scholarly journals Identification of landscape features influencing gene flow: How useful are habitat selection models?

2016 ◽  
Vol 9 (6) ◽  
pp. 805-817 ◽  
Author(s):  
Gretchen H. Roffler ◽  
Michael K. Schwartz ◽  
Kristy L. Pilgrim ◽  
Sandra L. Talbot ◽  
George K. Sage ◽  
...  
Keyword(s):  
Gene Flow ◽  
Habitat Selection ◽  
Selection Models ◽  
Landscape Features

scholarly journals Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Moritz Mercker ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Leonie Enners ◽  
Stefan Garthe
Keyword(s):  
Habitat Selection ◽  
Large Scale ◽  
Error Rates ◽  
Process Models ◽  
Statistical Hypothesis ◽  
Type I ◽  
Tracking Data ◽  
Selection Models ◽  
Animal Tracking ◽  
Step Selection

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

Evaluating relocation extent versus covariate resolution in habitat selection models across spatiotemporal scales

2018 ◽  
Vol 48 ◽  
pp. 245-256 ◽  
Author(s):  
Peter R. Nelson ◽  
Kyle Joly ◽  
Carl A. Roland ◽  
Bruce McCune
Keyword(s):  
Habitat Selection ◽  
Selection Models ◽  
Spatiotemporal Scales

Effects of landscape features on gene flow among urban frog populations

2019 ◽  
Vol 34 (4) ◽  
pp. 497-508 ◽  
Author(s):  
Hisanori Okamiya ◽  
Tamotsu Kusano
Keyword(s):  
Gene Flow ◽  
Landscape Features

scholarly journals Multi-scale selection models predict breeding habitat for two Arctic-breeding raptor species

2020 ◽  
Vol 6 (1) ◽  
pp. 24-40
Author(s):  
Philippe Galipeau ◽  
Alastair Franke ◽  
Mathieu Leblond ◽  
Joel Bêty
Keyword(s):  
Habitat Selection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Environmental Indicators ◽  
Breeding Habitat ◽  
Slope Aspect ◽  
Peregrine Falcon ◽  
Apex Predators ◽  
Selection Models

Raptors are important environmental indicators because they are apex predators and can be sensitive to disturbance. Few studies have addressed habitat preferences of tundra-nesting raptors, and those that exist have focused on fine-scale characteristics. With increasing economic development predicted to occur throughout the Canadian Arctic, the investigation of raptor breeding habitat at broad spatial scales is required. We modeled breeding habitat selection for two raptor species on north Baffin Island, NU, Canada. During aerial surveys conducted over six breeding seasons, we documented 172 peregrine falcon (Falco peregrinus tundrius) and 160 rough-legged hawk (Buteo lagopus) nesting sites. We used these locations in conjunction with remote sensing data to build habitat selection models at three spatial scales. Topography, distance to water, and normalized difference vegetation index explained selection at all scales; slope aspect was also important at the finest scale. To validate landscape scale models, we conducted a validation survey that resulted in the detection of 45 new nests (peregrine falcon n = 21, rough-legged hawk n = 24). We did not detect any new nests in areas where model-predicted occurrence was expected to be low. Conversely, we found more than half of previously undetected nests in areas where model-predicted occurrence was expected to be high.

scholarly journals Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range-wide gene flow in sage-grouse

2018 ◽  
Vol 11 (8) ◽  
pp. 1305-1321 ◽  
Author(s):  
Jeffrey R. Row ◽  
Kevin E. Doherty ◽  
Todd B. Cross ◽  
Michael K. Schwartz ◽  
Sara J. Oyler-McCance ◽  
...  
Keyword(s):  
Gene Flow ◽  
Functional Connectivity ◽  
Breeding Habitat ◽  
Sage Grouse ◽  
Landscape Features

Spatial variation in the response of tiger gene flow to landscape features and limiting factors

2019 ◽  
Vol 22 (5) ◽  
pp. 472-480 ◽  
Author(s):  
P. A. Reddy ◽  
J.‐P. Puyravaud ◽  
S. A. Cushman ◽  
H. Segu
Keyword(s):  
Gene Flow ◽  
Spatial Variation ◽  
Limiting Factors ◽  
Landscape Features
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