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En el instante 25 de junio de 2026, 12:31:58 UTC,
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Modificado el valor del campo
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a2026-06-25
en Can we model distribution of population abundance from wildlife–vehicles collision data? -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Can we model distribution of population abundance from wildlife–vehicles collision data?
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| 71 | "miteco_data_population": { | 85 | "miteco_data_population": { | ||
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| 81 | "name": "a4c6c3cd-c65a-58b9-8a1c-326013b92ebd", | 95 | "name": "a4c6c3cd-c65a-58b9-8a1c-326013b92ebd", | ||
| 82 | "notes": "Reliable estimates of the distribution of species | 96 | "notes": "Reliable estimates of the distribution of species | ||
| 83 | abundance are a key element in wildlife studies, but such information | 97 | abundance are a key element in wildlife studies, but such information | ||
| 84 | is usually difficult to obtain for large spatial or long temporal | 98 | is usually difficult to obtain for large spatial or long temporal | ||
| 85 | scales. Wildlife\u2013vehicle collision (WVC) data is systematically | 99 | scales. Wildlife\u2013vehicle collision (WVC) data is systematically | ||
| 86 | registered in many countries and could be used as a proxy of | 100 | registered in many countries and could be used as a proxy of | ||
| 87 | population abundance if the number of WVC in each territory increase | 101 | population abundance if the number of WVC in each territory increase | ||
| 88 | with the population abundance. However, factors such as road density | 102 | with the population abundance. However, factors such as road density | ||
| 89 | or human population should be controlled to obtain accurate abundance | 103 | or human population should be controlled to obtain accurate abundance | ||
| 90 | estimations from WVC data. Here, we propose a hierarchical modeling | 104 | estimations from WVC data. Here, we propose a hierarchical modeling | ||
| 91 | approach using the Royle\u2013Nichols model for | 105 | approach using the Royle\u2013Nichols model for | ||
| 92 | detection\u2013non-detection data to obtain population abundance | 106 | detection\u2013non-detection data to obtain population abundance | ||
| 93 | indices from WVC. Relative abundance and individual detectability were | 107 | indices from WVC. Relative abundance and individual detectability were | ||
| 94 | modeled for two species, wild boar Sus scrofa and roe deer Capreolus | 108 | modeled for two species, wild boar Sus scrofa and roe deer Capreolus | ||
| 95 | capreolus at 10 \u00d7 10 km cells in mainland Spain from WVC data | 109 | capreolus at 10 \u00d7 10 km cells in mainland Spain from WVC data | ||
| 96 | using environmental, anthropological and temporal covariates. For each | 110 | using environmental, anthropological and temporal covariates. For each | ||
| 97 | cell, a detection was annotated if at least one WVC was recorded at | 111 | cell, a detection was annotated if at least one WVC was recorded at | ||
| 98 | each month (used as survey occasion). The predicted abundance indices | 112 | each month (used as survey occasion). The predicted abundance indices | ||
| 99 | were compared with raw hunting statistics at region level to assess | 113 | were compared with raw hunting statistics at region level to assess | ||
| 100 | the performance of the modeling approach. Site specific covariates | 114 | the performance of the modeling approach. Site specific covariates | ||
| 101 | such as road density or administrative region and the month of the | 115 | such as road density or administrative region and the month of the | ||
| 102 | year, affected individual detectability, with higher WVC probability | 116 | year, affected individual detectability, with higher WVC probability | ||
| 103 | between October and December for wild boar and between April and July | 117 | between October and December for wild boar and between April and July | ||
| 104 | for roe deer. Wild boar and roe deer abundance can be explained by | 118 | for roe deer. Wild boar and roe deer abundance can be explained by | ||
| 105 | both, bioclimatic and land cover covariates. Abundance indices | 119 | both, bioclimatic and land cover covariates. Abundance indices | ||
| 106 | obtained from WVC data were significantly positively correlated with | 120 | obtained from WVC data were significantly positively correlated with | ||
| 107 | regional raw hunting yields for both species. We presented empirical | 121 | regional raw hunting yields for both species. We presented empirical | ||
| 108 | evidence supporting that accurate wildlife abundance indices at fine | 122 | evidence supporting that accurate wildlife abundance indices at fine | ||
| 109 | spatial resolution can be generated from WVC data when individual | 123 | spatial resolution can be generated from WVC data when individual | ||
| 110 | detectability is considered in the modeling process.", | 124 | detectability is considered in the modeling process.", | ||
| 111 | "notes_translated": { | 125 | "notes_translated": { | ||
| 112 | "en": "Reliable estimates of the distribution of species abundance | 126 | "en": "Reliable estimates of the distribution of species abundance | ||
| 113 | are a key element in wildlife studies, but such information is usually | 127 | are a key element in wildlife studies, but such information is usually | ||
| 114 | difficult to obtain for large spatial or long temporal scales. | 128 | difficult to obtain for large spatial or long temporal scales. | ||
| 115 | Wildlife\u2013vehicle collision (WVC) data is systematically | 129 | Wildlife\u2013vehicle collision (WVC) data is systematically | ||
| 116 | registered in many countries and could be used as a proxy of | 130 | registered in many countries and could be used as a proxy of | ||
| 117 | population abundance if the number of WVC in each territory increase | 131 | population abundance if the number of WVC in each territory increase | ||
| 118 | with the population abundance. However, factors such as road density | 132 | with the population abundance. However, factors such as road density | ||
| 119 | or human population should be controlled to obtain accurate abundance | 133 | or human population should be controlled to obtain accurate abundance | ||
| 120 | estimations from WVC data. Here, we propose a hierarchical modeling | 134 | estimations from WVC data. Here, we propose a hierarchical modeling | ||
| 121 | approach using the Royle\u2013Nichols model for | 135 | approach using the Royle\u2013Nichols model for | ||
| 122 | detection\u2013non-detection data to obtain population abundance | 136 | detection\u2013non-detection data to obtain population abundance | ||
| 123 | indices from WVC. Relative abundance and individual detectability were | 137 | indices from WVC. Relative abundance and individual detectability were | ||
| 124 | modeled for two species, wild boar\u00a0Sus scrofa\u00a0and roe | 138 | modeled for two species, wild boar\u00a0Sus scrofa\u00a0and roe | ||
| 125 | deer\u00a0Capreolus capreolus\u00a0at 10 \u00d7 10 km cells in | 139 | deer\u00a0Capreolus capreolus\u00a0at 10 \u00d7 10 km cells in | ||
| 126 | mainland Spain from WVC data using environmental, anthropological and | 140 | mainland Spain from WVC data using environmental, anthropological and | ||
| 127 | temporal covariates. For each cell, a detection was annotated if at | 141 | temporal covariates. For each cell, a detection was annotated if at | ||
| 128 | least one WVC was recorded at each month (used as survey occasion). | 142 | least one WVC was recorded at each month (used as survey occasion). | ||
| 129 | The predicted abundance indices were compared with raw hunting | 143 | The predicted abundance indices were compared with raw hunting | ||
| 130 | statistics at region level to assess the performance of the modeling | 144 | statistics at region level to assess the performance of the modeling | ||
| 131 | approach. Site specific covariates such as road density or | 145 | approach. Site specific covariates such as road density or | ||
| 132 | administrative region and the month of the year, affected individual | 146 | administrative region and the month of the year, affected individual | ||
| 133 | detectability, with higher WVC probability between October and | 147 | detectability, with higher WVC probability between October and | ||
| 134 | December for wild boar and between April and July for roe deer. Wild | 148 | December for wild boar and between April and July for roe deer. Wild | ||
| 135 | boar and roe deer abundance can be explained by both, bioclimatic and | 149 | boar and roe deer abundance can be explained by both, bioclimatic and | ||
| 136 | land cover covariates. Abundance indices obtained from WVC data were | 150 | land cover covariates. Abundance indices obtained from WVC data were | ||
| 137 | significantly positively correlated with regional raw hunting yields | 151 | significantly positively correlated with regional raw hunting yields | ||
| 138 | for both species. We presented empirical evidence supporting that | 152 | for both species. We presented empirical evidence supporting that | ||
| 139 | accurate wildlife abundance indices at fine spatial resolution can be | 153 | accurate wildlife abundance indices at fine spatial resolution can be | ||
| 140 | generated from WVC data when individual detectability is considered in | 154 | generated from WVC data when individual detectability is considered in | ||
| 141 | the modeling process.", | 155 | the modeling process.", | ||
| 142 | "es": "Reliable estimates of the distribution of species abundance | 156 | "es": "Reliable estimates of the distribution of species abundance | ||
| 143 | are a key element in wildlife studies, but such information is usually | 157 | are a key element in wildlife studies, but such information is usually | ||
| 144 | difficult to obtain for large spatial or long temporal scales. | 158 | difficult to obtain for large spatial or long temporal scales. | ||
| 145 | Wildlife\u2013vehicle collision (WVC) data is systematically | 159 | Wildlife\u2013vehicle collision (WVC) data is systematically | ||
| 146 | registered in many countries and could be used as a proxy of | 160 | registered in many countries and could be used as a proxy of | ||
| 147 | population abundance if the number of WVC in each territory increase | 161 | population abundance if the number of WVC in each territory increase | ||
| 148 | with the population abundance. However, factors such as road density | 162 | with the population abundance. However, factors such as road density | ||
| 149 | or human population should be controlled to obtain accurate abundance | 163 | or human population should be controlled to obtain accurate abundance | ||
| 150 | estimations from WVC data. Here, we propose a hierarchical modeling | 164 | estimations from WVC data. Here, we propose a hierarchical modeling | ||
| 151 | approach using the Royle\u2013Nichols model for | 165 | approach using the Royle\u2013Nichols model for | ||
| 152 | detection\u2013non-detection data to obtain population abundance | 166 | detection\u2013non-detection data to obtain population abundance | ||
| 153 | indices from WVC. Relative abundance and individual detectability were | 167 | indices from WVC. Relative abundance and individual detectability were | ||
| 154 | modeled for two species, wild boar Sus scrofa and roe deer Capreolus | 168 | modeled for two species, wild boar Sus scrofa and roe deer Capreolus | ||
| 155 | capreolus at 10 \u00d7 10 km cells in mainland Spain from WVC data | 169 | capreolus at 10 \u00d7 10 km cells in mainland Spain from WVC data | ||
| 156 | using environmental, anthropological and temporal covariates. For each | 170 | using environmental, anthropological and temporal covariates. For each | ||
| 157 | cell, a detection was annotated if at least one WVC was recorded at | 171 | cell, a detection was annotated if at least one WVC was recorded at | ||
| 158 | each month (used as survey occasion). The predicted abundance indices | 172 | each month (used as survey occasion). The predicted abundance indices | ||
| 159 | were compared with raw hunting statistics at region level to assess | 173 | were compared with raw hunting statistics at region level to assess | ||
| 160 | the performance of the modeling approach. Site specific covariates | 174 | the performance of the modeling approach. Site specific covariates | ||
| 161 | such as road density or administrative region and the month of the | 175 | such as road density or administrative region and the month of the | ||
| 162 | year, affected individual detectability, with higher WVC probability | 176 | year, affected individual detectability, with higher WVC probability | ||
| 163 | between October and December for wild boar and between April and July | 177 | between October and December for wild boar and between April and July | ||
| 164 | for roe deer. Wild boar and roe deer abundance can be explained by | 178 | for roe deer. Wild boar and roe deer abundance can be explained by | ||
| 165 | both, bioclimatic and land cover covariates. Abundance indices | 179 | both, bioclimatic and land cover covariates. Abundance indices | ||
| 166 | obtained from WVC data were significantly positively correlated with | 180 | obtained from WVC data were significantly positively correlated with | ||
| 167 | regional raw hunting yields for both species. We presented empirical | 181 | regional raw hunting yields for both species. We presented empirical | ||
| 168 | evidence supporting that accurate wildlife abundance indices at fine | 182 | evidence supporting that accurate wildlife abundance indices at fine | ||
| 169 | spatial resolution can be generated from WVC data when individual | 183 | spatial resolution can be generated from WVC data when individual | ||
| 170 | detectability is considered in the modeling process." | 184 | detectability is considered in the modeling process." | ||
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