{"help": "https://pro.iepnb.gob.es/catalogo/api/3/action/help_show?name=package_show", "success": true, "result": {"access_rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "alternate_identifier": "ISBN: 978-2-37180-153-0", "author": "Borda-de-Agua, L., Ascens\u00e3o, F., Barrientos, R. y Pereira, H.M.", "author_name": "Borda-de-Agua, L., Ascens\u00e3o, F., Barrientos, R. y Pereira, H.M.", "classification_variables": {"es": ""}, "conforms_to": ["https://www.boe.es/eli/es/res/2013/02/19/(4)"], "contact_email": "organismo@example.org", "contact_name": "Organismo publicador del Cat\u00e1logo", "contact_role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "contact_uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "contact_url": "https://organismo.example.org/", "created": "2025-05-23", "creator_user_id": "d24a314a-79ce-48c0-abd9-54cb42706da4", "dataset_scope": "non_spatial_dataset", "dcat_type": "http://purl.org/dc/dcmitype/Text", "encoding": "UTF-8", "featured": false, "graphic_overview": "https://i1.rgstatic.net/publication/323696417_IENE_2016_International_Conference_-_August_30th_-_September_2nd_2016_-_Lyon_France_International_Conference_on_Ecology_and_Transportation_Programme_and_Abstracts_Guinard_E_ed_2016_Lyon_France/links/5aa65e06aca272d448bae1b3/largepreview.png", "hvd": "non_hvd", "id": "6b7d4a8a-2f65-5598-a301-56c68a7a0c6f", "identifier": "6b7d4a8a-2f65-5598-a301-56c68a7a0c6f", "inspire_id": "", "isopen": true, "language": "http://publications.europa.eu/resource/authority/language/SPA", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "lineage_process_steps": [], "lineage_source": ["2016 IENE International Conference. Programme and abstracts. Vol. 3.11", "Num. 389", "pag. 283"], "maintainer": "", "maintainer_name": "", "metadata_created": "2026-06-23T14:54:32.493652", "metadata_modified": "2026-06-25T12:36:30.607199", "metadata_profile": ["https://www.w3.org/TR/vocab-dcat-3/"], "miteco_data_population": {"es": ""}, "miteco_data_territory": {"es": ""}, "miteco_dataset_type": "http://publications.europa.eu/resource/authority/dataset-type/STATISTICAL", "miteco_geo_level": "1", "modified": "2026-06-25", "name": "6b7d4a8a-2f65-5598-a301-56c68a7a0c6f", "notes": "One of the main tasks in road ecology is to identify hotspots of high mortality so that one can devise and implement mitigation measures. A common strategy to identify hotspots is to divide a road into several segments and determine when the number of collisions is above a given threshold, reflecting a desired significance level obtained assuming a probability distribution (often the Poisson). The problem of this approach when applied to each segment individually is that the probability of identifying false hotspots is very high, i.e., the probability of making a type I error is very high. For instance, if we establish the threshold based as the top of a 95% confidence interval, then one should expect to incorrectly identify just by chance five false hotspots in every 100 segments. Although one may argue that such overly cautionary approach may be beneficial from a biological conservation perspective, it may lead to the waste of resources and, probably worse, it may raise doubts on the methodology adopted and the credibility of those suggesting it. The problem of multiple comparison occurs in several scientific areas and several corrections have been suggested. Here, we apply three different approaches to the identification of hotspots: a method similar to that of the Bonferroni correction; the false discovery rate (FDR); and a Bayesian approach that consists of a hierarchical Poisson model. The Bonferroni approach reduces the probability of type I errors, yet the probability of type II errors (rejecting a true hotspot), is very high and thus this procedure has low power. FDR method increases the power of the test while keeping the probability of identifying false hotspots low. The Bayesian approach uses the information obtained from all segments to infer the probability of a segment being a hotspotand avoids some of the problems inherent to the two previous approaches. We discuss the application of these three methods and give recommendations to the identification of hotspots with a view to providing a wide range of practitioners\u2019 procedures that are reliable and simple to use in real situations.", "notes_translated": {"en": "One of the main tasks in road ecology is to identify hotspots of high mortality so that one can devise and implement mitigation measures. A common strategy to identify hotspots is to divide a road into several segments and determine when the number of collisions is above a given threshold, reflecting a desired significance level obtained assuming a probability distribution (often the Poisson). The problem of this approach when applied to each segment individually is that the probability of identifying false hotspots is very high, i.e., the probability of making a type I error is very high. For instance, if we establish the threshold based as the top of a 95% confidence interval, then one should expect to incorrectly identify just by chance five false hotspots in every 100 segments. Although one may argue\nthat such overly cautionary approach may be beneficial from a biological conservation perspective, it may lead to the waste of resources and, probably worse, it may raise doubts on the methodology adopted and the credibility of those suggesting it. The problem of multiple comparison occurs in several scientific areas and several corrections have been suggested. Here, we apply three different approaches to the identification of hotspots: a method similar to that of the Bonferroni correction; the false discovery rate (FDR); and a Bayesian approach that consists of a hierarchical Poisson model. 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The problem of multiple comparison occurs in several scientific areas and several corrections have been suggested. Here, we apply three different approaches to the identification of hotspots: a method similar to that of the Bonferroni correction; the false discovery rate (FDR); and a Bayesian approach that consists of a hierarchical Poisson model. The Bonferroni approach reduces the probability of type I errors, yet the probability of type II errors (rejecting a true hotspot), is very high and thus this procedure has low power. FDR method increases the power of the test while keeping the probability of identifying false hotspots low. The Bayesian approach uses the information obtained from all segments to infer the probability of a segment being a hotspotand avoids some of the problems inherent to the two previous approaches. 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