此資源sampling event的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 528 筆紀錄。

亦存在 1 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。

此 IPT 存放資料以提供資料儲存庫服務。資料與資源的詮釋資料可由「下載」單元下載。「版本」表格列出此資源的其它公開版本，以便利追蹤其隨時間的變更。

下載最新版本的 Darwin Core Archive (DwC-A) 資源，或資源詮釋資料的 EML 或 RTF 文字檔。

以下的表格只顯示可公開存取資源的已發布版本。

研究者應依照以下指示引用此資源。:

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

研究者應尊重以下權利聲明。:

此資料的發布者及權利單位為 Biodiversity Data Journal。 This work is licensed under a Creative Commons Attribution (CC-BY) 4.0 License.

此資源已向GBIF註冊，並指定以下之GBIF UUID: 88228250-e465-494e-858e-3bc327de01d7。  Biodiversity Data Journal 發佈此資源，並經由Participant Node Managers Committee同意向GBIF註冊成為資料發佈者。

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.

參與計畫的人員:

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).

方法步驟描述:

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.