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|National Wildlife Property Repository
The National Wildlife Property Repository is responsible for receiving wildlife items that have been forfeited or abandoned to the U. S. Fish and Wildlife Service. These items are stored in a secure environment, and disposed of in accordance with the law. Many of the items are donated to educational facilities, non profit organizations, and conservation agencies to aid in teaching about endangered species and other wildlife. Others items are sent to scientific institutions to be used in research to develop better identify and/or protect the wildlife. The facility is a 22,000 square foot office and warehouse located northeast of Denver, Colorado at the Rocky Mountain Arsenal. To obtain wildlife products please send a letter to the Repository detailing the following:
For further information, or to request wildlife items, contact the Repository:
For information, or to request eagle parts and feathers, visit National Eagle Repository:
To donate legally owned ivory in the United States to the U.S. Fish and Wildlife Service:
Last updated: February 10, 2013


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As summer sports and warmer temperatures approach our area, parents and coaches need to remember the importance of hydration and the prevention of heat illness.
Keep in mind that children and the elderly are at an increased risk to suffer from heat illness, so identifying the risk factors early is key! Following a few simple guidelines can greatly decrease the occurrence of dehydration and heat illness in players.
Heat illnesses are the result of elevated body temperatures due to an inability to dissipate the body's heat and/or a decreased fluid level. Always remember that mild heat illnesses have the potential of becoming severe life threatening emergencies if not treated properly. Recognizing the early signs and symptoms can keep players safe and healthy throughout their respective season.
Heat exhaustion happens when one is exposed to heat for a prolonged period of time. The body may become overwhelmed by heat when its mechanism for keeping cool (sweating) breaks down. Symptoms of heat exhaustion include but are not limited to: nausea, dizziness, weakness, headache, pale and moist skin, weak pulse, and disorientation.
Heat stroke, unlike heat exhaustion, strikes suddenly and with little warning. When the body's cooling system fails, the body's temperature rises quickly. Heat stroke can be life threatening. Signs of heat stroke include very high body temperature, hot, dry skin, lack of sweating, fast pulse, confusion, and possible loss of consciousness.
Signs and Symptoms of Heat Illness
- Dehydration
- Headache
- Dizziness
- Muscle weakness or cramps
- Nausea and vomiting - Thirst
- Loss of Coordination
- Sweating
- Fatigue
- Wear lightweight, light-colored, loose-fitting clothes.
- Protect yourself from the sun by wearing a hat or using an umbrella. Don't forget the sunscreen!
- Drink plenty of water before starting an outdoor activity. Drink extra water all day. Drink less tea, coffee, and soda.
- Schedule vigorous outdoor activities for cooler times of the day - before 10 a.m. and after 6 p.m.
During an outdoor activity, take frequent breaks and drink water or other fluids every 15 to 20 minutes, even if you don't feel thirsty. If you have clear, pale urine, you are probably drinking enough fluids.
- Have the person rest (lying down) in the shade.
- Replace fluid with a water/salt solution. Drink slowly, drinking too much, too fast very often causes nausea and vomiting.
- Remove clothing.
- Pour water on the extremities and fan the person to increase air circulation and evaporation.
- Immersing the person in cool (not cold) water is also useful.
- Extremities should be massaged vigorously to help propel the cooled blood back into the core.
The Salem Community Center and Center Circle Indoor Sports Complex offer a climate-controlled environment year round. We also offer many leagues and camps throughout the year. For more information, visit our website at www.salemcommunitycenter.com or call 330-332-5885.

subset: commoncrawl


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Timothy Steele
Associate Professor; Department Chair
(Musicology, Music), Music
(616) 526-8523
Timothy H. Steele is associate professor of music and chair of the Music Department at Calvin College. He is a founding member and past president of the Society for Christian Scholarship in Music and is director of the Christian Voices in Musicology Study Group. His research interests include settings of Latin psalms from the Middle Ages and Renaissance, and the use of Genevan Psalmody in twentieth-century Hungary. Before coming to Calvin College, Dr. Steele was director of the instrumental program, chair of the Department of Music, and dean of the Division of Arts and Letters at Covenant College. Prior to that he taught music history and theory at Palm Beach Atlantic University, where he also directed the Palm Beach Atlantic Symphony. He was a clarinet student of Anthony Gigliotti (Philadelphia Orchestra) and earned the B.Mus.Ed. at Temple University (1981).
In addition to teaching and research in music history, Dr. Steele performs extensively on early wind and string instruments and has directed the Calvin College Collegium Musicum. He has performed with and directed early music ensembles at Temple University, the University of Chicago, and Covenant College. He studied historical performance with Howard Mayer Brown (University of Chicago), Mary Springfels (The Newberry Consort), Mary Anne Ballard (The Baltimore Consort), Ben Harms (Calliope), and Lucy Bardo (The New York Consort of Viols). He has performed with the Orianna Singers in Chicago and in a production of the thirteenth-century Play of Daniel in Milwaukee, Wisconsin. He is a member of the Early Music Ensemble of Grand Rapids, and is musicological consultant to The Hildegard Singers. He contributed performances and arrangements to the CD Hildegard of Bingen (1098-1179): Songs from the Abbey (Alto Productions, 2011), for which he also provided the liner notes, and the DVD Hildegard of Bingen and the Living Light (Linn Maxwell, 2012).


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Lot # 1066 (of 1594) < Previous Lot | Next Lot >
1959 Home Run Derby Advertising Poster/Complete Uncut Set
Starting Bid - $200.00, Sold For - $3,525.00
This advertising poster (16 x 20 inches) pictures all twenty cards of the Home Run Derby card set, which was issued by American Motors in 1959 to promote the Home Run Derby television program, a show in which the game's greatest sluggers competed in a power-hitting contest. The poster is essentially a complete set of this rare card issue in uncut form with additional advertising for local cosponsor Esslinger Beer and WCAU-TV appearing in the lower margin. The Home Run Derby card set is comprised primarily of stars, featuring all of the great home run sluggers of the era including Aaron, Banks, Hodges, Kaline, Killebrew, Mantle, Mathews, Mays, F. Robinson, and Duke Snider. The poster has two natural fold lines (one vertical and one horizontal) where it was originally folded by the American Motors promotional department to allow for mailing along with other promotional materials in 1959. There are tape stains and minor paper loss in all four corners relating to its use for display in a store front window as intended, and some very light surface wrinkles. The sheet displays beautifully. This is a very interesting and unusual form in which to find this rare set, which as individual cards lists for many thousands of dollars. This is one of only two Home Run Derby card set advertising posters we have ever seen. Dimensions: 17 x 23 inches. Matted and framed to total dimensions of 21 x 27.75 inches. From the Barry Halper Collection. Reserve $200. Estimate $400/$800. SOLD FOR $3,525.00
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My definition of a "systems" programming language pretty much goes as this +- a couple nitpicky points:
- A program whether statically compiled or dynamic memory wise is completely self-contained when targeting a certain platform of execution on the target OS of ANY patchlevel which says it maintains compatibility.
- The resultant program must also play nicely in kernel space if required in which the host language both provides and has mechanisms and semantics to accommodate kernel module/driver style communication to enable sane and safe kernel citizenship.
BTW Charles I'm waiting for Niko Matsakis' Rust talk to be available if you would be so kind :)


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SEARCH: Spatially explicit animal response to composition of habitat
(Draft M)
Nicholas McCann
Purdue University, Department of Forestry and Natural Resources, 715 West State Street, West Lafayette, IN 47907, USA
Spatially explicit population models (SEPMs) show promise as tools for conservation, but have been criticized as being behaviorally minimalist. Behavior is important because it influences patterns of animal movement, is temporally dynamic, and can vary at fine spatial and temporal scales. To investigate questions related to underlying behavioral mechanisms that occur at fine scales, we present a SEPM that allows researchers to simulate animal dispersal across complex, realistic landscapes while modeling a range of search processes and habitat selection rules. SEARCH can be used to simulate the spatially explicit response of medium and wide-ranging, solitary animals to habitat composition over multiple successive dispersal seasons, at fine temporal resolutions, on temporally dynamic, high-resolution landscapes. Within SEARCH, animals move across a vector-based landscape of four GIS layers, with values assigned to each point in space on each layer to reflect animals' response habitat characteristics (e.g., movement sinuosity and energy use). Dynamic landscapes can be simulated in SEARCH to reflect changes in habitat quality and/or spatial arrangement by replacing any combination of the landscape maps at times and dates scheduled by the user. Animals in SEARCH are parameterized by the user to reflect behavior, energetics, and home range requirements and baseline landscape and animal parameter values can be modified by the user to reflect variability caused by gender, activity mode, behavioral mode, time of day, and date. Dispersing animals retain memory of habitat suitability and occupancy and query their memory in combination with user-parameterized selection rules to locate and delineate home ranges. As SEARCH can be run for multiple years, stochastic reproduction is modeled and resident females reproduce annually, giving birth to a user-parameterized number of young. Output at the individual-level includes the animal location, energetics data, risk of predation, movement parameters at each time step, and shapefiles depicting the area perceived during dispersal. At the population-level, shapefiles depicting the arrangement of home ranges is produced and population demographics (e.g., age structure) can be constructed from individual-level data. To illustrate SEARCH's capabilities we use a reintroduced American marten (Martes americana) population in the Chequamegon-Nicolet National Forest (CNNF), Wisconsin as a hypothetical study system. We first explored the theory that fishers (Martes pennanti) constrict marten populations by predation and competitive exclusion by testing the sensitivity of marten population expansion to increased predation and reduced food availability. We then explored the relative effects of forest management plans on marten population expansion by simulating marten dispersal on CNNF landscapes across a range of even-aged management scenarios. Results suggest that marten populations are constricted by fisher predation, competition with fishers, and forest management that increases the proportion of even-aged stands in the CNNF. These hypothetical case-studies demonstrate the capabilities of SEARCH for investigation of questions related to basic and applied science. SEARCH's numerous fine-scale inputs and range of behavioral parameters gives it the flexibility to be a useful tool for investigating behavioral mechanisms for numerous solitary animals.
Spatially explicit population models (SEPMs) offer researchers an ability to study landscape-level processes and responses to habitat change, such as management strategies or natural disturbance (Pulliam et al. 1992, Turner et al. 1994, Pulliam and Dunning 1995). They specify the location of each object on landscape maps using idealized (South 1999, Wiegland et al. 1999) or realistic landscapes that can be created using geographic information systems (GIS; Turner et al. 1994, Liu et al. 1995). Such specification of the spatial arrangement of habitat is critical for understanding the ecology of the species being studied as well as system processes because wildlife populations are mobile (Turner et al. 1995, Mladenoff 2004).
Unfortunately, the scope of many studies that employ SEPMs are often limited because many SEPMs consider simplified, static landscapes and coarse spatial and temporal resolutions (Wiegland et al. 1999, Kramer-Schadt et al. 2004) rather than complex, realistic, temporally dynamic landscapes at fine resolutions. This is important because animals' response to habitat can vary at fine spatial and temporal scales (CITE) and habitat-use can change daily or seasonally (Wolf 1978).
The scope of many SEPMs is also limited because they exclude behavior (South 1999). Behavior influences patterns of animal movement and incorporating behavior into SEPMs would yield a greater understanding of the mechanisms that underlie animal movement. For instance, modeling of decision rules that underlie habitat selection would allow researchers to investigate the interaction of management scenarios and behavior on landscape permeability and lead to new questions or approaches to unanswered questions. [Find something to cite here]
Many SEPMs are further constrained because they are species-specific, reducing their flexibility (Liu et al. 1995, Railsback and Harvey 2002, but see Gardner and Gustafson 2004). Adding flexibility allows researchers to avoid the often labor-intensive task of model development, allows researchers to use models in different systems, and facilitates model evolution (Mladenoff 2004). It also allows researcher to simulate different species' responses to potential management scenarios or disturbance regimes and creates a common structure from which to begin discussions. Increased model flexibility has been a goal of several widely-used forest models (LANDIS, Mladenoff 2004; JABOWA, CITE), but has been lacking from animal dispersal models (but see Gardner and Gustafson 2004).
We present a SEPM that simulates animal dispersal across complex, realistic landscapes while modeling a range of search processes and habitat selection rules. It allows researchers to investigate questions related to underlying mechanisms that occur at finer scales than other simulations [are there any others that do this?], such as resource selection. We model the spatially explicit animal response to composition of habitat (SEARCH) over multiple successive dispersal seasons, at fine temporal and spatial resolution using temporally dynamic, GIS-based landscape maps. SEARCH allows for increased flexibility as the user defines multiple animal, spatial, and temporal parameters. To illustrate its use for study of solitary carnivores, we present an example of SEARCH's application to an American marten (Martes americana) dataset.
Model overview
SEARCH simulates animal dispersal on GIS-based landscapes to investigate the sensitivity of dispersal patterns to variability in habitat quality, habitat arrangement, and animal physiological and behavioral parameters. SEARCH builds on findings from field studies of animal demographics, behavior, and physiology to identify population dispersal patterns that emerge from the movement of individuals. It may be parameterized for numerous species and employs dynamic landscapes to simulate habitat change. It was coded in the C# language (CITE Visual Studio; CITE), employs a graphic user interface (GUI) to prompt the user to input maps and animal parameters, and is available at: http://ranchdelux.com.
Process overview and scheduling. THIS NARRATIVE IS KEY, AND NEEDS MORE
Within SEARCH, animals move across a GIS-based landscape composed of four maps, with values assigned to each polygon in each map to reflect habitat characteristics (Table 1; Figure 2). The landscape is populated by residents or is unpopulated to reflect the system under study. Each of the landscape maps are queried at each time step for a user-defined dispersal period and number of years (Figure 1). At each time step, animal location, energy level, behavioral mode, memory, and activity state, and activity mode is updated. Animals employ a behavioral rule using landscape values and user-input values to simulate habitat selection.
To simulate a dynamic landscape, the user can schedule the replacement of any combination of the four maps at selected times and dates. This can be done to reflect changes in habitat quality and/or spatial arrangement.
Animals are parameterized by the user to reflect behavior, energetics, and space requirements (Table 3). Baseline landscape map values and animal parameter values can be modified by the user to reflect variability caused by gender, activity mode, behavioral mode, time of day, and date (Table 2).
Once a user-defined number of steps taken or number of suitable sites is traversed, animals select a home range center from areas searched during dispersal, move to the selected home range center, and attempt to establish a home range. Animals become residents once a home range is established, but die if a home range is not established during the dispersal period. In the period between dispersal periods, the inter-dispersal period, residents are subject to random mortality and females stochastically give birth to young which disperse the following dispersal season.
State variables and scales
Within SEARCH temporal resolution is represented as a discrete time step. The user defines the time step length (≥ 5 minutes), dispersal period (≥ 1 d), and the extent of simulation runs, which may range one or multiple years. The inter-dispersal period composes the remainder of the year, when dispersal does not occur.
Scales employed by SEARCH are the resolution and extent of user-input vector-based GIS maps. User-defined values are assigned to each GIS map classification and characterize the following state variables: correlated random walk movement parameters, the probability of acquiring prey, the probability of being depredated, the suitability for home range establishment, and occupancy status (Table 1).
Individual animals are characterized by the following state variables: identification number, gender, dispersal mode, behavioral mode, energy level, and location (Table 3). Population characteristics can also be derived from model output including: age structure, sex ratio, and mortality rates.
Design concepts
Emergent phenomena.
Patterns emerge at the population-level based-on movements of individuals. They develop from the interaction of animal parameter values, behavioral rules, stochasticity, and landscape map values. Emergent patterns include: habitat selection, dispersal paths, dispersal speeds and directions, home range locations, population growth and spatial expansion rates, mortality rates, and mortality locations.
Adaptation and Prediction.
Simulated animals to choose from alternative behaviors. For example, individuals select habitat during dispersal by comparing the probability of predation and prey acquisition at their current location with adjacent locations and also evaluate landscape values when selecting a location to center a home range. Also, animal activity mode may change based-on a user-defined energy threshold and behavior of active animals can be effected by their perceived risk of predation.
Interaction and Sensing.
Interaction between individuals is implied because animals of the same gender cannot establish overlapping home ranges. Additionally, users may employ one of two criteria to trigger selection of a home range center. This allows the user to compare the success of populations that select home ranges using the different criteria.
Animals respond to map polygon values assigned for: suitability for home range establishment and occupancy status, probability of predation, and probability of acquiring prey. They respond to these values according to their: gender, social status (resident or disperser), energy level, behavioral mode, activity state, activity mode, and if they are at a heightened risk of predation.
Animal movement is a correlated random walk sampled from a wrapped Cauchy distribution (CITE), with sinuosity based on user-defined map polygon values. Stochasticity is also incorporated when dispersing individuals select habitat, after comparing the quality of their current location with locations into which they may cross.
Home range establishment uses a random set of points within a user-defined distance (±SD) from the home range center point. Mortality due to predation and prey acquisition during dispersal is based on the GIS classification in which an animal is located, its current behavioral mode, and a random number draw. Mortality and female natality during the inter-dispersal period is probabilistic and irrespective of habitat type. Dispersing animals in the years following initialization begin at random points within the female parent's home range.
Note: I am missing behavioral mode changes due to perceived risk of predation.
Data output for individuals includes: habitat and individual state variables, location, date, and time during each time step. GIS maps that depict each animal movement path, home range, and perception of its travel path are also produced. Population-level maps are produced at user-defined intervals that depict home range and dispersal path arrangement. Additional population-level responses, such as sex ratio or population age structure, can be extrapolated from individual output.
Need to be careful throughout: residents vs. dispersers is an important distinction
Modifiers. The user modifies animal parameter values by multiplying them by a real number to reflect variation caused by gender, activity mode, behavioral mode, time of day, and date (Table 2). Temporal modifiers are used to alter parameters at times during the 24-hour period and dates scheduled by the user. For example, perception distance may be decreased at night.
Landscape. The user assigns values to map polygons that dispersing animals respond to (Table 1). A dynamic landscape can be simulated by scheduling the input of maps that have different values or spatial arrangement. These alternate maps are scheduled in a sequence of dates or times to reflect habitat management or disturbance. The user also assigns temporal parameters, including dispersal season dates and time step resolution (Table 3).
Home ranges. Two options are available to prompt selection of a home range center: (1) A minimum number of time steps occurred; or (2) A minimum number of suitable and unoccupied points were sampled by the dispersing animal. These minimum values are input by the user. Home range establishment requirements are also input by the user, including the minimum area for a home range and the mean distance (±SD) from the center point that will be used to create the home range (Table 3).
Energy. Energy parameters are defined by the user, including the initial energy within each animal and the minimum energy-level per animal below which mortality occurs due to starvation (Table 3). Energy is reduced at each time step due to the GIS classification the animal is in and gained if a prey item is acquired.
Activity states. Active, dispersing animals move across the landscape while resting animals do not. Temporal parameters reflect daily mean periods of activity or rest, after a user-defined daily start time. Variability among individuals is simulated by including a standard deviation measure with each mean.
Activity modes. Active, dispersing animals are either searching for habitat to establish a home range or foraging for prey. Variation of animal response to location because of activity mode is influenced by modifiers (Table 2). For example, the user may increase the probability of capturing prey when foraging versus searching. The default activity mode is searching but changes to foraging if the animal's energy-level drops below a user-defined threshold.
Behavioral modes. Active animals may exhibit different behavior because of a perceived risk of mortality. Animals are active in a mode that is risky or safe. Similar to activity mode, variation of animal response to habitat because of behavioral mode is influenced by modifiers (Table 2). For example, the user can increase the probability of predation for animals in risky behavior mode and reduce that probability when in safe mode.
Default animal activity is risky but may change to safe if the animal perceives a heightened risk of mortality. This is simulated when a number drawn from an even distribution (r) falls between (PD + Ms) and (PD + Mr), where PD is the probability of depredation for the current location, Ms is input by the user to modify the minimum value at which the animal will change to safe behavior, and Mr is input by the user to modify the minimum value at which the animal will return to risky behavior. The animal will return to risky behavior at subsequent steps if: r > (PD + Mr). [If r equals PD + Mx? Check code.]
Perception and memory. Memory in SEARCH is the area perceived during dispersal: a function of perception distance and the path traversed during dispersal. Perception distance is the distance at which animals perceive key habitat elements (Zollner and Lima 1997). In SEARCH, perception distance is the radius (m) from each point along an individual's travel path and habitat suitability and occupancy are the key habitat elements perceived by animals. Habitat suitability and occupancy are defined by a user input habitat map that is updated at each time step. Perception distance is user-defined and can be altered using modifiers to reflect differences caused by time of day or season. For example, the user can reduce nighttime animal perception distance relative to daytime perception distance. A circle with a radius of this distance is produced at each point along the line-segment connecting the animal's travel path. The animal's memory is the total area within these circles for all time steps. If a polygon is visited multiple times, the most recent status of the key habitat elements is recorded as memory. This assumes that animal's retain a complete memory of habitat suitability and occupancy during dispersal.
Inter-dispersal periods. The inter-dispersal period is defined as the period between user-defined dispersal period p and p + 1. Resident animals are subject to a user-defined mortality probability during this period. Additionally, a user defined proportion of randomly selected females give birth to a user-defined mean (±SD) number of young. Young begin dispersal in the subsequent season at a random point drawn from a uniform distribution within the female's home range.
One of three scenarios initialize the simulation: (1) Animals disperse from locations defined by an input map; (2) Dispersal begins at a random point within a random number of randomly selected female home ranges from an input map; or (3) A combination of these. In all 3 scenarios, the landscape map may be populated by existing home ranges. Scenario 2 requres this +++++ Scenario 1 most closely simulates a reintroduction, while scenario 2 simulates dispersal from young of a resident population. Combining the scenarios allows the user to simulate a reintroduction in areas where residents occur.
The landscape at initialization is defined by the user-input maps. ADD MORE+++++++
At initialization a memory map is created for each animal that is updated at each time step. The memory map records the area perceived at all points during dispersal. It simulates an animal's memory of the dispersal season, and is queried during home range establishment to identify optimal sites for home range establishment.
Question: In the previous draft you point out that this may be redundant; do you have suggestions as to how I might mention it here and above, while not being redundant. I did my best to not re-state details here, but I feel it is appropriate to mention it in both sections. MAYBE COMBINE IT HERE?
Crossing. During dispersal, animals choose between the GIS landscape classification they are moving toward and remaining in their current location (Table 1). This choice uses landscape map values to calculate the probability that an animal will cross into a different habitat type using: p = n/c, where p equals the percent chance of selecting the new classification, n equals the user-defined rank of the new location, and c equals the rank of the current location. A random number (r) is generated from a uniform distribution. The animal will move into the new habitat if p > r. If p < r the animal will reflect 180º at the boundary and continue in the current habitat type for the remainder of the time step.
Home range selection and establishment. To select a home range center, each animal queries its memory map to evaluate potential home range centers. A decision matrix is then employed that incorporates the probability of acquiring food (PA) or probability of depredation (PD) at each point, weighted by a proximity factor. PA or 1-PD is multiplied by a proximity factor equaling the distance from the point to its current location (d). The user may modify d using a root factor: , where n is a value input by the user. This allows the user to modify the impact of d on home range center point selection. Movement in subsequent time steps will be directed to the point of the highest value, where attempts at home range establishment will occur. This assumes that home range center selection is based upon prey availability, predation risk, and proximity, and not other factors, such as habitat structure , or proximity to disturbance.
Home range establishment attempts occur within 1 time step when the selected point is within the animal's perceptual range. To simulate exploration around the home range center, 30 points are randomly selected from a user-defined gender-specific mean (±SD) distance from the home range center point, if it is unoccupied by an animal of the same gender. At that point a 100% minimum convex polygon (MCP) is calculated using the suitable, unoccupied points (n ≥ 3). If the area within the MCP is not ≥ the minimum home range size required for animals of its gender, 30 additional points are randomly selected and a MCP is generated using all suitable points including suitable points from the first draw. This is repeated until a MCP of suitable size is generated or until 10 unsuccessful attempts are made. If an acceptable MCP is defined, it delineates the home range boundary for that individual. Once the MCP is defined, points within its bounds are occupied and home range establishment using those points by animals of the same gender is prohibited. If 10 unsuccessful attempts are made or if an animal arrives at a home range center point occupied by another animal of the same gender that animal queries its memory map, recalculates the decision matrix, and redirects its movement toward the highest ranking remaining home range center point where the process is repeated. Only animals that establish home ranges during dispersal survive to subsequent years.
Example of a generalized dataset
SEARCH allows users to simulate the dispersal of multiple species and is most appropriate for medium or wide-ranging, solitary carnivores. We present an example of how SEARCH may be parameterized to simulate the dispersal of a mesocarnivore, using the American marten (Martes americana) as a study system. Martens were extirpated from Wisconsin during its settlement in the late 19th and early 20th centuries and reintroduction efforts have been largely unsuccessful (Williams et al. 2006). The marten is the only mammal listed as endangered in Wisconsin (CITE) and understanding the effects of habitat composition and configuration on marten dispersal is important for management of the remaining populations and for identification of areas suitable for future introductions.
Parameter estimates, modifiers, and map scheduling
Landscape and animal parameter values (Table X) were collected from published marten field studies from throughout the marten range and ongoing studies in Wisconsin. Landscape maps are from the from the USDA Stands coverage for Wisconsin (CITE) [Add details of what it classifies and where it comes from].
Modifiers were applied to +++. Support the use of modifiers in text [or add to the Table if possible]. For example, if males have a XX% greater chance of being depredated, this can be cited and modified and reflected in the Table.
Different maps were scheduled to reflect: reduced food availability during winter, reduced food availability during nighttime hours, ADD +++
ü Review the info in this section to verify that it covers all parameters (either in text or tables)
Refer to figure 4 (?) – 4 maps depicting some outputs.
Look at Mladenoff 2004 as he warns-against using a lot of parameters… should this be handled up-front, in the Intro.?
Model not validated…
Table 1. Landscape parameters -- User-input maps and field definitions. Field values are input by the user to reflect variation in animal behavioral or physiological responses to different GIS classifications.
Map title
Range of input
Movement rule
Tortuosity of movement; where 0 produces a pure random walk and 1 produces a linear movement
Step length
Mean step length (m) per time step; includes a field for standard deviation (±SD)
Energy use
Energy (kCal) used per time step
Rank of location quality
Prey acquisition
Probability of capturing a prey item per time step
Mean size (g) of captured prey; includes a field for standard deviation (±SD)
Predation risk
Probability of mortality due to depredation per time step
Habitat suitability
Suitability of habitat for home range establishment
0 or 1
If occupied by a male
If occupied by a female
Location, number, and gender of animals upon initialization
Table 2. Modifiers of animal behavior employed in SEARCH. Modifiers allow the user to modify habitat values by multiplying them by a real number to reflect variation caused by gender, activity mode, behavioral mode, time of day, and date. Temporal parameters are scheduled by the user.
Parameter modified
Prey acquisition probability
Probability of acquiring prey in a given GIS classification
Predation probability
Probability of mortality due to predation in a given GIS classification
The energy used in each GIS classification
Movement speed
The movement distance per time step in each GIS classification
Movement tortuosity
The movement tortuosity in each GIS classification
Animal perception distance in each GIS classification
Table 3. Animal parameters.
Dispersal season dates
Start and end dates for dispersal season each year and the number of years to conduct simulation runs
Time step resolution
Number of minutes between time steps
Start time
Time of day that dispersal begins
Activity and resting periods
Hours (±SD) of activity and rest from start time
Home range center selection threshold
Number of steps or suitable and unoccupied sites traversed before selecting a home range center
Distance from home range center
Gender-specific mean (±SD) distance from home range center that points are selected for home range MCP construction
Minimum home range size
Gender-specific minimum area required for a home range
Minimum and maximum energy allwable for each animal. Death occurs below the minimum value
Search/forage trigger
Perception distance
Distance at which habitat suitability and occupancy are percieved during dispersal
Safe to risky
Risky to safe
Random mortality per time step and per inter-dispersal season; independent of probability of predation landscape map value
Table 4. Landscape parameters used in Sample. THIS may need to be pared-down for the publication; otherwise it'll be too big [?]
Forest cover-classification
Summer --- value used for sample
Winter --- value used for sample
Where values came from -- citation
Peter Pan et al. (1492)
Mixed forest
Gum drops
Table 5. Animal parameters.
Parameter value
Citation -- can be here or in text
September - January
5 min
5:00am-11:00am, SD +- 1 h; 5:00pm-8:00pm, SD +- 1 h
11:01am - 4:59pm, SD +- 1 h; 8:01pm - 4:59am, SD +- 1 h
Table 5. Modifier values.
Risky Search
Risky Foraging
Safe Search
Safe Foraging
Notes to self:
Add theoretical advantages to Introduction
Define dispersal in Introduction
Need to be consistent with cover class, cover classification, OR habitat type. ***Use GIS Classification
Process overview section needs to be more descriptive --- still unclear what happens
Assumptions… need improvement
Biology can be incorporated into details, etc.
Modifier descriptions are missing in text ~
Modifiers: the term defined is too restrictive --- use: 'influences' when applicable
Location VS habitat; habitat is used inappropriately
Map swapping values for Example?? Maybe just in text… or many of these Tables of parameter Examples can be on-line Appendices???
Figure 1. Flowchart.
Figure 2. A figure of sample output maps – in a set of 4;
Literature cited
**Karieva, P., and U. Wennergren. 1995. Connecting landscape patterns to ecosystem and population processes. Nature 373:299-302.
**Marsh, L.M., and R.E. Jones. 1988. The form and consequences of random walk movement models. Journal of Theoretical Biology 133:113-131.
Fahrig, L., and J. Paloheimo. 1988. Effect of spatial arrangement of habitat patches on local population size. Ecology 69(2):468-475.
Kramer-Schadt, S., E. Revilla, T. Wiegand, and U. Breitenmoser. 2004. Fragmented landscapes, road mortality and patch connectivity: modeling influences on the dispersal of Eurasian Lynx. Journal of Applied Ecology 41:711-723.
Koehler, G.M. 1990. Population and habitat characteristics of lynx and snowshoe hares in north central Washington. Canadian Journal of Zoology 68:845-851.
Liu, J. 1993. ECOLECON: An ECOLogical-ECONomic model for specis conservation in complex forest landscapes. Ecological Modeling 70:63-87.
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Shot on location in an effectively stark and arid border town in Mexico, Borderland certainly captures the "feel" that Berman was going for. This is not a polished or glitzed-up piece of survival horror, but a dark and frequently powerful thriller that seems fairly conventional in some respects -- but then throws you a few curve balls and surprises before capping it all off with an roughly pulse-doubling finale. The performances are a whole lot stronger than what's normally found in flicks of this ilk. Presley, Strong and Muxworthy make for an entirely believable trio, and they're given just enough character development to let us care about what happens down the road. Toss in a great turn by Damian Alcazar as the worn-out cop, a disconcertingly vicious performance by Sean Astin and the stunningly beautiful face of Martha Higareda, and you've got a surprisingly excellent cast anchoring this creepy ship.
Three young Americans travel to a border town just inside Mexico and get tangled up with a vicious cult of Satan worshipers. Sounds a lot like Eli Roth's Hostel or John Stockwell's (ugh) Turistas, doesn't it? That's what I thought at first, but once I realized that Zev Berman'sBorderland was based on actual (and truly horrific) events from 1989, I had a whole new perspective on the project. (That fact that Mr. Berman was actually in Mexico went these horrible events went down, and was even questioned by the Mexican authorities, adds a whole new layer of creepiness to the flick.) Borderland is a grim, gruesome and impressively well-mounted piece of genre filmmaking, and one that's a whole lot more unsettling when you realize it was based on an actual massacre.
Rider Strong, Jake Muxworthy and Brian Presley play a trio of recent high school grads who decide to drive down south for a night of drunkenness and debauchery, but when one of the crew gets kidnapped by a gang of Satan-lovin' drug runners, things go from mildly uncomfortable to downright terrifying. As the two other guys look around, question the (predictably unhelpful) authorities, and run up against some local villains, a former cop (now disgraced, of course) does all he can to shed some light on the subject. Of course nobody believes the burnout's raving stories about homicidal "satanicos" -- or they know about the criminals but are simply too terrified to help out.
The horror fans will most likely dig Borderland, and it'll probably send a few viewers home itching to "wiki" the actual truth of the story. So while the flick offers a fairly familiar tale, it does so in admirably gritty fashion. Berman balances the weight of the actual events with the desire to mount a simply effective horror story, and I think he pulls it off quite well. Boiled down to the basics: If you liked Hostel, I suspect you'll dig this one -- and if you actually paid money to see Turistas, then I definitely think you'll be a lot more satisfied with Borderland.


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Dear Kai and Noah, on your first day of preschool and kindergarten,
I tell you both that "Mamas cry a lot" when you ask me about the tears brimming my eyes at Kindergarten registration. Mamas cry when we're proud of you for being brave, when we're happy you survived another first. We cry when we're so frustrated we can't speak, when we watch a sad movie or a happy movie, when we're nostalgic for the dimples in your wrists, and when we're worried about how you'll navigate new friendships on school playgrounds. We cry that you're suddenly so grown up, and we cry when we realize you are still so very little.
The first day of kindergarten will be the newest of new, sweet Noah. You are leaving behind the familiar of your small preschool world, eight predictable friends in a cozy classroom, the smiling face of Ms. Lynne. I can't walk you in and linger in the classroom every day, watching you fall into your regular spot at circle time, so at home and comfortable. I won't know who you're playing with on the playground, and with 27 kids in your kindergarten class, your teacher won't be able to tell me exactly what you did all day.
So, when I see your little blond head bobbing out to the car each day after school, please wave to me and tell me about your day. I'll try to ask the right questions, but I don't want to hear your day was "fine" or that you "did stuff." You're going to do exciting things at Kindergarten – things you didn't even know were possible. You'll meet new friends, and you'll learn so much about yourself once you enter the big world of school. I want to hear about it all. Not just the first day, but the second day and the 120th day.
And little Kai, your world is about to open up, too. You're starting preschool! You've asked all summer when you can go to "pretty school," and the time is finally here. You are so ready in every way, and you know it. It will be your first time going somewhere without your best friend and brother to guide and boss you. It's your chance to discover what you like to do on your own – to design your own games and play with your own friends beyond your brother's shadow. I can't wait to see who you become once you gain your footing in preschool.
What I ask of you, my little introvert, is that you go easy on yourself. When the stimulation feels like a swirling wind, step back and take a deep breath. You are in a safe place, with kind teachers and other little kids who are scared about school. No one there expects perfection, so please don't expect too much of yourself. Look how far you've come with your apraxia. It was hard work, and you did it! You are smart, and kind, and brave. And you have fantastic dance moves, so shake that little booty whenever the music strikes you.
I'll walk you both into your classrooms for your first days next week, and we all know I'll cry. I'll probably cry when I pick you up the first day, too. While there will be tears of sadness over the speed with which time flies mixed in there, most of the tears will be the proud kind.


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Senior citizen lunches at Bay Path
Bay Path Regional Vocational High School, 57 Old Muggett Hill Rd., Charlton, invites all area senior citizens to come at 1 p.m. each Wednesday during the school year for a nutritious lunch for $1. Coffee is 25¢. The program is co-sponsored by Tri-Valley Elder Services and Bay Path. Seniors may buy fresh student-made baked goods at the school's Minuteman Shoppe during lunchtime. For more information, call (508) 248-5971.
November 8: chicken Parmesan with sauce, cold turkey wrap, potato rounds, vegetable soup with crackers, fresh fruit, dessert
November 15: roast turkey with gravy, whipped potatoes, steamed carrots, cranberry sauce, corn bread banana, dessert
November 22: half-day/no lunch November 29: cold turkey sandwich with lettuce, tomato and cheese, vegetable soup with crackers, potato rounds, apple juice, dessert


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GREENFORD - Fire Chief Todd Baird provided Green Township trustees with an alternate plan for the expansion of the fire station a Tuesday's meeting.
Last month, after hearing a presentation by Eric Hammer, president of the fire department, on an architect's recommendation for an electrical survey and engineering services, trustees wanted more time to consider the information.
The addition, priced at $100,000, was discussed at a Feb. 27 meeting.
Baird said the alternate plan won't add physically to the back of the building but will enlarge two garage doors, add insulation and lighting.
The scaled back plan is estimated at $18,269, Baird said adding the department could "get by" and it would allow them to keep a medical unit inside year-round.
He said a problem will be storing the annual fireman's festival equipment that is kept there now.
Trustee Chairman Dave Slagle asked, "What will you do with it?" Baird said there was no plan other than to move it to his residence.
With about $75,000 budgeted for upgrades to the station, Baird suggested the savings could be advanced to a new ambulance.
It was just an alternate plan, Baird said.
Trustee Mark Stepuk asked about the garage doors and Baird said they were 10 feet high and a little wider.
"Right now it's extremely difficult to back in," he said, adding they can widen both doors and put heat in the building.
He said it would also be nice to have water in the building.
No action was taken.
Trustees voted to allow Washingtonville to use its roller, dump truck, tractor and leveling blade in preparation for its chip and seal program. Slagle said he received a call from village Mayor Will Jones regarding the assistance.
Green Township will also provide the manpower to operate the equipment and Trustee George Toy said, "They've been good to our fire department. I think it will be a good gesture. We do it with Beaver and Perry (townships)."
Trustees voted unanimously to provide the assistance.
Trustees also approved a bid by Morrow Electric for $2,147 for underground wiring at Coy Park. It was one of two bids received.
The board also approved adding two firefighters and funds, totaling $2,450 that can be reimbursed with a state fire marshals grant, for additional equipment.
Also, Recycling Coordinator Janet Schaffer said she was waiting for a date for the Canfield tire drive and Stepuk said, "If they don't do one, we will."
Zoning Officer Bill Pitts said season is just getting started and he issued one permit in February. The zoning board met and produced new resolution on signs which he said is working its way to trustees for their decision.