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| 4 | "alternate_identifier": "DOI: 10.1016/j.jenvman.2019.109320", | 4 | "alternate_identifier": "DOI: 10.1016/j.jenvman.2019.109320", | ||
| 5 | "author": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, E.P. y | 5 | "author": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, E.P. y | ||
| 6 | Desbiez, A.", | 6 | Desbiez, A.", | ||
| 7 | "author_name": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, | 7 | "author_name": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, | ||
| 8 | E.P. y Desbiez, A.", | 8 | E.P. y Desbiez, A.", | ||
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| 33 | "created": "2025-05-23", | 33 | "created": "2025-05-23", | ||
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| 36 | "name": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, E.P. y | 36 | "name": "Ascens\u00e3o, F., Yogui, D., Alves, M., Medici, E.P. y | ||
| 37 | Desbiez, A." | 37 | Desbiez, A." | ||
| 38 | } | 38 | } | ||
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| 51 | h\u00e1bitat se transforma en una serie de parches m\u00e1s | 51 | h\u00e1bitat se transforma en una serie de parches m\u00e1s | ||
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| 53 | matriz de h\u00e1bitats distinta de la original", | 53 | matriz de h\u00e1bitats distinta de la original", | ||
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| 72 | "lineage_process_steps": [], | 72 | "lineage_process_steps": [], | ||
| 73 | "lineage_source": [ | 73 | "lineage_source": [ | ||
| 74 | "Journal of Environmental Management. Vol. 248", | 74 | "Journal of Environmental Management. Vol. 248", | ||
| 75 | "pag. 109320" | 75 | "pag. 109320" | ||
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| 94 | "name": "fcadbff5-6813-5a1b-8eb0-4ce902305c8f", | 94 | "name": "fcadbff5-6813-5a1b-8eb0-4ce902305c8f", | ||
| 95 | "notes": "We modelled the spatiotemporal patterns of road mortality | 95 | "notes": "We modelled the spatiotemporal patterns of road mortality | ||
| 96 | for seven medium-large mammals, using a roadkill dataset from Mato | 96 | for seven medium-large mammals, using a roadkill dataset from Mato | ||
| 97 | Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for | 97 | Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for | ||
| 98 | two years). We related roadkill presence-absence along the road | 98 | two years). We related roadkill presence-absence along the road | ||
| 99 | sections (1000 m) and across the survey dates with a collection of | 99 | sections (1000 m) and across the survey dates with a collection of | ||
| 100 | environmental variables, including land cover, forest cover, distance | 100 | environmental variables, including land cover, forest cover, distance | ||
| 101 | to rivers, temperature, precipitation and vegetation productivity. We | 101 | to rivers, temperature, precipitation and vegetation productivity. We | ||
| 102 | further included two variables aiming to reflect the intrinsic spatial | 102 | further included two variables aiming to reflect the intrinsic spatial | ||
| 103 | and temporal roadkill risk. Environmental variables were obtained | 103 | and temporal roadkill risk. Environmental variables were obtained | ||
| 104 | through remote sensing and weather stations, allowing the estimate of | 104 | through remote sensing and weather stations, allowing the estimate of | ||
| 105 | the roadkill risk for the entire surveyed roads and survey periods. | 105 | the roadkill risk for the entire surveyed roads and survey periods. | ||
| 106 | Overall, the models could explain a small fraction of the | 106 | Overall, the models could explain a small fraction of the | ||
| 107 | spatiotemporal patterns of roadkills (<0.23), probably due to species | 107 | spatiotemporal patterns of roadkills (<0.23), probably due to species | ||
| 108 | being habitat generalists, but still had reasonable discrimination | 108 | being habitat generalists, but still had reasonable discrimination | ||
| 109 | power (AUC averaging 0.70 \u00b1 0.07). The intrinsic spatial and | 109 | power (AUC averaging 0.70 \u00b1 0.07). The intrinsic spatial and | ||
| 110 | temporal roadkill risk were the most important variables, followed by | 110 | temporal roadkill risk were the most important variables, followed by | ||
| 111 | land cover, climate and NDVI. We show that identifying spatiotemporal | 111 | land cover, climate and NDVI. We show that identifying spatiotemporal | ||
| 112 | roadkill patterns may provide valuable information to define specific | 112 | roadkill patterns may provide valuable information to define specific | ||
| 113 | management actions focused on road sections and time periods, in | 113 | management actions focused on road sections and time periods, in | ||
| 114 | complement to permanent road mitigation measures. Our approach thus | 114 | complement to permanent road mitigation measures. Our approach thus | ||
| 115 | offers a new insight into the understanding of road effects and how to | 115 | offers a new insight into the understanding of road effects and how to | ||
| 116 | plan and strategize monitoring and mitigation.", | 116 | plan and strategize monitoring and mitigation.", | ||
| 117 | "notes_translated": { | 117 | "notes_translated": { | ||
| 118 | "en": "We modelled the spatiotemporal patterns of road mortality | 118 | "en": "We modelled the spatiotemporal patterns of road mortality | ||
| 119 | for seven medium-large mammals, using a\u00a0roadkill\u00a0dataset | 119 | for seven medium-large mammals, using a\u00a0roadkill\u00a0dataset | ||
| 120 | from Mato Grosso do Sul,\u00a0Brazil\u00a0(800\u202fkm of roads | 120 | from Mato Grosso do Sul,\u00a0Brazil\u00a0(800\u202fkm of roads | ||
| 121 | surveyed every two weeks, for two years). We | 121 | surveyed every two weeks, for two years). We | ||
| 122 | related\u00a0roadkill\u00a0presence-absence along the road sections | 122 | related\u00a0roadkill\u00a0presence-absence along the road sections | ||
| 123 | (1000\u202fm) and across the survey dates with a collection of | 123 | (1000\u202fm) and across the survey dates with a collection of | ||
| 124 | environmental variables, including land cover, forest cover, distance | 124 | environmental variables, including land cover, forest cover, distance | ||
| 125 | to rivers, temperature, precipitation and vegetation productivity. We | 125 | to rivers, temperature, precipitation and vegetation productivity. We | ||
| 126 | further included two variables aiming to reflect the intrinsic spatial | 126 | further included two variables aiming to reflect the intrinsic spatial | ||
| 127 | and temporal roadkill risk. Environmental variables were obtained | 127 | and temporal roadkill risk. Environmental variables were obtained | ||
| 128 | through\u00a0remote sensing\u00a0and weather stations, allowing the | 128 | through\u00a0remote sensing\u00a0and weather stations, allowing the | ||
| 129 | estimate of the roadkill risk for the entire surveyed roads and survey | 129 | estimate of the roadkill risk for the entire surveyed roads and survey | ||
| 130 | periods. Overall, the models could explain a small fraction of the | 130 | periods. Overall, the models could explain a small fraction of the | ||
| 131 | spatiotemporal patterns of roadkills (<0.23), probably due to species | 131 | spatiotemporal patterns of roadkills (<0.23), probably due to species | ||
| 132 | being habitat\u00a0generalists, but still had reasonable | 132 | being habitat\u00a0generalists, but still had reasonable | ||
| 133 | discrimination power (AUC averaging 0.70\u202f\u00b1\u202f0.07). The | 133 | discrimination power (AUC averaging 0.70\u202f\u00b1\u202f0.07). The | ||
| 134 | intrinsic spatial and temporal roadkill risk were the most important | 134 | intrinsic spatial and temporal roadkill risk were the most important | ||
| 135 | variables, followed by land cover, climate and\u00a0NDVI. We show that | 135 | variables, followed by land cover, climate and\u00a0NDVI. We show that | ||
| 136 | identifying spatiotemporal roadkill patterns may provide valuable | 136 | identifying spatiotemporal roadkill patterns may provide valuable | ||
| 137 | information to define specific management actions focused on road | 137 | information to define specific management actions focused on road | ||
| 138 | sections and time periods, in complement to permanent | 138 | sections and time periods, in complement to permanent | ||
| 139 | road\u00a0mitigation measures. Our approach thus offers a new insight | 139 | road\u00a0mitigation measures. Our approach thus offers a new insight | ||
| 140 | into the understanding of road effects and how to plan and strategize | 140 | into the understanding of road effects and how to plan and strategize | ||
| 141 | monitoring and mitigation.", | 141 | monitoring and mitigation.", | ||
| 142 | "es": "We modelled the spatiotemporal patterns of road mortality | 142 | "es": "We modelled the spatiotemporal patterns of road mortality | ||
| 143 | for seven medium-large mammals, using a roadkill dataset from Mato | 143 | for seven medium-large mammals, using a roadkill dataset from Mato | ||
| 144 | Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for | 144 | Grosso do Sul, Brazil (800 km of roads surveyed every two weeks, for | ||
| 145 | two years). We related roadkill presence-absence along the road | 145 | two years). We related roadkill presence-absence along the road | ||
| 146 | sections (1000 m) and across the survey dates with a collection of | 146 | sections (1000 m) and across the survey dates with a collection of | ||
| 147 | environmental variables, including land cover, forest cover, distance | 147 | environmental variables, including land cover, forest cover, distance | ||
| 148 | to rivers, temperature, precipitation and vegetation productivity. We | 148 | to rivers, temperature, precipitation and vegetation productivity. We | ||
| 149 | further included two variables aiming to reflect the intrinsic spatial | 149 | further included two variables aiming to reflect the intrinsic spatial | ||
| 150 | and temporal roadkill risk. Environmental variables were obtained | 150 | and temporal roadkill risk. Environmental variables were obtained | ||
| 151 | through remote sensing and weather stations, allowing the estimate of | 151 | through remote sensing and weather stations, allowing the estimate of | ||
| 152 | the roadkill risk for the entire surveyed roads and survey periods. | 152 | the roadkill risk for the entire surveyed roads and survey periods. | ||
| 153 | Overall, the models could explain a small fraction of the | 153 | Overall, the models could explain a small fraction of the | ||
| 154 | spatiotemporal patterns of roadkills (<0.23), probably due to species | 154 | spatiotemporal patterns of roadkills (<0.23), probably due to species | ||
| 155 | being habitat generalists, but still had reasonable discrimination | 155 | being habitat generalists, but still had reasonable discrimination | ||
| 156 | power (AUC averaging 0.70 \u00b1 0.07). The intrinsic spatial and | 156 | power (AUC averaging 0.70 \u00b1 0.07). The intrinsic spatial and | ||
| 157 | temporal roadkill risk were the most important variables, followed by | 157 | temporal roadkill risk were the most important variables, followed by | ||
| 158 | land cover, climate and NDVI. We show that identifying spatiotemporal | 158 | land cover, climate and NDVI. We show that identifying spatiotemporal | ||
| 159 | roadkill patterns may provide valuable information to define specific | 159 | roadkill patterns may provide valuable information to define specific | ||
| 160 | management actions focused on road sections and time periods, in | 160 | management actions focused on road sections and time periods, in | ||
| 161 | complement to permanent road mitigation measures. Our approach thus | 161 | complement to permanent road mitigation measures. Our approach thus | ||
| 162 | offers a new insight into the understanding of road effects and how to | 162 | offers a new insight into the understanding of road effects and how to | ||
| 163 | plan and strategize monitoring and mitigation." | 163 | plan and strategize monitoring and mitigation." | ||
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| 190 | Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto | 190 | Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto | ||
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