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En el instante 25 de junio de 2026, 12:27:07 UTC,
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Modificado el valor del campo
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a2026-06-25
en Estimating roadkill risk when there is no roadkill data. -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Estimating roadkill risk when there is no roadkill data.
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| 82 | "notes": "The most common way to quantify roadkill risk in different | 96 | "notes": "The most common way to quantify roadkill risk in different | ||
| 83 | sections of infrastructures is to collect information on the location | 97 | sections of infrastructures is to collect information on the location | ||
| 84 | of casualties and then, model the probability using the environmental | 98 | of casualties and then, model the probability using the environmental | ||
| 85 | and infrastructure variables associated with the roadkill sites. This | 99 | and infrastructure variables associated with the roadkill sites. This | ||
| 86 | approach is not applicable in roads with low traffic intensity as they | 100 | approach is not applicable in roads with low traffic intensity as they | ||
| 87 | have a small number of victims (e.g. unpaved roads), where there is a | 101 | have a small number of victims (e.g. unpaved roads), where there is a | ||
| 88 | high removal rate of casualties by scavengers (e.g. in natural areas), | 102 | high removal rate of casualties by scavengers (e.g. in natural areas), | ||
| 89 | or when it has to be estimated before the infrastructure is built. We | 103 | or when it has to be estimated before the infrastructure is built. We | ||
| 90 | developed an indirect approach to evaluate the risk of collisions with | 104 | developed an indirect approach to evaluate the risk of collisions with | ||
| 91 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | 105 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | ||
| 92 | abundance and phenology of species, the characteristics of the | 106 | abundance and phenology of species, the characteristics of the | ||
| 93 | environment, and traffic intensity. First we characterized the road | 107 | environment, and traffic intensity. First we characterized the road | ||
| 94 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | 108 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | ||
| 95 | 2% were paved; and extracted environmental variables for the complete | 109 | 2% were paved; and extracted environmental variables for the complete | ||
| 96 | network in sections of 200m. Then, we characterized the traffic using | 110 | network in sections of 200m. Then, we characterized the traffic using | ||
| 97 | data from automatic counting systems for main roads and for the rest | 111 | data from automatic counting systems for main roads and for the rest | ||
| 98 | we built a model of traffic intensity using data from a stratified | 112 | we built a model of traffic intensity using data from a stratified | ||
| 99 | sampling design in 62 sites using magnetometers, estimating traffic | 113 | sampling design in 62 sites using magnetometers, estimating traffic | ||
| 100 | intensity to the whole network of roads. We characterized the | 114 | intensity to the whole network of roads. We characterized the | ||
| 101 | abundance of multiple species using track censuses in 183 sites using | 115 | abundance of multiple species using track censuses in 183 sites using | ||
| 102 | 200 m transects; obtaining information on abundance, crossing | 116 | 200 m transects; obtaining information on abundance, crossing | ||
| 103 | intensity and the distance moved along the road (estimator of the time | 117 | intensity and the distance moved along the road (estimator of the time | ||
| 104 | of exposure to vehicles or exposure). With this information we created | 118 | of exposure to vehicles or exposure). With this information we created | ||
| 105 | a model of the number of crossing events per species in sections of | 119 | a model of the number of crossing events per species in sections of | ||
| 106 | 200 m using environmental predictors and applied the models to the | 120 | 200 m using environmental predictors and applied the models to the | ||
| 107 | whole network of roads. We estimated the roadkill risk using the index | 121 | whole network of roads. We estimated the roadkill risk using the index | ||
| 108 | risk = log (no. crossings x traffic intensity x exposure), | 122 | risk = log (no. crossings x traffic intensity x exposure), | ||
| 109 | standardized between 0 and 1. We calculated the index for the whole | 123 | standardized between 0 and 1. We calculated the index for the whole | ||
| 110 | network of roads. As an example, we show the predictions corresponding | 124 | network of roads. As an example, we show the predictions corresponding | ||
| 111 | to the roadkill risk for several species, clearly identifying areas of | 125 | to the roadkill risk for several species, clearly identifying areas of | ||
| 112 | high risk which are localized along roads with high traffic intensity | 126 | high risk which are localized along roads with high traffic intensity | ||
| 113 | and within them, specific sections with maximum risk. The predictions | 127 | and within them, specific sections with maximum risk. The predictions | ||
| 114 | matched well with the observations of road-killed data recorded in the | 128 | matched well with the observations of road-killed data recorded in the | ||
| 115 | area.", | 129 | area.", | ||
| 116 | "notes_translated": { | 130 | "notes_translated": { | ||
| 117 | "en": "The most common way to quantify roadkill risk in different | 131 | "en": "The most common way to quantify roadkill risk in different | ||
| 118 | sections of infrastructures is to collect information on the location | 132 | sections of infrastructures is to collect information on the location | ||
| 119 | of casualties and then, model the probability using the environmental | 133 | of casualties and then, model the probability using the environmental | ||
| 120 | and infrastructure variables associated with the roadkill sites. This | 134 | and infrastructure variables associated with the roadkill sites. This | ||
| 121 | approach is not applicable in roads with low traffic intensity as they | 135 | approach is not applicable in roads with low traffic intensity as they | ||
| 122 | have a small number of victims (e.g. unpaved roads), where there is a | 136 | have a small number of victims (e.g. unpaved roads), where there is a | ||
| 123 | high removal rate of casualties by scavengers (e.g. in natural areas), | 137 | high removal rate of casualties by scavengers (e.g. in natural areas), | ||
| 124 | or when it has to be estimated before the infrastructure is built. We | 138 | or when it has to be estimated before the infrastructure is built. We | ||
| 125 | developed an indirect approach to evaluate the risk of collisions with | 139 | developed an indirect approach to evaluate the risk of collisions with | ||
| 126 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | 140 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | ||
| 127 | abundance and phenology of species, the characteristics of the | 141 | abundance and phenology of species, the characteristics of the | ||
| 128 | environment, and traffic intensity. First we characterized the road | 142 | environment, and traffic intensity. First we characterized the road | ||
| 129 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | 143 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | ||
| 130 | 2% were paved; and extracted environmental variables for the complete | 144 | 2% were paved; and extracted environmental variables for the complete | ||
| 131 | network in sections of 200m. Then, we characterized the traffic using | 145 | network in sections of 200m. Then, we characterized the traffic using | ||
| 132 | data from automatic counting systems for main roads and for the rest | 146 | data from automatic counting systems for main roads and for the rest | ||
| 133 | we built a model of traffic intensity using data from a stratified | 147 | we built a model of traffic intensity using data from a stratified | ||
| 134 | sampling design in 62 sites using magnetometers, estimating traffic | 148 | sampling design in 62 sites using magnetometers, estimating traffic | ||
| 135 | intensity to the whole network of roads. We characterized the | 149 | intensity to the whole network of roads. We characterized the | ||
| 136 | abundance of multiple species using track censuses in 183 sites using | 150 | abundance of multiple species using track censuses in 183 sites using | ||
| 137 | 200 m transects; obtaining information on abundance, crossing | 151 | 200 m transects; obtaining information on abundance, crossing | ||
| 138 | intensity and the distance moved along the road (estimator of the time | 152 | intensity and the distance moved along the road (estimator of the time | ||
| 139 | of exposure to vehicles or exposure). With this information we created | 153 | of exposure to vehicles or exposure). With this information we created | ||
| 140 | a model of the number of crossing events per species in sections of | 154 | a model of the number of crossing events per species in sections of | ||
| 141 | 200 m using environmental predictors and applied the models to the | 155 | 200 m using environmental predictors and applied the models to the | ||
| 142 | whole network of roads. We estimated the roadkill risk using the index | 156 | whole network of roads. We estimated the roadkill risk using the index | ||
| 143 | risk = log (no. crossings x traffic intensity x exposure), | 157 | risk = log (no. crossings x traffic intensity x exposure), | ||
| 144 | standardized between 0 and 1. We calculated the index for the\nwhole | 158 | standardized between 0 and 1. We calculated the index for the\nwhole | ||
| 145 | network of roads. As an example, we show the predictions corresponding | 159 | network of roads. As an example, we show the predictions corresponding | ||
| 146 | to the roadkill risk for several species, clearly identifying areas of | 160 | to the roadkill risk for several species, clearly identifying areas of | ||
| 147 | high risk which are localized along roads with high traffic intensity | 161 | high risk which are localized along roads with high traffic intensity | ||
| 148 | and within them, specific sections with maximum risk. The predictions | 162 | and within them, specific sections with maximum risk. The predictions | ||
| 149 | matched well with the observations of road-killed data recorded in the | 163 | matched well with the observations of road-killed data recorded in the | ||
| 150 | area.", | 164 | area.", | ||
| 151 | "es": "The most common way to quantify roadkill risk in different | 165 | "es": "The most common way to quantify roadkill risk in different | ||
| 152 | sections of infrastructures is to collect information on the location | 166 | sections of infrastructures is to collect information on the location | ||
| 153 | of casualties and then, model the probability using the environmental | 167 | of casualties and then, model the probability using the environmental | ||
| 154 | and infrastructure variables associated with the roadkill sites. This | 168 | and infrastructure variables associated with the roadkill sites. This | ||
| 155 | approach is not applicable in roads with low traffic intensity as they | 169 | approach is not applicable in roads with low traffic intensity as they | ||
| 156 | have a small number of victims (e.g. unpaved roads), where there is a | 170 | have a small number of victims (e.g. unpaved roads), where there is a | ||
| 157 | high removal rate of casualties by scavengers (e.g. in natural areas), | 171 | high removal rate of casualties by scavengers (e.g. in natural areas), | ||
| 158 | or when it has to be estimated before the infrastructure is built. We | 172 | or when it has to be estimated before the infrastructure is built. We | ||
| 159 | developed an indirect approach to evaluate the risk of collisions with | 173 | developed an indirect approach to evaluate the risk of collisions with | ||
| 160 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | 174 | wildlife within Do\u00f1ana Natural Area (SW Spain), considering the | ||
| 161 | abundance and phenology of species, the characteristics of the | 175 | abundance and phenology of species, the characteristics of the | ||
| 162 | environment, and traffic intensity. First we characterized the road | 176 | environment, and traffic intensity. First we characterized the road | ||
| 163 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | 177 | network, corresponding to 2190 km of roads (4.04 km/km2) of which only | ||
| 164 | 2% were paved; and extracted environmental variables for the complete | 178 | 2% were paved; and extracted environmental variables for the complete | ||
| 165 | network in sections of 200m. Then, we characterized the traffic using | 179 | network in sections of 200m. Then, we characterized the traffic using | ||
| 166 | data from automatic counting systems for main roads and for the rest | 180 | data from automatic counting systems for main roads and for the rest | ||
| 167 | we built a model of traffic intensity using data from a stratified | 181 | we built a model of traffic intensity using data from a stratified | ||
| 168 | sampling design in 62 sites using magnetometers, estimating traffic | 182 | sampling design in 62 sites using magnetometers, estimating traffic | ||
| 169 | intensity to the whole network of roads. We characterized the | 183 | intensity to the whole network of roads. We characterized the | ||
| 170 | abundance of multiple species using track censuses in 183 sites using | 184 | abundance of multiple species using track censuses in 183 sites using | ||
| 171 | 200 m transects; obtaining information on abundance, crossing | 185 | 200 m transects; obtaining information on abundance, crossing | ||
| 172 | intensity and the distance moved along the road (estimator of the time | 186 | intensity and the distance moved along the road (estimator of the time | ||
| 173 | of exposure to vehicles or exposure). With this information we created | 187 | of exposure to vehicles or exposure). With this information we created | ||
| 174 | a model of the number of crossing events per species in sections of | 188 | a model of the number of crossing events per species in sections of | ||
| 175 | 200 m using environmental predictors and applied the models to the | 189 | 200 m using environmental predictors and applied the models to the | ||
| 176 | whole network of roads. We estimated the roadkill risk using the index | 190 | whole network of roads. We estimated the roadkill risk using the index | ||
| 177 | risk = log (no. crossings x traffic intensity x exposure), | 191 | risk = log (no. crossings x traffic intensity x exposure), | ||
| 178 | standardized between 0 and 1. We calculated the index for the whole | 192 | standardized between 0 and 1. We calculated the index for the whole | ||
| 179 | network of roads. As an example, we show the predictions corresponding | 193 | network of roads. As an example, we show the predictions corresponding | ||
| 180 | to the roadkill risk for several species, clearly identifying areas of | 194 | to the roadkill risk for several species, clearly identifying areas of | ||
| 181 | high risk which are localized along roads with high traffic intensity | 195 | high risk which are localized along roads with high traffic intensity | ||
| 182 | and within them, specific sections with maximum risk. The predictions | 196 | and within them, specific sections with maximum risk. The predictions | ||
| 183 | matched well with the observations of road-killed data recorded in the | 197 | matched well with the observations of road-killed data recorded in the | ||
| 184 | area." | 198 | area." | ||
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