scholarly journals A reformulation of the selection ratio shed light on resource selection functions and leads to a unified framework for habitat selection studies

2019 ◽  
Author(s):  
Simon Chamaillé-Jammes
Keyword(s):  
Habitat Selection ◽  
Resource Selection ◽  
Selection Effect ◽  
Added Value ◽  
Intermediate Step ◽  
List Type ◽  
Unified Framework ◽  
Resource Selection Functions ◽  
Selection Ratio ◽  
Continuous Predictors

AbstractThe selection ratio (SR), i.e. the ratio of proportional use of a habitat over proportional availability of this habitat, has for long been the standard metric of habitat selection analyses. It is easy to compute and directly estimates disproportionate use. Its apparent restriction to habitat selection analyses using categorical predictors led to the development of the resource selection functions (RSF) approach, which has now become the norm.The RSF approach has however led to debates and confusion. For instance, what functional form can be used remains debated, and the concept of relative probability of selection is often misunderstood.I propose a reformulation of the SR demonstrating that it can be estimated in a regression context, and thus even with continuous predictors. This reformulation suggests that RSF can be seen as an intermediate step in the calculation of SR. This reformulation also clarifies some long-standing debates about RSF and data-selection/fitting practices.I further suggest that SR estimates the strength of habitat selection, but that the contribution of selection in determining use, which should be more directly linked to fitness than selection per se, should be estimated by another metric, the selection effect on use (SE). SE could be estimated simply as the difference between proportional use and proportional availability, and can be computed from SR and a density estimation of availability.I conduct a habitat selection analysis of plains zebras to demonstrate the added-value of going beyond RSF scores and using SR estimated in a regression context, and of combining SR and SE.Overall, I highlight the inter-relation between various metrics used to study habitat selection (i.e., SR, other selection indices, RSF scores, marginality). I conclude by proposing that SR and SE can be the unifying metrics of habitat selection, as together they offer a comprehensive view on the strength of habitat selection and its effect on habitat use.

scholarly journals A Novel Framework to Predict Relative Habitat Selection in Aquatic Systems: Applying Machine Learning and Resource Selection Functions to Acoustic Telemetry Data From Multiple Shark Species

2021 ◽  
Vol 8 ◽  
Author(s):  
Lucas P. Griffin ◽  
Grace A. Casselberry ◽  
Kristen M. Hart ◽  
Adrian Jordaan ◽  
Sarah L. Becker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Habitat Selection ◽  
Acoustic Telemetry ◽  
Resource Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Shark Species ◽  
Telemetry Data ◽  
Resource Selection Functions ◽  
Resource Managers

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

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.

Spatial decomposition of predation risk using resource selection functions: an example in a wolf-elk predator-prey system

Oikos ◽  
2005 ◽  
Vol 111 (1) ◽  
pp. 101-111 ◽  
Author(s):  
M. Hebblewhite ◽  
E. H. Merrill ◽  
T. L. McDonald
Keyword(s):  
Predation Risk ◽  
Resource Selection ◽  
Resource Selection Functions ◽  
Predator Prey ◽  
Spatial Decomposition ◽  
Prey System

scholarly journals Human-induced changes in habitat preference by capybaras (Hydrochoerus hydrochaeris) and their potential effect on zoonotic disease transmission

2020 ◽  
Author(s):  
Thiago C. Dias ◽  
Jared A. Stabach ◽  
Qiongyu Huang ◽  
Marcelo B. Labruna ◽  
Peter Leimgruber ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Disease Transmission ◽  
Resource Selection ◽  
Spotted Fever ◽  
Space Use ◽  
Zoonotic Disease ◽  
Disease Spread ◽  
Selection Strategies ◽  
Hydrochoerus Hydrochaeris ◽  
Natural Landscapes

AbstractHuman activities are changing landscape structure and function globally, affecting wildlife space use, and ultimately increasing human-wildlife conflicts and zoonotic disease spread. Capybara (Hydrochoerus hydrochaeris) is a conflict species that has been implicated in the spread and amplification of the most lethal tick-borne disease in the world, the Brazilian spotted fever (BSF). Even though essential to understand the link between capybaras, ticks and the BSF, many knowledge gaps still exist regarding the effects of human disturbance in capybara space use. Here, we analyzed diurnal and nocturnal habitat selection strategies of capybaras across natural and human-modified landscapes using resource selection functions (RSF). Selection for forested habitats was high across human- modified landscapes, mainly during day- periods. Across natural landscapes, capybaras avoided forests during both day- and night periods. Water was consistently selected across both landscapes, during day- and nighttime. This variable was also the most important in predicting capybara habitat selection across natural landscapes. Capybaras showed slightly higher preferences for areas near grasses/shrubs across natural landscapes, and this variable was the most important in predicting capybara habitat selection across human-modified landscapes. Our results demonstrate human-driven variation in habitat selection strategies by capybaras. This behavioral adjustment across human-modified landscapes may be related to BSF epidemiology.

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

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

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.

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