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a2026-06-25
en Comparing spatial statistical methods to detect amphibian road mortality hotspots. -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Comparing spatial statistical methods to detect amphibian road mortality hotspots.
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| 76 | "notes": "Animal mortality on roads is one of the main concerns on | 90 | "notes": "Animal mortality on roads is one of the main concerns on | ||
| 77 | wildlife conservation. Due to their habitat requirements, amphibians | 91 | wildlife conservation. Due to their habitat requirements, amphibians | ||
| 78 | became one of the most commonly road-killed group and this may affect | 92 | became one of the most commonly road-killed group and this may affect | ||
| 79 | their population viability. Implementation of mitigation measures may | 93 | their population viability. Implementation of mitigation measures may | ||
| 80 | overcome the problem. However, due to the extensive road network, | 94 | overcome the problem. However, due to the extensive road network, | ||
| 81 | their application is very expensive and required a better | 95 | their application is very expensive and required a better | ||
| 82 | understanding in where they should be implemented. Mortality hotspots | 96 | understanding in where they should be implemented. Mortality hotspots | ||
| 83 | can be identified as clusters of road-killed records) using GIS | 97 | can be identified as clusters of road-killed records) using GIS | ||
| 84 | (Geographic Information Systems). Although there are several | 98 | (Geographic Information Systems). Although there are several | ||
| 85 | statistical methods available, it is lacking a comparison analysis of | 99 | statistical methods available, it is lacking a comparison analysis of | ||
| 86 | them in order to understand their pros and contras. The aim of this | 100 | them in order to understand their pros and contras. The aim of this | ||
| 87 | study was to analyse possible differences between global, multi-scale | 101 | study was to analyse possible differences between global, multi-scale | ||
| 88 | and local spatial analysis methods in defining hotspots using | 102 | and local spatial analysis methods in defining hotspots using | ||
| 89 | amphibian road fatality data collected in northern Portugal country | 103 | amphibian road fatality data collected in northern Portugal country | ||
| 90 | roads. We calculated the Nearest neighbor index, Morans I and | 104 | roads. We calculated the Nearest neighbor index, Morans I and | ||
| 91 | Getis-ord General in order to compare the global clustering of points | 105 | Getis-ord General in order to compare the global clustering of points | ||
| 92 | in seven sampled roads, and three were identified as clustered. We | 106 | in seven sampled roads, and three were identified as clustered. We | ||
| 93 | used Ripley K-function, Ripley L-function and F function to calculate | 107 | used Ripley K-function, Ripley L-function and F function to calculate | ||
| 94 | the best scale for Malo's equation and Kernel density analysis in | 108 | the best scale for Malo's equation and Kernel density analysis in | ||
| 95 | detecting hotspots and we compared their detection performance with | 109 | detecting hotspots and we compared their detection performance with | ||
| 96 | Local Indicators of Association (LISA) (i.e Local Moran's I and | 110 | Local Indicators of Association (LISA) (i.e Local Moran's I and | ||
| 97 | Getis-ord Gi*). Three different GIS software applications were used: | 111 | Getis-ord Gi*). Three different GIS software applications were used: | ||
| 98 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | 112 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | ||
| 99 | Results showed the importance of using multidistance spatial cluster | 113 | Results showed the importance of using multidistance spatial cluster | ||
| 100 | analysis to define the best scale for hotspot detection with | 114 | analysis to define the best scale for hotspot detection with | ||
| 101 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | 115 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | ||
| 102 | the advantages of Local Indicators of Association (LISA) for detecting | 116 | the advantages of Local Indicators of Association (LISA) for detecting | ||
| 103 | clusters with the contribution of each individual observation (Local | 117 | clusters with the contribution of each individual observation (Local | ||
| 104 | Morans I and Getis-ord Gi*).", | 118 | Morans I and Getis-ord Gi*).", | ||
| 105 | "notes_translated": { | 119 | "notes_translated": { | ||
| 106 | "en": "Animal mortality on roads is one of the main concerns on | 120 | "en": "Animal mortality on roads is one of the main concerns on | ||
| 107 | wildlife conservation. Due to their habitat requirements, amphibians | 121 | wildlife conservation. Due to their habitat requirements, amphibians | ||
| 108 | became one of the most commonly road-killed group and this may affect | 122 | became one of the most commonly road-killed group and this may affect | ||
| 109 | their population viability. Implementation of mitigation measures may | 123 | their population viability. Implementation of mitigation measures may | ||
| 110 | overcome the problem. However, due to the extensive road network, | 124 | overcome the problem. However, due to the extensive road network, | ||
| 111 | their application is very expensive and required a better | 125 | their application is very expensive and required a better | ||
| 112 | understanding in where they should be implemented. Mortality hotspots | 126 | understanding in where they should be implemented. Mortality hotspots | ||
| 113 | can be identified as clusters of road-killed records) using GIS | 127 | can be identified as clusters of road-killed records) using GIS | ||
| 114 | (Geographic Information Systems). Although there are several | 128 | (Geographic Information Systems). Although there are several | ||
| 115 | statistical methods available, it is lacking a comparison analysis of | 129 | statistical methods available, it is lacking a comparison analysis of | ||
| 116 | them in order to understand their pros and contras. The aim of this | 130 | them in order to understand their pros and contras. The aim of this | ||
| 117 | study was to analyse possible differences between global, multi-scale | 131 | study was to analyse possible differences between global, multi-scale | ||
| 118 | and local spatial analysis methods in defining hotspots using | 132 | and local spatial analysis methods in defining hotspots using | ||
| 119 | amphibian road fatality data collected in northern Portugal country | 133 | amphibian road fatality data collected in northern Portugal country | ||
| 120 | roads. We calculated the Nearest neighbor index, Morans I and | 134 | roads. We calculated the Nearest neighbor index, Morans I and | ||
| 121 | Getis-ord General in order to compare the global clustering of points | 135 | Getis-ord General in order to compare the global clustering of points | ||
| 122 | in seven sampled roads, and three were identified as clustered. We | 136 | in seven sampled roads, and three were identified as clustered. We | ||
| 123 | used Ripley K-function, Ripley L-function and F function to calculate | 137 | used Ripley K-function, Ripley L-function and F function to calculate | ||
| 124 | the best scale for Malo's equation and Kernel density analysis in | 138 | the best scale for Malo's equation and Kernel density analysis in | ||
| 125 | detecting hotspots and we compared their detection performance with | 139 | detecting hotspots and we compared their detection performance with | ||
| 126 | Local Indicators of Association (LISA) (i.e Local Moran's I and | 140 | Local Indicators of Association (LISA) (i.e Local Moran's I and | ||
| 127 | Getis-ord Gi*). Three different GIS software applications were used: | 141 | Getis-ord Gi*). Three different GIS software applications were used: | ||
| 128 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | 142 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | ||
| 129 | Results showed the importance of using multidistance spatial cluster | 143 | Results showed the importance of using multidistance spatial cluster | ||
| 130 | analysis to define the best scale for hotspot detection with | 144 | analysis to define the best scale for hotspot detection with | ||
| 131 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | 145 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | ||
| 132 | the advantages of Local Indicators of Association (LISA) for detecting | 146 | the advantages of Local Indicators of Association (LISA) for detecting | ||
| 133 | clusters with the contribution of each individual observation (Local | 147 | clusters with the contribution of each individual observation (Local | ||
| 134 | Morans I and Getis-ord Gi*).", | 148 | Morans I and Getis-ord Gi*).", | ||
| 135 | "es": "Animal mortality on roads is one of the main concerns on | 149 | "es": "Animal mortality on roads is one of the main concerns on | ||
| 136 | wildlife conservation. Due to their habitat requirements, amphibians | 150 | wildlife conservation. Due to their habitat requirements, amphibians | ||
| 137 | became one of the most commonly road-killed group and this may affect | 151 | became one of the most commonly road-killed group and this may affect | ||
| 138 | their population viability. Implementation of mitigation measures may | 152 | their population viability. Implementation of mitigation measures may | ||
| 139 | overcome the problem. However, due to the extensive road network, | 153 | overcome the problem. However, due to the extensive road network, | ||
| 140 | their application is very expensive and required a better | 154 | their application is very expensive and required a better | ||
| 141 | understanding in where they should be implemented. Mortality hotspots | 155 | understanding in where they should be implemented. Mortality hotspots | ||
| 142 | can be identified as clusters of road-killed records) using GIS | 156 | can be identified as clusters of road-killed records) using GIS | ||
| 143 | (Geographic Information Systems). Although there are several | 157 | (Geographic Information Systems). Although there are several | ||
| 144 | statistical methods available, it is lacking a comparison analysis of | 158 | statistical methods available, it is lacking a comparison analysis of | ||
| 145 | them in order to understand their pros and contras. The aim of this | 159 | them in order to understand their pros and contras. The aim of this | ||
| 146 | study was to analyse possible differences between global, multi-scale | 160 | study was to analyse possible differences between global, multi-scale | ||
| 147 | and local spatial analysis methods in defining hotspots using | 161 | and local spatial analysis methods in defining hotspots using | ||
| 148 | amphibian road fatality data collected in northern Portugal country | 162 | amphibian road fatality data collected in northern Portugal country | ||
| 149 | roads. We calculated the Nearest neighbor index, Morans I and | 163 | roads. We calculated the Nearest neighbor index, Morans I and | ||
| 150 | Getis-ord General in order to compare the global clustering of points | 164 | Getis-ord General in order to compare the global clustering of points | ||
| 151 | in seven sampled roads, and three were identified as clustered. We | 165 | in seven sampled roads, and three were identified as clustered. We | ||
| 152 | used Ripley K-function, Ripley L-function and F function to calculate | 166 | used Ripley K-function, Ripley L-function and F function to calculate | ||
| 153 | the best scale for Malo's equation and Kernel density analysis in | 167 | the best scale for Malo's equation and Kernel density analysis in | ||
| 154 | detecting hotspots and we compared their detection performance with | 168 | detecting hotspots and we compared their detection performance with | ||
| 155 | Local Indicators of Association (LISA) (i.e Local Moran's I and | 169 | Local Indicators of Association (LISA) (i.e Local Moran's I and | ||
| 156 | Getis-ord Gi*). Three different GIS software applications were used: | 170 | Getis-ord Gi*). Three different GIS software applications were used: | ||
| 157 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | 171 | ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). | ||
| 158 | Results showed the importance of using multidistance spatial cluster | 172 | Results showed the importance of using multidistance spatial cluster | ||
| 159 | analysis to define the best scale for hotspot detection with | 173 | analysis to define the best scale for hotspot detection with | ||
| 160 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | 174 | Malo\u00b4s equation and Kernel density analysis. Here we also suggest | ||
| 161 | the advantages of Local Indicators of Association (LISA) for detecting | 175 | the advantages of Local Indicators of Association (LISA) for detecting | ||
| 162 | clusters with the contribution of each individual observation (Local | 176 | clusters with the contribution of each individual observation (Local | ||
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