Cambios
En el instante 23 de junio de 2026, 15:51:39 UTC,
-
Modificado el valor del campo
spatial_coverage
a[{'bbox': '{"type": "Polygon", "coordinates": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}', 'centroid': '{"type": "Point", "coordinates": [-6.92, 35.715]}', 'text': 'España', 'uri': 'http://datos.gob.es/recurso/sector-publico/territorio/Pais/España'}]
en Landscape connectivity estimates are affected by spatial resolution, habitat seasonality and population trends.
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| 82 | "notes": "Connectivity assessments and corridor delineation are key | 82 | "notes": "Connectivity assessments and corridor delineation are key | ||
| 83 | contributions to landscape management and biodiversity conservation. | 83 | contributions to landscape management and biodiversity conservation. | ||
| 84 | We examined the influence of three potentially crucial factors on the | 84 | We examined the influence of three potentially crucial factors on the | ||
| 85 | results of connectivity analyses, using the two subpopulations of the | 85 | results of connectivity analyses, using the two subpopulations of the | ||
| 86 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | 86 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | ||
| 87 | study. First, we evaluated the spatial resolution of vegetation data, | 87 | study. First, we evaluated the spatial resolution of vegetation data, | ||
| 88 | using three types of datasets ranging from coarse resolution | 88 | using three types of datasets ranging from coarse resolution | ||
| 89 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | 89 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | ||
| 90 | variation in the distribution of habitat resources and in the species | 90 | variation in the distribution of habitat resources and in the species | ||
| 91 | use of the landscape. Third, multi-annual periods with different | 91 | use of the landscape. Third, multi-annual periods with different | ||
| 92 | population status. The estimates of subpopulation isolation (effective | 92 | population status. The estimates of subpopulation isolation (effective | ||
| 93 | distances) and the trajectory of the identified corridors were | 93 | distances) and the trajectory of the identified corridors were | ||
| 94 | substantially influenced by (i) the spatial resolution of vegetation | 94 | substantially influenced by (i) the spatial resolution of vegetation | ||
| 95 | data; the more robust results were obtained when incorporating | 95 | data; the more robust results were obtained when incorporating | ||
| 96 | fine-scale LiDAR data; (ii) the season over which species occurrence | 96 | fine-scale LiDAR data; (ii) the season over which species occurrence | ||
| 97 | data and landscape characteristics were considered; the spring mating | 97 | data and landscape characteristics were considered; the spring mating | ||
| 98 | season yielded higher connectivity estimates than any other season; | 98 | season yielded higher connectivity estimates than any other season; | ||
| 99 | (iii) the status of the populations, with higher landscape | 99 | (iii) the status of the populations, with higher landscape | ||
| 100 | connectivity estimated for expanding populations. Our study reveals | 100 | connectivity estimated for expanding populations. Our study reveals | ||
| 101 | that the use of coarse-resolution data may underestimate the | 101 | that the use of coarse-resolution data may underestimate the | ||
| 102 | resistance of the non-habitat landscape matrix to species movements. | 102 | resistance of the non-habitat landscape matrix to species movements. | ||
| 103 | The use of year-round estimates of habitat connectivity may miss the | 103 | The use of year-round estimates of habitat connectivity may miss the | ||
| 104 | key seasonal temporal windows for species movements. Landscape | 104 | key seasonal temporal windows for species movements. Landscape | ||
| 105 | resistance may be overestimated when data from periods with declining | 105 | resistance may be overestimated when data from periods with declining | ||
| 106 | or restricted populations are used. We recommend carefully | 106 | or restricted populations are used. We recommend carefully | ||
| 107 | disentangling the effects of demography and landscape heterogeneity on | 107 | disentangling the effects of demography and landscape heterogeneity on | ||
| 108 | realized species dispersal movements for improving the insights from | 108 | realized species dispersal movements for improving the insights from | ||
| 109 | connectivity modelling.", | 109 | connectivity modelling.", | ||
| 110 | "notes_translated": { | 110 | "notes_translated": { | ||
| 111 | "en": "Connectivity assessments and corridor delineation are key | 111 | "en": "Connectivity assessments and corridor delineation are key | ||
| 112 | contributions to landscape management and biodiversity conservation. | 112 | contributions to landscape management and biodiversity conservation. | ||
| 113 | We examined the influence of three potentially crucial factors on the | 113 | We examined the influence of three potentially crucial factors on the | ||
| 114 | results of connectivity analyses, using the two subpopulations of the | 114 | results of connectivity analyses, using the two subpopulations of the | ||
| 115 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | 115 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | ||
| 116 | study. First, we evaluated the spatial resolution of vegetation data, | 116 | study. First, we evaluated the spatial resolution of vegetation data, | ||
| 117 | using three types of datasets ranging from coarse resolution | 117 | using three types of datasets ranging from coarse resolution | ||
| 118 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | 118 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | ||
| 119 | variation in the distribution of habitat resources and in the species | 119 | variation in the distribution of habitat resources and in the species | ||
| 120 | use of the landscape. Third, multi-annual periods with different | 120 | use of the landscape. Third, multi-annual periods with different | ||
| 121 | population status. The estimates of subpopulation isolation (effective | 121 | population status. The estimates of subpopulation isolation (effective | ||
| 122 | distances) and the trajectory of the identified corridors were | 122 | distances) and the trajectory of the identified corridors were | ||
| 123 | substantially influenced by (i) the spatial resolution of vegetation | 123 | substantially influenced by (i) the spatial resolution of vegetation | ||
| 124 | data; the more robust results were obtained when incorporating | 124 | data; the more robust results were obtained when incorporating | ||
| 125 | fine-scale LiDAR data; (ii) the season over which species occurrence | 125 | fine-scale LiDAR data; (ii) the season over which species occurrence | ||
| 126 | data and landscape characteristics were considered; the spring mating | 126 | data and landscape characteristics were considered; the spring mating | ||
| 127 | season yielded higher connectivity estimates than any other season; | 127 | season yielded higher connectivity estimates than any other season; | ||
| 128 | (iii) the status of the populations, with higher landscape | 128 | (iii) the status of the populations, with higher landscape | ||
| 129 | connectivity estimated for expanding populations. Our study reveals | 129 | connectivity estimated for expanding populations. Our study reveals | ||
| 130 | that the use of coarse-resolution data may underestimate the | 130 | that the use of coarse-resolution data may underestimate the | ||
| 131 | resistance of the non-habitat landscape matrix to species movements. | 131 | resistance of the non-habitat landscape matrix to species movements. | ||
| 132 | The use of year-round estimates of habitat connectivity may miss the | 132 | The use of year-round estimates of habitat connectivity may miss the | ||
| 133 | key seasonal temporal windows for species movements. Landscape | 133 | key seasonal temporal windows for species movements. Landscape | ||
| 134 | resistance may be overestimated when data from periods with declining | 134 | resistance may be overestimated when data from periods with declining | ||
| 135 | or restricted populations are used. We recommend carefully | 135 | or restricted populations are used. We recommend carefully | ||
| 136 | disentangling the effects of demography and landscape heterogeneity on | 136 | disentangling the effects of demography and landscape heterogeneity on | ||
| 137 | realized species dispersal movements for improving the insights from | 137 | realized species dispersal movements for improving the insights from | ||
| 138 | connectivity modelling.", | 138 | connectivity modelling.", | ||
| 139 | "es": "Connectivity assessments and corridor delineation are key | 139 | "es": "Connectivity assessments and corridor delineation are key | ||
| 140 | contributions to landscape management and biodiversity conservation. | 140 | contributions to landscape management and biodiversity conservation. | ||
| 141 | We examined the influence of three potentially crucial factors on the | 141 | We examined the influence of three potentially crucial factors on the | ||
| 142 | results of connectivity analyses, using the two subpopulations of the | 142 | results of connectivity analyses, using the two subpopulations of the | ||
| 143 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | 143 | endangered brown bear in the Cantabrian Range (NW Spain) as a case | ||
| 144 | study. First, we evaluated the spatial resolution of vegetation data, | 144 | study. First, we evaluated the spatial resolution of vegetation data, | ||
| 145 | using three types of datasets ranging from coarse resolution | 145 | using three types of datasets ranging from coarse resolution | ||
| 146 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | 146 | land-cover maps to high-resolution LiDAR data. Second, the seasonal | ||
| 147 | variation in the distribution of habitat resources and in the species | 147 | variation in the distribution of habitat resources and in the species | ||
| 148 | use of the landscape. Third, multi-annual periods with different | 148 | use of the landscape. Third, multi-annual periods with different | ||
| 149 | population status. The estimates of subpopulation isolation (effective | 149 | population status. The estimates of subpopulation isolation (effective | ||
| 150 | distances) and the trajectory of the identified corridors were | 150 | distances) and the trajectory of the identified corridors were | ||
| 151 | substantially influenced by (i) the spatial resolution of vegetation | 151 | substantially influenced by (i) the spatial resolution of vegetation | ||
| 152 | data; the more robust results were obtained when incorporating | 152 | data; the more robust results were obtained when incorporating | ||
| 153 | fine-scale LiDAR data; (ii) the season over which species occurrence | 153 | fine-scale LiDAR data; (ii) the season over which species occurrence | ||
| 154 | data and landscape characteristics were considered; the spring mating | 154 | data and landscape characteristics were considered; the spring mating | ||
| 155 | season yielded higher connectivity estimates than any other season; | 155 | season yielded higher connectivity estimates than any other season; | ||
| 156 | (iii) the status of the populations, with higher landscape | 156 | (iii) the status of the populations, with higher landscape | ||
| 157 | connectivity estimated for expanding populations. Our study reveals | 157 | connectivity estimated for expanding populations. Our study reveals | ||
| 158 | that the use of coarse-resolution data may underestimate the | 158 | that the use of coarse-resolution data may underestimate the | ||
| 159 | resistance of the non-habitat landscape matrix to species movements. | 159 | resistance of the non-habitat landscape matrix to species movements. | ||
| 160 | The use of year-round estimates of habitat connectivity may miss the | 160 | The use of year-round estimates of habitat connectivity may miss the | ||
| 161 | key seasonal temporal windows for species movements. Landscape | 161 | key seasonal temporal windows for species movements. Landscape | ||
| 162 | resistance may be overestimated when data from periods with declining | 162 | resistance may be overestimated when data from periods with declining | ||
| 163 | or restricted populations are used. We recommend carefully | 163 | or restricted populations are used. We recommend carefully | ||
| 164 | disentangling the effects of demography and landscape heterogeneity on | 164 | disentangling the effects of demography and landscape heterogeneity on | ||
| 165 | realized species dispersal movements for improving the insights from | 165 | realized species dispersal movements for improving the insights from | ||
| 166 | connectivity modelling." | 166 | connectivity modelling." | ||
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