Shrub Responses After Fire in an Idaho Ponderosa Pine Community

Buring at 10-15-year intervals has been recommended in the warm, grand fir (Abies grandis)-myrtle pachistima (Pachistima Lochsa River of northern Idaho (Leege 1979). Spring and autumn burning generally promote sprouting of most shrub species in this community and produce valuable browse (Leege and Hickey 1971, Wright 1978). Fire is also a valuable tool in shrub and timber management in the more xeric ponderosa pine (Pinus ponderosa)-common snowberry (Symphoricarpos albus) communities (Davis et al. 1980). Effects of fire on production and mineral content of shrubs in this community have not been documented

Psychology and aggression

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68264/2/10.1177_002200275900300301.pd

Shrub Responses After Fire in an Idaho Ponderosa Pine Community

Buring at 10-15-year intervals has been recommended in the warm, grand fir (Abies grandis)-myrtle pachistima (Pachistima Lochsa River of northern Idaho (Leege 1979). Spring and autumn burning generally promote sprouting of most shrub species in this community and produce valuable browse (Leege and Hickey 1971, Wright 1978). Fire is also a valuable tool in shrub and timber management in the more xeric ponderosa pine (Pinus ponderosa)-common snowberry (Symphoricarpos albus) communities (Davis et al. 1980). Effects of fire on production and mineral content of shrubs in this community have not been documented

Adaptive models for large herbivore movements in heterogeneous landscapes

It is usually assumed that landscape heterogeneity influences animal movements, but understanding of such processes is limited. Understanding the effects of landscape heterogeneity on the movements of large herbivores such as North American elk is considered very important for their management. Most simulation studies on movements of large herbivores use predetermined behavioral rules based on empirical observations, or simply on what seems reasonable for animals to do. Here we did not impose movement rules but instead we considered that animals had higher fitness (hence better performance) when they managed to avoid predators, and when they acquired important fat reserves before winter. Individual decision-making was modeled with neural networks that received as input those variables suspected to be important in determining movement efficiency. Energetic gains and losses were tracked based on known physiological characteristics of ruminants. A genetic algorithm was used to improve the overall performance of the decision processes in different landscapes and ultimately to select certain movement behaviors. We found more variability in movement patterns in heterogeneous landscapes. Emergent properties of movement paths were concentration of activities in well-defined areas and an alternation between small, localized movement with larger, exploratory movements. Even though our simulated individuals moved shorter distances that actual elk, we found similarities in several aspects of their movement patterns such as in the distributions of distance moved and turning angles, and a tendency to return to previously visited areas

Can habitat selection predict abundance?

. Habitats have substantial influence on the distribut ion and abundance of animals. Ani- mals’ selective mo vement yields their habitat use. Animals generally are more abundant in habitats that are selected most strongly. 2. Models of habitat selection can be used to distribute animals on the landscape or their distribution can be modelled based on data of habitat use, occupancy, intensity of use or counts of animals. When the population is at carrying capacity or in an ideal-free distri- bution, habitat selection and related metrics of habitat use can be used to estimate abun- dance. 3. If the population is not at equilibrium, models have the flexibility to incorporate density into models of habitat selection; but abundance might be influenced by factors influencing fit- ness that are not directly related to habitat thereby compromising the use of habitat-based models for predicting population size. 4. Scale and domain of the sampling fram e, both in time and space, are crucial consider- ations limiting application of these models. Ultimately, identifying reliable models for predict- ing abundance from habitat data requires an understanding of the mechanisms underlying population regulation and limitation. animal movement, occupancy, population estimation, population size, presence- only data, resource selection function

Multiscale population genetic analysis of mule deer (Odocoileus hemionus hemionus) in western Canada sheds new light on the spread of chronic wasting disease

To successfully manage wildlife diseases, it is necessary to understand factors that influence spread. One approach is to analyze host movement and social structure, as these b

Wave-like patterns of plant phenology determine ungulate movement tactics

Animals exhibit a diversity of movement tactics [1]. Tracking resources that change across space and time is predicted to be a fundamental driver of animal movement [2]. For example, some migratory ungulates (i.e., hooved mammals) closely track the progression of highly nutritious plant green-up, a phenomenon called ‘‘green-wave surfing’’ [3–5]. Yet general principles describing how the dynamic nature of resources determine movement tactics are lacking [6]. We tested an emerging theory that predicts surfing and the existence of migratory behavior will be favored in environments where green-up is fleeting and moves sequentially across large landscapes (i.e., wave-like green-up) [7]. Landscapes exhibiting wave-like patterns of greenup facilitated surfing and explained the existence of migratory behavior across 61 populations of four ungulate species on two continents (n = 1,696 individuals). At the species level, foraging benefits were equivalent between tactics, suggesting that each movement tactic is fine-tuned to local patterns of plant phenology. For decades, ecologists have sought to understand how animals move to select habitat, commonly defining habitat as a set of static patches [8, 9]. Our findings indicate that animal movement tactics emerge as a function of the flux of resources across space and time, underscoring the need to redefine habitat to include its dynamic attributes. As global habitats continue to be modified by anthropogenic disturbance and climate change [10], our synthesis provides a generalizable framework to understand how animal movement will be influenced by altered patterns of resource phenology

Energy content of stools in normal healthy controls and patients with cystic fibrosis

Stool energy losses and the sources of energy within the stool were determined in 20 healthy controls and 20 patients with cystic fibrosis while on their habitual pancreatic enzyme replacement treatment. Stool energy losses were equivalent to 3.5% of gross energy intake in healthy children (range 1.3-5.8%). Despite a comparable gross energy intake, stool energy losses were three times greater in patients with cystic fibrosis than controls averaging 10.6% of gross energy intake (range 4.9-19.7%). Stool lipid could account for only 29% and 41% of the energy within the stool in controls and patients with cystic fibrosis respectively and was poorly related to stool energy. Approximately 30% of the energy within the stool could be attributable to colonic bacteria in both the healthy children and patients with cystic fibrosis. These results suggest that stool energy losses in healthy children are relatively modest but that even when patients with cystic fibrosis are symptomatically well controlled on pancreatic enzyme replacement, raised stool energy losses may continue to contribute towards an energy deficit sufficient to limit growth in cystic fibrosis. As the energy content per gram wet weight remains relatively constant (8 kJ/g), stool energy losses may be estimated from simple measurements of stool wet weight