Observation of phytoplankton in réserve naturelle du Luitel during the summer of 2024

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版本

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如何引用

研究者應依照以下指示引用此資源。:

RAFAI S, VOGIN A, RIGAUD Q, NÉMERY J, COSTREL DE CORAINVILLE S, BAILLOT S, DESPLANQUES C (2026). Observation of phytoplankton in réserve naturelle du Luitel during the summer of 2024. Version 1.0. LIPhy CNRS Univ Grenoble Alpes. Occurrence dataset. https://ipt.gbif.fr/resource?r=lacoscope_luitel&v=1.0

權利

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 LIPhy CNRS Univ Grenoble Alpes。 This work is licensed under a Creative Commons Attribution Non Commercial (CC-BY-NC 4.0) License.

GBIF 註冊

此資源已向GBIF註冊，並指定以下之GBIF UUID: 13c67ec0-8900-4278-9f03-ff3a8957905c。  LIPhy CNRS Univ Grenoble Alpes 發佈此資源，並經由GBIF France同意向GBIF註冊成為資料發佈者。

關鍵字

Occurrence; phytoplancton; lake

聯絡資訊

地理涵蓋範圍

N/A

分類群涵蓋範圍

無相關描述

時間涵蓋範圍

計畫資料

The Lacoscope project aims to involve a large number of citizens (non-expert general public) in the collaborative production of images of plankton in Alpine lakes. A low-cost, portable microscopy platform (PlanktoScope), built by students and supported by scientific mediators, will allow the monitoring of plankton dynamics as an indicator of environnemental changes.

參與計畫的人員:

取樣方法

We sampled surface plankton using a horizontal tow with a 20--\,\textmu m mesh plankton net over an \textasciitilde 10 m path at constant speed.

方法步驟描述:

1. Sampling at Luitel was carried out using a horizontal plankton net with a mesh size of 20 µm, a classic method widely used to collect microalgae and microorganisms at the surface (Lund et al., 1958). The net was towed for approximately 10 m at a constant speed in order to obtain a representative volume of filtered water from the study area. The samples were then analyzed using the PlanktoScope, a low-tech, open-source imaging device that provides a standardized stream of plankton images (Pollina et al., 2023). The segmented objects were then imported into the EcoTaxa platform, a collaborative reference tool for the semi-automated taxonomic classification of plankton images (EcoTaxa Development Team, 2024) with combination of machine learning algorithms and expert validation (Picheral et al., 2017; Irisson et al., 2022). Finally, the validated data were analyzed in terms of occurrences and community structure using dedicated Python scripts.

額外的詮釋資料