Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry
Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the approp...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Suárez-Seoane, Susana [verfasserIn] Jiménez-Alfaro, Borja [verfasserIn] Obeso, Jose Ramón [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Biodiversity and conservation - Dordrecht : Springer Netherlands, 1992, 29(2019), 3 vom: 17. Dez., Seite 987-1008 |
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| Übergeordnetes Werk: |
volume:29 ; year:2019 ; number:3 ; day:17 ; month:12 ; pages:987-1008 |
| Links: |
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| DOI / URN: |
10.1007/s10531-019-01922-5 |
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SPR01093507X |
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| 520 | |a Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. | ||
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| 700 | 1 | |a Obeso, Jose Ramón |e verfasserin |4 aut | |
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10.1007/s10531-019-01922-5 doi (DE-627)SPR01093507X (SPR)s10531-019-01922-5-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl 43.31 bkl Suárez-Seoane, Susana verfasserin aut Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 Jiménez-Alfaro, Borja verfasserin aut Obeso, Jose Ramón verfasserin aut Enthalten in Biodiversity and conservation Dordrecht : Springer Netherlands, 1992 29(2019), 3 vom: 17. Dez., Seite 987-1008 (DE-627)31751055X (DE-600)2000787-5 1572-9710 nnns volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 https://dx.doi.org/10.1007/s10531-019-01922-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_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_120 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_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_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_2107 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_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE 43.31 ASE AR 29 2019 3 17 12 987-1008 |
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10.1007/s10531-019-01922-5 doi (DE-627)SPR01093507X (SPR)s10531-019-01922-5-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl 43.31 bkl Suárez-Seoane, Susana verfasserin aut Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 Jiménez-Alfaro, Borja verfasserin aut Obeso, Jose Ramón verfasserin aut Enthalten in Biodiversity and conservation Dordrecht : Springer Netherlands, 1992 29(2019), 3 vom: 17. Dez., Seite 987-1008 (DE-627)31751055X (DE-600)2000787-5 1572-9710 nnns volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 https://dx.doi.org/10.1007/s10531-019-01922-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_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_120 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_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_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_2107 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_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE 43.31 ASE AR 29 2019 3 17 12 987-1008 |
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10.1007/s10531-019-01922-5 doi (DE-627)SPR01093507X (SPR)s10531-019-01922-5-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl 43.31 bkl Suárez-Seoane, Susana verfasserin aut Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 Jiménez-Alfaro, Borja verfasserin aut Obeso, Jose Ramón verfasserin aut Enthalten in Biodiversity and conservation Dordrecht : Springer Netherlands, 1992 29(2019), 3 vom: 17. Dez., Seite 987-1008 (DE-627)31751055X (DE-600)2000787-5 1572-9710 nnns volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 https://dx.doi.org/10.1007/s10531-019-01922-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_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_120 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_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_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_2107 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_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE 43.31 ASE AR 29 2019 3 17 12 987-1008 |
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10.1007/s10531-019-01922-5 doi (DE-627)SPR01093507X (SPR)s10531-019-01922-5-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl 43.31 bkl Suárez-Seoane, Susana verfasserin aut Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 Jiménez-Alfaro, Borja verfasserin aut Obeso, Jose Ramón verfasserin aut Enthalten in Biodiversity and conservation Dordrecht : Springer Netherlands, 1992 29(2019), 3 vom: 17. Dez., Seite 987-1008 (DE-627)31751055X (DE-600)2000787-5 1572-9710 nnns volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 https://dx.doi.org/10.1007/s10531-019-01922-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_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_120 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_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_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_2107 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_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE 43.31 ASE AR 29 2019 3 17 12 987-1008 |
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10.1007/s10531-019-01922-5 doi (DE-627)SPR01093507X (SPR)s10531-019-01922-5-e DE-627 ger DE-627 rakwb eng 570 ASE 42.90 bkl 43.31 bkl Suárez-Seoane, Susana verfasserin aut Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 Jiménez-Alfaro, Borja verfasserin aut Obeso, Jose Ramón verfasserin aut Enthalten in Biodiversity and conservation Dordrecht : Springer Netherlands, 1992 29(2019), 3 vom: 17. Dez., Seite 987-1008 (DE-627)31751055X (DE-600)2000787-5 1572-9710 nnns volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 https://dx.doi.org/10.1007/s10531-019-01922-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-GGO SSG-OPC-ASE 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_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_120 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_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 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_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_2107 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_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.90 ASE 43.31 ASE AR 29 2019 3 17 12 987-1008 |
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Enthalten in Biodiversity and conservation 29(2019), 3 vom: 17. Dez., Seite 987-1008 volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 |
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Enthalten in Biodiversity and conservation 29(2019), 3 vom: 17. Dez., Seite 987-1008 volume:29 year:2019 number:3 day:17 month:12 pages:987-1008 |
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Suárez-Seoane, Susana @@aut@@ Jiménez-Alfaro, Borja @@aut@@ Obeso, Jose Ramón @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR01093507X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519191549.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10531-019-01922-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR01093507X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10531-019-01922-5-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="084" ind1=" " ind2=" "><subfield code="a">43.31</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Suárez-Seoane, Susana</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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 Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Habitat maps</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stationary responses</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Truncated responses</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation predictive models</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiménez-Alfaro, Borja</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Obeso, Jose Ramón</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Biodiversity and conservation</subfield><subfield code="d">Dordrecht : Springer Netherlands, 1992</subfield><subfield code="g">29(2019), 3 vom: 17. 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Suárez-Seoane, Susana |
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Suárez-Seoane, Susana ddc 570 bkl 42.90 bkl 43.31 misc Habitat maps misc Stationary responses misc Truncated responses misc Vegetation predictive models Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry |
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570 ASE 42.90 bkl 43.31 bkl Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry Habitat maps (dpeaa)DE-He213 Stationary responses (dpeaa)DE-He213 Truncated responses (dpeaa)DE-He213 Vegetation predictive models (dpeaa)DE-He213 |
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Elektronische Aufsätze |
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Suárez-Seoane, Susana |
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10.1007/s10531-019-01922-5 |
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habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the european bilberry |
| title_auth |
Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry |
| abstract |
Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. |
| abstractGer |
Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. |
| abstract_unstemmed |
Abstract Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. |
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Habitat-partitioning improves regional distribution models in multi-habitat species: a case study with the European bilberry |
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Jiménez-Alfaro, Borja Obeso, Jose Ramón |
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| score |
7.401412 |