Long-term monitoring of mammal communities in the Peneda-Gerês National Park using camera trap data
We provide a dataset from long-term camera trap monitoring in the parishes of Castro Laboreiro and Lamas de Mouro, Peneda-Gerês National Park, between 2015 and 2021. We established a 16 km² grid of 64 cameras deployed yearly during the summer months. Here, we publish the data and images collected between 2015 and 2021, using GBIF Darwin Event Core. 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.
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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
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Samplingevent; camera traps; mammal; Portugal; long-term monitoring; Samplingevent
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Castro Laboreiro and Lamas de Mouro, Peneda-Gerês National Park, Portugal.
|Coordenadas límite||Latitud Mínima Longitud Mínima [41,997, -8,205], Latitud Máxima Longitud Máxima [42,036, -8,158]|
Mammals and birds were identified to the species level where possible.
|Class||Mammalia (Mammals), Aves (Birds)|
|Fecha Inicial / Fecha Final||2015-04-19 / 2015-08-19|
|Fecha Inicial / Fecha Final||2016-04-13 / 2016-08-27|
|Fecha Inicial / Fecha Final||2017-05-08 / 2017-10-03|
|Fecha Inicial / Fecha Final||2018-05-05 / 2018-10-15|
|Fecha Inicial / Fecha Final||2019-05-07 / 2019-10-08|
|Fecha Inicial / Fecha Final||2020-06-02 / 2021-05-07|
Datos del Proyecto
We provide a dataset from long-term camera trap monitoring in the Peneda-Gerês National Park, between 2015 and 2021. We established a 16 km² grid of 64 cameras deployed yearly during the summer months and obtained a total of 934,810 pictures on 41,234 trap nights. The pictures were automatically grouped into sequences with each sequence representing a distinct occurrence event, resulting in 80,191 occurrences. Out of those, 14,442 contained observations of a species, while the remaining were either blank or the species was not identifiable. 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.
|Título||Long-term monitoring of mammal communities in the Peneda-Gerês National Park using camera trap data|
|Descripción del Área de Estudio||The study was conducted in the parishes of Castro Laboreiro and Lamas de Mouro in the Peneda-Gerês National Park in Northern Portugal. The park was created in 1971 with the aim to protect the high natural value landscape. It is the oldest protected area and the only national park in Portugal. Over the past 60 years the region underwent large land-use changes. Especially in the area surrounding Castro Laboreiro where historically agro-pastoral activities were common, with seasonal migration from summer villages at the plateau to winter villages in the valley, socio-economic changes lead to a large population decline. This abandonment opened possibilities for natural succession and passive rewilding in the area. Today the area supports a great diversity of habitats consisting of small agricultural fields in the valley, shrublands and oak forest patches in the hillside and pastures for cattle and agricultural fields in the plateau. Since 1971, Castro Laboreiro is part of the Peneda-Gerês National Park and since 1997, it is also part of the European protected area network Natura 2000. The elevation in the area ranges from 300 to 1,340 m. As it is located at the transition between the Mediterranean and Atlantic biogeographic zones, the region has a temperate Mediterranean climate, characterized by cold and rainy winters and warm summers. The average annual temperature throughout the study duration was 11.9 °C at 800m elevation, with an average annual precipitation of 1858 mm between 1985 and 2015. Within the camera trap grid, we identified seven different land-use types from aerial imagery in 2020, with bare rock accounting for almost half of the area (45.30%) followed by low shrub (20.13%) and oak forest (18.77%). While agriculture (2.42%) and urban infrastructure (1.11%) only represent a considerably small part of the area, high shrub and pine forest make up for 8.64% and 3.63% respectively.|
|Descripción del Diseño||The project aims to (1) assess the population trends of the medium and large size mammals of the region over time; (2) analyse the effects of passive rewilding and other environmental variables on the occurence and abundance; (3) look at potential interactions between wild and domestic species and (4) analyze effects of environmental and anthropogenic variables on their behavior (e.g. activity patterns).|
Personas asociadas al proyecto:
Métodos de Muestreo
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).
|Área de Estudio||The dataset is obtained from a long-term monitoring campaign that is conducted every year since 2015. Currently, images from 2015 to 2021 are classified, but further data will be included in the future. In 2015 and 2016 camera traps were deployed from April to August and from 2017 to 2019 from May to October. In 2020, because of travel restrictions during the Covid pandemic camera traps could only be deployed in June and were left in the field until May, 2021. Due to theft and malfunction the number of operative cameras ranged from 61 in 2016 to 48 in 2020. Trap nights per year ranged from 4,236 in 2015 to 9,606 in 2020/21.|
|Control de Calidad||To ensure using the updated scientific name and common name of species, the taxonomic nomenclature followed the catalogue of life (https://www.catalogueoflife.org/). Additionally, we checked every species in the database of the IUCN Red List of Threatened species (https://www.iucnredlist.org) for their conservation status and populations trends.|
Descripción de la metodología paso a paso:
- 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.