File Name: territoriality and home range concepts as applied to mammals .zip
Wayne D. Animals concentrate their activities within areas we call home ranges because information about places increases fitness. Most animals, and certainly all mammals, store information about places in cognitive maps—or neurally encoded representations of the geometric relations among places—and learn to associate objects or events with places on their map.
I define the value of information as a time-dependent increment it adds to any appropriate currency of fitness for an informed versus an uninformed forager, and integrate it into simple conceptual models that help explain movements of animals that learn, forget, and use information. Unlike other space-use models, these recognize that movement decisions are based on an individual's imperfect and ever-changing expectancies about the environment—rather than omniscience or ignorance.
These models also provide insights about interindividual spacing patterns, from exclusive home ranges whether defended as territories or not to broadly overlapping or shared ranges.
Incorporating this dynamic view of animal expectancies and information value into more-complex and realistic movement models, such as random-walk, Bayesian foraging, and multi-individual movement models, should facilitate a more comprehensive and empirical understanding of animal space-use phenomena. The fitness value of cognitive maps and the selective exploitation of spatial information support a general theory of animal space use, which explains why mammals have home ranges and how they use them.
How animals distribute their activities in space and time is of central importance in behavioral and population ecology. Theoretical biologists have developed diverse theories to explain space-use patterns at different spatiotemporal scales, ranging from the transient movement decisions of individual foragers e. Population spacing patterns, in turn, strongly influence theories concerning the evolution of social systems Carr and Macdonald ; Horn ; Orians ; Slobodchikoff ; Smith ; Waser and Wiley ; Wittenberger Recently, a growing number of animal movement modelers have recognized the importance of learning and memory in animal space-use decisions e.
Here I review these concepts and models, and argue that a unified theory of animal space use requires understanding the cognitive processes animals use to exploit information. This approach emphasizes that what we call the home range is an emergent property of the movements of an animal that benefits from spatial information.
In other words, home ranges exist because information about places is useful—and the home range may best be defined as that area over which an animal regularly exploits and updates information stored in a cognitive map. I then illustrate these concepts using simple movement models for individuals using a 1-dimensional 1-D , homogenous habitat, followed by extensions to consider heterogeneous habitats and 2-dimensional 2-D habitats.
Animal space-use models assume that individuals space themselves and move through their environments in ways that increase fitness via the efficient exploitation of resources and avoidance of risks e. Until recently, most models have assumed that foragers are either ignorant or omniscient about resource distributions.
For example, most optimal foraging models assume that encounters with resources are random in space or time e. In reality, resource availability in space and time is usually only partly predictable, due to numerous ecological and stochastic processes, such as birth, death, growth, phenology, movement, or exploitation by other foragers. For such expectancies to be useful i.
Cognitive maps and the nature of spatial information. In mammals, cognitive mapping is made possible by the hippocampus Fyhn et al. Places per se do not move around or disappear, and spatial memories can be highly resistant to memory decay Kamil and Roitblat ; Nadel ; O'Keefe and Nadel ; Staddon In contrast, the things or events associated with places may move or change over time.
Thus, the use of cognitive maps involves integrating temporally stable and temporally variable information Olton In my theory, cognitive maps allow for navigation from place to place, whereas expectancies based on learned, site-specific information influence an animal's motivations for where and when to go.
As animals move they build and improve cognitive maps and update expectancies about sites on maps. They may approach or avoid sites in a goal-directed fashion Nadel et al. Repeated use of an area increases the richness and accuracy of map information, which allows more efficient navigation among sites.
These assumptions and observations are consistent with an ecological view of cognition as reflecting adaptations to an animal's niche Gallistel ; Healy and Braithwaite ; Real ; Staddon ; Stephens Because space and time are common to all niches, diverse taxa adapt to the temporal and spatial properties of the environment in similar ways, whereas their cognitive processes for dealing with niche-specific or species-specific characteristics vary Staddon Because the physical environment and geometric relations among places are generally stable, animals can learn the locations of fitness-affecting features in a single encounter and remember them for long periods, whereas learning tasks that are ecologically arbitrary may take many trials O'Keefe and Nadel ; Olton ; Staddon Spatial predictability and the value of information.
For simplicity, I initially define information in terms of foraging returns. Thus, learned information about resource locations has value at a later time if anticipating resource locations increases foraging returns e.
Animals may have some average or a priori expectancy about places they have never visited, but they are assumed to have specific information only about those they have visited. Predictability is likely to decline over time after learning because of prey movements, phenological changes, exploitation by other foragers, and so on.
Loss of predictability favors using information before conditions change. Exploited or depressed sensu Charnov et al. The optimal time to return to a resource site should, therefore, be a compromise between that favored by decreasing information and that favored by resource renewal Spencer Assume that resources renew according to a negative exponential function with the intrinsic rate of resource renewal, r.
Then, the expected value of a site x to a forager at time t is:. Expected value varies over time as shown in Fig. The expected value of the site to the forager is initially lower than that of unexploited sites because of its reduced resource density. With time, the expected value rises above the unexploited background value, peaks, and finally asymptotes on the background value as information value declines toward zero.
The optimal time to reuse a site is at the peak in the V t hump, which is always earlier than predicted by resource renewal alone. Logically, as the rate of resource renewal increases, optimal return time comes earlier Fig. If information value declines rapidly, optimal return time also decreases but with lower expected value than for sites with slower information decay Fig. Higher initial value of information also favors earlier return time, potentially with large returns Fig.
Temporal decision profiles showing optimal return times given that resources renew and information values decay with time since a previous visit. In all figures, the decreasing dotted curve s is an animal's decaying information about resources, the monotonically increasing dashed curve s is the renewing resources, and the solid humped curve s represents the forager's expected foraging returns based on both resource level and information from equation 1.
The horizontal line represents the average expected returns for a forager that does not use information. Vertical arrows show optimal return times.
A Standard curves with exponential information decay and negative exponential resource renewal. B Effects of resource renewal rate: faster renewal leads to earlier optimal return time and higher expected value. C Effects of information decay rate: faster decay leads to earlier optimal return time but lower expected value. D Effects of initial information value: higher initial value leads to earlier optimal return time and higher expected value. The shape of a temporal decision profile depends on parameter values as shown in Fig.
Nevertheless, all forms of depletion and renewal functions can result in a humped value curve and yield qualitatively similar predictions to those described here for linear depletion and exponential renewal Spencer An important general conclusion results from humped decision profiles: So long as acquired information has value, an informed forager should revisit sites and therefore limit its use of space to a finite set of sites even if available sites are infinite.
Hence, exploiting information leads to formation of a home range within which the forager can update its cognitive map in a positive feedback process. Before further discussing use of information in complex environments, I 1st illustrate use of decision profiles in some simple movement models Spencer for foragers constrained to quasi 1-D environments, such as a pied wagtail Motacilla alba yarrellii — Davies and Houston or a mink Mustela vison — Gerell ; Yamaguchi and Macdonald foraging along a riverbank.
At any moment, such a forager has 3 options: proceed forward, stay put, or turn to retrace its previous route. Thus, all sites along the linear environment are initially equivalent in expected value. Further assume that geometric information the locations of specific sites along the linear environment neither improves nor decays once obtained, so that changing information values represent changes in foraging rate due only to gaining and losing site-specific resource information and can be ignored but see below for inclusion of travel costs.
After passing through x , the forager leaves an ending value of resources, V 0 :. Resources renew following exploitation along a negative exponential trajectory. However, the forager also may gain information during its pass through x that could elevate foraging returns in x despite resource depression. The question is: Under what conditions does the value of this information more than compensate for the negative effects of resource depression if the forager turns around?
That is, when does it pay to turn back and use information? Let I x, t be the increment to foraging at site x due to information about x used at time t. I x,0 is the value of this information given perfect predictability, that is, before the resource distribution at x changes. I x, t declines from this maximum as the distribution changes.
In the presence of a lone forager, the value of any place x at time t is:. Furthermore, resources are renewing, and the farther back the forager goes after turning the longer each site has had to renew. Spatial decision profiles. In spatial decision profiles, the x-axis is distance or location and values represent those expected upon arrival at each site, rather than at the time of evaluation, because site value will continue to change with resource renewal and information decay after a forager has decided to turn around.
Hence, the forager's expectancies reflect the total round-trip time or cost to a site rather than the time passed when it makes the decision to turn around. An example of this scenario Fig.
Spatial decision profiles for a forager with a linear cognitive map. A Resource density, information value, and expected site value at a moment in time as a function of distance back over the length of habitat just covered, compared with the value of unexploited sites ahead.
Going farther than x min and then returning will increase expected returns relative to continuing indefinitely in 1 direction. B Optimal turnaround decisions for the forager in Fig. Longer T leads to many possible rules depending on other assumptions, as discussed in the text.
If the forager turns earlier than t turn or has less than t min available for foraging , the potential gains it foregoes for the 1st sites revisited exceed the elevated gains of the remaining sites.
Nevertheless, solving for a globally optimum or evolutionarily stable— Maynard Smith turnaround strategy requires additional assumptions about the time available to the forager, how the forager perceives time, and whether the forager can predict its own influence on future resource values or whether the forager can plan more than 1 turn in advance Spencer For a simple example, consider a forager with a deadline, such as a diurnal forager that must maximize returns by sunset and does not anticipate its future effects on resource values beyond 1 turnaround.
T is the length of the available foraging period. As T becomes larger, solving for the optimum decision rule becomes a complex problem best handled by simulation, because each turn changes the decision profile nonsymmetrically Fig. Value profiles for a forager at 3 times during a trip up and back over a length of habitat starting at position 0. Arrows indicate the forager's direction of movement over the previous time periods.
Note asymmetries in resource density, the value of information, and expected value to forager. Note that the further reduction of resources on the return trip makes turning again at position 0 a poor strategy, so the forager will continue moving farther left before eventually turning again.
For a forager not facing an imminent deadline, if it pays once to turn around to use information, it pays eventually to turn again. Because the forager's expectancies were assumed to be homogenous at time 0, they also were symmetrical.
Extending this argument, as long as the value of information creates an advantage to turning around at least once, a forager should turn repeatedly after moving some distance each time. The movement pattern generated is not necessarily periodic or cyclic, however, with the forager continually covering the same stretch of habitat. The time and distance moved after each turn varies, because the decision profile changes with each pass, as demonstrated by the following simple simulation models Spencer
Inferring the role of interactions in territorial animals relies upon accurate recordings of the behaviour of neighbouring individuals. Such accurate recordings are rarely available from field studies. As a result, quantification of the interaction mechanisms has often relied upon theoretical approaches, which hitherto have been limited to comparisons of macroscopic population-level predictions from un-tested interaction models. Here we present a quantitative framework that possesses a microscopic testable hypothesis on the mechanism of conspecific avoidance mediated by olfactory signals in the form of scent marks. We find that the key parameters controlling territoriality are two: the average territory size, i.
Baird, T. Lizards and other reptiles as model systems for the study of contest behaviour. Hardy and M. Briffa Eds. Cambridge, England: Cambridge University Press. Age and gender-related differences in the social behavior and mating success of free-living collared lizards, Crotaphytus collaris.
D Corresponding author. Email: ronald. Radio-telemetry was used to investigate the home range and den characteristics of the brush-tailed rabbit-rat Conilurus penicillatus from three sites in the monsoonal tropics of the Northern Territory, Australia. Radio-tracking was conducted in a series of discontinuous 4—day sessions, over a 2-year period. The home ranges of 61 C.
The utility of the definition of territory proposed by Davies individual animals or groups are spaced out more than would be expected from a random occupation of suitable habitats was tested by applying it to an analysis of spacing behavior in voles, Clethrionomys rufocanus bedfordiae Thomas. The examination of the Nearest Neighbor Distance and spatial distribution of home range suggested that, by this definition, adult females had territories in both winter and spring, while adult males had them only in winter. Download to read the full article text.
We characterized territories and ranging patterns by analyzing six variables:, 1 territory size, 2 overlap zone, 3 territory utilization, 4 core area, 5 territory shift, and 6 travel distance. Data collection covered a period of 10 mo, during which we simultaneously sampled the local positions of mostly large parties, including males in each community, in min intervals. Although overlap zones between study communities mainly represented infrequently visited peripheral areas, overlap zones with all neighboring communities also included intensively used central areas. Territory utilization was not strongly seasonal, with no major shift of activity center or shift of areas used over consecutive months.
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William Henry Burt; Territoriality and Home Range Concepts as Applied to Mammals, Journal of Mammalogy, Volume 24, Issue 3, 17 August , Pages.Reply
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Territoriality and Home Range Concepts as Applied to Mammals. William Henry Burt. Journal of Mammalogy, Vol. 24, No. 3. (Aug., ), pp.Reply