Using Ships of Opportunity to Assess Winter Habitat Associations of Seabirds in Subarctic Coastal Alaska

2015 ◽  
Vol 89 (2) ◽  
pp. 111-128 ◽  
Author(s):  
Neil M. Dawson ◽  
Mary A. Bishop ◽  
Kathy J. Kuletz ◽  
Alain F. Zuur
2017 ◽  
Vol 81 (6) ◽  
pp. 1042-1050 ◽  
Author(s):  
Emily D. Thorne ◽  
Charles Waggy ◽  
David S. Jachowski ◽  
Marcella J. Kelly ◽  
W. Mark Ford

Bird Study ◽  
2003 ◽  
Vol 50 (2) ◽  
pp. 116-130 ◽  
Author(s):  
Mark H. Hancock ◽  
Jeremy D. Wilson

2019 ◽  
Vol 222 ◽  
pp. 214-225
Author(s):  
Sarah E. Gutowsky ◽  
Robert A. Ronconi ◽  
Lee F.G. Gutowsky ◽  
Mark F. Elderkin ◽  
Julie Paquet ◽  
...  

2009 ◽  
Vol 16 (3) ◽  
pp. 471-480 ◽  
Author(s):  
Thomas S. Jung ◽  
Tony E. Chubbs ◽  
Colin G. Jones ◽  
Frank R. Phillips ◽  
Robert D. Otto

2006 ◽  
Vol 84 (12) ◽  
pp. 1823-1832 ◽  
Author(s):  
Kim G. Poole ◽  
Kari Stuart-Smith

Winter range has been identified as an important component of moose ( Alces alces (L., 1758)) conservation in managed forests, yet there have been few studies on habitat associations in montane ecosystems. We investigated habitat selection by moose at landscape and stand scales during late winter in southeastern British Columbia using global positioning system (GPS) collars on 24 adult moose cows in each of two winters. The strongest determinant of late-winter range at the landscape scale was decreasing elevation, while moose also selected for areas of gentler slopes and higher solar insolation. Elevation likely is a surrogate for snow depth, which is probably the primary causative factor influencing late-winter distribution of moose. Within late-winter range, topographic variables had little influence on moose habitat selection. Lower crown closure was the strongest determinant of stand-scale selection, although the resultant model was weak. We found no disproportionate selection for stands with high crown closure, and there was little evidence for greater use of cover stands with increasing snow as winter progressed. Within late-winter range, moose selected forage habitats (42% use vs. 30% availability) over cover habitats (22% use vs. 37% availability). The delineation of late-winter moose range can be based on snow depth, or elevation as its surrogate.


2013 ◽  
Vol 125 (3) ◽  
pp. 502-512 ◽  
Author(s):  
Marianne G. Korosy ◽  
Joshua S. Reece ◽  
Reed F. Noss

1985 ◽  
Vol 6 (2) ◽  
pp. 52-58 ◽  
Author(s):  
Susan T. Bagley

AbstractThe genus Klebsiella is seemingly ubiquitous in terms of its habitat associations. Klebsiella is a common opportunistic pathogen for humans and other animals, as well as being resident or transient flora (particularly in the gastrointestinal tract). Other habitats include sewage, drinking water, soils, surface waters, industrial effluents, and vegetation. Until recently, almost all these Klebsiella have been identified as one species, ie, K. pneumoniae. However, phenotypic and genotypic studies have shown that “K. pneumoniae” actually consists of at least four species, all with distinct characteristics and habitats. General habitat associations of Klebsiella species are as follows: K. pneumoniae—humans, animals, sewage, and polluted waters and soils; K. oxytoca—frequent association with most habitats; K. terrigena— unpolluted surface waters and soils, drinking water, and vegetation; K. planticola—sewage, polluted surface waters, soils, and vegetation; and K. ozaenae/K. rhinoscleromatis—infrequently detected (primarily with humans).


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Mohammadi ◽  
K. Almasieh ◽  
D. Nayeri ◽  
F. Ataei ◽  
A. Khani ◽  
...  

AbstractIran lies at the southernmost range limit of brown bears globally. Therefore, understanding the habitat associations and patterns of population connectivity for brown bears in Iran is relevant for the species’ conservation. We applied species distribution modeling to predict habitat suitability and connectivity modeling to identify population core areas and corridors. Our results showed that forest density, topographical roughness, NDVI and human footprint were the most influential variables in predicting brown bear distribution. The most crucial core areas and corridor networks for brown bear are concentrated in the Alborz and Zagros Mountains. These two core areas were predicted to be fragmented into a total of fifteen isolated patches if dispersal of brown bear across the landscape is limited to 50,000 cost units, and aggregates into two isolated habitat patches if the species is capable of dispersing 400,000 cost units. We found low overlap between corridors, and core habitats with protected areas, suggesting that the existing protected area network may not be adequate for the conservation of brown bear in Iran. Our results suggest that effective conservation of brown bears in Iran requires protection of both core habitats and the corridors between them, especially outside Iran’s network of protected areas.


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