000 04227nam a22005655i 4500
001 978-3-319-09608-7
003 DE-He213
005 20160405110601.0
007 cr nn 008mamaa
008 141001s2014 gw | s |||| 0|eng d
020 _a9783319096087
_9978-3-319-09608-7
024 7 _a10.1007/978-3-319-09608-7
_2doi
050 4 _aQH75-77
072 7 _aRNK
_2bicssc
072 7 _aNAT011000
_2bisacsh
082 0 4 _a577
_223
100 1 _aKeller, Jeffrey K.
_eauthor.
245 1 0 _aImproving GIS-based Wildlife-Habitat Analysis
_h[electronic resource] /
_cby Jeffrey K. Keller, Charles R. Smith.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIII, 132 p. 14 illus., 6 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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 -- Chapter 4. Landscape Sampling Area vs. Actual Location of Taxonomic Survey -- Chapter 5. 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. However, 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. Additionally, underlying assumptions of the study design or technology may have unrecognized consequences. This 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. Overall, most studies in this realm employ relatively low resolution imagery that allows neither identification nor accurate classification of habitat components. Additionally, 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 ‘solid’ and ‘edge’ habitat types. Keller 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 species’ detection locations and scales of habitat use.
650 0 _aLife sciences.
650 0 _aGeographical information systems.
650 0 _aRemote sensing.
650 0 _aEcosystems.
650 0 _aConservation biology.
650 0 _aEcology.
650 0 _aNature conservation.
650 1 4 _aLife Sciences.
650 2 4 _aConservation Biology/Ecology.
650 2 4 _aGeographical Information Systems/Cartography.
650 2 4 _aEcosystems.
650 2 4 _aNature Conservation.
650 2 4 _aAnimal Systematics/Taxonomy/Biogeography.
650 2 4 _aRemote Sensing/Photogrammetry.
700 1 _aSmith, Charles R.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319096070
830 0 _aSpringerBriefs in Ecology,
_x2192-4759
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-09608-7
912 _aZDB-2-SBL
999 _c2937
_d2937