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On April 12, 2022 at 11:43:39 AM UTC, Administrator:
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in LiDAR metrics predict suitable forest foraging areas of endangered Mouse- eared bats (Myotis myotis)
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47 | "license_title": "WSL Data Policy", | 47 | "license_title": "WSL Data Policy", | ||
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51 | \"martin.obrist@wsl.ch\", \"given_name\": \"Martin K.\", | 51 | \"martin.obrist@wsl.ch\", \"given_name\": \"Martin K.\", | ||
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54 | "metadata_created": "2022-04-08T08:45:26.157941", | 54 | "metadata_created": "2022-04-08T08:45:26.157941", | ||
n | 55 | "metadata_modified": "2022-04-12T11:42:55.685736", | n | 55 | "metadata_modified": "2022-04-12T11:43:39.074208", |
56 | "name": "lidar_forest_myotis-myotis", | 56 | "name": "lidar_forest_myotis-myotis", | ||
57 | "notes": "Habitat shift caused by human impact on vegetation | 57 | "notes": "Habitat shift caused by human impact on vegetation | ||
58 | structure poses a great threat to species which are special- ized on | 58 | structure poses a great threat to species which are special- ized on | ||
59 | unique habitats. Single layered beech forests, the main foraging | 59 | unique habitats. Single layered beech forests, the main foraging | ||
60 | habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened | 60 | habitat of Greater Mouse-eared Bats (My- otis myotis), are threatened | ||
61 | by recent changes in forest structure. After this species suffered | 61 | by recent changes in forest structure. After this species suffered | ||
62 | considerable popula- tion losses until the 1970s, their roosts in | 62 | considerable popula- tion losses until the 1970s, their roosts in | ||
63 | buildings are strictly protected. However, some populations are still | 63 | buildings are strictly protected. However, some populations are still | ||
64 | de- clining. Thus, the spatial identification of suitable foraging | 64 | de- clining. Thus, the spatial identification of suitable foraging | ||
65 | habitat would be essential to ensure conservation pol- icy. The aim of | 65 | habitat would be essential to ensure conservation pol- icy. The aim of | ||
66 | this study was (a) to verify the relevance of forest structural | 66 | this study was (a) to verify the relevance of forest structural | ||
67 | variables for the activity of M. myotis and (b) to evaluate the | 67 | variables for the activity of M. myotis and (b) to evaluate the | ||
68 | potential of LiDAR (Light Detection and Ranging) in predicting | 68 | potential of LiDAR (Light Detection and Ranging) in predicting | ||
69 | suitable foraging habitat of the species. We systematically sampled | 69 | suitable foraging habitat of the species. We systematically sampled | ||
70 | bat activity in forests close to 18 maternity roosts in Switzerland | 70 | bat activity in forests close to 18 maternity roosts in Switzerland | ||
71 | and applied a generalized linear mixed model (GLMM) to fit the | 71 | and applied a generalized linear mixed model (GLMM) to fit the | ||
72 | activity data to forest structure variables recorded in the field and | 72 | activity data to forest structure variables recorded in the field and | ||
73 | derived from LiDAR. We found that suitable forest foraging habitat is | 73 | derived from LiDAR. We found that suitable forest foraging habitat is | ||
74 | defined by single layered for- est, dense canopy, no shrub layer and a | 74 | defined by single layered for- est, dense canopy, no shrub layer and a | ||
75 | free flight space. Most importantly, this key foraging habitat can be | 75 | free flight space. Most importantly, this key foraging habitat can be | ||
76 | well predicted by airborne LiDAR data. This allows for the first time | 76 | well predicted by airborne LiDAR data. This allows for the first time | ||
77 | to create nationwide prediction maps of potential foraging habitats of | 77 | to create nationwide prediction maps of potential foraging habitats of | ||
78 | this species to inform conservation management. This method has a | 78 | this species to inform conservation management. This method has a | ||
79 | special significance for endangered species with large spatial use, | 79 | special significance for endangered species with large spatial use, | ||
80 | whose key resources are hard to identify and widely distributed across | 80 | whose key resources are hard to identify and widely distributed across | ||
81 | the landscape.", | 81 | the landscape.", | ||
82 | "num_resources": 3, | 82 | "num_resources": 3, | ||
83 | "num_tags": 5, | 83 | "num_tags": 5, | ||
84 | "organization": { | 84 | "organization": { | ||
85 | "approval_status": "approved", | 85 | "approval_status": "approved", | ||
86 | "created": "2018-05-18T09:12:30.577470", | 86 | "created": "2018-05-18T09:12:30.577470", | ||
87 | "description": "We study the relationship between threatened | 87 | "description": "We study the relationship between threatened | ||
88 | species and their habitat with the objective to develop evidence-based | 88 | species and their habitat with the objective to develop evidence-based | ||
89 | instruments for conservation. The viability of populations of habitat | 89 | instruments for conservation. The viability of populations of habitat | ||
90 | specialists is often limited due to restricted availability of | 90 | specialists is often limited due to restricted availability of | ||
91 | suitable habitat or discontinuous distribution. We focus on selected | 91 | suitable habitat or discontinuous distribution. We focus on selected | ||
92 | animal species of conservation concern (forest birds, bats, forest | 92 | animal species of conservation concern (forest birds, bats, forest | ||
93 | insects) and analyse species-habitat relationships at a scale ranging | 93 | insects) and analyse species-habitat relationships at a scale ranging | ||
94 | from single subpopulations to entire metapopulations. We strive | 94 | from single subpopulations to entire metapopulations. We strive | ||
95 | towards standardised recording of elusive species with novel | 95 | towards standardised recording of elusive species with novel | ||
96 | techniques, assessing environmental variables and combining these | 96 | techniques, assessing environmental variables and combining these | ||
97 | field data with data from remote sensing. We determine limiting | 97 | field data with data from remote sensing. We determine limiting | ||
98 | factors (habitat, climate, resources) and predict species occurrence | 98 | factors (habitat, climate, resources) and predict species occurrence | ||
99 | and habitat suitability with modelling techniques.\r\n\r\nThese models | 99 | and habitat suitability with modelling techniques.\r\n\r\nThese models | ||
100 | allow to predict species occurrence patterns and to delineate | 100 | allow to predict species occurrence patterns and to delineate | ||
101 | evidence-based priority areas for conservation. Our long-term | 101 | evidence-based priority areas for conservation. Our long-term | ||
102 | collaboration with federal and cantonal stakeholders of forest and | 102 | collaboration with federal and cantonal stakeholders of forest and | ||
103 | nature conservation enables the combination of research results with | 103 | nature conservation enables the combination of research results with | ||
104 | conservation requirements (e.g. species action plans, forest | 104 | conservation requirements (e.g. species action plans, forest | ||
105 | biodiversity expert panel, scientific counsel for bat protection). | 105 | biodiversity expert panel, scientific counsel for bat protection). | ||
106 | While we investigate complete distribution ranges in bats, we focus on | 106 | While we investigate complete distribution ranges in bats, we focus on | ||
107 | forest habitats in birds and insects given the large extent and high | 107 | forest habitats in birds and insects given the large extent and high | ||
108 | importance of forests for biodiversity conservation in Switzerland.", | 108 | importance of forests for biodiversity conservation in Switzerland.", | ||
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111 | "is_organization": true, | 111 | "is_organization": true, | ||
112 | "name": "conservation-biology", | 112 | "name": "conservation-biology", | ||
113 | "state": "active", | 113 | "state": "active", | ||
114 | "title": "Conservation Biology", | 114 | "title": "Conservation Biology", | ||
115 | "type": "organization" | 115 | "type": "organization" | ||
116 | }, | 116 | }, | ||
117 | "owner_org": "32672ec5-c180-471e-92f7-ef58e00eb1fb", | 117 | "owner_org": "32672ec5-c180-471e-92f7-ef58e00eb1fb", | ||
118 | "private": false, | 118 | "private": false, | ||
119 | "publication": "{\"publication_year\": \"2022\", \"publisher\": | 119 | "publication": "{\"publication_year\": \"2022\", \"publisher\": | ||
120 | \"EnviDat\"}", | 120 | \"EnviDat\"}", | ||
t | 121 | "publication_state": "approved", | t | 121 | "publication_state": "published", |
122 | "related_datasets": "activity data, environment data, explanatory | 122 | "related_datasets": "activity data, environment data, explanatory | ||
123 | file, R code", | 123 | file, R code", | ||
124 | "related_publications": "Katja Rauchenstein, Klaus Ecker, Elias | 124 | "related_publications": "Katja Rauchenstein, Klaus Ecker, Elias | ||
125 | Bader, Christian Ginzler, Christoph D\u00fcggelin, Fabio Bontadina, | 125 | Bader, Christian Ginzler, Christoph D\u00fcggelin, Fabio Bontadina, | ||
126 | Martin K. Obrist (2022) LiDAR metrics predict suitable forest foraging | 126 | Martin K. Obrist (2022) LiDAR metrics predict suitable forest foraging | ||
127 | areas of endangered Mouse- eared bats (Myotis myotis). Forest Ecology | 127 | areas of endangered Mouse- eared bats (Myotis myotis). Forest Ecology | ||
128 | and Management. \r\nhttps://doi.org/10.1016/j.foreco.2022.120210", | 128 | and Management. \r\nhttps://doi.org/10.1016/j.foreco.2022.120210", | ||
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227 | "tags": [ | 227 | "tags": [ | ||
228 | { | 228 | { | ||
229 | "display_name": "CHIROPTERA", | 229 | "display_name": "CHIROPTERA", | ||
230 | "id": "647209a0-71b8-4c1a-9322-f04be60d4485", | 230 | "id": "647209a0-71b8-4c1a-9322-f04be60d4485", | ||
231 | "name": "CHIROPTERA", | 231 | "name": "CHIROPTERA", | ||
232 | "state": "active", | 232 | "state": "active", | ||
233 | "vocabulary_id": null | 233 | "vocabulary_id": null | ||
234 | }, | 234 | }, | ||
235 | { | 235 | { | ||
236 | "display_name": "ECHOLOCATION", | 236 | "display_name": "ECHOLOCATION", | ||
237 | "id": "bb44fe1e-80d1-4737-bc12-a1570a60c6a6", | 237 | "id": "bb44fe1e-80d1-4737-bc12-a1570a60c6a6", | ||
238 | "name": "ECHOLOCATION", | 238 | "name": "ECHOLOCATION", | ||
239 | "state": "active", | 239 | "state": "active", | ||
240 | "vocabulary_id": null | 240 | "vocabulary_id": null | ||
241 | }, | 241 | }, | ||
242 | { | 242 | { | ||
243 | "display_name": "FOREST MANAGEMENT", | 243 | "display_name": "FOREST MANAGEMENT", | ||
244 | "id": "d4404f05-ef28-4de6-b269-17713089be84", | 244 | "id": "d4404f05-ef28-4de6-b269-17713089be84", | ||
245 | "name": "FOREST MANAGEMENT", | 245 | "name": "FOREST MANAGEMENT", | ||
246 | "state": "active", | 246 | "state": "active", | ||
247 | "vocabulary_id": null | 247 | "vocabulary_id": null | ||
248 | }, | 248 | }, | ||
249 | { | 249 | { | ||
250 | "display_name": "HABITAT MODELLING", | 250 | "display_name": "HABITAT MODELLING", | ||
251 | "id": "2a24ea65-92cc-405c-88cd-29497bb481f6", | 251 | "id": "2a24ea65-92cc-405c-88cd-29497bb481f6", | ||
252 | "name": "HABITAT MODELLING", | 252 | "name": "HABITAT MODELLING", | ||
253 | "state": "active", | 253 | "state": "active", | ||
254 | "vocabulary_id": null | 254 | "vocabulary_id": null | ||
255 | }, | 255 | }, | ||
256 | { | 256 | { | ||
257 | "display_name": "VEGETATION STRUCTURE", | 257 | "display_name": "VEGETATION STRUCTURE", | ||
258 | "id": "4c0a535d-bafb-4744-a904-0bd0f79c9268", | 258 | "id": "4c0a535d-bafb-4744-a904-0bd0f79c9268", | ||
259 | "name": "VEGETATION STRUCTURE", | 259 | "name": "VEGETATION STRUCTURE", | ||
260 | "state": "active", | 260 | "state": "active", | ||
261 | "vocabulary_id": null | 261 | "vocabulary_id": null | ||
262 | } | 262 | } | ||
263 | ], | 263 | ], | ||
264 | "title": "LiDAR metrics predict suitable forest foraging areas of | 264 | "title": "LiDAR metrics predict suitable forest foraging areas of | ||
265 | endangered Mouse- eared bats (Myotis myotis)", | 265 | endangered Mouse- eared bats (Myotis myotis)", | ||
266 | "type": "dataset", | 266 | "type": "dataset", | ||
267 | "url": null, | 267 | "url": null, | ||
268 | "version": "1.0" | 268 | "version": "1.0" | ||
269 | } | 269 | } |