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Modificado el valor del campo
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a2026-06-25
en Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models.
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| 5 | "author": "Ferreira, E. M., Valerio, F., Medinas, D., Fernandes, N., | 5 | "author": "Ferreira, E. M., Valerio, F., Medinas, D., Fernandes, N., | ||
| 6 | Craveiro, J., Costa, P., Silva, J.P., Carrapato, C., Mira, A. y | 6 | Craveiro, J., Costa, P., Silva, J.P., Carrapato, C., Mira, A. y | ||
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| 81 | "name": "f9fa4e48-1eb2-5493-a387-f31fb7b6dfc7", | 95 | "name": "f9fa4e48-1eb2-5493-a387-f31fb7b6dfc7", | ||
| 82 | "notes": "Anthropogenic infrastructures and land-use changes are | 96 | "notes": "Anthropogenic infrastructures and land-use changes are | ||
| 83 | major threats to animal movements across heterogeneous landscapes. | 97 | major threats to animal movements across heterogeneous landscapes. | ||
| 84 | Yet, the behavioural consequences of such constraints remain poorly | 98 | Yet, the behavioural consequences of such constraints remain poorly | ||
| 85 | understood. We investigated the relationship between the behaviour of | 99 | understood. We investigated the relationship between the behaviour of | ||
| 86 | the Common genet (Genetta genetta) and road proximity, within a | 100 | the Common genet (Genetta genetta) and road proximity, within a | ||
| 87 | dominant mixed forest-agricultural landscape in southern Portugal, | 101 | dominant mixed forest-agricultural landscape in southern Portugal, | ||
| 88 | fragmented by roads. Specifically, we aimed to: (i) identify and | 102 | fragmented by roads. Specifically, we aimed to: (i) identify and | ||
| 89 | characterise the behavioural states displayed by genets and related | 103 | characterise the behavioural states displayed by genets and related | ||
| 90 | movement patterns; and (ii) understand how behavioural states are | 104 | movement patterns; and (ii) understand how behavioural states are | ||
| 91 | influenced by proximity to main paved roads and landscape features. We | 105 | influenced by proximity to main paved roads and landscape features. We | ||
| 92 | used a multivariate Hidden Markov Model (HMM) to characterise the | 106 | used a multivariate Hidden Markov Model (HMM) to characterise the | ||
| 93 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | 107 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | ||
| 94 | 187 nights (mean 27 days per individual) during the period | 108 | 187 nights (mean 27 days per individual) during the period | ||
| 95 | 2016\u20132019, using distance to major paved roads and landscape | 109 | 2016\u20132019, using distance to major paved roads and landscape | ||
| 96 | features as predictors. Our findings indicated that genet\u2019s | 110 | features as predictors. Our findings indicated that genet\u2019s | ||
| 97 | movement patterns were composed of three basic behavioural states, | 111 | movement patterns were composed of three basic behavioural states, | ||
| 98 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | 112 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | ||
| 99 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | 113 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | ||
| 100 | [mean = 46.1 m] and with a wide range in turning angle) and | 114 | [mean = 46.1 m] and with a wide range in turning angle) and | ||
| 101 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | 115 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | ||
| 102 | mainly linear movements). Within the genet\u2019s main activity-period | 116 | mainly linear movements). Within the genet\u2019s main activity-period | ||
| 103 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | 117 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | ||
| 104 | of their time travelling, 35.4% foraging and 28.0% resting. The | 118 | of their time travelling, 35.4% foraging and 28.0% resting. The | ||
| 105 | probability of genets displaying the travelling state was highest in | 119 | probability of genets displaying the travelling state was highest in | ||
| 106 | areas far away from roads (> 500 m), whereas foraging and resting | 120 | areas far away from roads (> 500 m), whereas foraging and resting | ||
| 107 | states were more likely in areas relatively close to roads (up to 500 | 121 | states were more likely in areas relatively close to roads (up to 500 | ||
| 108 | m). Landscape features also had a pronounced effect on behaviour state | 122 | m). Landscape features also had a pronounced effect on behaviour state | ||
| 109 | occurrence. More specifically, travelling was most likely to occur in | 123 | occurrence. More specifically, travelling was most likely to occur in | ||
| 110 | areas with lower forest edge density and close to riparian habitats, | 124 | areas with lower forest edge density and close to riparian habitats, | ||
| 111 | while foraging was more likely to occur in areas with higher forest | 125 | while foraging was more likely to occur in areas with higher forest | ||
| 112 | edge density and far away from riparian habitats. The results suggest | 126 | edge density and far away from riparian habitats. The results suggest | ||
| 113 | that, although roads represent a behavioural barrier to the movement | 127 | that, although roads represent a behavioural barrier to the movement | ||
| 114 | of genets, they also take advantage of road proximity as foraging | 128 | of genets, they also take advantage of road proximity as foraging | ||
| 115 | areas. Our study demonstrates that the HMM approach is useful for | 129 | areas. Our study demonstrates that the HMM approach is useful for | ||
| 116 | disentangling movement behaviour and understanding how animals respond | 130 | disentangling movement behaviour and understanding how animals respond | ||
| 117 | to roadsides and fragmented habitats. We emphasise that road-engaged | 131 | to roadsides and fragmented habitats. We emphasise that road-engaged | ||
| 118 | stakeholders need to consider movement behaviour of genets when | 132 | stakeholders need to consider movement behaviour of genets when | ||
| 119 | targeting management practices to maximise road permeability for | 133 | targeting management practices to maximise road permeability for | ||
| 120 | wildlife.", | 134 | wildlife.", | ||
| 121 | "notes_translated": { | 135 | "notes_translated": { | ||
| 122 | "en": "Anthropogenic infrastructures and land-use changes are | 136 | "en": "Anthropogenic infrastructures and land-use changes are | ||
| 123 | major threats to animal movements across heterogeneous landscapes. | 137 | major threats to animal movements across heterogeneous landscapes. | ||
| 124 | Yet, the behavioural consequences of such constraints remain poorly | 138 | Yet, the behavioural consequences of such constraints remain poorly | ||
| 125 | understood. We investigated the relationship between the behaviour of | 139 | understood. We investigated the relationship between the behaviour of | ||
| 126 | the Common genet (Genetta\u00a0genetta) and road proximity, within a | 140 | the Common genet (Genetta\u00a0genetta) and road proximity, within a | ||
| 127 | dominant mixed forest-agricultural landscape in southern Portugal, | 141 | dominant mixed forest-agricultural landscape in southern Portugal, | ||
| 128 | fragmented by roads. Specifically, we aimed to: (i) identify and | 142 | fragmented by roads. Specifically, we aimed to: (i) identify and | ||
| 129 | characterise the behavioural states displayed by genets and related | 143 | characterise the behavioural states displayed by genets and related | ||
| 130 | movement patterns; and (ii) understand how behavioural states are | 144 | movement patterns; and (ii) understand how behavioural states are | ||
| 131 | influenced by proximity to main paved roads and landscape features. We | 145 | influenced by proximity to main paved roads and landscape features. We | ||
| 132 | used a multivariate Hidden Markov Model (HMM) to characterise the | 146 | used a multivariate Hidden Markov Model (HMM) to characterise the | ||
| 133 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | 147 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | ||
| 134 | 187 nights (mean 27 days per individual) during the period | 148 | 187 nights (mean 27 days per individual) during the period | ||
| 135 | 2016\u20132019, using distance to major paved roads and landscape | 149 | 2016\u20132019, using distance to major paved roads and landscape | ||
| 136 | features as predictors. Our findings indicated that genet\u2019s | 150 | features as predictors. Our findings indicated that genet\u2019s | ||
| 137 | movement patterns were composed of three basic behavioural states, | 151 | movement patterns were composed of three basic behavioural states, | ||
| 138 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | 152 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | ||
| 139 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | 153 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | ||
| 140 | [mean = 46.1 m] and with a wide range in turning angle) and | 154 | [mean = 46.1 m] and with a wide range in turning angle) and | ||
| 141 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | 155 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | ||
| 142 | mainly linear movements). Within the genet\u2019s main activity-period | 156 | mainly linear movements). Within the genet\u2019s main activity-period | ||
| 143 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | 157 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | ||
| 144 | of their time travelling, 35.4% foraging and 28.0% resting. The | 158 | of their time travelling, 35.4% foraging and 28.0% resting. The | ||
| 145 | probability of genets displaying the travelling state was highest in | 159 | probability of genets displaying the travelling state was highest in | ||
| 146 | areas far away from roads (> 500 m), whereas foraging and resting | 160 | areas far away from roads (> 500 m), whereas foraging and resting | ||
| 147 | states were more likely in areas relatively close to roads (up to 500 | 161 | states were more likely in areas relatively close to roads (up to 500 | ||
| 148 | m). Landscape features also had a pronounced effect on behaviour state | 162 | m). Landscape features also had a pronounced effect on behaviour state | ||
| 149 | occurrence. More specifically, travelling was most likely to occur in | 163 | occurrence. More specifically, travelling was most likely to occur in | ||
| 150 | areas with lower forest edge density and close to riparian habitats, | 164 | areas with lower forest edge density and close to riparian habitats, | ||
| 151 | while foraging was more likely to occur in areas with higher forest | 165 | while foraging was more likely to occur in areas with higher forest | ||
| 152 | edge density and far away from riparian habitats. The results suggest | 166 | edge density and far away from riparian habitats. The results suggest | ||
| 153 | that, although roads represent a behavioural barrier to the movement | 167 | that, although roads represent a behavioural barrier to the movement | ||
| 154 | of genets, they also take advantage of road proximity as foraging | 168 | of genets, they also take advantage of road proximity as foraging | ||
| 155 | areas. Our study demonstrates that the\u00a0HMM\u00a0approach is | 169 | areas. Our study demonstrates that the\u00a0HMM\u00a0approach is | ||
| 156 | useful for disentangling movement behaviour and understanding how | 170 | useful for disentangling movement behaviour and understanding how | ||
| 157 | animals respond to roadsides and fragmented habitats. We emphasise | 171 | animals respond to roadsides and fragmented habitats. We emphasise | ||
| 158 | that road-engaged stakeholders need to consider movement behaviour of | 172 | that road-engaged stakeholders need to consider movement behaviour of | ||
| 159 | genets when targeting management practices to maximise road | 173 | genets when targeting management practices to maximise road | ||
| 160 | permeability for wildlife.", | 174 | permeability for wildlife.", | ||
| 161 | "es": "Anthropogenic infrastructures and land-use changes are | 175 | "es": "Anthropogenic infrastructures and land-use changes are | ||
| 162 | major threats to animal movements across heterogeneous landscapes. | 176 | major threats to animal movements across heterogeneous landscapes. | ||
| 163 | Yet, the behavioural consequences of such constraints remain poorly | 177 | Yet, the behavioural consequences of such constraints remain poorly | ||
| 164 | understood. We investigated the relationship between the behaviour of | 178 | understood. We investigated the relationship between the behaviour of | ||
| 165 | the Common genet (Genetta genetta) and road proximity, within a | 179 | the Common genet (Genetta genetta) and road proximity, within a | ||
| 166 | dominant mixed forest-agricultural landscape in southern Portugal, | 180 | dominant mixed forest-agricultural landscape in southern Portugal, | ||
| 167 | fragmented by roads. Specifically, we aimed to: (i) identify and | 181 | fragmented by roads. Specifically, we aimed to: (i) identify and | ||
| 168 | characterise the behavioural states displayed by genets and related | 182 | characterise the behavioural states displayed by genets and related | ||
| 169 | movement patterns; and (ii) understand how behavioural states are | 183 | movement patterns; and (ii) understand how behavioural states are | ||
| 170 | influenced by proximity to main paved roads and landscape features. We | 184 | influenced by proximity to main paved roads and landscape features. We | ||
| 171 | used a multivariate Hidden Markov Model (HMM) to characterise the | 185 | used a multivariate Hidden Markov Model (HMM) to characterise the | ||
| 172 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | 186 | fine-scale movements (10-min fixes GPS) of seven genets tracked during | ||
| 173 | 187 nights (mean 27 days per individual) during the period | 187 | 187 nights (mean 27 days per individual) during the period | ||
| 174 | 2016\u20132019, using distance to major paved roads and landscape | 188 | 2016\u20132019, using distance to major paved roads and landscape | ||
| 175 | features as predictors. Our findings indicated that genet\u2019s | 189 | features as predictors. Our findings indicated that genet\u2019s | ||
| 176 | movement patterns were composed of three basic behavioural states, | 190 | movement patterns were composed of three basic behavioural states, | ||
| 177 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | 191 | classified as \u201cresting\u201d (short step-lengths [mean = 10.6 m] | ||
| 178 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | 192 | and highly tortuous), \u201cforaging\u201d (intermediate step-lengths | ||
| 179 | [mean = 46.1 m] and with a wide range in turning angle) and | 193 | [mean = 46.1 m] and with a wide range in turning angle) and | ||
| 180 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | 194 | \u201ctravelling\u201d (longer step-lengths [mean = 113.7 m] and | ||
| 181 | mainly linear movements). Within the genet\u2019s main activity-period | 195 | mainly linear movements). Within the genet\u2019s main activity-period | ||
| 182 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | 196 | (17.00 h-08.00 h), the movement model predicts that genets spend 36.7% | ||
| 183 | of their time travelling, 35.4% foraging and 28.0% resting. The | 197 | of their time travelling, 35.4% foraging and 28.0% resting. The | ||
| 184 | probability of genets displaying the travelling state was highest in | 198 | probability of genets displaying the travelling state was highest in | ||
| 185 | areas far away from roads (> 500 m), whereas foraging and resting | 199 | areas far away from roads (> 500 m), whereas foraging and resting | ||
| 186 | states were more likely in areas relatively close to roads (up to 500 | 200 | states were more likely in areas relatively close to roads (up to 500 | ||
| 187 | m). Landscape features also had a pronounced effect on behaviour state | 201 | m). Landscape features also had a pronounced effect on behaviour state | ||
| 188 | occurrence. More specifically, travelling was most likely to occur in | 202 | occurrence. More specifically, travelling was most likely to occur in | ||
| 189 | areas with lower forest edge density and close to riparian habitats, | 203 | areas with lower forest edge density and close to riparian habitats, | ||
| 190 | while foraging was more likely to occur in areas with higher forest | 204 | while foraging was more likely to occur in areas with higher forest | ||
| 191 | edge density and far away from riparian habitats. The results suggest | 205 | edge density and far away from riparian habitats. The results suggest | ||
| 192 | that, although roads represent a behavioural barrier to the movement | 206 | that, although roads represent a behavioural barrier to the movement | ||
| 193 | of genets, they also take advantage of road proximity as foraging | 207 | of genets, they also take advantage of road proximity as foraging | ||
| 194 | areas. Our study demonstrates that the HMM approach is useful for | 208 | areas. Our study demonstrates that the HMM approach is useful for | ||
| 195 | disentangling movement behaviour and understanding how animals respond | 209 | disentangling movement behaviour and understanding how animals respond | ||
| 196 | to roadsides and fragmented habitats. We emphasise that road-engaged | 210 | to roadsides and fragmented habitats. We emphasise that road-engaged | ||
| 197 | stakeholders need to consider movement behaviour of genets when | 211 | stakeholders need to consider movement behaviour of genets when | ||
| 198 | targeting management practices to maximise road permeability for | 212 | targeting management practices to maximise road permeability for | ||
| 199 | wildlife." | 213 | wildlife." | ||
| 200 | }, | 214 | }, | ||
| 201 | "num_resources": 1, | 215 | "num_resources": 1, | ||
| 202 | "num_tags": 2, | 216 | "num_tags": 2, | ||
| 203 | "organization": { | 217 | "organization": { | ||
| 204 | "approval_status": "approved", | 218 | "approval_status": "approved", | ||
| 205 | "created": "2026-06-23T14:36:08.942021", | 219 | "created": "2026-06-23T14:36:08.942021", | ||
| 206 | "description": "", | 220 | "description": "", | ||
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