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en What drives the spatial distribution and temporal persistence of bat road kill hotspots? -
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del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en What drives the spatial distribution and temporal persistence of bat road kill hotspots?
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| 78 | "notes": "Road kill hotspots are frequently used to identify | 92 | "notes": "Road kill hotspots are frequently used to identify | ||
| 79 | priority locations for mitigation measures. However, understanding the | 93 | priority locations for mitigation measures. However, understanding the | ||
| 80 | landscape context and temporal dynamics of these hotspots is a | 94 | landscape context and temporal dynamics of these hotspots is a | ||
| 81 | challenge. We investigate the factors that drive the spatiotemporal | 95 | challenge. We investigate the factors that drive the spatiotemporal | ||
| 82 | variation of bat mortality hotspots in a Mediterranean landscape. We | 96 | variation of bat mortality hotspots in a Mediterranean landscape. We | ||
| 83 | hypothesise that hotspot locations occur at places where bat activity | 97 | hypothesise that hotspot locations occur at places where bat activity | ||
| 84 | is higher. Additionally, we hypothesise that this activity is related | 98 | is higher. Additionally, we hypothesise that this activity is related | ||
| 85 | to vegetation density and productivity because this is related to | 99 | to vegetation density and productivity because this is related to | ||
| 86 | insect prey abundance. We used a combination of spatiotemporal | 100 | insect prey abundance. We used a combination of spatiotemporal | ||
| 87 | analysis and generalised mixed models to evaluate the effect of the | 101 | analysis and generalised mixed models to evaluate the effect of the | ||
| 88 | local spatial variation of vegetation productivity (as measured by the | 102 | local spatial variation of vegetation productivity (as measured by the | ||
| 89 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | 103 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | ||
| 90 | combined this information with bat road kill locations to predict and | 104 | combined this information with bat road kill locations to predict and | ||
| 91 | validate spatial and temporal variation on road kill hotspot | 105 | validate spatial and temporal variation on road kill hotspot | ||
| 92 | locations. During three years (2009, 2010, and 2011) we conducted | 106 | locations. During three years (2009, 2010, and 2011) we conducted | ||
| 93 | daily surveys (n=690) along a 51 km long transect that incorporates | 107 | daily surveys (n=690) along a 51 km long transect that incorporates | ||
| 94 | different types of roads in southern Portugal, searching for bat | 108 | different types of roads in southern Portugal, searching for bat | ||
| 95 | casualties. Overall, we found 474 bat casualties during the sampling | 109 | casualties. Overall, we found 474 bat casualties during the sampling | ||
| 96 | period. Then, to conduct the bat mortality analyses we assigned each | 110 | period. Then, to conduct the bat mortality analyses we assigned each | ||
| 97 | road kill to the corresponding 500 m-road segment. We identified | 111 | road kill to the corresponding 500 m-road segment. We identified | ||
| 98 | mortality hotspots for each year; segments where the number of | 112 | mortality hotspots for each year; segments where the number of | ||
| 99 | casualties exceeded the upper 80% confidence limit of the mean, | 113 | casualties exceeded the upper 80% confidence limit of the mean, | ||
| 100 | assuming a Poisson distribution of road kill per road segment, and | 114 | assuming a Poisson distribution of road kill per road segment, and | ||
| 101 | analysed if they changed their location over the years. Overall, only | 115 | analysed if they changed their location over the years. Overall, only | ||
| 102 | 10% of segments were identified as hotspots during the whole year. 37% | 116 | 10% of segments were identified as hotspots during the whole year. 37% | ||
| 103 | of road segments are intermittent road kill hotspots, i.e., they are | 117 | of road segments are intermittent road kill hotspots, i.e., they are | ||
| 104 | classified as hotspots only in one or two years. 53% of road segments | 118 | classified as hotspots only in one or two years. 53% of road segments | ||
| 105 | had very few bat casualties and were not identified as hotspots. For | 119 | had very few bat casualties and were not identified as hotspots. For | ||
| 106 | 20% of the road segments, we did not find any bat casualties. Thus, | 120 | 20% of the road segments, we did not find any bat casualties. Thus, | ||
| 107 | the non-persistent hotspots were the most frequent category. Further | 121 | the non-persistent hotspots were the most frequent category. Further | ||
| 108 | analysis of this type of hotspots showed that the spatiotemporal | 122 | analysis of this type of hotspots showed that the spatiotemporal | ||
| 109 | congruence of hotspots locations declined with decreasing vegetation | 123 | congruence of hotspots locations declined with decreasing vegetation | ||
| 110 | production and the associated reduced bat activity on the proximity of | 124 | production and the associated reduced bat activity on the proximity of | ||
| 111 | road segments. This supports our hypothesis showing that a decline in | 125 | road segments. This supports our hypothesis showing that a decline in | ||
| 112 | overall vegetation productivity, and the presumed lower abundance of | 126 | overall vegetation productivity, and the presumed lower abundance of | ||
| 113 | prey have a significant effect on the decrease of bat road kill. In | 127 | prey have a significant effect on the decrease of bat road kill. In | ||
| 114 | this study, we show for the first time that using readily available | 128 | this study, we show for the first time that using readily available | ||
| 115 | series remote sensing data, and the indices that can be calculated | 129 | series remote sensing data, and the indices that can be calculated | ||
| 116 | based on this information, such as the NDVI, can be a powerful tool to | 130 | based on this information, such as the NDVI, can be a powerful tool to | ||
| 117 | predict bat road kill hotspots and their persistence in time. Thus, | 131 | predict bat road kill hotspots and their persistence in time. Thus, | ||
| 118 | NDVI can be used in road planning, to prioritise location of | 132 | NDVI can be used in road planning, to prioritise location of | ||
| 119 | mitigation measures or to identify essential road habitats for | 133 | mitigation measures or to identify essential road habitats for | ||
| 120 | conservation.", | 134 | conservation.", | ||
| 121 | "notes_translated": { | 135 | "notes_translated": { | ||
| 122 | "en": "Road kill hotspots are frequently used to identify priority | 136 | "en": "Road kill hotspots are frequently used to identify priority | ||
| 123 | locations for mitigation measures. However, understanding the | 137 | locations for mitigation measures. However, understanding the | ||
| 124 | landscape context and temporal dynamics of these hotspots is a | 138 | landscape context and temporal dynamics of these hotspots is a | ||
| 125 | challenge. We investigate the factors that drive the spatiotemporal | 139 | challenge. We investigate the factors that drive the spatiotemporal | ||
| 126 | variation of bat mortality hotspots in a Mediterranean landscape. We | 140 | variation of bat mortality hotspots in a Mediterranean landscape. We | ||
| 127 | hypothesise that hotspot locations occur at places where bat activity | 141 | hypothesise that hotspot locations occur at places where bat activity | ||
| 128 | is higher. Additionally, we hypothesise that this activity is related | 142 | is higher. Additionally, we hypothesise that this activity is related | ||
| 129 | to vegetation density and productivity because this is related to | 143 | to vegetation density and productivity because this is related to | ||
| 130 | insect prey abundance. We used a combination of spatiotemporal | 144 | insect prey abundance. We used a combination of spatiotemporal | ||
| 131 | analysis and generalised mixed models to evaluate the effect of the | 145 | analysis and generalised mixed models to evaluate the effect of the | ||
| 132 | local spatial variation of vegetation productivity (as measured by the | 146 | local spatial variation of vegetation productivity (as measured by the | ||
| 133 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | 147 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | ||
| 134 | combined\nthis information with bat road kill locations to predict and | 148 | combined\nthis information with bat road kill locations to predict and | ||
| 135 | validate spatial and temporal variation on road kill hotspot | 149 | validate spatial and temporal variation on road kill hotspot | ||
| 136 | locations. During three years (2009, 2010, and 2011) we conducted | 150 | locations. During three years (2009, 2010, and 2011) we conducted | ||
| 137 | daily surveys (n=690) along a 51 km long transect that incorporates | 151 | daily surveys (n=690) along a 51 km long transect that incorporates | ||
| 138 | different types of roads in southern Portugal, searching for bat | 152 | different types of roads in southern Portugal, searching for bat | ||
| 139 | casualties. Overall, we found 474 bat casualties during the sampling | 153 | casualties. Overall, we found 474 bat casualties during the sampling | ||
| 140 | period. Then, to conduct the bat mortality analyses we assigned each | 154 | period. Then, to conduct the bat mortality analyses we assigned each | ||
| 141 | road kill to the corresponding 500 m-road segment. We identified | 155 | road kill to the corresponding 500 m-road segment. We identified | ||
| 142 | mortality hotspots for each year; segments where the number of | 156 | mortality hotspots for each year; segments where the number of | ||
| 143 | casualties exceeded the upper 80% confidence limit of the mean, | 157 | casualties exceeded the upper 80% confidence limit of the mean, | ||
| 144 | assuming a Poisson distribution of road kill per road segment, and | 158 | assuming a Poisson distribution of road kill per road segment, and | ||
| 145 | analysed if they changed their location over the years. Overall, only | 159 | analysed if they changed their location over the years. Overall, only | ||
| 146 | 10% of segments were identified as hotspots during the whole year. 37% | 160 | 10% of segments were identified as hotspots during the whole year. 37% | ||
| 147 | of road segments are intermittent road kill hotspots, i.e., they are | 161 | of road segments are intermittent road kill hotspots, i.e., they are | ||
| 148 | classified as hotspots only in one or two years. 53% of road segments | 162 | classified as hotspots only in one or two years. 53% of road segments | ||
| 149 | had very few bat casualties and were not identified as hotspots. For | 163 | had very few bat casualties and were not identified as hotspots. For | ||
| 150 | 20% of the road segments, we did not find any bat casualties. Thus, | 164 | 20% of the road segments, we did not find any bat casualties. Thus, | ||
| 151 | the non-persistent hotspots were the most frequent category. Further | 165 | the non-persistent hotspots were the most frequent category. Further | ||
| 152 | analysis of this type of hotspots showed that the spatiotemporal | 166 | analysis of this type of hotspots showed that the spatiotemporal | ||
| 153 | congruence of hotspots locations declined with decreasing vegetation | 167 | congruence of hotspots locations declined with decreasing vegetation | ||
| 154 | production\nand the associated reduced bat activity on the proximity | 168 | production\nand the associated reduced bat activity on the proximity | ||
| 155 | of road segments. This supports our hypothesis showing that a decline | 169 | of road segments. This supports our hypothesis showing that a decline | ||
| 156 | in overall vegetation productivity, and the presumed lower abundance | 170 | in overall vegetation productivity, and the presumed lower abundance | ||
| 157 | of prey have a significant effect on the decrease of bat road kill. In | 171 | of prey have a significant effect on the decrease of bat road kill. In | ||
| 158 | this study, we show for the first time that using readily available | 172 | this study, we show for the first time that using readily available | ||
| 159 | series remote sensing data, and the indices that can be calculated | 173 | series remote sensing data, and the indices that can be calculated | ||
| 160 | based on this information, such as the NDVI, can be a powerful tool to | 174 | based on this information, such as the NDVI, can be a powerful tool to | ||
| 161 | predict bat road kill hotspots and their persistence in time. Thus, | 175 | predict bat road kill hotspots and their persistence in time. Thus, | ||
| 162 | NDVI can be used in road planning, to prioritise location of | 176 | NDVI can be used in road planning, to prioritise location of | ||
| 163 | mitigation measures or to identify essential road habitats for | 177 | mitigation measures or to identify essential road habitats for | ||
| 164 | conservation.", | 178 | conservation.", | ||
| 165 | "es": "Road kill hotspots are frequently used to identify priority | 179 | "es": "Road kill hotspots are frequently used to identify priority | ||
| 166 | locations for mitigation measures. However, understanding the | 180 | locations for mitigation measures. However, understanding the | ||
| 167 | landscape context and temporal dynamics of these hotspots is a | 181 | landscape context and temporal dynamics of these hotspots is a | ||
| 168 | challenge. We investigate the factors that drive the spatiotemporal | 182 | challenge. We investigate the factors that drive the spatiotemporal | ||
| 169 | variation of bat mortality hotspots in a Mediterranean landscape. We | 183 | variation of bat mortality hotspots in a Mediterranean landscape. We | ||
| 170 | hypothesise that hotspot locations occur at places where bat activity | 184 | hypothesise that hotspot locations occur at places where bat activity | ||
| 171 | is higher. Additionally, we hypothesise that this activity is related | 185 | is higher. Additionally, we hypothesise that this activity is related | ||
| 172 | to vegetation density and productivity because this is related to | 186 | to vegetation density and productivity because this is related to | ||
| 173 | insect prey abundance. We used a combination of spatiotemporal | 187 | insect prey abundance. We used a combination of spatiotemporal | ||
| 174 | analysis and generalised mixed models to evaluate the effect of the | 188 | analysis and generalised mixed models to evaluate the effect of the | ||
| 175 | local spatial variation of vegetation productivity (as measured by the | 189 | local spatial variation of vegetation productivity (as measured by the | ||
| 176 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | 190 | Normalized Vegetation Index -NDVI) on bats space use. Then, we | ||
| 177 | combined this information with bat road kill locations to predict and | 191 | combined this information with bat road kill locations to predict and | ||
| 178 | validate spatial and temporal variation on road kill hotspot | 192 | validate spatial and temporal variation on road kill hotspot | ||
| 179 | locations. During three years (2009, 2010, and 2011) we conducted | 193 | locations. During three years (2009, 2010, and 2011) we conducted | ||
| 180 | daily surveys (n=690) along a 51 km long transect that incorporates | 194 | daily surveys (n=690) along a 51 km long transect that incorporates | ||
| 181 | different types of roads in southern Portugal, searching for bat | 195 | different types of roads in southern Portugal, searching for bat | ||
| 182 | casualties. Overall, we found 474 bat casualties during the sampling | 196 | casualties. Overall, we found 474 bat casualties during the sampling | ||
| 183 | period. Then, to conduct the bat mortality analyses we assigned each | 197 | period. Then, to conduct the bat mortality analyses we assigned each | ||
| 184 | road kill to the corresponding 500 m-road segment. We identified | 198 | road kill to the corresponding 500 m-road segment. We identified | ||
| 185 | mortality hotspots for each year; segments where the number of | 199 | mortality hotspots for each year; segments where the number of | ||
| 186 | casualties exceeded the upper 80% confidence limit of the mean, | 200 | casualties exceeded the upper 80% confidence limit of the mean, | ||
| 187 | assuming a Poisson distribution of road kill per road segment, and | 201 | assuming a Poisson distribution of road kill per road segment, and | ||
| 188 | analysed if they changed their location over the years. Overall, only | 202 | analysed if they changed their location over the years. Overall, only | ||
| 189 | 10% of segments were identified as hotspots during the whole year. 37% | 203 | 10% of segments were identified as hotspots during the whole year. 37% | ||
| 190 | of road segments are intermittent road kill hotspots, i.e., they are | 204 | of road segments are intermittent road kill hotspots, i.e., they are | ||
| 191 | classified as hotspots only in one or two years. 53% of road segments | 205 | classified as hotspots only in one or two years. 53% of road segments | ||
| 192 | had very few bat casualties and were not identified as hotspots. For | 206 | had very few bat casualties and were not identified as hotspots. For | ||
| 193 | 20% of the road segments, we did not find any bat casualties. Thus, | 207 | 20% of the road segments, we did not find any bat casualties. Thus, | ||
| 194 | the non-persistent hotspots were the most frequent category. Further | 208 | the non-persistent hotspots were the most frequent category. Further | ||
| 195 | analysis of this type of hotspots showed that the spatiotemporal | 209 | analysis of this type of hotspots showed that the spatiotemporal | ||
| 196 | congruence of hotspots locations declined with decreasing vegetation | 210 | congruence of hotspots locations declined with decreasing vegetation | ||
| 197 | production and the associated reduced bat activity on the proximity of | 211 | production and the associated reduced bat activity on the proximity of | ||
| 198 | road segments. This supports our hypothesis showing that a decline in | 212 | road segments. This supports our hypothesis showing that a decline in | ||
| 199 | overall vegetation productivity, and the presumed lower abundance of | 213 | overall vegetation productivity, and the presumed lower abundance of | ||
| 200 | prey have a significant effect on the decrease of bat road kill. In | 214 | prey have a significant effect on the decrease of bat road kill. In | ||
| 201 | this study, we show for the first time that using readily available | 215 | this study, we show for the first time that using readily available | ||
| 202 | series remote sensing data, and the indices that can be calculated | 216 | series remote sensing data, and the indices that can be calculated | ||
| 203 | based on this information, such as the NDVI, can be a powerful tool to | 217 | based on this information, such as the NDVI, can be a powerful tool to | ||
| 204 | predict bat road kill hotspots and their persistence in time. Thus, | 218 | predict bat road kill hotspots and their persistence in time. Thus, | ||
| 205 | NDVI can be used in road planning, to prioritise location of | 219 | NDVI can be used in road planning, to prioritise location of | ||
| 206 | mitigation measures or to identify essential road habitats for | 220 | mitigation measures or to identify essential road habitats for | ||
| 207 | conservation." | 221 | conservation." | ||
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