Building a shared vision of the future for multifunctional agricultural landscapes. Lessons from a long term socio-ecological research site in south-western France

Ouin, Annie; Andrieu, Émilie; Vialatte, Aude; Balent, Gérard; Barbaro, Luc; Blanco, Julien; Ceschia, Éric; Clément, Floriane; Fauvel, Mathieu; Gallai, Nicola; Hewison, A. J. Mark; Jean-François, Dejoux; Képhaliacos, Charilaos; Macary, Francis; Probst, Anne; Probst, Jean‐Luc; Ryschawy, Julie; Sourdril, Anne; Tallec, Tiphaine; Verheyden, Hélène

doi:10.1016/bs.aecr.2021.05.001

Cited by 13 publications

(7 citation statements)

References 122 publications

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“…Our first dataset, hereafter referred to as ZA-Pygar, consists of a total of 237 1-minute recordings collected from 2019 to 2022 in 79 different sampling sites in the French region of Occitanie (Barbaro et al, 2022). More precisely, the region sampled is part of the Zone Atelier Pyrénées Garonne (Ouin et al, 2021), considered a long-term socio-ecological research site by French research institute CNRS since 2017. Out of the 79 sampling sites from which recordings were obtained, 60 are found in the Aurignac canton, 16 are found either in alpine meadows or at the edge between alpine meadows and subalpine forests at altitudes between 1400 and 2100 m in the Ariège department, and the remaining 3 are found in lowland agricultural areas in South-West Occitanie (Fig.…”

Section: Methodsmentioning

confidence: 99%

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

Funosas,

Barbaro,

Schillé

et al. 2023

Preprint

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Passive acoustic monitoring has become increasingly popular as a practical and cost-effective way of obtaining highly reliable acoustic data in ecological research projects. Increased ease of collecting these data means that, currently, the main bottleneck in ecoacoustic monitoring projects is often the time required for the manual analysis of passively collected recordings. In this study we evaluate the potential and current limitations of BirdNET-Analyzer v2.4, the most advanced and generic deep learning algorithm for bird recognition to date, as a tool to assess bird community composition through the automated analysis of large-scale ecoacoustic data.To this end, we study 3 acoustic datasets comprising a total of 629 environmental soundscapes collected in 194 different sites spread across a 19° latitude span in Europe. We analyze these recordings both with BirdNET and by manual listening by local expert birders, and we then compare the results obtained through the two methods to evaluate the performance of the algorithm both at the level of each single vocalization and for entire recording sequences (1, 5 or 10 min).Our analyses reveal that BirdNET identifications can be highly reliable if a sufficiently high minimum confidence threshold is used. However, the current recall of the algorithm is markedly low when the minimum confidence threshold is adjusted to ensure high levels of precision. Thus, we found that F1-scores remain moderate (<0.5) for all datasets and confidence thresholds studied. We therefore estimate that acoustic datasets of extended duration are currently necessary for BirdNET to provide a reliable and minimally comprehensive picture of the target bird community. Our results also suggest that BirdNET performance is not significantly influenced by the type of recorder used or the habitat recorded but is modulated by the volume of species-specific acoustic data available online.We conclude that a judicious use of AI-based IDs provided by BirdNET can represent a novel and powerful method to assist in the assessment of bird community composition through the automated analysis of large-scale ecoacoustic data. Finally, we provide best use recommendations to ensure optimal results from the algorithm for the ecological study of bird communities.

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Section: Methodsmentioning

confidence: 99%

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

Funosas,

Barbaro,

Schillé

et al. 2023

Preprint

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“…The straight line distance between both sites is 12 kilometers. Both experimental plots are part of the Regional Spatial Observatory South West (OSR SW), the regional Zone Atelier Pyrénées-Garonne (ZA PYGAR, [7] ), the national research infrastructure Critical Zone Observatories: Research and Applications (OZCAR; [8] ) and the Integrated Carbon Observation System (ICOS; [9] ) European network.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

Ganeva,

Tallec,

Brut

et al. 2023

Data in Brief

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“…The study area is located in south‐western France in the Vallées et Coteaux de Gascogne site in the long term socio‐ecological research area (LTSER ZA PYGAR) ‘Pyrénées Garonne' (Ouin et al 2022) and covers approximatively 2500 km 2 (50 × 50 km, Fig. 3).…”

Section: Methodsmentioning

confidence: 99%

How far is enough? Prediction of the scale of effect for wild bees

Desaegher

Ouin²,

Gravel³

2022

Ecography

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A crucial issue for landscape ecologists is identifying the spatial extents at which a landscape affects species occurrence. Multi‐scale analyses are usually conducted to identify the ‘scale of effect', that is, the spatial extent associated with the best relationship between landscape variables and species occurrence, which is assumed to be related to species traits. However, few guidelines exist to determine the range of distances to be investigated. Based on the foraging distances of wild bee species, our main goal was to estimate the maximum distance of effect, that is, the distance beyond which the scale of effect for wild bee species is unlikely to be detected. Using the InVEST pollination model, we 1) modelled bee categories with distinct foraging distances and identified the scale of effecton their simulated abundance 2) defined an index, noted λ, that estimates the distance beyond which landscape composition has only negligible effects on simulated abundances. We validated our results by identifying the scale of effecton the abundances of 16 bee species collected in south‐western France. We detected a significant positive relationship between the average foraging distance (α) of the modelled bees and their scale of effect. The λ index was linearly related to the average foraging distances of bees (λ = 5.4α + 253) and was above the identified scale of effect for the modelled bees. The λ was also found to be above the scale of effect for 93% of the observed bee species. Our results suggest that the λ index is a good estimator of the upper limit of the scale of effect for wild bees. The λ index could be used to identify the minimum distance between sampling sites before setting up an experiment and the maximum buffer size required in multi‐scale analysis to detect the scale of effect.

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Building a shared vision of the future for multifunctional agricultural landscapes. Lessons from a long term socio-ecological research site in south-western France

Cited by 13 publications

References 122 publications

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

How far is enough? Prediction of the scale of effect for wild bees

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