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a2026-06-25
en Relative effects of road risk, habitat suitability, and connectivity on wildlife roadkills: the case of tawny owls (Strix aluco). -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Relative effects of road risk, habitat suitability, and connectivity on wildlife roadkills: the case of tawny owls (Strix aluco).
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| 78 | "notes": "Despite its importance for reducing wildlife-vehicle | 92 | "notes": "Despite its importance for reducing wildlife-vehicle | ||
| 79 | collisions, there is still incomplete understanding of factors | 93 | collisions, there is still incomplete understanding of factors | ||
| 80 | responsible for high road mortality. In particular, few empirical | 94 | responsible for high road mortality. In particular, few empirical | ||
| 81 | studies examined the idea that spatial variation in roadkills is | 95 | studies examined the idea that spatial variation in roadkills is | ||
| 82 | influenced by a complex interplay between road-related factors, and | 96 | influenced by a complex interplay between road-related factors, and | ||
| 83 | species-specific habitat quality and landscape connectivity. In this | 97 | species-specific habitat quality and landscape connectivity. In this | ||
| 84 | study we addressed this issue, using a 7-year dataset of tawny owl | 98 | study we addressed this issue, using a 7-year dataset of tawny owl | ||
| 85 | (Strix aluco) roadkills recorded along 37 km of road in southern | 99 | (Strix aluco) roadkills recorded along 37 km of road in southern | ||
| 86 | Portugal. We used a multi-species roadkill index as a surrogate of | 100 | Portugal. We used a multi-species roadkill index as a surrogate of | ||
| 87 | intrinsic road risk, and we used a Maxent distribution model to | 101 | intrinsic road risk, and we used a Maxent distribution model to | ||
| 88 | estimate habitat suitability. Landscape connectivity was estimated | 102 | estimate habitat suitability. Landscape connectivity was estimated | ||
| 89 | from least-cost paths between tawny owl territories, using habitat | 103 | from least-cost paths between tawny owl territories, using habitat | ||
| 90 | suitability as a resistance surface. We defined 10 alternative | 104 | suitability as a resistance surface. We defined 10 alternative | ||
| 91 | scenarios to compute connectivity, based on variation in potential | 105 | scenarios to compute connectivity, based on variation in potential | ||
| 92 | movement patterns according to territory quality and dispersal | 106 | movement patterns according to territory quality and dispersal | ||
| 93 | distance thresholds. Hierarchical partitioning of a regression model | 107 | distance thresholds. Hierarchical partitioning of a regression model | ||
| 94 | indicated that independent variation in tawny owl roadkills was | 108 | indicated that independent variation in tawny owl roadkills was | ||
| 95 | explained primarily by the roadkill index (70.5%) and, to a much | 109 | explained primarily by the roadkill index (70.5%) and, to a much | ||
| 96 | lesser extent, by landscape connectivity (26.2%), while habitat | 110 | lesser extent, by landscape connectivity (26.2%), while habitat | ||
| 97 | suitability had minor effects (3.3%). Analysis of connectivity | 111 | suitability had minor effects (3.3%). Analysis of connectivity | ||
| 98 | scenarios suggested that owl roadkills were primarily related to short | 112 | scenarios suggested that owl roadkills were primarily related to short | ||
| 99 | range movements (<5 km) between high quality territories. Tawny owl | 113 | range movements (<5 km) between high quality territories. Tawny owl | ||
| 100 | roadkills were spatially autocorrelated, but the introduction of | 114 | roadkills were spatially autocorrelated, but the introduction of | ||
| 101 | spatial filters in the regression model did not change the type and | 115 | spatial filters in the regression model did not change the type and | ||
| 102 | relative contribution of environmental variables. Overall, results | 116 | relative contribution of environmental variables. Overall, results | ||
| 103 | suggest that road-related factors may have a dominant influence on | 117 | suggest that road-related factors may have a dominant influence on | ||
| 104 | roadkill patterns, particularly in areas like ours where habitat | 118 | roadkill patterns, particularly in areas like ours where habitat | ||
| 105 | quality and landscape connectivity are globally high for the study | 119 | quality and landscape connectivity are globally high for the study | ||
| 106 | species. Nevertheless, the study supported the view that functional | 120 | species. Nevertheless, the study supported the view that functional | ||
| 107 | connectivity should be incorporated whenever possible in roadkill | 121 | connectivity should be incorporated whenever possible in roadkill | ||
| 108 | models, as it may greatly increase their power to predict the location | 122 | models, as it may greatly increase their power to predict the location | ||
| 109 | of roadkill hotspots.\n Palabras clave: Bird, Connectivity, Habitat", | 123 | of roadkill hotspots.\n Palabras clave: Bird, Connectivity, Habitat", | ||
| 110 | "notes_translated": { | 124 | "notes_translated": { | ||
| 111 | "en": "Background Despite its importance for reducing | 125 | "en": "Background Despite its importance for reducing | ||
| 112 | wildlife-vehicle collisions, there is still incomplete understanding | 126 | wildlife-vehicle collisions, there is still incomplete understanding | ||
| 113 | of factors responsible for high road mortality. In particular, few | 127 | of factors responsible for high road mortality. In particular, few | ||
| 114 | empirical studies examined the idea that spatial variation in | 128 | empirical studies examined the idea that spatial variation in | ||
| 115 | roadkills is influenced by a complex interplay between road-related | 129 | roadkills is influenced by a complex interplay between road-related | ||
| 116 | factors, and species-specific habitat quality and landscape | 130 | factors, and species-specific habitat quality and landscape | ||
| 117 | connectivity. Methodology/Principal Findings In this study we | 131 | connectivity. Methodology/Principal Findings In this study we | ||
| 118 | addressed this issue, using a 7-year dataset of tawny owl (Strix | 132 | addressed this issue, using a 7-year dataset of tawny owl (Strix | ||
| 119 | aluco) roadkills recorded along 37 km of road in southern Portugal. We | 133 | aluco) roadkills recorded along 37 km of road in southern Portugal. We | ||
| 120 | used a multi-species roadkill index as a surrogate of intrinsic road | 134 | used a multi-species roadkill index as a surrogate of intrinsic road | ||
| 121 | risk, and we used a Maxent distribution model to estimate habitat | 135 | risk, and we used a Maxent distribution model to estimate habitat | ||
| 122 | suitability. Landscape connectivity was estimated from least-cost | 136 | suitability. Landscape connectivity was estimated from least-cost | ||
| 123 | paths between tawny owl territories, using habitat suitability as a | 137 | paths between tawny owl territories, using habitat suitability as a | ||
| 124 | resistance surface. We defined 10 alternative scenarios to compute | 138 | resistance surface. We defined 10 alternative scenarios to compute | ||
| 125 | connectivity, based on variation in potential movement patterns | 139 | connectivity, based on variation in potential movement patterns | ||
| 126 | according to territory quality and dispersal distance thresholds. | 140 | according to territory quality and dispersal distance thresholds. | ||
| 127 | Hierarchical partitioning of a regression model indicated that | 141 | Hierarchical partitioning of a regression model indicated that | ||
| 128 | independent variation in tawny owl roadkills was explained primarily | 142 | independent variation in tawny owl roadkills was explained primarily | ||
| 129 | by the roadkill index (70.5%) and, to a much lesser extent, by | 143 | by the roadkill index (70.5%) and, to a much lesser extent, by | ||
| 130 | landscape connectivity (26.2%), while habitat suitability had minor | 144 | landscape connectivity (26.2%), while habitat suitability had minor | ||
| 131 | effects (3.3%). Analysis of connectivity scenarios suggested that owl | 145 | effects (3.3%). Analysis of connectivity scenarios suggested that owl | ||
| 132 | roadkills were primarily related to short range movements (<5 km) | 146 | roadkills were primarily related to short range movements (<5 km) | ||
| 133 | between high quality territories. Tawny owl roadkills were spatially | 147 | between high quality territories. Tawny owl roadkills were spatially | ||
| 134 | autocorrelated, but the introduction of spatial filters in the | 148 | autocorrelated, but the introduction of spatial filters in the | ||
| 135 | regression model did not change the type and relative contribution of | 149 | regression model did not change the type and relative contribution of | ||
| 136 | environmental variables. Conclusions Overall, results \u2026", | 150 | environmental variables. Conclusions Overall, results \u2026", | ||
| 137 | "es": "Despite its importance for reducing wildlife-vehicle | 151 | "es": "Despite its importance for reducing wildlife-vehicle | ||
| 138 | collisions, there is still incomplete understanding of factors | 152 | collisions, there is still incomplete understanding of factors | ||
| 139 | responsible for high road mortality. In particular, few empirical | 153 | responsible for high road mortality. In particular, few empirical | ||
| 140 | studies examined the idea that spatial variation in roadkills is | 154 | studies examined the idea that spatial variation in roadkills is | ||
| 141 | influenced by a complex interplay between road-related factors, and | 155 | influenced by a complex interplay between road-related factors, and | ||
| 142 | species-specific habitat quality and landscape connectivity. In this | 156 | species-specific habitat quality and landscape connectivity. In this | ||
| 143 | study we addressed this issue, using a 7-year dataset of tawny owl | 157 | study we addressed this issue, using a 7-year dataset of tawny owl | ||
| 144 | (Strix aluco) roadkills recorded along 37 km of road in southern | 158 | (Strix aluco) roadkills recorded along 37 km of road in southern | ||
| 145 | Portugal. We used a multi-species roadkill index as a surrogate of | 159 | Portugal. We used a multi-species roadkill index as a surrogate of | ||
| 146 | intrinsic road risk, and we used a Maxent distribution model to | 160 | intrinsic road risk, and we used a Maxent distribution model to | ||
| 147 | estimate habitat suitability. Landscape connectivity was estimated | 161 | estimate habitat suitability. Landscape connectivity was estimated | ||
| 148 | from least-cost paths between tawny owl territories, using habitat | 162 | from least-cost paths between tawny owl territories, using habitat | ||
| 149 | suitability as a resistance surface. We defined 10 alternative | 163 | suitability as a resistance surface. We defined 10 alternative | ||
| 150 | scenarios to compute connectivity, based on variation in potential | 164 | scenarios to compute connectivity, based on variation in potential | ||
| 151 | movement patterns according to territory quality and dispersal | 165 | movement patterns according to territory quality and dispersal | ||
| 152 | distance thresholds. Hierarchical partitioning of a regression model | 166 | distance thresholds. Hierarchical partitioning of a regression model | ||
| 153 | indicated that independent variation in tawny owl roadkills was | 167 | indicated that independent variation in tawny owl roadkills was | ||
| 154 | explained primarily by the roadkill index (70.5%) and, to a much | 168 | explained primarily by the roadkill index (70.5%) and, to a much | ||
| 155 | lesser extent, by landscape connectivity (26.2%), while habitat | 169 | lesser extent, by landscape connectivity (26.2%), while habitat | ||
| 156 | suitability had minor effects (3.3%). Analysis of connectivity | 170 | suitability had minor effects (3.3%). Analysis of connectivity | ||
| 157 | scenarios suggested that owl roadkills were primarily related to short | 171 | scenarios suggested that owl roadkills were primarily related to short | ||
| 158 | range movements (<5 km) between high quality territories. Tawny owl | 172 | range movements (<5 km) between high quality territories. Tawny owl | ||
| 159 | roadkills were spatially autocorrelated, but the introduction of | 173 | roadkills were spatially autocorrelated, but the introduction of | ||
| 160 | spatial filters in the regression model did not change the type and | 174 | spatial filters in the regression model did not change the type and | ||
| 161 | relative contribution of environmental variables. Overall, results | 175 | relative contribution of environmental variables. Overall, results | ||
| 162 | suggest that road-related factors may have a dominant influence on | 176 | suggest that road-related factors may have a dominant influence on | ||
| 163 | roadkill patterns, particularly in areas like ours where habitat | 177 | roadkill patterns, particularly in areas like ours where habitat | ||
| 164 | quality and landscape connectivity are globally high for the study | 178 | quality and landscape connectivity are globally high for the study | ||
| 165 | species. Nevertheless, the study supported the view that functional | 179 | species. Nevertheless, the study supported the view that functional | ||
| 166 | connectivity should be incorporated whenever possible in roadkill | 180 | connectivity should be incorporated whenever possible in roadkill | ||
| 167 | models, as it may greatly increase their power to predict the location | 181 | models, as it may greatly increase their power to predict the location | ||
| 168 | of roadkill hotspots.\n Palabras clave: Bird, Connectivity, Habitat" | 182 | of roadkill hotspots.\n Palabras clave: Bird, Connectivity, Habitat" | ||
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