Assessing functional connectivity using empirical data

Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however,...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kadoya, Taku [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Complex life cycle Foraging theory Matrix structure Patch connectivity Structural connectivity

Übergeordnetes Werk:	Enthalten in: Population ecology - Hoboken, NJ : Wiley, 2000, 51(2008), 1 vom: 14. Nov., Seite 5-15
Übergeordnetes Werk:	volume:51 ; year:2008 ; number:1 ; day:14 ; month:11 ; pages:5-15

Links:	Volltext

DOI / URN:	10.1007/s10144-008-0120-6

Katalog-ID:	SPR008889813

Internformat


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520			\|a Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data.
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912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_266
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
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912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
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912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2108
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912			\|a GBV_ILN_2116
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912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
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publishDate	2008
allfields	10.1007/s10144-008-0120-6 doi (DE-627)SPR008889813 (SPR)s10144-008-0120-6-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl Kadoya, Taku verfasserin aut Assessing functional connectivity using empirical data 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data. Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213 Enthalten in Population ecology Hoboken, NJ : Wiley, 2000 51(2008), 1 vom: 14. Nov., Seite 5-15 (DE-627)269533516 (DE-600)1474902-6 1438-390X nnns volume:51 year:2008 number:1 day:14 month:11 pages:5-15 https://dx.doi.org/10.1007/s10144-008-0120-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE AR 51 2008 1 14 11 5-15
spelling	10.1007/s10144-008-0120-6 doi (DE-627)SPR008889813 (SPR)s10144-008-0120-6-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl Kadoya, Taku verfasserin aut Assessing functional connectivity using empirical data 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data. Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213 Enthalten in Population ecology Hoboken, NJ : Wiley, 2000 51(2008), 1 vom: 14. Nov., Seite 5-15 (DE-627)269533516 (DE-600)1474902-6 1438-390X nnns volume:51 year:2008 number:1 day:14 month:11 pages:5-15 https://dx.doi.org/10.1007/s10144-008-0120-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE AR 51 2008 1 14 11 5-15
allfields_unstemmed	10.1007/s10144-008-0120-6 doi (DE-627)SPR008889813 (SPR)s10144-008-0120-6-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl Kadoya, Taku verfasserin aut Assessing functional connectivity using empirical data 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data. Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213 Enthalten in Population ecology Hoboken, NJ : Wiley, 2000 51(2008), 1 vom: 14. Nov., Seite 5-15 (DE-627)269533516 (DE-600)1474902-6 1438-390X nnns volume:51 year:2008 number:1 day:14 month:11 pages:5-15 https://dx.doi.org/10.1007/s10144-008-0120-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE AR 51 2008 1 14 11 5-15
allfieldsGer	10.1007/s10144-008-0120-6 doi (DE-627)SPR008889813 (SPR)s10144-008-0120-6-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl Kadoya, Taku verfasserin aut Assessing functional connectivity using empirical data 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data. Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213 Enthalten in Population ecology Hoboken, NJ : Wiley, 2000 51(2008), 1 vom: 14. Nov., Seite 5-15 (DE-627)269533516 (DE-600)1474902-6 1438-390X nnns volume:51 year:2008 number:1 day:14 month:11 pages:5-15 https://dx.doi.org/10.1007/s10144-008-0120-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE AR 51 2008 1 14 11 5-15
allfieldsSound	10.1007/s10144-008-0120-6 doi (DE-627)SPR008889813 (SPR)s10144-008-0120-6-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl Kadoya, Taku verfasserin aut Assessing functional connectivity using empirical data 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data. Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213 Enthalten in Population ecology Hoboken, NJ : Wiley, 2000 51(2008), 1 vom: 14. Nov., Seite 5-15 (DE-627)269533516 (DE-600)1474902-6 1438-390X nnns volume:51 year:2008 number:1 day:14 month:11 pages:5-15 https://dx.doi.org/10.1007/s10144-008-0120-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE AR 51 2008 1 14 11 5-15
language	English
source	Enthalten in Population ecology 51(2008), 1 vom: 14. Nov., Seite 5-15 volume:51 year:2008 number:1 day:14 month:11 pages:5-15
sourceStr	Enthalten in Population ecology 51(2008), 1 vom: 14. Nov., Seite 5-15 volume:51 year:2008 number:1 day:14 month:11 pages:5-15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Complex life cycle Foraging theory Matrix structure Patch connectivity Structural connectivity
dewey-raw	570
isfreeaccess_bool	false
container_title	Population ecology
authorswithroles_txt_mv	Kadoya, Taku @@aut@@
publishDateDaySort_date	2008-11-14T00:00:00Z
hierarchy_top_id	269533516
dewey-sort	3570
id	SPR008889813
language_de	englisch
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author	Kadoya, Taku
spellingShingle	Kadoya, Taku ddc 570 bkl 42.90 misc Complex life cycle misc Foraging theory misc Matrix structure misc Patch connectivity misc Structural connectivity Assessing functional connectivity using empirical data
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topic_title	570 ASE 42.90 bkl Assessing functional connectivity using empirical data Complex life cycle (dpeaa)DE-He213 Foraging theory (dpeaa)DE-He213 Matrix structure (dpeaa)DE-He213 Patch connectivity (dpeaa)DE-He213 Structural connectivity (dpeaa)DE-He213
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title	Assessing functional connectivity using empirical data
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title_full	Assessing functional connectivity using empirical data
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title_sort	assessing functional connectivity using empirical data
title_auth	Assessing functional connectivity using empirical data
abstract	Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data.
abstractGer	Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data.
abstract_unstemmed	Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data.
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title_short	Assessing functional connectivity using empirical data
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up_date	2024-07-03T23:42:29.793Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR008889813</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519115531.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2008 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10144-008-0120-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR008889813</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10144-008-0120-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kadoya, Taku</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Assessing functional connectivity using empirical data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2008</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The potential for connectivity to impact populations in heterogeneous landscapes, and the obvious implications for conservation biology, has led to increasing interest in connectivity and a proliferation of connectivity measures. Despite the pivotal role of this measure in ecology, however, there is no generally accepted and employed formal definition of connectivity. In addition, despite the strong desire from conservationists, who are increasingly asked to design and implement corridor plans, empirically determining measures of movement and dispersal, and assessing connectivity from field data remain challenging tasks in spatial ecology. Here I summarize the current use of connectivity concepts in terms of both metapopulation and landscape ecology, and present recently developed promising techniques in spatial ecology, such as graph theory, pattern-oriented modeling, and state–space modeling, which will help to improve assessment of species-centered or functional connectivity based on empirical data.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Complex life cycle</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Foraging theory</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Matrix structure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Patch connectivity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Structural connectivity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Population ecology</subfield><subfield code="d">Hoboken, NJ : Wiley, 2000</subfield><subfield code="g">51(2008), 1 vom: 14. 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