body
measured in a chamber with coil
Sutcliffe and Smith (1995); Scott and
others (2001)
Bioelectrical impedance (BIA)
Electrical conductivity of body through
attached electrodes
Deurenberg (1995); van Marken
Lichtenbelt (2001)
Whole body counting
Whole body liquid scintillation counting
Zane and others (1997)
Ultrasound scanning
Reflection intensity and timing of sound
energy
Hermes and others (2000); Starck and
others (2001); Battely and others (2004);
Dietz and others (1999)
Dual energy X-ray absorptiometry
Attenuation of photons as they pass through
an absorber
Prentice (1995); Nagy (2001); Haderslev
and others (2005)
Computed tomography CT
Attenuation of photons as they pass through
an absorber
Grauer and others (1984); Nordoy and
Blix (1985)
In vivo Neutron Activation analysis
IVNAA
Emitted radiations
Ryde (1995)
Magnetic resonance imaging (MRI)
Absorption and reradiation of electromatic
frequency pulse by body protons
Brambilla and others (1995); Scollan and
others (1998)
Infrared interactance
Optical density
Conway and others (1984)
Ecological
Stoichiometry
Dynamic Energy
Budgets
Biophysical
Ecology
Other
Environment
ECS
Other
Other
Hydrogen
Protein
Blood
ECF
Bone
Adipose
Tissue
Lipid
Carbon
Cell
Mass
Water
Skeletal
Muscle
Oxygen
Whole Body
Tissue-System
Cellular
Atomic
Molecular
Fig. 1 Analysis of body composition at 5 different levels of biological organization from the atom to the whole body.
Ecological stoichiometry applied at the atomic level. Most traditional methods of body-composition analysis provide
data at the molecular level (see also Table 4). Dynamic-energy budget models sometimes focus on partitioning energy
among growth, reproduction and storage components but these models also deal with nutrient balance. Biophysical
ecological models explicitly link the exchange of mass and energy between an organism and its environment. Modified
from Wang and others (1992). ECS and ECF symbolize extracellular solids and extracellular fluids, respectively.
1181
Condition indices for conservation
chemical factories, either continuously or by batches,
have been applied to the digestive systems of animals.
In contrast to the mechanical perspective, biologists
have extensively documented a much more dynamic
view of physiological processes in which organisms
repair and modify themselves as they meet the
challenges of new environments. It is now recognized,
for example, that birds regulate muscle mass during
migration and that snakes regrow their digestive
systems after catching a prey item. While no one
doubts the dynamic nature of biological systems,
homeostatic mechanisms regulate body structures
within narrow limits of mass, making the analogy to
a human-built structure a good first approximation.
For purposes of studying body mass composition,
the perspective outlined above suggests two contrasting models that represent the mechanical and dynamic
perspectives. The mechanical perspective we deem the
“cargo” or “structural” model, for which the underlying assumption is that the body is a fixed structure
that can carry a load (Kooijman 2001). This load
might be food from a meal, eggs being developed for
reproduction, or fat stored for migration. This static
view of the organism, while not explicitly stated, is
often an underlying assumption of many CIs. In
contrast, the “dynamical regulation” view assumes
that body structures are constantly remade in ways
that match the tasks being undertaken. Migrating
birds are good examples. They reduce mass of flight
muscles in accordance with their power needs as they
burn fat along the migration route (Battley and others
1999; Lindstrom and others 2000; Dekinga and others
2001; Bauchinger 2005). Red knots can upregulate and
downregulate the mass of their digestive systems in
response to diet and to migration phase (Dekinga and
others 2001; van Gils and others 2003). Snakes provide
another clear example. Because most snakes so rarely
eat, it is a better strategy to maintain the digestive
tissues at minimal level most of the time and then
rebuild the digestive system to process the meal after
the prey has been swallowed (Secor and others 1994;
Secor and Diamond 1995; Starck and Beese 2001;
Starck and Beese 2002). Some evidence suggests that
dramatic changes in organ size are accomplished
by changing cell volume rather than cell number
(Johnson 1973; Starck and Beese 2002), but few
studies address this issue and very little is known
about the control signals that regulate body mass
(see Adams and others 2001).
Summary comments
CIs will continue to be applied widely in ecological
and conservation studies. Scientists find these simple
metrics convenient for documenting differences
among individuals and between populations. As yet,
there is no clear consensus as to which morphological
metrics are best but if sufficient data exist the relative
mass, Wr seems to be preferred. We suggest that
researchers consider carefully the statistical consequences of the indices they choose and the inferences
that can be drawn from the data. If measurements are
made at more than one level of biological organization
(Table 2) such data are likely to add complementary
and helpful information.
Challenges
1. Can physiologists develop guidelines as to the best
CIs and statistical procedures to use? In other words,
can physiologists agree on an equivalent of BMI that
will work across species and provide a tool that would
permit a much wider group of scientists and citizen
scientists to gather data that could be shared?
Outside of fisheries research and studies of humans,
there are few, if any, guidelines as to which metrics are
best. Fisheries scientists have begun to establish
standards and it may be that the broader community
could build from their experiences. These recommendations would need to acknowledge the wide range of
contexts in which CIs are used, address statistical
issues and explore the relationship between CIs and
shape metrics.
2. Can the scientific community compile data on
body mass and body mass changes for animals in
different environmental and physiological states and
make it available in public databases analogous to
current practices for DNA, RNA and many protein
molecules?
Body mass and body mass changes are at the center
of many ecophysiological interactions. Compiling
information in a standard format would advance the
ability to test ideas about body mass and body mass
changes. Prentice and Jebb (2001) suggested this kind
of compilation for humans.
3. Can physiologists invent better tools to measure
body composition that are portable and require less
experience to operate?
For all biologists, nondestructive methods that
can be readily applied in the field will help advance
the ability to quantify individual health and the
relationship between individual success and species
survival. Environmental and conservation physiologists can contribute to this goal by validating CIs using
advanced instrumentation, comparing body CIs with a
variety of other CIs that have been used.
4. Can physiologists produce a theory of body mass
change based on first principles?
1182
There are currently a variety of frameworks such
as behavioral models, biophysical ecology, dynamic
energy budgets, the metabolic theory of ecology
and ecological stoichiometry that can provide insights
into the regulation and change of body mass. These
modeling paradigms provide insight and can predict
CIs but as yet a clear understanding of the
physiological processes and molecular circuits that
regulate body mass are lacking.
Acknowledgments
We wish to thank Celia Morris and Susan Speak for
their advice about the organization and flow of the
text. Cody Choate and Derek Berezdivin helped find
and organize the literature. This work was supported
in part by award 0344822 from the National Science
Foundation.
Conflict of interest: None declared.
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