This is a system for
evaluating habitat quality for black-tailed deer and moose on the basis of
available food, its nutritional quality, and the nutritional requirements of
the adult female segment of the population.
We focus on food because it clearly sets the potential upper limit on the
number of animals a habitat can support.
Forage resources (vegetation and nutritional quality) can be measured in the
field and can be manipulated by land management.
We focus on digestible energy and digestible protein, because they are
the two most common nutritional limiting factors for wild ungulates, and their
requirements are reasonably well known for black-tailed deer and moose.
We focus on adult females because they are the productive segment of the
population, the animals that produce young.
Nutritional requirements vary seasonally and with reproductive status (e.g.,
maintenance versus lactation). This
system is suitable for any habitat and any species of deer (Cervidae) where the
availability of forage, its nutritional quality, and the nutritional
requirements of the deer are known.
analytical system provides a “snapshot” analysis at one, user-specified point
of time. It is assumed that all
available vegetation is potential food, and there is no accounting for
herbivore-plant dynamics (effects of overbrowsing).
This is not a simulation model.
Rather, it is a calculator: given
specific values of available forage biomass, nutritional quality, and animal
requirements, it calculates the maximum number of “animal days” (one adult
female for one day) that can be sustained by the forage resource. The
animal-day values are best considered in a relative (comparative) sense, not as
absolute values, because they are the maximum number of animal days (at
one point in time) that can be supported by the food
if all the suitable food were eaten then (no herbivore-plant feedback).
The instantaneous, food-based “carrying
capacity” of a habitat
varies daily with
marked changes in plant phenology and
requirements during summer and with variation in snowpack during
winter. Our snapshot values are
valid for the point of measurement and analysis; they are not an annual, or
even seasonal, average. They are
best used for comparing two or more habitats, or the same habitat(s) in two or
more different states (times or post-manipulation, succession, etc.).
Additionally, a large scale, GIS application of the system is useful for
comparing various landscapes or patterns within the same landscape – where size
and spatial configuration of habitats are important considerations.
The FRESH system includes linked databases of understory biomass and
forage-specific nutritional data for a variety of habitats and forages.
The databases provide users examples or reference points (in the case of
habitats) and ballpark-level default estimates (in the case of forages) for
data that the user might not have.
Although field data directly from the user’s study area always are best, a user
can use the FRESH system to explore habitat relations with estimates of habitat
variables based on the examples and default values from the linked databases
stored within the FRESH system.
The calculation of animal days is accomplished
with a linear programming model. Linear
programming models are optimization algorithms, where an objective is maximized
(or minimized) within a set of maximum or minimum constraints.
They are called linear programming models because they consist of a
series of equations, each of which is written as a linear equation (e.g.,
Y > a +
bc + de +fg),
and the solution is found by solving all equations simultaneously.
In our case, the objective that is to be maximized is the quantity of
forage biomass (kg/ha) that can be pooled from all available biomass while
meeting (or exceeding) specified minimum constraints for digestible energy and
digestible protein. The
FRESH-moose linear program also includes an additional constraint for time
costs of feeding (ingestion) as a function of twig size; the objective
(suitable biomass) must be maximized within a specified maximum amount of time
spent feeding (minutes/day) in addition to the minimum concentrations of
digestible energy and digestible protein.
We are essentially answering the question, “If one were to harvest all
the available current annual growth of plants in a given area and bring it into
a captive animal facility for feeding, what is the maximum number of animal
days that could be supported while meeting (or exceeding) a user-specified
level of metabolic requirements (mean concentration of digestible energy and
digestible protein)?” We answer
that question by using a linear programming model to determine the maximum
amount of forage biomass that can be pooled (from the combination of all
available forages) while meeting the specified constraints.
The maximum suitable biomass (kg/ha) is then divided by the
user-specified daily dry-matter intake of an adult female (kg/day), yielding
the maximum number of animal days (days/ha) that can be supported within the
The data-entry requirements for analyzing any given habitat are the
following: (1) a list of all
forage is a plant part with a unique nutritional composition – e.g.,
different species are different forages; shrub leaves are different forages
from shrub twigs, even within the same species); (2) the available biomass
(kg/ha, dry weight) of each forage; (3) the concentration of digestible energy
(kJ/g) of each forage; (4) the concentration of digestible protein (percent,
dry weight) of each forage; (5) the daily energy requirement (kJ/day) of an
adult female; the daily digestible protein requirement (g/day) of an adult
female; and the daily dry-matter intake (g/day) of an adult female.
In the case of the FRESH-moose calculations, the forage data also must
include the distribution of twig sizes (basal diameters, mm) and the total time
available for feeding by the moose (min/day).
We provide guidelines for all the animal data inputs; the forage variables
(biomass and nutritional quality) are the chief data requirements from the
user. If the user does not have
their own plant nutritional data, they can use our plant nutritional database
(automatically linked database) to provide estimates.
Affecting Nutrition and Palatability
Animal requirements for digestible energy and digestible
protein are well known because energy and protein are such basic currencies,
and common limiting factors, in animal nutrition that they have been studied
extensively. Energy is needed for
maintaining body heat, fueling activity and growth, and conducting basal
metabolic processes. Protein is
needed for building and maintaining body tissue.
Not all energy and protein consumed by an animal, however, is
metabolically available to the animal.
“Gross energy” (kJ/g) is the total energy contained in the food – the amount
that would be released by burning it in a fire.
Only the energy in the digestible
portion of a food, however, is available to the animal.
Energy in the indigestible fraction passes from the animal in excretory
products (feces and, to a lesser extent, urine), unused by the animal.
The energy in the digestible fraction is called “digestible energy.”
Some of that is lost as heat in conversion to “metabolic” energy, but
the metabolic energy coefficient for most forages is nearly constant (at about
0.85), so we have focused the analysis on digestible energy rather than
metabolic energy per se.
Similarly, “crude protein” (which is calculated simply as 6.25 times the
nitrogen concentration of a forage) is only a rough index of the true protein
content of a forage. More
importantly, however, the digestible fraction of the protein in a forage can
vary greatly, especially for wild forages commonly eaten by deer and moose,
which may contain tannins and other protein digestion-reducing compounds.
Thus, the “digestible protein” concentration of forage is much more
important than its crude protein concentration when evaluating forages for deer
Because digestible energy and digestible protein are so important to
animals and have been so widely studied, there exist good laboratory analytical
techniques for measuring or estimating their values in forages, some
specifically designed for deer and moose.
Similarly, much research has focused on energy and protein requirements of
animals, including deer and moose, so those requirements also are reasonably
well known. Thus, digestible
energy and digestible protein are our most sound currencies for evaluating
forage quality for deer and moose.
The nutritional quality of plant material, however, is far more complex
than simply that of digestible energy and digestible protein.
Animals also require vitamins, minerals, and micronutrients.
Wild forages for herbivores like deer and moose, moreover, also contain
many noxious compounds – some affecting palatability, some affecting
digestibility, and some even toxic.
The bacterial flora in the ruminant stomach of deer and moose synthesizes
vitamins, so vitamins are not an important limiting factor.
Minerals and micronutrients are important, but usually they are in
sufficient concentration in forage, so they are less likely a limiting factor
than is energy or protein. However,
that is not always true, and in some cases, minerals and/or micronutrients may
be a very important constraint – either by being in too short supply, or by
being so abundant as to be toxic. For
most deer and moose ranges, however, that is not the situation.
Therefore, mineral and micronutrient requirements of deer and moose have
received much less scientific investigation than have energy and protein, and
they are much more poorly understood.
We have not included mineral or micronutrient requirements as constraints in
our linear programs.
Noxious organic compounds abound in wild plants, especially in forbs,
ferns, shrubs, and trees – all the forages commonly consumed by deer and moose.
Ecologists have frequently termed these compounds “secondary
compounds” (secondary to basic plant metabolism) and have considered them as
defensive mechanisms protecting plants from herbivory.
They are considered in two groups, differing functionally in the
herbivore: (1) digestion-reducing
compounds, which decrease the value of a forage by decreasing its energy and/or
protein digestibility; and (2) toxins, which produce acute debilitating effects
in the herbivore. The two groups
are not necessarily exclusive. Plant
secondary chemistry is an enormously complex subject, with thousands of
compounds in the environment and different compounds in virtually every forage.
The effects of one major class of compounds (tannins) have been studied
well enough to be incorporated into laboratory analytical techniques for
estimating digestible energy and digestible protein, but most effects of most
compounds are very poorly understood.
Plant secondary chemistry is far too complex to model, yet it has very real
effects on herbivore use of plants – either through learned behavior or through
innate palatability preferences/avoidances of specific forages.
“Nutritional wisdom” (the ability of animals to select their food on the
basis of its nutritional value) is a very old concept, yet it has never been
found to be true. Palatability of
forages and diet selection in large herbivores are far more complex than simply
the nutritional value of the food.
Plant secondary chemistry complicates the problem even further.
We must acknowledge that forage quality is more than simply digestible
energy and digestible protein, and we must account for the fact that some
forages are avoided (unpalatable) regardless of their apparently high quality
in terms of digestible energy and digestible protein.
But we must do this within a relatively simple model.
We have taken a very pragmatic approach to the solution of the
“unpalatability” problem – the problem of apparently good forages (in terms of
digestible energy and protein) not being eaten or eaten in only small
proportions by deer and moose, despite an abundant availability.
This could pose a significant problem in the linear programming
solution, for example, when a forage such as alder (Alnus spp.) is relatively
abundant, has high digestible energy and protein concentrations, and yet is
eaten in only very small proportions by deer or moose.
The linear programming solution would include much or all of the alder,
regardless if it comprised a large proportion of the solution, and therefore,
would inflate the apparent value of the habitat beyond its true value.
Our approach to resolving this problem in a simple, pragmatic way is to
add additional, forage-specific constraints to the linear programming model,
whereby an upper limit (maximum constraint) can be specified for each forage
known to be relatively unpalatable, regardless of its digestible energy and
protein concentrations. Thus, if
3.0% is specified as the maximum constraint for alder, then that will be the
maximum amount of alder in the linear programming solution.
Values for the forage-specific constraints are user-specified.
However, the user can base their choice of such values on the results of
diet composition studies (e.g., fecal composition or rumen analyses) of deer or
moose in similar habitats. Thus,
the forage-specific constraint can be based on field data, not just
professional opinion. This
provides a relatively simple and workable solution to a problem that is far too
complex to model biologically. The
linear programming solution is driven by forage availability, digestible
energy, and digestible protein, but it is restricted by empirically determined
limits on palatability of any given forage.
capacity” is a very ambiguous term in ecology.
It is usually meant to be the maximum number of animals (of a given
species) that a given habitat can support indefinitely.
The “indefinitely” aspect requires that trophic dynamics
(herbivore-plant, predator-prey) be considered and that the system maintains a
stable equilibrium for long (indefinite) time.
It is a useful concept for theoretical system modeling, but it is very
problematic for practical application.
In the real world, virtually no habitat is stable indefinitely.
Seasonal variations occur throughout the year; annual variations occur
between years (e.g., weather); and disturbances, succession, ecological change
are present over both short and long time scales.
“Maximum number of animals” can vary, depending on sex and age
composition desired or assumed, as well as expectations for productivity of the
population (e.g., “maximum sustained yield”).
Herbivore-plant dynamics are seldom, if ever, truly stable (even within
“dynamic equilibrium bounds”).
In practical application, carrying capacity is best determined
empirically, after carefully defining exactly what is meant about location,
time scale, reasonable limits of natural variation (e.g., are droughts or
floods included?), and animal population demographics.
This has been done occasionally for closely managed deer populations,
and it has been done extensively for managing livestock grazing.
It requires much empirical experience with the animals and the habitats,
including sound data on animal demographics and vegetation dynamics, and
usually, careful manipulation of the animal population.
Yet, extrapolation to other habitats than those specific lands studied
involves much uncertainty. For
most large, free-ranging populations of deer and moose, empirical determination
of carrying capacity is impossible.
Theoretical calculation or estimation of carrying capacity for large
herbivores is confounded by two major problems:
(1) Food quantity and quality are not substitutable for one another, and
(2) the diet selection process, central to predicting diet composition, has
remained an exceedingly difficult process to model or predict.
The combination of these two problems has implications well beyond
estimating carrying capacity on the basis of food supplies.
It also confounds the interpretation of results from habitat use studies
(e.g., habitat selection or preferences) and models of habitat quality derived
from such data (e.g., “resource utilization functions”).
The problem of “nonsubstitutability” of quantity and quality of food is
that poor quality food cannot be substituted for deficiencies of high quality
food – in other words, much poor food is not equal to less good food.
The reason for this is that herbivores are limited by the amount of food
that they can process (ingestion, digestion and passage, or both).
Once they reach their limit of intake, they cannot consume more; they
cannot make up for poor quality food by eating more of it.
The most common cause is bulk-passage limitations through the
gastrointestinal tract (especially problematic for ruminants, with their
four-chambered stomach). Thus, one
cannot simply multiply the biomass of forages by their digestible energy (or
protein) concentrations, sum for all forages, and then divide by the animal’s
daily metabolic requirement to determine the number of animal days that the
food can sustain. Furthermore,
virtually every forage in the habitat is unique – it has a unique combination
of biomass and nutritional quality.
Thus, it is the combination (mix) of forages that must be optimized in order to
quantify the quality of a habitat’s food resources.
Some of the potential foods may or may not be suitable, depending on
what else and how much of it is in the diet.
Empirical observations of diet composition of herbivores in the habitat
provide a way of determining a suitable mix of potential foods.
Some models of carrying capacity apply empirically observed diet
composition to the array of potential foods in the habitat to determine the
maximum quantity of food that can be mixed in that same proportion.
The problem with this is that diet composition is not a static, fixed
attribute of an animal-habitat interaction.
It varies with the relative availabilities of the various foods, and therefore,
varies with animal population density as well.
As population density increases (“how many animals can the habitat
support?”), for example, the most preferred foods decrease in availability, and
diet composition shifts, yet it still might yield a diet that is well above
minimal requirements. This is the
similar problem with habitat use studies – habitat preferences (and resource
utilization functions), should be expected to shift with changes in relative
availabilities of habitats in a landscape and
with population densities of the animal, the latter because food
quantity and quality are not substitutable for one another.
The best habitat for an individual animal (the level at which habitat
preferences are chosen) may be high quality, low quantity food at low
population densities; but at high population densities (i.e., carrying
capacity), the habitat that supports the most animals may be lower (but
sufficient) quality, high quantity food – i.e., exactly the opposite patterns
of habitat use. An accurate
diet prediction model (e.g., “optimal foraging theory”) would enable us to
account for the interaction of forage availability, nutritional quality, and
herbivore population density; but no such model exists.
We have taken a very pragmatic approach to resolving this dilemma:
we have avoided the prediction of diet composition altogether;
similarly, we do not predict habitat preference or use, either.
Rather, we move directly to the question, “what is the maximum number of
animals (or animal days) that can be supported by a given food resource at a
given level of metabolic requirement?”
Our answer is the maximum number of animal days that can be supported at that
instant of analysis (the time when food supplies were measured), without
concern for an “indefinite” or “stable” concept of carrying capacity
(herbivore-plant interactions) or the composition of diets at anything less
than the maximum density of animals.
We calculate the dietary mix that solves the optimization problem of the linear
program: maximize the quantity of
suitable forage biomass within the specified minimum constraints of digestible
energy concentration and digestible protein concentration (and for moose, the
maximum constraint of foraging time) and maximum, forage-specific limits for
forages known to be unpalatable. Our
solution would be the “optimal diet” only at that population density that is
the maximum for the habitat. This
is not a diet prediction model; it is an animal feeding capacity calculator.
It is something more akin to what a feedlot manager would use than to
what an ecologist would find exciting.
calculators: (1) stand-level application and (2) landscape-level application
system consists of two levels of application – a web-based, stand-level module;
and a GIS-based, landscape-level module.
The web-based application runs on our server.
The user imports their stand-level data, or types it in directly, or accesses
stand-level data from our linked database.
Typical applications might be comparison of results from silviculture
treatments, or before-versus-after treatment, or various types of old-growth
forests, or for maintenance versus reproductive requirements, etc.
Data are analyzed for one stand or habitat – one array of forage
availability – at a time.
landscape pattern of habitats is an important consideration, then the GIS-based
application is needed. The GIS
application is downloaded from our website directly onto the user’s computer;
the user must also download a GIS dataset onto their computer; and then all
calculations take place on the user’s computer.
The user must have a description of the forage resources and overstory canopy
cover of each habitat type in the landscape.
The habitat types must be mapped in the GIS system; and for winter analyses,
elevation must be mapped in the GIS system.
Additionally, the user must specify a mean home-range size (ha) for the animal.
The FRESH system then analyzes all GIS cells within a block the size of
the mean home range, computing available forage after accounting for habitat
type, elevation, and burial by snow (if winter).
It compiles all available forage within the home range into one composite array
of available forage, and then it uses the same linear programming algorithm as
in the stand-level module to calculate an animal-days value for that “home
range.” It then moves over, by a
user-specified amount of space, to another, overlapping area of home range, and
repeats the process. After all the
landscape has been analyzed in this manner, the mean is calculated across all
deer-day calculations for all home ranges.
This is known as a “moving window” analysis.
It accounts for the size and spatial locations of each habitat
within a scale appropriate for the animal.
The underlying assumption is that each animal knows an area the size of its
home range and can mix forages from anywhere within that home range.
The synergistic effect of combining forages from different habitats can
be greater than the sum of the values of each habitat considered in isolation.
However, the combination of habitats must occur at the scale of what an
individual animal knows – i.e., its home range – because diet composition and
nutritional requirements operate at the scale of the individual animal.
applications of the GIS-based module might be comparisons of landscape-level
management areas, or the same area under various potential management
alternatives or temporal patterns for a landscape as it changes with plant
succession, etc. Data are analyzed for
all stands in the landscape, all within the same run, and all at the same point
deer (Cervidae) species: (1)black-tailed deer and (2)moose
The FRESH system can be applied to any species of large, generalist
herbivore (i.e., herbivores that consume a mixed-species diet without
specializing on one or a few forages) for which its nutritional requirements
are reasonably well known and the food resources of its habitat can be
quantified. It can be applied
anywhere. If additional
constraints are needed (e.g., concerning micronutrient limitation in a
particular locale), then the FRESH system cannot be applied directly.
Its theory would still be applicable, but the system would need to be
modified to incorporate the new constraint(s).
The current system has been developed for black-tailed deer and moose in
. The moose
application is basically the same model as that for deer, except that it
includes an additional constraint for foraging time.
The time constraint is needed because browse forages consumed by moose
often occur as very large twigs, and the size of bite taken while feeding on
such twigs involves a trade-off between nutritional quality and time costs of
harvesting. Large bites are more
time efficient (g/min) than are small bites, but small bites (distal ends of
twigs) have higher concentrations of digestible energy and digestible protein
than do large bites. Thus, each
twig presents an array of opportunity (availability) to the moose.
Part of the optimization problem is choosing the appropriate bite
size(s) for each twig (browse) species.
Of course, forage resources of deer and moose range, and metabolic
requirements of deer and moose, differ substantially between the species.
However, those are simple matters to adjust through user-specified data
input in the FRESH system.
status and and future plans
As of January, 2006, FRESH-Deer is fully operational at both the
stand-level (web-based) and landscape-level (GIS-based) scales of application.
We are currently working with the
to increase the linked databases for both habitat biomass and
forage nutritional quality. We
will add data to both databases as they become available.
We also anticipate updating and improving documentation and user-guide
information. Users are encouraged
to offer suggestions at our “Contact Us” link
[this is a link that we need to add to the homepage].
FRESH-Moose is still in the development stage.
We anticipate both stand-level and landscape-level applications to be
available by December 2006, but database development for both habitats and
forages will require more time than that needed for FRESH-Deer, as habitat data
for moose ranges have not been collected in the manner needed for FRESH-Moose.
Ultimately, we intend to expand the analytical framework of the FRESH
system to include other factors affecting habitat quality at the landscape
scale, beyond forage alone. We
plan to do this by considering the current FRESH food-based estimate of
carrying capacity as only the maximum potential (upper limit) of the habitat,
and then modifying that value by the “probability of use” of the habitat,
determined from a broad “resource utilization function” model.
In other words, the current, food-based FRESH estimate is the carrying
capacity that could be achieved if the habitat were fully acceptable to the
animal, whereas the resource utilization function model provides an estimate of
probability of use of any given patch (GIS cell) of habitat.
Resource utilization functions are calculated from habitat-use data,
usually radio-telemetry studies, and are best considered descriptions of
observed (past) patterns of use, rather than predictions of future patterns.
However, when multiple habitat-use datasets are available over a wide
geographic area and time period, then a resource utilization function model
calculated from a meta-analysis of those datasets should be reasonably robust
and might offer some predictive insight.
For example, during the 1980s and 1990s in southeastern
Alaska, there were four major radio-telemetry
studies of habitat-use by black-tailed deer – on Admiralty Island, Prince of
. Together, those
studies cover a broad range of southeastern
over a nearly 20-year period. Resource
utilization functions that are consistent across all four studies are likely
robust for most of southeastern
’s islands and for deer population densities similar to those of
the past two decades.
The combination of a food-based upper limit and a behavior-based
probability of use should yield some interesting insights into habitat value
for deer. Also, the differences
between the two should be insightful as well.
A. Hanley, Chief Wildlife Biologist and Team Leader, Alaska Wildlife
Habitat Team (Juneau) and Boreal Ecology Cooperative Research Unit (Fairbanks),
USDA Forest Service, Pacific Northwest Research Station, Juneau, Alaska
Donald E. Spalinger [link
to CV], Associate Professor, Department of Biological Sciences,
J. Mock, Associate Professor, Department of Computer Science,
Oran Weaver [link to CV],
student, Department of Computer Science,
Grant M. Harris [link to
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