RELATING RESOURCES TO A PROBABILISTIC MEASURE OF SPACE USE: FOREST FRAGMENTS AND STELLER'S JAYS

By John M. Marzluff, Joshua J. Millspaugh, P. Hurvitz, Mark S. Handcock. 2004

376 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/3e56d614a89b6de35dde7a97c1da8d9eff861236

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Citations in the corpus

Analyzing animal movements using Brownian bridges. (2007, methodology): “Lastly, the ability to describe long-distance movements (e.g., migration routes) probabilistically represents a significant improvement over methods that simply connect locations using straight lines (e.g., Stokes et al. 1998, Saher and Schmiegelow 2004).”
Correcting Home-Range Models for Observation Bias (2007, methodology): “Home ranges have been used to gain insight into important resources (Marzluff et al. 2004), predator–prey relationships (Norbury et al. 1998), and competition (Minta 1992), as well as social pressures and mating systems (Conner et al. 1999).”
Modelling distribution and abundance with presence‐only data (2006, background): “Marzluff et al. (2004) have extended this approach by modelling the intensity of use relative to environmental covariates.”
Kernel-based home range method for data with irregular sampling intervals (2006, methodology): “…the loss of However, animals are unlikely to use their home range in a uniform manner and consequently, probabilistic methods can be more useful in habitat related studies than the MCP method or flat kernel (Worton, 1987; Harris et al., 1990; Seaman and Powell, 1996; Marzluff et al., 2001, 2004).”
Linking Movement Ecology with Wildlife Management and Conservation (2016, methodology): “These methods have specific requirements regarding the quality of data needed, as few as 10 locations per month may be sufficient to estimate a home range but this would not be sufficient for understanding resource use (Marzluff et al., 2004; Börger et al., 2006).”
Likelihood Cross-Validation Versus Least Squares Cross-Validation for Choosing the Smoothing Parameter in Kernel Home-Range Analysis (2006, methodology): “And recently, Marzluff et al. (2004) suggested a new analytic technique that used areas of high use estimated from kernel density to determine resource preferences for Steller’s jays (Cyanocitta stelleri).”
QUANTIFYING HOME-RANGE OVERLAP: THE IMPORTANCE OF THE UTILIZATION DISTRIBUTION (2005, background): “For example, the UD may provide the most informative measure of use in habitat selection studies (Marzluff et al. 2001, 2004).”
Study Designs and Tests for Comparing Resource Use and Availability II (2006, methodology): “…et al. 1998, Cooper and Millspaugh 1999, Manly et al. 2002, Ramsey and Usner 2003, Thomas et al. 2006) and regression of resource attributes on probabilities (or intensities) of use from estimated utilization distributions (Marzluff et al. 2004, Millspaugh et al. 2006, Sawyer et al. 2006).”
Effects of sampling regime on the mean and variance of home range size estimates. (2006, background): “The kernel can account for multiple centres of activity (Powell 2000; Kenward 2001; Kernohan et al. 2001), is robust to changes in the spatial resolution of the data (Hansteen et al. 1997) and is becoming the method of choice for modern studies of animal‐habitat relationships ( Marzluff et al. 2004 ”
Effects of hunter access and habitat security on elk habitat selection in landscapes with a public and private land matrix (2013, methodology): “We treated individual animal selection coefficients as a random sample from a normal distribution with the mean representing the population-level effect of a covariate on the relative probability of selection (Marzluff et al. 2004, Sawyer et al. 2006).”
Estimating Utilization Distributions with Kernel Versus Local Convex Hull Methods (2011, methodology): “Similarly, tools such as the volume-of-intersection (VI) index (Seidel 1992, Millspaugh et al. 2004) and resource utilization functions (Marzluff et al. 2004) analyze social interactions or resource selection based on the conditional probability that an animal shares space with another individual…”