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| 79 | "notes": "Road networks are the main source of mortality for many | 93 | "notes": "Road networks are the main source of mortality for many | ||
| 80 | species. Amphibians, which are in global decline, are the most | 94 | species. Amphibians, which are in global decline, are the most | ||
| 81 | road-killed fauna group, due to their activity patterns and preferred | 95 | road-killed fauna group, due to their activity patterns and preferred | ||
| 82 | habitats. Many different methodologies have been applied in modeling | 96 | habitats. Many different methodologies have been applied in modeling | ||
| 83 | the relationship between environment and road-kills events, such as | 97 | the relationship between environment and road-kills events, such as | ||
| 84 | logistic regression. Here, we compared the performance of five | 98 | logistic regression. Here, we compared the performance of five | ||
| 85 | regression techniques to relate amphibians\u2019 road-kill frequency | 99 | regression techniques to relate amphibians\u2019 road-kill frequency | ||
| 86 | to environmental variables. For this, we surveyed three country roads | 100 | to environmental variables. For this, we surveyed three country roads | ||
| 87 | in northern Portugal in search of road-killed amphibians. To explain | 101 | in northern Portugal in search of road-killed amphibians. To explain | ||
| 88 | the presence of road-kills, we selected a set of environmental | 102 | the presence of road-kills, we selected a set of environmental | ||
| 89 | variables important for the presence of amphibians and the occurrence | 103 | variables important for the presence of amphibians and the occurrence | ||
| 90 | of road-kills. We compared the performances of five modeling | 104 | of road-kills. We compared the performances of five modeling | ||
| 91 | techniques: (i) generalized linear models, (ii) generalized additive | 105 | techniques: (i) generalized linear models, (ii) generalized additive | ||
| 92 | models, (iii) random forest, (iv) boosted regression trees, and (v) | 106 | models, (iii) random forest, (iv) boosted regression trees, and (v) | ||
| 93 | geographically weighted regression. The boosted regression trees and | 107 | geographically weighted regression. The boosted regression trees and | ||
| 94 | geographically weighted regression techniques performed the best, with | 108 | geographically weighted regression techniques performed the best, with | ||
| 95 | a percentage of deviance explained between 61.8% and 76.6% and between | 109 | a percentage of deviance explained between 61.8% and 76.6% and between | ||
| 96 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | 110 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | ||
| 97 | regression showed a great advantage over the other techniques, as it | 111 | regression showed a great advantage over the other techniques, as it | ||
| 98 | allows mapping local parameter coefficients as well as local model | 112 | allows mapping local parameter coefficients as well as local model | ||
| 99 | performance (pseudo-R2). The results suggest that geographically | 113 | performance (pseudo-R2). The results suggest that geographically | ||
| 100 | weighted regression is a useful tool for road-kill modeling, as well | 114 | weighted regression is a useful tool for road-kill modeling, as well | ||
| 101 | as to better visualize and map the spatial variability of the | 115 | as to better visualize and map the spatial variability of the | ||
| 102 | models.", | 116 | models.", | ||
| 103 | "notes_translated": { | 117 | "notes_translated": { | ||
| 104 | "en": "Road networks are the main source of mortality for many | 118 | "en": "Road networks are the main source of mortality for many | ||
| 105 | species. Amphibians, which are in global decline, are the most | 119 | species. Amphibians, which are in global decline, are the most | ||
| 106 | road-killed fauna group, due to their activity patterns and preferred | 120 | road-killed fauna group, due to their activity patterns and preferred | ||
| 107 | habitats. Many different methodologies have been applied in modeling | 121 | habitats. Many different methodologies have been applied in modeling | ||
| 108 | the relationship between environment and road-kills events, such as | 122 | the relationship between environment and road-kills events, such as | ||
| 109 | logistic regression. Here, we compared the performance of five | 123 | logistic regression. Here, we compared the performance of five | ||
| 110 | regression techniques to relate amphibians\u2019 road-kill frequency | 124 | regression techniques to relate amphibians\u2019 road-kill frequency | ||
| 111 | to environmental variables. For this, we surveyed three country roads | 125 | to environmental variables. For this, we surveyed three country roads | ||
| 112 | in northern Portugal in search of road-killed amphibians. To explain | 126 | in northern Portugal in search of road-killed amphibians. To explain | ||
| 113 | the presence of road-kills, we selected a set of environmental | 127 | the presence of road-kills, we selected a set of environmental | ||
| 114 | variables important for the presence of amphibians and the occurrence | 128 | variables important for the presence of amphibians and the occurrence | ||
| 115 | of road-kills. We compared the performances of five modeling | 129 | of road-kills. We compared the performances of five modeling | ||
| 116 | techniques: (i) generalized linear models, (ii) generalized additive | 130 | techniques: (i) generalized linear models, (ii) generalized additive | ||
| 117 | models, (iii) random forest, (iv) boosted regression trees, and (v) | 131 | models, (iii) random forest, (iv) boosted regression trees, and (v) | ||
| 118 | geographically weighted regression. The boosted regression trees and | 132 | geographically weighted regression. The boosted regression trees and | ||
| 119 | geographically weighted regression techniques performed the best, with | 133 | geographically weighted regression techniques performed the best, with | ||
| 120 | a percentage of deviance explained between 61.8% and 76.6% and between | 134 | a percentage of deviance explained between 61.8% and 76.6% and between | ||
| 121 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | 135 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | ||
| 122 | regression showed a great advantage over the other techniques, as it | 136 | regression showed a great advantage over the other techniques, as it | ||
| 123 | allows mapping local parameter coefficients as well as local model | 137 | allows mapping local parameter coefficients as well as local model | ||
| 124 | performance (pseudo-R2). The results suggest that geographically | 138 | performance (pseudo-R2). The results suggest that geographically | ||
| 125 | weighted regression is a useful tool for road-kill modeling, as well | 139 | weighted regression is a useful tool for road-kill modeling, as well | ||
| 126 | as to better visualize and map the spatial variability of the | 140 | as to better visualize and map the spatial variability of the | ||
| 127 | models.", | 141 | models.", | ||
| 128 | "es": "Road networks are the main source of mortality for many | 142 | "es": "Road networks are the main source of mortality for many | ||
| 129 | species. Amphibians, which are in global decline, are the most | 143 | species. Amphibians, which are in global decline, are the most | ||
| 130 | road-killed fauna group, due to their activity patterns and preferred | 144 | road-killed fauna group, due to their activity patterns and preferred | ||
| 131 | habitats. Many different methodologies have been applied in modeling | 145 | habitats. Many different methodologies have been applied in modeling | ||
| 132 | the relationship between environment and road-kills events, such as | 146 | the relationship between environment and road-kills events, such as | ||
| 133 | logistic regression. Here, we compared the performance of five | 147 | logistic regression. Here, we compared the performance of five | ||
| 134 | regression techniques to relate amphibians\u2019 road-kill frequency | 148 | regression techniques to relate amphibians\u2019 road-kill frequency | ||
| 135 | to environmental variables. For this, we surveyed three country roads | 149 | to environmental variables. For this, we surveyed three country roads | ||
| 136 | in northern Portugal in search of road-killed amphibians. To explain | 150 | in northern Portugal in search of road-killed amphibians. To explain | ||
| 137 | the presence of road-kills, we selected a set of environmental | 151 | the presence of road-kills, we selected a set of environmental | ||
| 138 | variables important for the presence of amphibians and the occurrence | 152 | variables important for the presence of amphibians and the occurrence | ||
| 139 | of road-kills. We compared the performances of five modeling | 153 | of road-kills. We compared the performances of five modeling | ||
| 140 | techniques: (i) generalized linear models, (ii) generalized additive | 154 | techniques: (i) generalized linear models, (ii) generalized additive | ||
| 141 | models, (iii) random forest, (iv) boosted regression trees, and (v) | 155 | models, (iii) random forest, (iv) boosted regression trees, and (v) | ||
| 142 | geographically weighted regression. The boosted regression trees and | 156 | geographically weighted regression. The boosted regression trees and | ||
| 143 | geographically weighted regression techniques performed the best, with | 157 | geographically weighted regression techniques performed the best, with | ||
| 144 | a percentage of deviance explained between 61.8% and 76.6% and between | 158 | a percentage of deviance explained between 61.8% and 76.6% and between | ||
| 145 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | 159 | 55.3% and 66.7%, respectively. Moreover, the geographically weighted | ||
| 146 | regression showed a great advantage over the other techniques, as it | 160 | regression showed a great advantage over the other techniques, as it | ||
| 147 | allows mapping local parameter coefficients as well as local model | 161 | allows mapping local parameter coefficients as well as local model | ||
| 148 | performance (pseudo-R2). The results suggest that geographically | 162 | performance (pseudo-R2). The results suggest that geographically | ||
| 149 | weighted regression is a useful tool for road-kill modeling, as well | 163 | weighted regression is a useful tool for road-kill modeling, as well | ||
| 150 | as to better visualize and map the spatial variability of the models." | 164 | as to better visualize and map the spatial variability of the models." | ||
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