Accounting for animal movement in estimation of resource selection functions: sampling and data analysis

Ecology ◽  
2009 ◽  
Vol 90 (12) ◽  
pp. 3554-3565 ◽  
Author(s):  
James D. Forester ◽  
Hae Kyung Im ◽  
Paul J. Rathouz
Keyword(s):  
Data Analysis ◽  
Resource Selection ◽  
Animal Movement ◽  
Resource Selection Functions

Resource selection by grazing herbivores on post-fire regrowth in a West African woodland savanna

2007 ◽  
Vol 34 (2) ◽  
pp. 77 ◽  
Author(s):  
Erik Klop ◽  
Janneke van Goethem ◽  
Hans H. de Iongh
Keyword(s):  
Resource Selection ◽  
West African ◽  
Tree Cover ◽  
Resource Selection Functions ◽  
Risk Of Predation ◽  
Savanna Fires ◽  
Grass Sward ◽  
Burnt Areas ◽  
Good Visibility ◽  
Selection Of

The preference of grazing herbivores to feed on grass regrowth following savanna fires rather than on unburnt grass swards is widely recognised. However, there is little information on which factors govern patterns of resource selection within burnt areas. In this study, we attempted to disentangle the effects of different habitat and grass sward characteristics on the utilisation of post-fire regrowth by nine species of ungulates in a fire-dominated woodland savanna in north Cameroon. We used resource-selection functions based on logistic regression. Overall, the resource-selection functions identified the time elapsed since burning as the most influential parameter in determining probability of use by ungulates, as most species strongly selected swards that were recently burned. This pattern might be related to nutrient levels in the grass sward. In addition, most species selected areas with high grass cover and avoided grass swards with high amounts of dead stem material. This is likely to increase bite mass and, hence, intake rates. The avoidance of high tree cover by some species may suggest selection for open areas with good visibility and, hence, reduced risk of predation. Body mass seemed to have no effect on differential selection of post-fire regrowth, irrespective of feeding style.

Process-focussed, multi-grain resource selection functions

2015 ◽  
Vol 305 ◽  
pp. 10-21 ◽  
Author(s):  
Michel P. Laforge ◽  
Eric Vander Wal ◽  
Ryan K. Brook ◽  
Erin M. Bayne ◽  
Philip D. McLoughlin
Keyword(s):  
Resource Selection ◽  
Resource Selection Functions

scholarly journals Evaluating resource selection functions

2002 ◽  
Vol 157 (2-3) ◽  
pp. 281-300 ◽  
Author(s):  
Mark S Boyce ◽  
Pierre R Vernier ◽  
Scott E Nielsen ◽  
Fiona K.A Schmiegelow
Keyword(s):  
Resource Selection ◽  
Resource Selection Functions

scholarly journals Mapping resource selection functions in wildlife studies: Concerns and recommendations

2016 ◽  
Vol 76 ◽  
pp. 173-183 ◽  
Author(s):  
Lillian R. Morris ◽  
Kelly M. Proffitt ◽  
Jason K. Blackburn
Keyword(s):  
Resource Selection ◽  
Resource Selection Functions ◽  
Mapping Resource

scholarly journals Correlation and studies of habitat selection: problem, red herring or opportunity?

2010 ◽  
Vol 365 (1550) ◽  
pp. 2233-2244 ◽  
Author(s):  
John Fieberg ◽  
Jason Matthiopoulos ◽  
Mark Hebblewhite ◽  
Mark S. Boyce ◽  
Jacqueline L. Frair
Keyword(s):  
Habitat Selection ◽  
Resource Selection ◽  
New Technologies ◽  
Animal Movement ◽  
Mixed Effects ◽  
Correlated Data ◽  
Two Stage ◽  
Wide Range ◽  
Spatio Temporal ◽  
Post Hoc

With the advent of new technologies, animal locations are being collected at ever finer spatio-temporal scales. We review analytical methods for dealing with correlated data in the context of resource selection, including post hoc variance inflation techniques, ‘two-stage’ approaches based on models fit to each individual, generalized estimating equations and hierarchical mixed-effects models. These methods are applicable to a wide range of correlated data problems, but can be difficult to apply and remain especially challenging for use–availability sampling designs because the correlation structure for combinations of used and available points are not likely to follow common parametric forms. We also review emerging approaches to studying habitat selection that use fine-scale temporal data to arrive at biologically based definitions of available habitat, while naturally accounting for autocorrelation by modelling animal movement between telemetry locations. Sophisticated analyses that explicitly model correlation rather than consider it a nuisance, like mixed effects and state-space models, offer potentially novel insights into the process of resource selection, but additional work is needed to make them more generally applicable to large datasets based on the use–availability designs. Until then, variance inflation techniques and two-stage approaches should offer pragmatic and flexible approaches to modelling correlated data.

scholarly journals Transcending scale dependence in identifying habitat with resource selection functions

2012 ◽  
Vol 22 (4) ◽  
pp. 1068-1083 ◽  
Author(s):  
Nicholas J. DeCesare ◽  
Mark Hebblewhite ◽  
Fiona Schmiegelow ◽  
David Hervieux ◽  
Gregory J. McDermid ◽  
...  
Keyword(s):  
Resource Selection ◽  
Scale Dependence ◽  
Resource Selection Functions

scholarly journals Temporal variation and scale in movement-based resource selection functions

2014 ◽  
Vol 17 ◽  
pp. 82-98 ◽  
Author(s):  
M.B. Hooten ◽  
E.M. Hanks ◽  
D.S. Johnson ◽  
M.W. Alldredge
Keyword(s):  
Temporal Variation ◽  
Resource Selection ◽  
Resource Selection Functions

Resource selection functions for age-0 Arctic grayling (Thymallus arcticus) and their application to stream habitat compensation

2004 ◽  
Vol 61 (9) ◽  
pp. 1736-1746 ◽  
Author(s):  
Nicholas E Jones ◽  
William M Tonn
Keyword(s):  
Habitat Use ◽  
Water Depth ◽  
Resource Selection ◽  
Productive Capacity ◽  
Resource Selection Functions ◽  
Arctic Grayling ◽  
Artificial Stream ◽  
Average Water ◽  
Thymallus Arcticus ◽  
Habitat Compensation

We developed resource selection functions (RSFs) for young-of-the-year (YOY) Arctic grayling (Thymallus arcticus) in a natural Barrenlands stream and used them to assess the habitat in an artificial stream created as part of a habitat compensation agreement in the Canadian Arctic. The model for small (15–21 mm) grayling explained 55% of the variation in habitat use and included water velocity, average water depth, and percentage of detritus and fines. The model for large (38–57 mm) grayling explained 36% of the variation in habitat use and included water depth, percentage of detritus and fines, and several cover variables. Model validation using a withheld sample of data indicated that the models provided good fits to the data, correctly classifying 71–75% of habitat-use locations. Applying the RSFs to observed habitat use in the artificial stream indicated an abundance of quality habitat for small grayling, but a paucity for the larger YOY. These results reflect an ontogenetic shift in habitat requirements, from the simple needs of small YOY to the more complex demands of larger YOY, demands that could not be well met by the artificial stream. We suggest that this inability contributed to the poor productive capacity of the artificial stream.

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