No abstract

Habitat requirements of arctic loons (Ga@ia arctica) and red-throated loons (Guviu stellata) were studied at Storkersen Point on the Arctic coastal plain of Alaska from 1971 to 1975. Nest success ranged from 28 to 92 per cent and 33 to 78 per cent for arctic and red-throated loons, respectively. Loons were ecologically isolated in their feeding habits and use of wetlands. Arctic loons fed to their young invertebrates captured in the nest pond, and red-throated loons fed to theirs fish captured from the Beaufort Sea. Both species preferred islands as nest substrates, but arctic loons utilized large ponds with stands of Arctophila fulva wetlands for nesting, whereas, red-throated loons used smaller, partially-drained basins most frequently. RfjSUMk. Observations sur le huard arctique et sur le huard Ct gorge rousse, d Srorkersen Point, en Alaska. Entre 1971 et 1975, on a men6 des Ctudea sur lea conditions d'habitat du huard arctique (Gavia wctica) et du huard à gorge rousse (Gavia stellata), 21 Storkersen Point, dans la plaine c8tihre de l'Alaska. V i-h u i t B quatre-vingt-douze pourcent des huards arctiques, et trente-trois B soixante-dixhuit pourcent des huards B gorge rousse ont rCussi B Ctablir leurs nids en cet endroit. Les deux esphces de huard Ctaient isolkq du point de vue blogique: chacune avait ses habitudes d'alimentation et habitait un type particulier de mar& cage. Les huards arctiques ont nourri leurs couvQs d'invertCbrCs capturcS dans 1'6tang p r b du nid; les huards B gorge rousse ont d o n d B lems petits des poissons pêch6s dans la mer de Beaufort. Les deux & e s d'oiseaux ont pr6ffC6 construire leurs nids sur des îles, mais les huards arctiques ont établi leurs nids dans de grands 6tangs oh poussait l'arctophila fulva, alors que les huards B gorge rousse ont la plupart du temps utilise de plus petits 6tangs partiellement ass6ch6. PesmMe. Ha6amaenus 30 w nmobod u xpacumobdi eazapdi e nynxme Cmop~epcelta wa A~urcl~e. B nepHon 19'6-76 rr. B n y~~~e CTopxepcem, pacnonomeHaoM H& ~B H H H H O~ ~C T E apxTxsecxoro no6epemm AJLIICI~H, HsysaJracb YCJIOBEI~ O~H T~H H S sepaoso6oB (Gavia arctioa) E xpacHoso6oP (Gavia stellah) rarapu. &nS ycnalu~o saxomemm rResA xone6anacb B npenenax OT Z S S AO 92% ws sepH* H8OJIEpOBaHbI B CBWM o6pase IIHTaHHR E O~H T~H H R Ha S&JIO ¶eH€IOB MeCTHOCTE. S&B E OT 33% A0 78% xpac~oao60ft rarapw. h ' a bI ~~I J I E BKOJIOOFEI ¶eCf EH %pHO806bIe I'rtrapbI KOPMHnH lFTeHqOB BbIJI&BJIEB&e"H E8 MeCTHbM IXp AOB 6t?8IIOSBOHO ¶HbIME, a ~cpac~osobb~e rarapbI I I p E H O C a W CBOeMy IIOTOMCTBY pu&' E8 MOPS BdOpTa. 06a BHA& lIpe~lIO ¶ETaJIE X'He8AOBaTbCR Ha OCTPOBaX, IXpH ¶eM KpSOH0806bIe I'MYbphI ~b16xpam 6on~1u~e lTOpOCIIIHe &pIETO~HJIOfi pbmre& IIpYRbI, MeHblIIHx pa8MepOB. a xpac~oso6b1e rarapM game cenxmcb H a xacTmHo npemposamm Bonoemx

No abstract

New camera technology is allowing avian ecologists to perform detailed studies of avian behavior, nesting strategies and predation in areas where it was previously impossible to gather data. Unfortunately, studies have shown mechanical triggers and a variety of sensors to be inadequate in capturing footage of small predators (e.g., snakes, rodents) or events in dense vegetation. Because of this, continuous camera recording is currently the most robust solution for avian monitoring, especially in ground nesting species. However, continuous video footage results in a data deluge, as monitoring enough nests to make biologically significant inferences results in massive amounts of data which is unclassifiable by humans alone. In the summer of 2012, Dr. Ellis-Felege gathered video footage from 63 sharp-tailed grouse (Tympanuchus phasianellus) nests, as well as preliminary interior least tern (Sternula antillarum) and piping plover (Charadrius melodus) nests, resulting in over 20,000 hours of video footage. In order to effectively analyze this video, a project combining both crowd sourcing and volunteer computing was developed, where volunteers can stream nesting video and report their observations, as well as have their computers download video for analysis by computer vision techniques. This provides a robust way to analyze the video, as user observations are validated by multiple views as well as the results of the computer vision techniques. This work provides initial results analyzing the effectiveness of the crowd sourced observations and computer vision techniques.

No abstract