Den‐Site Characteristics of Black Bears in Rocky Mountain National Park, Colorado

Baldwin, Roger A.; Bender, Louis C.

doi:10.2193/2007-393

Cited by 44 publications

(40 citation statements)

References 21 publications

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“…For context, the behavior of Yellowstone wolves near roads within the park and the absence of hunting and poaching along those roads suggest that they would not perceive those roads as a threat. A similar idea was proposed for black bears in Rocky Mountain National Park, where bears chose den sites close to roads despite humans' heavy use of these roads (Baldwin and Bender 2008). Wolf den sites are often located near roads in the Northern Range of the Yellowstone National Park, and this partly explains wolf preference for roads in the summers from 2000 to 2002.…”

Section: Discussionmentioning

confidence: 77%

Long‐ and short‐term temporal variability in habitat selection of a top predator

et al. 2015

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Abstract. Considerable theory explains the importance of understanding temporal variation in ecological processes. Nevertheless, long-term variability in habitat selection is rarely assessed or even acknowledged. We explored temporal variability in the habitat selection of a top-predator, the wolf (Canis lupus), at two time scales: interannual and seasonal variability. To do this, we developed resource utilization functions to relate wolf habitat selection to environmental variables in different years and seasons. We used radiotelemetry data collected from a wolf population in Yellowstone National Park during a 10-year period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) and added a Year variable in the models to account for interannual variation in the studied processes. We also used a three-year data set (nested within the 10-year data set) to incorporate additional variables in the models and test for differences in short-and long-term patterns of habitat selection. Wolves exhibited seasonal variation in habitat selection with respect to distance from roads, elevation, openness, and habitat type. Habitat selection was considerably more complicated during the winter compared to summer, when wolves only selected habitat based on distance from roads. We detected clear patterns of habitat selection in the three-year data set that could not be detected in the 10-year data set, despite the longer data set had more statistical power for pattern detection. This observation is likely the result of the longer data set being comprised of several shorter-term and countervailing patterns. This explanation is also consistent with having detected significant year effects in the 10-year data set. Insomuch as habitat selection is important to conservation and management, this research is significant for demonstrating the different impressions that can be given by short-term and long-term studies. It may be common for short-term data sets to suggest patterns of habitat selection that do not prevail over longer periods of time.

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Section: Discussionmentioning

confidence: 77%

Long‐ and short‐term temporal variability in habitat selection of a top predator

et al. 2015

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“…Variable importance can be determined in two ways. First, Maxent provides the percent contribution of each variable to the final model [10,11]. This is a heuristic approach to model importance in which the contribution values are determined by the increase in gain in the model provided by each variable [11].…”

Section: Variable Responsementioning

confidence: 99%

“…For example, is a species more likely to be found closer to water, at sites with greater annual precipitation, or at lower elevations? Because Maxent is an exponential model, the probability assigned to a location is proportional to the exponential of the selected combination of variables, thus allowing construction of response curves to illustrate the effect of selected variables on probability of use [10][11][12][13]. These response curves consist of a chart with specified metrics for the variable in question represented on the x-axis and the predicted probability of suitable conditions as defined by the logistic output when all other variables are set to their average values over all other presence locations along the y-axis [11].…”

Section: Variable Responsementioning

confidence: 99%

“…These maps can be constructed to represent the probability of finding a particular species in a given area (e.g., black bear, Ursus americanus, den sites [10]) or to indicate whether a species is likely to be present or absent in a given area (e.g., Javan slow loris, Nycticebus javanicus, locations [20]). For the former, the logistic output is typically used to represent the probability of occurrence, while for the latter, a defined threshold is used to indicate if a species is likely to be found (defined as present or absent) in a given area.…”

Section: Mapping Featurementioning

confidence: 99%

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Use of Maximum Entropy Modeling in Wildlife Research

Baldwin

2009

Entropy

578

415

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Maximum entropy (Maxent) modeling has great potential for identifying distributions and habitat selection of wildlife given its reliance on only presence locations. Recent studies indicate Maxent is relatively insensitive to spatial errors associated with location data, requires few locations to construct useful models, and performs better than other presence-only modeling approaches. Further advances are needed to better define model thresholds, to test model significance, and to address model selection. Additionally, development of modeling approaches is needed when using repeated sampling of known individuals to assess habitat selection. These advancements would strengthen the utility of Maxent for wildlife research and management.

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“…The number of successes was obtained by quantifying the validation records with prediction probability values higher than the previously defi ned threshold (Baldwin and Bender, 2008). In the same way, the proportions test was used to evaluate the accuracy between HMF map and the placement of the Asteraceae records on it.…”

Section: Statistical Analysesmentioning

confidence: 99%

Potential Distribution of Humid Mountain Forest in Mexico

et al. 2012

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Abstract:Humid Mountain Forest is one of the most important Mexican biomes due to the high fl oristic richness and endemism found within its small geographical extension. The distribution of this biome is not well known in the country; accordingly, the goal of this paper is to delimit its distribution by using potential distribution models of species characteristic to the biome as surrogates. Based on herbarium specimens housed at the National Herbarium of Mexico, a database of 78 species restricted or mostly restricted to Humid Mountain Forest was constructed. In addition, 56 environmental covariates (26 climatic, nine soil properties, nine topographic attributes, and 14 remote sensing data) were assembled. Species modeling was done using MaxEnt. Twenty-three covariates defi ned the Humid Mountain Forest distribution in Mexico. Among these, normalized vegetation index for May, total annual precipitation, and organic carbon content in the soil were the most important. According to the models, Humid Mountain Forest in Mexico represents 7% of the total surface of the country and it is found in 25 of the 32 states. Chiapas, Guerrero, Michoacán, and Oaxaca are the most outstanding states since they collectively account for 74% of the total Humid Mountain Forest surface. Keywords: maximum entropy, MODIS remote sensing data, soil properties.Resumen: El Bosque Húmedo de Montaña es uno de los biomas más importantes de México, pues allí se encuentra una gran riqueza fl orística y de endemismos, restringida a una pequeña extensión geográfi ca. Hasta la fecha no se sabe a ciencia cierta la extensión real de este bioma en el país; por tal razón, el objetivo de este trabajo es delimitar su distribución utilizando como variables substitutas la distribución potencial de especies vegetales características de este bioma. Con base en el estudio de especímenes de herbario depositados en el Herbario Nacional de México, se generó una base de datos de 78 especies restringidas o casi restringidas al Bosque Húmedo de Montaña. Por otra parte, se usaron 56 covariables ambientales (26 de clima, nueve propiedades de suelos, nueve atributos topográfi cos y 14 datos de sensores remotos). Los modelos de distribución potencial fueron elaborados utilizando el programa Maxent. Las covariables que resultaron ser signifi cativas para la modelación de la distribución del Bosque Húmedo de Montaña fueron 23; entre las más importantes se encuentran el índice de vegetación normalizada del mes de mayo, la precipitación total anual y el contenido de carbono orgánico en el suelo. De acuerdo con el modelo, el Bosque Húmedo de Montaña en México representa 7% de su territorio y se encuentra en 25 de sus 32 entidades políticas; Chiapas, Guerrero, Michoacán y Oaxaca son los estados más importantes, pues en su territorio se ubica 74% de la superfi cie total del Bosque Húmedo de Montaña. Palabras Clave: datos de sensores remotos de MODIS, máxima entropía, propiedades de suelos. ECOLOGY

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Den‐Site Characteristics of Black Bears in Rocky Mountain National Park, Colorado

Cited by 44 publications

References 21 publications

Long‐ and short‐term temporal variability in habitat selection of a top predator

Long‐ and short‐term temporal variability in habitat selection of a top predator

Use of Maximum Entropy Modeling in Wildlife Research

Potential Distribution of Humid Mountain Forest in Mexico

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