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En el instante 25 de junio de 2026, 12:30:22 UTC,
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Modificado el valor del campo
modified
a2026-06-25
en Global impact of roads on carnivores: which species and where? -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Global impact of roads on carnivores: which species and where?
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| 81 | "notes": "Carnivores have life histories that can render them | 95 | "notes": "Carnivores have life histories that can render them | ||
| 82 | susceptible to roads, such as low population growth rates and great | 96 | susceptible to roads, such as low population growth rates and great | ||
| 83 | mobility. However, little is known about the effect of roads on | 97 | mobility. However, little is known about the effect of roads on | ||
| 84 | population viability. In this study we determined which carnivore | 98 | population viability. In this study we determined which carnivore | ||
| 85 | species are more affected by roads at the global level, and the | 99 | species are more affected by roads at the global level, and the | ||
| 86 | spatial match between the number of species affected and road density. | 100 | spatial match between the number of species affected and road density. | ||
| 87 | We used a reaction-diffusion model describing population dynamics to | 101 | We used a reaction-diffusion model describing population dynamics to | ||
| 88 | predict the impact of a road network on a population including the | 102 | predict the impact of a road network on a population including the | ||
| 89 | following parameters: dispersal distance, growth rate in favorable | 103 | following parameters: dispersal distance, growth rate in favorable | ||
| 90 | natural habitat patches, and growth rate in unfavorable habitats | 104 | natural habitat patches, and growth rate in unfavorable habitats | ||
| 91 | (roads). We applied this approach to 230 carnivore species at a global | 105 | (roads). We applied this approach to 230 carnivore species at a global | ||
| 92 | level. To rank the species most affected by roads we used maximum road | 106 | level. To rank the species most affected by roads we used maximum road | ||
| 93 | density, and the minimum size of the patches between roads, above or | 107 | density, and the minimum size of the patches between roads, above or | ||
| 94 | below which populations cannot persist. We addressed the following | 108 | below which populations cannot persist. We addressed the following | ||
| 95 | tasks: 1) for each species we computed the maximum road density and | 109 | tasks: 1) for each species we computed the maximum road density and | ||
| 96 | the minimum patch size between roads that allow species to occur, | 110 | the minimum patch size between roads that allow species to occur, | ||
| 97 | using species-specific life histories and road mortality data; 2) we | 111 | using species-specific life histories and road mortality data; 2) we | ||
| 98 | obtained the road density, and the number and size of the patches | 112 | obtained the road density, and the number and size of the patches | ||
| 99 | between roads that are observed within each species range, by | 113 | between roads that are observed within each species range, by | ||
| 100 | intersecting each species IUCN range map with roads (density) map from | 114 | intersecting each species IUCN range map with roads (density) map from | ||
| 101 | openstreetmap; 3) we computed for each species the ratio of maximum to | 115 | openstreetmap; 3) we computed for each species the ratio of maximum to | ||
| 102 | observed road density and the number and area of patches that are | 116 | observed road density and the number and area of patches that are | ||
| 103 | bigger than the minimum patch size; 4) we selected the species within | 117 | bigger than the minimum patch size; 4) we selected the species within | ||
| 104 | the 5% percentile for these quantities as the most affected species. | 118 | the 5% percentile for these quantities as the most affected species. | ||
| 105 | We found that family Ursidae has the highest percentage (43%) of | 119 | We found that family Ursidae has the highest percentage (43%) of | ||
| 106 | species within the 5% most affected species, followed by family | 120 | species within the 5% most affected species, followed by family | ||
| 107 | Felidae and family Canidae. We also found that 54% of the most | 121 | Felidae and family Canidae. We also found that 54% of the most | ||
| 108 | affected species are not threatened by roads according to the IUCN, | 122 | affected species are not threatened by roads according to the IUCN, | ||
| 109 | including 10 species that currently have an IUCN \u201cLeast | 123 | including 10 species that currently have an IUCN \u201cLeast | ||
| 110 | Concern\u201d status. The highest numbers of species affected by roads | 124 | Concern\u201d status. The highest numbers of species affected by roads | ||
| 111 | are found in Europe, North and Central America, South of Asia and | 125 | are found in Europe, North and Central America, South of Asia and | ||
| 112 | China, and central-east Africa. However, while in Europe this high | 126 | China, and central-east Africa. However, while in Europe this high | ||
| 113 | number of species is matched by high road density, this is not | 127 | number of species is matched by high road density, this is not | ||
| 114 | necessarily the case in the other regions, indicating that species can | 128 | necessarily the case in the other regions, indicating that species can | ||
| 115 | be affected even at low road densities. Our approach can be extended | 129 | be affected even at low road densities. Our approach can be extended | ||
| 116 | to any species for which the necessary life history data can be | 130 | to any species for which the necessary life history data can be | ||
| 117 | obtained, and can assist in developing conservation and mitigation | 131 | obtained, and can assist in developing conservation and mitigation | ||
| 118 | measures. Furthermore, it can be applied at different spatial or | 132 | measures. Furthermore, it can be applied at different spatial or | ||
| 119 | temporal scales, such as projecting the impact of future road network | 133 | temporal scales, such as projecting the impact of future road network | ||
| 120 | development.", | 134 | development.", | ||
| 121 | "notes_translated": { | 135 | "notes_translated": { | ||
| 122 | "en": "Carnivores have life histories that can render them | 136 | "en": "Carnivores have life histories that can render them | ||
| 123 | susceptible to roads, such as low population growth rates and great | 137 | susceptible to roads, such as low population growth rates and great | ||
| 124 | mobility. However, little is known about the effect of roads on | 138 | mobility. However, little is known about the effect of roads on | ||
| 125 | population viability. In this study we determined which carnivore | 139 | population viability. In this study we determined which carnivore | ||
| 126 | species are more affected by roads at the global level, and the | 140 | species are more affected by roads at the global level, and the | ||
| 127 | spatial match between the number of species affected and road density. | 141 | spatial match between the number of species affected and road density. | ||
| 128 | We used a reaction-diffusion model describing population dynamics to | 142 | We used a reaction-diffusion model describing population dynamics to | ||
| 129 | predict the impact of a road network on a population including the | 143 | predict the impact of a road network on a population including the | ||
| 130 | following parameters: dispersal distance, growth rate in favorable | 144 | following parameters: dispersal distance, growth rate in favorable | ||
| 131 | natural habitat patches, and growth rate in unfavorable habitats | 145 | natural habitat patches, and growth rate in unfavorable habitats | ||
| 132 | (roads). We applied this approach to 230 carnivore species at a global | 146 | (roads). We applied this approach to 230 carnivore species at a global | ||
| 133 | level. To rank the species most affected by roads we used maximum road | 147 | level. To rank the species most affected by roads we used maximum road | ||
| 134 | density, and the minimum size of the patches between roads, above or | 148 | density, and the minimum size of the patches between roads, above or | ||
| 135 | below which populations cannot persist. We addressed the following | 149 | below which populations cannot persist. We addressed the following | ||
| 136 | tasks: 1) for each species we computed the maximum road density and | 150 | tasks: 1) for each species we computed the maximum road density and | ||
| 137 | the minimum patch size between roads that allow species to occur, | 151 | the minimum patch size between roads that allow species to occur, | ||
| 138 | using species-specific life histories and road mortality data; 2) we | 152 | using species-specific life histories and road mortality data; 2) we | ||
| 139 | obtained the road density, and the number and size of the patches | 153 | obtained the road density, and the number and size of the patches | ||
| 140 | between roads that are observed within each species range, by | 154 | between roads that are observed within each species range, by | ||
| 141 | intersecting each species IUCN range map with roads (density) map from | 155 | intersecting each species IUCN range map with roads (density) map from | ||
| 142 | openstreetmap; 3) we computed for each species the ratio of maximum to | 156 | openstreetmap; 3) we computed for each species the ratio of maximum to | ||
| 143 | observed road density and the number and area of patches that are | 157 | observed road density and the number and area of patches that are | ||
| 144 | bigger than the minimum patch size; 4) we selected the species within | 158 | bigger than the minimum patch size; 4) we selected the species within | ||
| 145 | the 5% percentile for these quantities as\nthe most affected species. | 159 | the 5% percentile for these quantities as\nthe most affected species. | ||
| 146 | We found that family Ursidae has the highest percentage (43%) of | 160 | We found that family Ursidae has the highest percentage (43%) of | ||
| 147 | species within the 5% most affected species, followed by family | 161 | species within the 5% most affected species, followed by family | ||
| 148 | Felidae and family Canidae. We also found that 54% of the most | 162 | Felidae and family Canidae. We also found that 54% of the most | ||
| 149 | affected species are not threatened by roads according to the IUCN, | 163 | affected species are not threatened by roads according to the IUCN, | ||
| 150 | including 10 species that currently have an IUCN \u201cLeast | 164 | including 10 species that currently have an IUCN \u201cLeast | ||
| 151 | Concern\u201d status. The highest numbers of species affected by roads | 165 | Concern\u201d status. The highest numbers of species affected by roads | ||
| 152 | are found in Europe, North and Central America, South of Asia and | 166 | are found in Europe, North and Central America, South of Asia and | ||
| 153 | China, and central-east Africa. However, while in Europe this high | 167 | China, and central-east Africa. However, while in Europe this high | ||
| 154 | number of species is matched by high road density, this is not | 168 | number of species is matched by high road density, this is not | ||
| 155 | necessarily the case in the other regions, indicating that species can | 169 | necessarily the case in the other regions, indicating that species can | ||
| 156 | be affected even at low road densities. Our approach can be extended | 170 | be affected even at low road densities. Our approach can be extended | ||
| 157 | to any species for which the necessary life history data can be | 171 | to any species for which the necessary life history data can be | ||
| 158 | obtained, and can assist in developing conservation and mitigation | 172 | obtained, and can assist in developing conservation and mitigation | ||
| 159 | measures. Furthermore, it can be applied at different spatial or | 173 | measures. Furthermore, it can be applied at different spatial or | ||
| 160 | temporal scales, such as projecting the impact of future road network | 174 | temporal scales, such as projecting the impact of future road network | ||
| 161 | development.", | 175 | development.", | ||
| 162 | "es": "Carnivores have life histories that can render them | 176 | "es": "Carnivores have life histories that can render them | ||
| 163 | susceptible to roads, such as low population growth rates and great | 177 | susceptible to roads, such as low population growth rates and great | ||
| 164 | mobility. However, little is known about the effect of roads on | 178 | mobility. However, little is known about the effect of roads on | ||
| 165 | population viability. In this study we determined which carnivore | 179 | population viability. In this study we determined which carnivore | ||
| 166 | species are more affected by roads at the global level, and the | 180 | species are more affected by roads at the global level, and the | ||
| 167 | spatial match between the number of species affected and road density. | 181 | spatial match between the number of species affected and road density. | ||
| 168 | We used a reaction-diffusion model describing population dynamics to | 182 | We used a reaction-diffusion model describing population dynamics to | ||
| 169 | predict the impact of a road network on a population including the | 183 | predict the impact of a road network on a population including the | ||
| 170 | following parameters: dispersal distance, growth rate in favorable | 184 | following parameters: dispersal distance, growth rate in favorable | ||
| 171 | natural habitat patches, and growth rate in unfavorable habitats | 185 | natural habitat patches, and growth rate in unfavorable habitats | ||
| 172 | (roads). We applied this approach to 230 carnivore species at a global | 186 | (roads). We applied this approach to 230 carnivore species at a global | ||
| 173 | level. To rank the species most affected by roads we used maximum road | 187 | level. To rank the species most affected by roads we used maximum road | ||
| 174 | density, and the minimum size of the patches between roads, above or | 188 | density, and the minimum size of the patches between roads, above or | ||
| 175 | below which populations cannot persist. We addressed the following | 189 | below which populations cannot persist. We addressed the following | ||
| 176 | tasks: 1) for each species we computed the maximum road density and | 190 | tasks: 1) for each species we computed the maximum road density and | ||
| 177 | the minimum patch size between roads that allow species to occur, | 191 | the minimum patch size between roads that allow species to occur, | ||
| 178 | using species-specific life histories and road mortality data; 2) we | 192 | using species-specific life histories and road mortality data; 2) we | ||
| 179 | obtained the road density, and the number and size of the patches | 193 | obtained the road density, and the number and size of the patches | ||
| 180 | between roads that are observed within each species range, by | 194 | between roads that are observed within each species range, by | ||
| 181 | intersecting each species IUCN range map with roads (density) map from | 195 | intersecting each species IUCN range map with roads (density) map from | ||
| 182 | openstreetmap; 3) we computed for each species the ratio of maximum to | 196 | openstreetmap; 3) we computed for each species the ratio of maximum to | ||
| 183 | observed road density and the number and area of patches that are | 197 | observed road density and the number and area of patches that are | ||
| 184 | bigger than the minimum patch size; 4) we selected the species within | 198 | bigger than the minimum patch size; 4) we selected the species within | ||
| 185 | the 5% percentile for these quantities as the most affected species. | 199 | the 5% percentile for these quantities as the most affected species. | ||
| 186 | We found that family Ursidae has the highest percentage (43%) of | 200 | We found that family Ursidae has the highest percentage (43%) of | ||
| 187 | species within the 5% most affected species, followed by family | 201 | species within the 5% most affected species, followed by family | ||
| 188 | Felidae and family Canidae. We also found that 54% of the most | 202 | Felidae and family Canidae. We also found that 54% of the most | ||
| 189 | affected species are not threatened by roads according to the IUCN, | 203 | affected species are not threatened by roads according to the IUCN, | ||
| 190 | including 10 species that currently have an IUCN \u201cLeast | 204 | including 10 species that currently have an IUCN \u201cLeast | ||
| 191 | Concern\u201d status. The highest numbers of species affected by roads | 205 | Concern\u201d status. The highest numbers of species affected by roads | ||
| 192 | are found in Europe, North and Central America, South of Asia and | 206 | are found in Europe, North and Central America, South of Asia and | ||
| 193 | China, and central-east Africa. However, while in Europe this high | 207 | China, and central-east Africa. However, while in Europe this high | ||
| 194 | number of species is matched by high road density, this is not | 208 | number of species is matched by high road density, this is not | ||
| 195 | necessarily the case in the other regions, indicating that species can | 209 | necessarily the case in the other regions, indicating that species can | ||
| 196 | be affected even at low road densities. Our approach can be extended | 210 | be affected even at low road densities. Our approach can be extended | ||
| 197 | to any species for which the necessary life history data can be | 211 | to any species for which the necessary life history data can be | ||
| 198 | obtained, and can assist in developing conservation and mitigation | 212 | obtained, and can assist in developing conservation and mitigation | ||
| 199 | measures. Furthermore, it can be applied at different spatial or | 213 | measures. Furthermore, it can be applied at different spatial or | ||
| 200 | temporal scales, such as projecting the impact of future road network | 214 | temporal scales, such as projecting the impact of future road network | ||
| 201 | development." | 215 | development." | ||
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