Los datos en este evento de muestreo recurso han sido publicados como Archivo Darwin Core(DwC-A), el cual es un formato estándar para compartir datos de biodiversidad como un conjunto de una o más tablas de datos. La tabla de datos del core contiene 528 registros.

también existen 1 tablas de datos de extensiones. Un registro en una extensión provee información adicional sobre un registro en el core. El número de registros en cada tabla de datos de la extensión se ilustra a continuación.

Este IPT archiva los datos, sirviendo así como repositorio de datos. Los datos y metadatos están disponibles para descargar en la sección de descargas. La tabla de versiones muestra otras versiones del recurso que se han hecho accesibles al público y permite el seguimiento de los cambios hechos al recurso en el tiempo.

Descargue la última versión de los datos como un Archivo Darwin Core (DwC-A) o los metadatos como EML o RTF:

La siguiente tabla muestra sólo las versiones publicadas del recurso que son de acceso público.

Los usuarios deben citar este trabajo de la siguiente manera:

Zuleger A, Perino A, Wolf F, Wheeler H, Pereira H (2022): Long-term monitoring of mammal communities in the Peneda-Gerês National Park using camera trap data. v1.2. Biodiversity Data Journal. Dataset/Samplingevent. https://ipt.pensoft.net/resource?r=ct_peneda&v=1.2

Los usuarios deben respetar los siguientes derechos de uso:

El publicador y propietario de los derechos de este trabajo es Biodiversity Data Journal. This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.

Este recurso ha sido registrado en GBIF con el siguiente UUID: 88228250-e465-494e-858e-3bc327de01d7.  Biodiversity Data Journal publica este recurso, y está registrado en GBIF como un publicador de datos avalado por Participant Node Managers Committee.

Samplingevent; camera traps; mammal; Portugal; long-term monitoring; Samplingevent

Castro Laboreiro and Lamas de Mouro, Peneda-Gerês National Park, Portugal.

Mammals and birds were identified to the species level where possible.

We provide a dataset from long-term camera trap monitoring in the Peneda-Gerês National Park that is on-going since 2015. We established a 16 km² grid of 64 cameras deployed yearly during the summer months. Since 2020 camera traps are also deployed during the winter and remain active all year with maintainance and data collection in May and October. The pictures are automatically grouped into sequences with each sequence representing a distinct occurrence event. We only obtained the information whether a species was present or absent on a picture, disregarding the number of individuals. Most observations were of domestic cattle (Bos taurus) and horses (Equus caballus) followed by European roe deer (Capreolus capreolus) and wild boar (Sus scrofa). Further observations include red fox (Vulpes vulpes), gray wolf (Canis lupus), Eurasian badger (Meles meles), stone marten (Martes foina), common genet (Genetta genetta), Iberian ibex (Capra pyrenaica) and red deer (Cervus elaphus). The project is on-going and additional data will be included in the future. The dataset is freely available for ecological analysis but also for trainining machine learning systems in automated image classification as all pictures have been manually classified.

Personas asociadas al proyecto:

For the study, 64 camera traps (Reconyx Hyperfire HC600, Holmen, WI, USA) were deployed in a 16 km² grid South-West of Castro Laboreiro. They were distributed as uniformly as possible across the different land-use types (e.g. 10% of cameras in land-use types that cover 10% of the area) with one camera per 0.25 ha grid cell (approx. 500 m spacing between each camera). Real locations could deviate by up to 100 meters from the planned locations due to accessibility and placement possibilities. Further, some locations had to be adjusted throughout the years because of changes in the vegetation structure or due to theft, but new locations were within 100 m of the original location and placed in similar habitats. The coordinates in the dataset were rounded to three decimals for safety reasons. The cameras remained active for 24 hours per day and were programmed on motion sensor to take three consecutive pictures each time they were trigged by an animal with no delay after a trigger event. The sensitivity of the sensor was set to high in 2015, 2016 and 2019, medium in 2017 and 2018 and medium/high in 2020 (see eventRemarks). Sampling effort was measured as the number of camera traps multiplied by the number of days they remained active (Rovero et al. 2010).

Descripción de la metodología paso a paso:

1. Each image obtained from the camera traps was classified manually. The images were later imported into Agouti (https://www.agouti.eu/) to be made publicly available. There they were automatically grouped into sequences of contiguous images with each sequence representing one distinct occurrence event. The pre-classified observations were linked to the respective sequences using the image name. Starting with the data from 2020, the images were directly imported into Agouti and classified within the software. The dataset was exported from Agouti in Camtrap DP format and manually converted to Darwin Core standard. It is structured as a sample event dataset including the event and the occurrence data and published as a Darwin Core Archive (DwC-A). Here, an event refers to a camera trap deployment at a certain location over a certain amount of time (equivalent to "deployment" in Camtrap DP format) and an occurrence refers to a distinct occurrence event (here: sequence, equivalent to "observation" in Camtrap DP format). The published DwC-A dataset only includes events that contained observations of an animal. As the DwC-A format currently doesn't allow for hierachical datasets with more than two levels, we included the first ten images of each occurrence event as associatedMedia in the occurence table. The original Camtrap DP dataset including also blank and unknown occurrences will be published as supplemental material to this publication and made available through GBIF in the future.