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| 5 | "author": "Cisneros-Araujo, P., Goicolea, T., Mateo-S\u00e1nchez, | 5 | "author": "Cisneros-Araujo, P., Goicolea, T., Mateo-S\u00e1nchez, | ||
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| 8 | "author_name": "Cisneros-Araujo, P., Goicolea, T., | 8 | "author_name": "Cisneros-Araujo, P., Goicolea, T., | ||
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| 84 | "name": "f97e0139-5c7e-5580-aabf-31134ddb87df", | 84 | "name": "f97e0139-5c7e-5580-aabf-31134ddb87df", | ||
| 85 | "notes": "Ecological modeling requires sufficient spatial resolution | 85 | "notes": "Ecological modeling requires sufficient spatial resolution | ||
| 86 | and a careful selection of environmental variables to achieve good | 86 | and a careful selection of environmental variables to achieve good | ||
| 87 | predictive performance. Although national and international | 87 | predictive performance. Although national and international | ||
| 88 | administrations offer fine-scale environmental data, they usually have | 88 | administrations offer fine-scale environmental data, they usually have | ||
| 89 | limited spatial coverage (country or continent). Alternatively, | 89 | limited spatial coverage (country or continent). Alternatively, | ||
| 90 | optical and radar satellite imagery is available with high | 90 | optical and radar satellite imagery is available with high | ||
| 91 | resolutions, global coverage and frequent revisit intervals. Here, we | 91 | resolutions, global coverage and frequent revisit intervals. Here, we | ||
| 92 | compared the performance of ecological models trained with free | 92 | compared the performance of ecological models trained with free | ||
| 93 | satellite data with models fitted using regionally restricted spatial | 93 | satellite data with models fitted using regionally restricted spatial | ||
| 94 | datasets. We developed brown bear habitat suitability and connectivity | 94 | datasets. We developed brown bear habitat suitability and connectivity | ||
| 95 | models from three datasets with different spatial coverage and | 95 | models from three datasets with different spatial coverage and | ||
| 96 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | 96 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | ||
| 97 | cover map (global coverage); (2) pan-European vegetation and land | 97 | cover map (global coverage); (2) pan-European vegetation and land | ||
| 98 | cover layers (continental coverage); and (3) LiDAR data and the Forest | 98 | cover layers (continental coverage); and (3) LiDAR data and the Forest | ||
| 99 | Map of Spain (national coverage). Results show that Sentinel imagery | 99 | Map of Spain (national coverage). Results show that Sentinel imagery | ||
| 100 | and pan-European datasets are powerful sources to estimate vegetation | 100 | and pan-European datasets are powerful sources to estimate vegetation | ||
| 101 | variables for habitat and connectivity modeling. However, Sentinel | 101 | variables for habitat and connectivity modeling. However, Sentinel | ||
| 102 | data could be limited for understanding precise habitat\u2013species | 102 | data could be limited for understanding precise habitat\u2013species | ||
| 103 | associations if the derived discrete variables do not distinguish a | 103 | associations if the derived discrete variables do not distinguish a | ||
| 104 | wide range of vegetation types. Therefore, more effort should be taken | 104 | wide range of vegetation types. Therefore, more effort should be taken | ||
| 105 | to improving the thematic resolution of satellite-derived vegetation | 105 | to improving the thematic resolution of satellite-derived vegetation | ||
| 106 | variables. Our findings support the application of ecological modeling | 106 | variables. Our findings support the application of ecological modeling | ||
| 107 | worldwide and can help select spatial datasets according to their | 107 | worldwide and can help select spatial datasets according to their | ||
| 108 | coverage and resolution for habitat suitability and connectivity | 108 | coverage and resolution for habitat suitability and connectivity | ||
| 109 | modeling.", | 109 | modeling.", | ||
| 110 | "notes_translated": { | 110 | "notes_translated": { | ||
| 111 | "en": "Ecological modeling requires sufficient spatial resolution | 111 | "en": "Ecological modeling requires sufficient spatial resolution | ||
| 112 | and a careful selection of environmental variables to achieve good | 112 | and a careful selection of environmental variables to achieve good | ||
| 113 | predictive performance. Although national and international | 113 | predictive performance. Although national and international | ||
| 114 | administrations offer fine-scale environmental data, they usually have | 114 | administrations offer fine-scale environmental data, they usually have | ||
| 115 | limited spatial coverage (country or continent). Alternatively, | 115 | limited spatial coverage (country or continent). Alternatively, | ||
| 116 | optical and radar satellite imagery is available with high | 116 | optical and radar satellite imagery is available with high | ||
| 117 | resolutions, global coverage and frequent revisit intervals. Here, we | 117 | resolutions, global coverage and frequent revisit intervals. Here, we | ||
| 118 | compared the performance of ecological models trained with free | 118 | compared the performance of ecological models trained with free | ||
| 119 | satellite data with models fitted using regionally restricted spatial | 119 | satellite data with models fitted using regionally restricted spatial | ||
| 120 | datasets. We developed brown bear habitat suitability and connectivity | 120 | datasets. We developed brown bear habitat suitability and connectivity | ||
| 121 | models from three datasets with different spatial coverage and | 121 | models from three datasets with different spatial coverage and | ||
| 122 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | 122 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | ||
| 123 | cover map (global coverage); (2) pan-European vegetation and land | 123 | cover map (global coverage); (2) pan-European vegetation and land | ||
| 124 | cover layers (continental coverage); and (3) LiDAR data and the Forest | 124 | cover layers (continental coverage); and (3) LiDAR data and the Forest | ||
| 125 | Map of Spain (national coverage). Results show that Sentinel imagery | 125 | Map of Spain (national coverage). Results show that Sentinel imagery | ||
| 126 | and pan-European datasets are powerful sources to estimate vegetation | 126 | and pan-European datasets are powerful sources to estimate vegetation | ||
| 127 | variables for habitat and connectivity modeling. However, Sentinel | 127 | variables for habitat and connectivity modeling. However, Sentinel | ||
| 128 | data could be limited for understanding precise habitat\u2013species | 128 | data could be limited for understanding precise habitat\u2013species | ||
| 129 | associations if the derived discrete variables do not distinguish a | 129 | associations if the derived discrete variables do not distinguish a | ||
| 130 | wide range of vegetation types. Therefore, more effort should be taken | 130 | wide range of vegetation types. Therefore, more effort should be taken | ||
| 131 | to improving the thematic resolution of satellite-derived vegetation | 131 | to improving the thematic resolution of satellite-derived vegetation | ||
| 132 | variables. Our findings support the application of ecological modeling | 132 | variables. Our findings support the application of ecological modeling | ||
| 133 | worldwide and can help select spatial datasets according to their | 133 | worldwide and can help select spatial datasets according to their | ||
| 134 | coverage and resolution for habitat suitability and connectivity | 134 | coverage and resolution for habitat suitability and connectivity | ||
| 135 | modeling.", | 135 | modeling.", | ||
| 136 | "es": "Ecological modeling requires sufficient spatial resolution | 136 | "es": "Ecological modeling requires sufficient spatial resolution | ||
| 137 | and a careful selection of environmental variables to achieve good | 137 | and a careful selection of environmental variables to achieve good | ||
| 138 | predictive performance. Although national and international | 138 | predictive performance. Although national and international | ||
| 139 | administrations offer fine-scale environmental data, they usually have | 139 | administrations offer fine-scale environmental data, they usually have | ||
| 140 | limited spatial coverage (country or continent). Alternatively, | 140 | limited spatial coverage (country or continent). Alternatively, | ||
| 141 | optical and radar satellite imagery is available with high | 141 | optical and radar satellite imagery is available with high | ||
| 142 | resolutions, global coverage and frequent revisit intervals. Here, we | 142 | resolutions, global coverage and frequent revisit intervals. Here, we | ||
| 143 | compared the performance of ecological models trained with free | 143 | compared the performance of ecological models trained with free | ||
| 144 | satellite data with models fitted using regionally restricted spatial | 144 | satellite data with models fitted using regionally restricted spatial | ||
| 145 | datasets. We developed brown bear habitat suitability and connectivity | 145 | datasets. We developed brown bear habitat suitability and connectivity | ||
| 146 | models from three datasets with different spatial coverage and | 146 | models from three datasets with different spatial coverage and | ||
| 147 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | 147 | accessibility. These datasets comprised (1) a Sentinel-1 and 2 land | ||
| 148 | cover map (global coverage); (2) pan-European vegetation and land | 148 | cover map (global coverage); (2) pan-European vegetation and land | ||
| 149 | cover layers (continental coverage); and (3) LiDAR data and the Forest | 149 | cover layers (continental coverage); and (3) LiDAR data and the Forest | ||
| 150 | Map of Spain (national coverage). Results show that Sentinel imagery | 150 | Map of Spain (national coverage). Results show that Sentinel imagery | ||
| 151 | and pan-European datasets are powerful sources to estimate vegetation | 151 | and pan-European datasets are powerful sources to estimate vegetation | ||
| 152 | variables for habitat and connectivity modeling. However, Sentinel | 152 | variables for habitat and connectivity modeling. However, Sentinel | ||
| 153 | data could be limited for understanding precise habitat\u2013species | 153 | data could be limited for understanding precise habitat\u2013species | ||
| 154 | associations if the derived discrete variables do not distinguish a | 154 | associations if the derived discrete variables do not distinguish a | ||
| 155 | wide range of vegetation types. Therefore, more effort should be taken | 155 | wide range of vegetation types. Therefore, more effort should be taken | ||
| 156 | to improving the thematic resolution of satellite-derived vegetation | 156 | to improving the thematic resolution of satellite-derived vegetation | ||
| 157 | variables. Our findings support the application of ecological modeling | 157 | variables. Our findings support the application of ecological modeling | ||
| 158 | worldwide and can help select spatial datasets according to their | 158 | worldwide and can help select spatial datasets according to their | ||
| 159 | coverage and resolution for habitat suitability and connectivity | 159 | coverage and resolution for habitat suitability and connectivity | ||
| 160 | modeling." | 160 | modeling." | ||
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| 303 | "title": "The role of remote sensing data in habitat suitability and | 303 | "title": "The role of remote sensing data in habitat suitability and | ||
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