この sampling event リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、528 レコードが含まれています。

拡張データ テーブルは1 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。

この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。

DwC-A形式のリソース データまたは EML / RTF 形式のリソース メタデータの最新バージョンをダウンロード：

次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。

研究者はこの研究内容を以下のように引用する必要があります。:

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

研究者は権利に関する下記ステートメントを尊重する必要があります。:

パブリッシャーとライセンス保持者権利者は Biodiversity Data Journal。 This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.

このリソースをはGBIF と登録されており GBIF UUID: 88228250-e465-494e-858e-3bc327de01d7が割り当てられています。   Participant Node Managers Committee によって承認されたデータ パブリッシャーとして GBIF に登録されているBiodiversity Data Journal が、このリソースをパブリッシュしました。

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.

プロジェクトに携わる要員:

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).

Method step description:

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.