A test of a generalized, optimal habitat selection model for drift‐feeding fishes: brook charr

2021 ◽  
Author(s):  
R. Sliger ◽  
G. D. Grossman
Keyword(s):  
Habitat Selection ◽  
Selection Model ◽  
Brook Charr ◽  
Drift Feeding ◽  
Optimal Habitat

Multi-scale habitat selection model assessing potential gray wolf den habitat and dispersal corridors in Michigan, USA

2019 ◽  
Vol 397 ◽  
pp. 84-94 ◽  
Author(s):  
Heather K. Stricker ◽  
Thomas M. Gehring ◽  
Deahn Donner ◽  
Tyler Petroelje
Keyword(s):  
Habitat Selection ◽  
Selection Model ◽  
Gray Wolf ◽  
Multi Scale ◽  
Dispersal Corridors

Examining feeding strategies and position choice of drift-feeding salmonids using an individual-based, mechanistic foraging model

2001 ◽  
Vol 58 (3) ◽  
pp. 446-457 ◽  
Author(s):  
G R Guensch ◽  
T B Hardy ◽  
R C Addley
Keyword(s):  
Habitat Selection ◽  
Energy Intake ◽  
Brown Trout ◽  
Salmo Trutta ◽  
Prey Capture ◽  
Feeding Strategies ◽  
Net Energy ◽  
Intake Rate ◽  
Drift Feeding ◽  
Energy Intake Rate

We demonstrated the ability of a mechanistic habitat selection model to predict habitat selection of brown trout (Salmo trutta) and mountain whitefish (Prosopium williamsoni) during summer and winter conditions in the Blacksmith Fork River, Utah. By subtracting energy costs and losses from the gross energy intake rate (GEI) obtained through simulation of prey capture, the model calculates the potential net energy intake rate (NEI) of a given stream position, which is essentially the rate of energy intake available for growth and reproduction. The prey capture model incorporates the size, swimming speed, and reaction distance of the fish; the velocity, depth, temperature, and turbidity of the water; and the density and size composition of the drifting invertebrates. The results suggest that during both summer and winter, the brown trout and mountain whitefish in our study reach avoided locations providing low NEI and preferred locations providing a high ratio of NEI to the swimming cost (SC) at the focal position of the fish (NEI/SC). This supports the idea that the drift-feeding fish in this study selected stream positions that provided adequate NEI for the least amount of swimming effort.

scholarly journals Scale and Landscape Features Matter for Understanding Waterbird Habitat Selection

2021 ◽  
Vol 13 (21) ◽  
pp. 4397
Author(s):  
Jinya Li ◽  
Yang Zhang ◽  
Lina Zhao ◽  
Wanquan Deng ◽  
Fawen Qian ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Landscape Composition ◽  
Selection Model ◽  
Landscape Configuration ◽  
Tracking Data ◽  
Search Range ◽  
Landscape Features ◽  
Daily Movement ◽  
Habitat Features ◽  
The Impact

Clarifying species-environment relationships is crucial for the development of efficient conservation and restoration strategies. However, this work is often complicated by a lack of detailed information on species distribution and habitat features and tends to ignore the impact of scale and landscape features. Here, we tracked 11 Oriental White Storks (Ciconia boyciana) with GPS loggers during their wintering period at Poyang Lake and divided the tracking data into two parts (foraging and roosting states) according to the distribution of activity over the course of a day. Then, a three-step multiscale and multistate approach was employed to model habitat selection characteristics: (1) first, we minimized the search range of the scale for these two states based on daily movement characteristics; (2) second, we identified the optimized scale of each candidate variable; and (3) third, we fit a multiscale, multivariable habitat selection model in relation to natural features, human disturbance and especially landscape composition and configuration. Our findings reveal that habitat selection of the storks varied with spatial scale and that these scaling relationships were not consistent across different habitat requirements (foraging or roosting) and environmental features. Landscape configuration was a more powerful predictor for storks’ foraging habitat selection, while roosting was more sensitive to landscape composition. Incorporating high-precision spatiotemporal satellite tracking data and landscape features derived from satellite images from the same periods into a multiscale habitat selection model can greatly improve the understanding of species-environmental relationships and guide efficient recovery planning and legislation.

scholarly journals A SPATIALLY EXPLICIT HABITAT SELECTION MODEL INCORPORATING HOME RANGE BEHAVIOR

Ecology ◽  
2005 ◽  
Vol 86 (5) ◽  
pp. 1199-1205 ◽  
Author(s):  
Jonathan R. Rhodes ◽  
Clive A. McAlpine ◽  
Daniel Lunney ◽  
Hugh P. Possingham
Keyword(s):  
Habitat Selection ◽  
Home Range ◽  
Selection Model ◽  
Spatially Explicit

Optimal Stopover Model: a state dependent habitat selection model for staging passerines

2021 ◽  
Author(s):  
Adi Domer ◽  
Ehud Vinepinsky ◽  
Amos Bouskila ◽  
Eyal Shochat ◽  
Ofer Ovadia
Keyword(s):  
Habitat Selection ◽  
Selection Model ◽  
State Dependent

The Shape of a Gerbil Isocline Measured Using Principles of Optimal Habitat Selection

Ecology ◽  
1991 ◽  
Vol 72 (1) ◽  
pp. 329-340 ◽  
Author(s):  
Z. Abramsky ◽  
M. L. Rosenzweig ◽  
B. Pinshow
Keyword(s):  
Habitat Selection ◽  
Optimal Habitat
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