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003			ES-MaUEC
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007			cr nn 008mamaa
008			141001s2014 gw | s |||| 0|eng d
020			_a9783319096087
024	7		_a10.1007/978-3-319-09608-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH541.15.R4 _b2014 EB
100	1		_aKeller, Jeffrey K. _986858
245	1	0	_aImproving GIS-based Wildlife-Habitat Analysis _cby Jeffrey K. Keller, Charles R. Smith.
264		1	_aCham _bSpringer International Publishing _c2014.
300			_a1 recurso en línea (XIII, 132 p.) : _b14 ilustraciones, 6 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aSpringerBriefs in Ecology _x2192-4759
505	0		_aChapter 1. Working Definitions -- Chapter 2. Image Resolution vs. Habitat Selection Scale in a Remote Sensing Context -- Chapter 3. Explanatory Variables -- ChapterÂ{u4BA0}Landscape Sampling Area vs. Actual Location of Taxonomic Survey -- ChapterÂ{u5BA0}Refining Habitat Specificity -- Chapter 6. Example Using High-resolution Imagery and Taxon-specific Variables.
520			_aGeographic Information Systems (GIS) provide a powerful tool for the investigation of species-habitat relationships and the development of wildlife management and conservation programs.Â{u8BF7}wever, the relative ease of data manipulation and analysis using GIS, associated landscape metrics packages, and sophisticated statistical tests may sometimes cause investigators to overlook important species-habitat functional relationships.Â{u1924}ditionally, underlying assumptions of the study design or technology may have unrecognized consequences.Â{u4A29}s volume examines how initial researcher choices of image resolution, scale(s) of analysis, response and explanatory variables, and location and area of samples can influence analysis results, interpretation, predictive capability, and study-derived management prescriptions.Â{uFDA5}erall, most studies in this realm employ relatively low resolution imagery that allows neither identification nor accurate classification of habitat components.Â{u1924}ditionally, the landscape metrics typically employed do not adequately quantify component spatial arrangement associated with species occupation. To address this latter issue, the authors introduce two novel landscape metrics that measure the functional size and location in the landscape of taxon-specific â€s̃olidâ€{u086E}d â€ẽdgeâ€{u0A21}bitat types.Â{uB96C}ller and Smith conclude that investigators conducting GIS-based analyses of species-habitat relationships should more carefully 1) match the resolution of remotely sensed imagery to the scale of habitat functional relationships of the focal taxon, 2) identify attributes (explanatory variables) of habitat architecture, size, configuration, quality, and context that reflect the way the focal taxon uses the subset of the landscape it occupies, and 3) match the location and scale of habitat samples, whether GIS- or ground-based, to corresponding speciesâ€{u0925}tection locations and scales of habitat use.
988			_aEBOOK, EBSPRINGER
650		7	_aTeledetección _2embne _9142193
650		7	_aGestión de la fauna _2embne _9160586
650		7	_aSistemas de información geográfica _2embne _9263853
700	1		_aSmith, Charles R. _986859
710	2		_aSpringerLink (Online service) _0Local _9106996
830		0	_aSpringerBriefs in Ecology _x2192-4759 _9133238
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-09608-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2020 _dz _eo