Measurement and Analysis of Animal Home Range Patterns

The measurements of home range size shown in Figures 1 and 2 come from animals fitted with radio telemetry collars. Developed in the late 1950s, radio telemetry revolutionized the study of animal movement, enabling routine, systematic measurement of animal locations. Since its introduction, the technique has been successfully used to study the movement behavior of mammals, birds, reptiles, amphibians, fish, and even insects, and has become a widely used approach in wildlife studies. The recent advent of global positioning system (GPS)-based telemetry is further enhancing the scope of the technique, allowing researchers to track animals, in some cases in near-real time, regardless of weather conditions, distance moved, and terrain covered.

The spatial distributions of animal relocations recorded in telemetry studies are translated into estimates of home range size using statistical home range models. A widely used approach is the minimum convex polygon (MCP) method, which characterizes the animal's home range as the smallest-sized polygon encompassing the

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1000 100 10 1

1000 100 10 1

Carnivores H = 0.520 7M120

Omnivores H = 0.1587 M112

Herbivores H = 0.020 5M102

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Herbivores H = 0.020 5M102

100 1000

Figure 2 The scaling of home range size with body mass across carnivores, omnivores, and herbivores. In each panel, the circles and solid lines indicate, respectively, the observed and fitted relationship between body size (M in kg) and home range size in (H, km). The coefficients of the relationship are also shown. The relationship between 1/population density (i.e., area per capita) and body mass is also plotted (triangles, and dashed line). In all cases, the slope of this line is shallower than the slope of the relationship between home range size and body size. From Jetz W, Carbone C, Fulford J, and Brown JH (2004) The scaling of animal space use. Science 306: 266-268.

observed relocations (usually 5-10% of the outermost relocations are excluded as outliers) (Figure 3 a). A number of density estimation methods have also been developed, in which the animal's home range is characterized using two-dimensional statistical probability density distribution fitted to the observed distribution of relocations (Figure 3 b).

Statistical home range models such as those shown in Figure 3 provide a useful way to summarize observed spatial patterns of space use; however, the models are purely descriptive, and thus yield little insight into the underlying causes for an animal's pattern of space use. Another approach, resource selection analysis, has become a widely used method for identifying underlying environmental correlates of animal space-use patterns. In contrast to the spatially explicit nature of statistical home range models, resource selection analysis uses a spatially implicit approach to identify habitats that are used disproportionately in relation to their occurrence through the examination ratios of habitat utilization relative to a measure of habitat availability. For example, Table 1 shows a resource selection analysis of elk home range relocations in western United States. The measurements in the table indicate that the elk preferentially utilize habitats that have intermediate levels of forest canopy cover. A plausible explanation for this is that the elk utilize habitats that balance their competing needs of having access to open areas that contain forage, and forest cover that provides a degree of protection from predators.

More recently, a new framework for analyzing patterns of animal home ranges has emerged in the form of mechanistic home range models. In contrast to resource selection analysis, mechanistic home range models yield spatially explicit predictions for patterns of animal space use by modeling the process of individual movement. Mathematically, this involves characterizing the fine-scale movement behavior of individuals as an underlying stochastic movement process that specifies the probability of an animal situated at a given location moving to a subsequent location in the time between relocations (Figure 4a). Relevant behavioral and ecological factors

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g 4 391200 rdi or o C

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g 4 391200 rdi or o C

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Figure 3 Examples of statistical home range models. (a) Minimum convex polygon method and (b) kernel method. Redrawn from White and Garrott (1997) (panel a) and Worton (1989) (panel b).

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Figure 3 Examples of statistical home range models. (a) Minimum convex polygon method and (b) kernel method. Redrawn from White and Garrott (1997) (panel a) and Worton (1989) (panel b).

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0

Table 1 Estimated habitat preferences of elk for habitats with different levels of forest canopy cover

Forest canopy

Number of

Landscape

Expected utilization

Selection ratio

Standardized selection

cover class

relocations (U)

availability (A)

Ei = A?pUi

Wi = U/Ei

index Bi = w^w.

0%

3

0.075

24.4

0.12

0.04

1-25%

90

0.305

99.1

0.91

0.29

26-75%

181

0.420

136.5

1.32

0.42

>75%

51

0.200

65.0

0.79

0.25

Total

325

1.000

325

3.14

1.0

Values of the selection ratio greater than one indicate habitats that are utilized at a higher frequency than their availability. As the numbers indicate, elk preferentially use habitat with intermediate (26-75%) canopy cover rather than habitats with either more open or more closed canopy cover. From Manly B, McDonald L, and Thomas D (1993) Resource Selection by Animals: Statistical Design and Analysis for Field Studies. New York: Chapman and Hall.

Values of the selection ratio greater than one indicate habitats that are utilized at a higher frequency than their availability. As the numbers indicate, elk preferentially use habitat with intermediate (26-75%) canopy cover rather than habitats with either more open or more closed canopy cover. From Manly B, McDonald L, and Thomas D (1993) Resource Selection by Animals: Statistical Design and Analysis for Field Studies. New York: Chapman and Hall.

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Figure 4 (a) Schematic illustrating the underlying model of individual movement behavior that underpins a mechanistic home range model. The movement of trajectory of individuals is characterized as a stochastic movement process, defined in terms of sequences of movements between successive relocations (i = 1 m) of distance pi and directions jI drawn from statistical distributions of these quantities that are influenced by relevant factors affecting the movement behavior of individuals. (b) Colored contour lines showing fit of a mechanistic home range model to relocations (filled circles) obtained from five adjacent coyote packs in Lamar Valley Yellowstone National Park. As described in the text, the PA + CA mechanistic home range model used in this study incorporates a foraging response to small mammal prey availability plus a conspecific avoidance response to the scent-marks of individuals in neighboring packs. The home range centers for each of the packs are also shown by triangles, and the grayscale background indicates small mammal prey density (kg ha~1) in the different habitat types. Moorcroft PR and Lewis MA (2006) Mechanistic Home Range Analysis. Princeton: Princeton University Press.

influencing the movements of individuals can be incorporated into this description of the fine-scale stochastic movement process. For example, a recent analysis of coyote home ranges in Yellowstone used a 'prey availability plus conspecific avoidance' (PA + CA) mechanistic home range model to account for the observed patterns of coyotes home ranges within the park. In the PA + CA model, individuals exhibit: (1) an avoidance response to encounters with foreign scent marks, (2) an over-marking response to encounters with foreign scent marks, and (3) a foraging response to prey availability, in which individuals decreased their mean step length in response to small mammal abundance.

As Figure 4b shows, the patterns of space use predicted by the PA + CA mechanistic home range model correctly capture the observed spatial distribution of relocations of five adjacent coyote packs in the study region, implying that the combined influence of resource availability and avoidance responses to neighboring groups is responsible for the observed pattern of coyote space use across the region. A nice feature of the mechanistic approach of 'modeling the movement process' is that mechanistic home range models can be used to predict patterns of space use following perturbation. For example, analysis showed that the PA + CA model shown in Figure 4b correctly predicted the shifts in patterns of coyote space use that occurred following the loss of one of the packs in the study area.

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