Vilis O. Nams

Using animal movement paths to measure response to spatial scale

Animals live in an environment that is patchy and hierarchical. I present a method of detecting the scales at which animals perceive their world. The hierarchical nature of habitat causes movement path structure to vary with spatial scale, and the patchy nature of habitat causes movement path structure to vary throughout space. These responses can be measured by a combination of path tortuousity (measured with fractal dimension) versus spatial scale, the variation in tortuousity of small path segments along the movement path, and the correlation between tortuousities of adjacent path segments. These statistics were tested using simulated animal movements. When movement paths contained no spatial heterogeneity, then fractal D and variance continuously increased with scale, and correlation was zero at all scales. When movement paths contained spatial heterogeneity, then fractal D sometimes showed a discontinuity at transitions between domains of scale, variation showed peaks at transitions, and correlations showed a statistically significant positive value at scales smaller than patch size, decreasing to below zero at scales greater than patch size. I illustrated these techniques with movement paths from deer mice and red-backed voles. These new analyses should help understand how animals perceive and react to their landscape structure at various spatial scales, and to answer questions about how habitat structure affects animal movement patterns.

The effect of phenotypic traits and external cues on natal dispersal movements

1. Natal dispersal has the potential to affect most ecological and evolutionary processes. However, despite its importance, this complex ecological process still represents a significant gap in our understanding of animal ecology due to both the lack of empirical data and the intrinsic complexity of dispersal dynamics. 2. By studying natal dispersal of 74 radiotagged juvenile eagle owls Bubo bubo (Linnaeus), in both the wandering and the settlement phases, we empirically addressed the complex interactions by which individual phenotypic traits and external cues jointly shape individual heterogeneity through the different phases of dispersal, both at nightly and weekly temporal scales. 3. Owls in poorer physical conditions travelled shorter total distances during the wandering phase, describing straighter paths and moving slower, especially when crossing heterogeneous habitats. In general, the owls in worse condition started dispersal later and took longer times to find further settlement areas. Net distances were also sex biased, with females settling at further distances. Dispersing individuals did not seem to explore wandering and settlement areas by using a search image of their natal surroundings. Eagle owls showed a heterogeneous pattern of patch occupancy, where few patches were highly visited by different owls whereas the majority were visited by just one individual. During dispersal, the routes followed by owls were an intermediate solution between optimized and randomized ones. Finally, dispersal direction had a marked directionality, largely influenced by dominant winds. These results suggest an asymmetric and anisotropic dispersal pattern, where not only the number of patches but also their functions can affect population viability. 4. The combination of the information coming from the relationships among a large set of factors acting and integrating at different spatial and temporal scales, under the perspective of heterogeneous life histories, are a fruitful ground for future understanding of natal dispersal.

Detecting oriented movement of animals

A nimal movement affects biological processes at many organizational levels. Sometimes when animals travel they orient themselves towards a given goal, and whether or not they do so affects biological processes very differently. For example, on a small organizational level, when animals disperse to find new habitat patches, then very directed travel leads to a more heterogeneous distribution of animals in the landscape (Hein et al. 2004). On a higher organizational level, these differences in movement can affect population dynamics. For example, modelled brown treecreeper, Climacteris picumnits, populations increase when individuals travel towards other individuals, but not when travelling randomly (Cooper et al. 2002). On a still higher organizational level, differences in movement mode affect biodiversity. While directed movement can lead to a spatial partitioning of species, random movement can maintain biodiversity through local coexistence (Armsworth & Roughgarden 2005). Thus, to understand how movement of specific animals affects biological processes, we need to know whether those animals travel in an oriented manner. To test for movement oriented towards a given goal (hereafter ‘oriented’ movement), it helps to understand the biological distinctions between oriented movement and other types of movement. The key issue is the scale of the movement mechanism. Some animals orient towards certain locations (e.g. homing wood mice, Apodemus sylvaticus; Jamon & Benhamou 1989), some orient towards certain habitats (e.g. perceptual range of white-footed mice, Peromyscus leucopus; Zollner & Lima 1997), and some towards certain directions (e.g. cactus bugs, Chelinidea vittiger orient with wind; Schooley & Wiens 2003). All of these imply a long-distant orientation mechanism. If animals do not orient themselves towards a given goal, then the behavioural mechanisms governing movement will act at a small spatial scale (hereafter ‘unoriented’ movement). Some of these mechanisms may involve orientation, but they affect movement towards goals at a small scale. Some animals orient towards individual prey that they detect using their sense of smell (e.g. striped skunks, Mephitis mephitis; Nams 1991) or sound (e.g. coyotes, Canis latrans; Wells & Bekoff 1982). Alternatively, these mechanisms may just involve simple movement rules. Often the behavioural mechanism itself is ignored and the movement is modelled as a correlated random walk (CRW), where an animal makes discrete steps, and at each step, the turning angle is independent of the previous turning angle (Kareiva & Shigesada 1983; McCulloch & Cain 1989). The smaller the turning angle, the straighter the overall movement path. But there are many types of unoriented movements that cannot be modelled by CRWs. Some animals alternate turns, leading to straighter paths (e.g. cactus bugs; Schooley & Wiens 2003). Some animals tend to make successive turns in the same direction, leading to shorter paths (e.g. caribou, Rangifer tarandus; Bergman et al. 2000) or a looping pattern (e.g. Collembola; Bengtsson et al. 2004). Some animals also have autocorrelated step lengths (e.g. tundra swans, Cygnus columbianus; Nolet & Mooij 2002). All of these mechanisms act at a small spatial scale. We can use these behavioural differences to test for oriented versus unoriented travel. There are two types of tests, and the choice of which test to use depends on the circumstances. For the first test, one must know a priori where the animal is supposed to be orienting. For example, in homing experiments, the animal is supposed to be orienting towards its home area. One then measures orientation based either on the success of the animal locating this home area, or on whether the path of the animal is in the direction of the home area (e.g. Mitamura et al. 2002). In measuring perceptual range, the animal is supposed to be orienting towards a certain habitat. One then measures the distance at which the animal changes movement path characteristics, or the success at finding the habitat (e.g. Zollner & Lima 1997; Gillis & Nams 1998). These types of tests are quite straightforward. The second type of test is much more difficult because one does not know a priori where the animal is supposed to be orienting. Suppose one is tracking dispersing Correspondence: V. O. Nams, Department of Environmental Sciences, NSAC, Box 550, Truro, NS B2N 5E3, Canada (email: vnams@ nsac.ca).

Changes of movement patterns from early dispersal to settlement

Moving and spatial learning are two intertwined processes: (a) changes in movement behavior determine the learning of the spatial environment, and (b) information plays a crucial role in several animal decision-making processes like movement decisions. A useful way to explore the interactions between movement decisions and learning of the spatial environment is by comparing individual behaviors during the different phases of natal dispersal (when individuals move across more or less unknown habitats) with movements and choices of breeders (who repeatedly move within fixed home ranges), that is, by comparing behaviors between individuals who are still acquiring information vs. individuals with a more complete knowledge of their surroundings. When analyzing movement patterns of eagle owls, Bubo bubo, belonging to three status classes (floaters wandering across unknown environments, floaters already settled in temporary settlement areas, and territory owners with a well-established home range), we found that: (1) wandering individuals move faster than when established in a more stable or fixed settlement area, traveling larger and straighter paths with longer move steps; and (2) when floaters settle in a permanent area, then they show movement behavior similar to territory owners. Thus, movement patterns show a transition from exploratory strategies, when animals have incomplete environmental information, to a more familiar way to exploit their activity areas as they get to know the environment better.

Changes in Tracking Tube use by Small Mammals Over Time

Abstract Tracking tubes offer an efficient alternative to live-trapping for studies that require very large sample sizes. However, it is necessary to know how small mammals change in their tendency to enter tracking tubes with time. We measured this change in response in a region of boreal forest in northern Nova Scotia, Canada, in 1994–1998. Over 4 weeks, small mammals increased in their tendency to enter tubes, with no difference in response among species. Thus, when designing studies using tracking tubes, one needs to adjust for differences in duration—one cannot simply calculate a tracks/tube-night measure. We show how to statistically remove the effect of duration.

Second cropping of wild blueberries — Effects of management practices

Wild blueberries (Vaccinium angustifolium Ait.) are normally managed on a biennial basis. Pruning forces the plant into a vegetative year without fruit, followed by the first crop year, which provides the greatest harvest. In subsequent years, harvest levels drop dramatically. Prior to the introduction of selective herbicides, second crop yields were too low to allow the adoption of a double harvest. This study was initiated to compare production and incomes of a single cropping (2-yr management cycle, the present system) versus a double cropping system (3-yr management cycle), using systems that include herbicides. Total yields and net incomes over the 12-yr study were affected by fertilizer applications, but not by management system (2-yr management cycle versus 3-yr management cycle) or pruning (burning versus mowing). Yields in second crop plots were lower than those in first crop plots, even though blossom numbers were higher. Fertilizer affected many aspects of blueberry plant development, including...

Honey bee stocking numbers and wild blueberry production in Nova Scotia

Eaton, L. J. and Nams, V. O. 2012. Honey bee stocking numbers and wild blueberry production in Nova Scotia. Can. J. Plant Sci. 92: 1305-1310. Wild blueberries (Vaccinium angustifolium Ait.) require cross pollination by insects. Introduction of managed species such as honey bees (Apis mellifera L.) and alfalfa leafcutting bees (Megachile rotundata Fabr.) is costly. We assessed the effects of stocking rates of honey bee hives and the interacting effects of the numbers of honey bees and other bees on yield of blueberries in commercial fields. Blueberry fields were sampled from 101 fields in years 1991 to 2010 in Nova Scotia. We recorded field size, numbers of beehives, yield, densities of bees, numbers of buds, blossoms and set fruit. Yields increased linearly with numbers of beehives, up to~4 hives/hectare, but at higher stocking rates there was too much variation to adequately determine the effects. Yields also increased linearly with numbers of honey bees, but there was an interaction with other bees that decreased the effects of honey bees, such that at maximum densities of other bees, there was no effect of honey bees on yield. These results suggest that other bees and honey bees compete for pollination. If producers have limited numbers of beehives, we suggest that more should be placed in areas where densities of other bees are lower, up to approximately 4 hives/hectare.

Seedling establishment in a patchy environment

AbstractModels of gene flow in plants have often assumed a homogeneous landscape in which seed survivorship is independent of dispersal distance. However, most landscapes are patchy, which may have important implications for gene flow. We consider the situation where plants have already dispersed into an area, and consider the effects of the (i) proportion of uninhabitable patches; (ii) proportion of habitable patches that are empty; (iii) survivorship of plants; and (iv) proportion of seed dispersed out of the patch of origin. We looked at the effects of these on the ratio of mean seedling establishment distance to mean seed dispersal distance. For most combinations of these parameters, mean seed dispersal distance is an underestimation of gene flow. This effect can be large when most seeds remain in the parent patch, a situation common in nature. The model predicts that dispersal increases as the number of local patches becoming temporarily extinct increases, while dispersal decreases, as the number of ...

Plane-mirror inversion

It has been suggested that the left-to-right inversion of mirror images is only apparent, that it is based on psychology rather than physics. Why then are mirror images reversed left-to-right for all people, rather than top-to-bottom for some and left-to-right for others? This inversion occurs because whenever we rotate objects we do so around a vertical axis - thus when we compare mirror images to rotate objects in our minds, we see a left-to-right inversion. A thought experiment is presented as support. We rotate around a vertical axis to preserve the vertical reference that gravity gives us.