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en Influence of landscape factors on amphibian roadkills at the national level. -
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) en Influence of landscape factors on amphibian roadkills at the national level.
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| 82 | "notes": "Roads exert multiple effects on wildlife, from animal | 96 | "notes": "Roads exert multiple effects on wildlife, from animal | ||
| 83 | mortality, habitat and population fragmentation, to modification of | 97 | mortality, habitat and population fragmentation, to modification of | ||
| 84 | animal reproductive behaviour. Amphibians are the most frequently | 98 | animal reproductive behaviour. Amphibians are the most frequently | ||
| 85 | road-killed animal group. Many studies have attempted to analyse the | 99 | road-killed animal group. Many studies have attempted to analyse the | ||
| 86 | factors driving amphibian casualties on roads, but these factors are | 100 | factors driving amphibian casualties on roads, but these factors are | ||
| 87 | limited to the roads themselves (eg, traffic, type of roads, roads | 101 | limited to the roads themselves (eg, traffic, type of roads, roads | ||
| 88 | crossing water bodies) or to structures along them (eg, ditches, | 102 | crossing water bodies) or to structures along them (eg, ditches, | ||
| 89 | walls). Sometimes, roadkills are related to land use along the roads. | 103 | walls). Sometimes, roadkills are related to land use along the roads. | ||
| 90 | We analysed the influence of landscape factors on roadkill hotspots at | 104 | We analysed the influence of landscape factors on roadkill hotspots at | ||
| 91 | the national level (Slovenia). Specifically, we aimed at:(1) | 105 | the national level (Slovenia). Specifically, we aimed at:(1) | ||
| 92 | identifying hotspots of roadkills,(2) analysing whether records of | 106 | identifying hotspots of roadkills,(2) analysing whether records of | ||
| 93 | amphibian presence on roads are related to the distribution of water | 107 | amphibian presence on roads are related to the distribution of water | ||
| 94 | bodies and (3) analysing which factors (proximity to water bodies or | 108 | bodies and (3) analysing which factors (proximity to water bodies or | ||
| 95 | human factors) explain the distribution of hotspots. Hotspots were | 109 | human factors) explain the distribution of hotspots. Hotspots were | ||
| 96 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | 110 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | ||
| 97 | for the first time in Slovenia. The relationships between amphibian | 111 | for the first time in Slovenia. The relationships between amphibian | ||
| 98 | presence and hotspots with factors were analysed with GLM. A total of | 112 | presence and hotspots with factors were analysed with GLM. A total of | ||
| 99 | 237 road sections were identified as hotspots, corresponding to 8% of | 113 | 237 road sections were identified as hotspots, corresponding to 8% of | ||
| 100 | road sections and containing 90% of road-killed individuals. Proximity | 114 | road sections and containing 90% of road-killed individuals. Proximity | ||
| 101 | to forests, meadows and agricultural land were the most important | 115 | to forests, meadows and agricultural land were the most important | ||
| 102 | variables in Maxent models. The number of roadkills depended on the | 116 | variables in Maxent models. The number of roadkills depended on the | ||
| 103 | proximity to agricultural land, forests, water bodies and wetland | 117 | proximity to agricultural land, forests, water bodies and wetland | ||
| 104 | areas, while the number of hotspots additionally depended on the | 118 | areas, while the number of hotspots additionally depended on the | ||
| 105 | proximity to urban settlements.\n Palabras clave: Amphibian, | 119 | proximity to urban settlements.\n Palabras clave: Amphibian, | ||
| 106 | Landscape, Motorway", | 120 | Landscape, Motorway", | ||
| 107 | "notes_translated": { | 121 | "notes_translated": { | ||
| 108 | "en": "Roads exert multiple effects on wildlife, from animal | 122 | "en": "Roads exert multiple effects on wildlife, from animal | ||
| 109 | mortality, habitat and population fragmentation, to modification of | 123 | mortality, habitat and population fragmentation, to modification of | ||
| 110 | animal reproductive behaviour. Amphibians are the most frequently | 124 | animal reproductive behaviour. Amphibians are the most frequently | ||
| 111 | road-killed animal group. Many studies have attempted to analyse the | 125 | road-killed animal group. Many studies have attempted to analyse the | ||
| 112 | factors driving amphibian casualties on roads, but these factors are | 126 | factors driving amphibian casualties on roads, but these factors are | ||
| 113 | limited to the roads themselves (eg, traffic, type of roads, roads | 127 | limited to the roads themselves (eg, traffic, type of roads, roads | ||
| 114 | crossing water bodies) or to structures along them (eg, ditches, | 128 | crossing water bodies) or to structures along them (eg, ditches, | ||
| 115 | walls). Sometimes, roadkills are related to land use along the roads. | 129 | walls). Sometimes, roadkills are related to land use along the roads. | ||
| 116 | We analysed the influence of landscape factors on roadkill hotspots at | 130 | We analysed the influence of landscape factors on roadkill hotspots at | ||
| 117 | the national level (Slovenia). Specifically, we aimed at:(1) | 131 | the national level (Slovenia). Specifically, we aimed at:(1) | ||
| 118 | identifying hotspots of roadkills,(2) analysing whether records of | 132 | identifying hotspots of roadkills,(2) analysing whether records of | ||
| 119 | amphibian presence on roads are related to the distribution of water | 133 | amphibian presence on roads are related to the distribution of water | ||
| 120 | bodies and (3) analysing which factors (proximity to water bodies or | 134 | bodies and (3) analysing which factors (proximity to water bodies or | ||
| 121 | human factors) explain the distribution of hotspots. Hotspots were | 135 | human factors) explain the distribution of hotspots. Hotspots were | ||
| 122 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | 136 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | ||
| 123 | for the first time in Slovenia. The relationships between amphibian | 137 | for the first time in Slovenia. The relationships between amphibian | ||
| 124 | presence and hotspots with factors were analysed with GLM. A total of | 138 | presence and hotspots with factors were analysed with GLM. A total of | ||
| 125 | 237 road sections were identified as hotspots, corresponding to 8% of | 139 | 237 road sections were identified as hotspots, corresponding to 8% of | ||
| 126 | road sections and containing 90% of road-killed individuals. Proximity | 140 | road sections and containing 90% of road-killed individuals. Proximity | ||
| 127 | to forests, meadows and agricultural land were the most important | 141 | to forests, meadows and agricultural land were the most important | ||
| 128 | variables in Maxent models. The number of roadkills depended on the | 142 | variables in Maxent models. The number of roadkills depended on the | ||
| 129 | proximity to agricultural land, forests, water bodies and wetland | 143 | proximity to agricultural land, forests, water bodies and wetland | ||
| 130 | areas, while the number of hotspots additionally depended on the | 144 | areas, while the number of hotspots additionally depended on the | ||
| 131 | proximity to urban settlements.", | 145 | proximity to urban settlements.", | ||
| 132 | "es": "Roads exert multiple effects on wildlife, from animal | 146 | "es": "Roads exert multiple effects on wildlife, from animal | ||
| 133 | mortality, habitat and population fragmentation, to modification of | 147 | mortality, habitat and population fragmentation, to modification of | ||
| 134 | animal reproductive behaviour. Amphibians are the most frequently | 148 | animal reproductive behaviour. Amphibians are the most frequently | ||
| 135 | road-killed animal group. Many studies have attempted to analyse the | 149 | road-killed animal group. Many studies have attempted to analyse the | ||
| 136 | factors driving amphibian casualties on roads, but these factors are | 150 | factors driving amphibian casualties on roads, but these factors are | ||
| 137 | limited to the roads themselves (eg, traffic, type of roads, roads | 151 | limited to the roads themselves (eg, traffic, type of roads, roads | ||
| 138 | crossing water bodies) or to structures along them (eg, ditches, | 152 | crossing water bodies) or to structures along them (eg, ditches, | ||
| 139 | walls). Sometimes, roadkills are related to land use along the roads. | 153 | walls). Sometimes, roadkills are related to land use along the roads. | ||
| 140 | We analysed the influence of landscape factors on roadkill hotspots at | 154 | We analysed the influence of landscape factors on roadkill hotspots at | ||
| 141 | the national level (Slovenia). Specifically, we aimed at:(1) | 155 | the national level (Slovenia). Specifically, we aimed at:(1) | ||
| 142 | identifying hotspots of roadkills,(2) analysing whether records of | 156 | identifying hotspots of roadkills,(2) analysing whether records of | ||
| 143 | amphibian presence on roads are related to the distribution of water | 157 | amphibian presence on roads are related to the distribution of water | ||
| 144 | bodies and (3) analysing which factors (proximity to water bodies or | 158 | bodies and (3) analysing which factors (proximity to water bodies or | ||
| 145 | human factors) explain the distribution of hotspots. Hotspots were | 159 | human factors) explain the distribution of hotspots. Hotspots were | ||
| 146 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | 160 | identified by Malo\u2019s method. Roadkills were modelled with Maxent | ||
| 147 | for the first time in Slovenia. The relationships between amphibian | 161 | for the first time in Slovenia. The relationships between amphibian | ||
| 148 | presence and hotspots with factors were analysed with GLM. A total of | 162 | presence and hotspots with factors were analysed with GLM. A total of | ||
| 149 | 237 road sections were identified as hotspots, corresponding to 8% of | 163 | 237 road sections were identified as hotspots, corresponding to 8% of | ||
| 150 | road sections and containing 90% of road-killed individuals. Proximity | 164 | road sections and containing 90% of road-killed individuals. Proximity | ||
| 151 | to forests, meadows and agricultural land were the most important | 165 | to forests, meadows and agricultural land were the most important | ||
| 152 | variables in Maxent models. The number of roadkills depended on the | 166 | variables in Maxent models. The number of roadkills depended on the | ||
| 153 | proximity to agricultural land, forests, water bodies and wetland | 167 | proximity to agricultural land, forests, water bodies and wetland | ||
| 154 | areas, while the number of hotspots additionally depended on the | 168 | areas, while the number of hotspots additionally depended on the | ||
| 155 | proximity to urban settlements.\n Palabras clave: Amphibian, | 169 | proximity to urban settlements.\n Palabras clave: Amphibian, | ||
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