# Leveraging passive acoustic monitoring for result-based agri-environmental schemes: Opportunities, challenges and next steps

Anna F Cord, Kevin Darras, Ryo Ogawa, Luc Barbaro, Charlotte Gerling, Maria Kernecker, Nonka Markova-Nenova, Gabriela Rodriguez-Barrera, Felix Zichner, Frank Wätzold

<figure><img src="image_1.png" /></figure>

## To cite this version:

Anna F Cord, Kevin Darras, Ryo Ogawa, Luc Barbaro, Charlotte Gerling, et al.. Leveraging passive acoustic monitoring for result-based agri-environmental schemes: Opportunities, challenges and next steps. Biological Conservation, 2025, 305, pp.111042. 10.1016/j.biocon.2025.111042. hal-05004399

HAL Id: hal-05004399

https://hal.inrae.fr/hal-05004399v1

Submitted on 25 Mar 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Leveraging passive acoustic monitoring for result-based agri-environmental schemes: Opportunities, challenges and next steps☆

Anna F. Cord<sup>a,b,*</sup>, Kevin Darras<sup>b,c</sup>, Ryo Ogawa<sup>a,b</sup>, Luc Barbaro<sup>d</sup>, Charlotte Gerling<sup>e</sup>, Maria Kernecker<sup>f</sup>, Nonka Markova-Nenova<sup>e</sup>, Gabriela Rodriguez-Barrera<sup>b</sup>, Felix Zichner<sup>b</sup>, Frank Wätzold<sup>e</sup>

## ARTICLE INFO

### Keywords:

- Standardisation

- Farmland biodiversity

- Bioacoustics

- Result-oriented payments

- Performance-based payments

- Cost-effectiveness

## ABSTRACT

Despite over three decades of agri-environmental schemes (AES) in Europe, farmland biodiversity continues to decline. Most AES are action-based, compensating farmers for implementing biodiversity-friendly management practices. In contrast, result-based payments (RBP) are AES that reward farmers for achieving specific biodiversity targets, making them a promising alternative for farmland species conservation. RBP have largely focused on vascular plants due to the difficulty and expense of monitoring elusive or mobile species. Passive Acoustic Monitoring (PAM) offers new opportunities for low-cost, standardised monitoring of soniferous animals' occurrence, activity and diversity. This study conceptually explores the integration of PAM as a novel monitoring approach within RBP. Using established RBP design frameworks, we examine key considerations for employing PAM in future RBP. We identify appropriate biodiversity targets at species and ecological community levels as well as indicators that effectively represent these targets. We further assess how the use of PAM affects the costs associated with RBP, with a focus on expenses for monitoring. Farmer acceptance is key to the success of AES, so we discuss farmers' willingness to adopt PAM technology and their perspectives on using ecological communities as targets in RBP. Finally, we propose pathways to implement PAM-based RBP pilot projects, emphasising collaboration with farmers and authorities, and provide an example for the whinchat as a target species. Such pilots can test the feasibility of integrating PAM in practical settings while addressing challenges identified in this study at the conceptual level, such as cost, technical implementation, and farmer engagement.

## 1. Introduction

Farmland biodiversity is undergoing a significant and widespread decline in Europe. This trend is largely driven by the intensification of agricultural practices (Raven and Wagner, 2021; Rigal et al., 2023), but also the abandonment of extensively managed agricultural land in marginal areas (Ubach et al., 2020; Uchida and Ushimaru, 2014). In response, Europe has primarily relied on agri-environmental schemes (AES, see Box 1 - Glossary) as a policy instrument. AES provide financial compensation to farmers in exchange for delivering public goods and

services, including biodiversity conservation (Ansell et al., 2016; Batáry et al., 2015). The predominant form of AES is action-based, where farmers receive payments for implementing specific measures such as hedgerow planting (Image et al., 2023), flower strip creation (Bullock et al., 2021), or adoption of extensive mowing or grazing practices (Wätzold et al., 2016). Action-based AES have been criticized for not incorporating appropriate conservation measures (Armsworth et al., 2012; Markova-Nenova et al., 2023b) and for not accounting for the structure of the surrounding landscape, which can greatly influence biodiversity outcomes (Geppert et al., 2020; Roilo et al., 2023). Further,

<footnote><sup>a</sup> Agro-Ecological Modeling Group, Institute of Crop Science and Resource Conservation, University of Bonn, Niebuhrstr. 1a, 53113 Bonn, Germany</footnote>

<footnote><sup>b</sup> Chair of Computational Landscape Ecology, TUD Dresden University of Technology, Helmholtzstr. 10, 01069 Dresden, Germany</footnote>

<footnote><sup>c</sup> EFNO, INRAE, Nogent-sur-Vernisson 45290, France</footnote>

<footnote><sup>d</sup> Dynafor, INRAE-INPT, University of Toulouse, Castanet-Tolosan, France</footnote>

<footnote><sup>e</sup> Chair of Environmental Economics, Brandenburg University of Technology Cottbus-Senftenberg, Erich-Weinert-Str. 1, 03046 Cottbus, Germany</footnote>

<footnote><sup>f</sup> Leibniz Centre for Agricultural Landscape Research (ZALF), Germany</footnote>

<footnote>* This article is part of a special issue entitled: Technologies for new AES published in Biological Conservation.</footnote>

<footnote>* Corresponding author at: Agro-Ecological Modeling Group, University of Bonn, Germany.</footnote>

<footnote>E-mail address: anna.cord@uni-bonn.de (A.F. Cord).</footnote>
action-based AES often have rigid implementation requirements, such as specific timings for management actions (Wätzold et al., 2016). Introducing more flexibility into these requirements could potentially enhance conservation benefits, reduce costs (Hecker et al., 2024), and increase the willingness of farmers to participate (Bartkowski et al., 2023).

As a promising alternative to action-based AES (Wuepper and Huber, 2022), result-based payments (RBP) are gaining traction in Europe (Elmiger et al., 2023; Hagemann et al., 2025). Under RBP, farmers receive payments only upon achieving a specific biodiversity target, such as the presence of endangered plant species on their land (Fleury et al., 2015). This approach inherently ensures a high level of ecological effectiveness (Birge et al., 2017; Sidemo-Holm et al., 2018). RBP further empowers farmers with autonomy as well as flexibility and stimulate innovation by providing financial incentives for implementing practices that minimise costs or demonstrate greater effectiveness in conserving target species (Wätzold and Drechsler, 2005).

Nevertheless, there are still significant barriers to the widespread adoption and implementation of RBP, including: (i) heightened risks for farmers in failing to meet biodiversity targets, requiring compensation through increased payments (thus diminishing the cost-effectiveness of RBP; Drechsler, 2017), and (ii) limitations in monitoring capabilities for biodiversity targets (Burton and Schwarz, 2013; Schöttker et al., 2023). This is particularly true for elusive or mobile animals, where monitoring proves to be highly challenging or, in economic terms, costly (Zabel et al., 2014). Consequently, current RBP primarily focus on vascular plants as biodiversity targets (Elmiger et al., 2023; Hagemann et al., 2025). However, new workflows encompassing the collection, storage, and processing of automated biodiversity monitoring data in space and time have recently emerged (Besson et al., 2022). These workflows, which leverage novel technologies for monitoring and often use machine learning for information extraction, have recently been proposed as promising approaches to improve the design of future RBP (Elmiger et al., 2023; Markova-Nenova et al., 2023a; Schöttker et al., 2023).

Here, we conceptually explore the potential of Passive Acoustic Monitoring (PAM; see Box 2) as an emerging technology for assessing the achievement of biodiversity targets within the context of RBP. PAM

is now widely applied for monitoring sound-producing animals such as birds, bats, orthopterans, and anurans (Sugai et al., 2019), which can serve as indicator species for ecosystem conditions (Fraixedas et al., 2020) and can directly benefit from certain agri-environmental practices (Froidevaux et al., 2019). PAM offers significant potential as a monitoring technology in the context of RBP for several reasons. First, it broadens the scope of taxa that can be considered as biodiversity targets. Second, it enables the simultaneous detection of multiple species and taxa, providing insight into the composition and interactions within ecological communities (Farina, 2018). Third, it generates audio recordings that can serve as objective, observer-independent evidence, with timestamp and location data offering a verifiable, unbiased record for later review and validation (Metcalf et al., 2023). Relatively affordable, flexible, automated sound recorders (Hill et al., 2019) have facilitated data collection over extensive spatial and temporal scales (Darras et al., 2019; Markova-Nenova et al., 2023a). While human expertise in verification and ecological analysis is still needed (e.g., species validation, range and results calibration; Funosas et al., 2024; Haupert et al., 2023; Wood et al., 2024), advances in machine learning and automated analysis workflows (Kahl et al., 2021; Mac Aodha et al., 2022) are improving the speed and accuracy of processing increasingly larger amounts of verifiable and re-analysable records.

In our conceptual analysis of PAM's potential for verifying biodiversity target achievements within RBP, we follow the steps outlined by Keenleyside et al. (2014) and Maher et al. (2018) for designing an RBP, but focus exclusively on the aspects relevant to PAM-based RBP. In step one, *selecting the biodiversity target*, we present potential ecological targets (at species and ecological community levels) that PAM can reliably monitor. In step two, *identifying result indicators*, we analyse indicators that can represent these levels of ecological targets in potential RBP. In step three, *costs and payment levels*, we assess how costs change with the use of PAM. Step four, *acceptance*, addresses aspects such as technology adoption, trust, as well as guidance and training.

## 2. Selecting the biodiversity target: Species and communities

RBP require clearly defined biodiversity targets, as farmer

**Box 1**

**Glossary.**

* Acoustic communities are spatio-temporal assemblages of species that produce sound through either internal mechanisms, such as vocal cords in mammals or syrinxes in birds, or external sound-producing means, such as woodpeckers pecking on tree trunks or beavers slapping their tails on the water surface (Farina and James, 2016).

* Action-based payments compensate farmers for implementing a pre-defined management measure, such as planting hedges, establishing flower strips or adopting extensive mowing or grazing practices (Jongeneel and Gonzalez-Martinez, 2023).

* Agri-environmental schemes (AES) are policy instruments that provide financial incentives for farmers to implement management measures aimed at, for example, biodiversity conservation, soil and water protection or climate change mitigation/adaptation (Batary et al., 2015).

* Cost-effectiveness means achieving a pre-defined environmental target at least cost, or maximizing an environmental target for a given cost (Wätzold and Schwerdtner, 2005).

* Opportunity costs of land use, in the context of AES, refer to the forgone income of farmers when implementing conservation measures on their land instead of pursuing profit-maximizing land use practices (see discussion in Schaub et al., 2023).

* Passive Acoustic Monitoring (PAM) is the measurement and analysis of sound and/or vibration in air, water and/or solids in the ultrasonic, infrasonic, and human audible range. Sound sources include biological organisms, geophysical phenomena, and human or technological activities (adapted from Pijanowski and Brown, 2022). In the context of AES, PAM may be conducted with automated sound recorders to track the presence, activity and diversity of terrestrial wildlife.

* Result-based payments (RBP) are granted for achieving specific environmental outcomes (e.g. the presence of indicator species), without prescribing specific agricultural management (Burton and Schwarz, 2013).

* Risk premium: When deciding about RBP scheme participation, farmers have to consider that, despite appropriate management, the target species may not occur due to factors beyond their control, such as predation. Therefore, they weigh the costs of implementing management measures against the expected payment (i.e., the payment in case of conservation success multiplied by the probability of target species' occurrence). Risk-averse farmers will only participate if a risk premium is added to the expected payment (Drechsler, 2017).

* Soundscapes are collections of sounds of biological (e.g., crickets), geophysical (e.g., wind) and anthropogenic (e.g., car engine noise) origin that emanate from a landscape and which vary over space and time (adapted from Pijanowski et al., 2011).
**Box 2**

Brief introduction to Passive Acoustic Monitoring (PAM).

PAM involves the deployment of automated sound recorders in the field and subsequent processing of the recorded sound data (Fig. 1). While some guidelines for terrestrial monitoring and acoustic analysis are available (Metcalf et al., 2023), standardised protocols are currently lacking.

**Field setup:** Recording schedules and sampling frequencies must be designed to match the activity periods and the vocalisation frequency of the target species or community. Currently, data collection methods are highly varied, with sound being recorded continuously (e.g. for rare species), at predetermined numerical times or solar times (e.g. at sunrise for the dawn chorus), intermittently using duty cycles (for phenological studies), or after triggers when an intensity threshold is exceeded (as is commonly done for bats). Recorders can sample sound over several days to ensure site representativeness. Similarly, spatial sampling designs can be tailored to various budgets, replication requirements, and constraints related to storage and energy consumption (Sugai et al., 2020).

**Data processing:** A variety of software tools address different parts of the data workflow (Darras et al., 2024a). Audio recordings need to be managed and stored alongside metadata, including time, location, recorder model and settings. However, reporting standards are not yet established, despite ongoing harmonisation efforts (Darras et al., 2024b). Signal pre-processing can involve splitting, amplifying, re-sampling, or compressing recordings to produce comparable and analysable data subsets. Spectrograms, waveforms and playback are commonly used to verify and interpret sound patterns. Acoustic analysis is frequently performed automatically using species recognition models, such as Convolutional Neural Networks (CNNs), which annotate recordings with species labels (Nieto-Mora et al., 2023). It is increasingly recognised that taxonomic experts should first validate these annotations to assess the algorithms' performance (Pérez-Granados, 2023). PAM readily provides data on species presence or species richness, but deriving abundance or density estimates remains challenging (see section 3). There are currently no widely accepted procedures for generating standardised biodiversity data from PAM.

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Passive Acoustic Monitoring workflow in practice. a) Field setup with example photograph. The detection space represents the area over which the recorder captures particular sounds, varying with environmental and sonic conditions. b) Data retrieval: Acoustic data are collected from the recorders. c) Data management and storage: Acoustic data need to be backed up, archived, and organised systematically by space, time, and other metadata. d) Signal processing: Recordings can be amplified, re-sampled, split, filtered or compressed to enable subsequent workflow steps. e) Data visualisation: Sound recordings can be represented using spectrograms (as shown here) or waveforms, allowing navigation in the temporal and spectral dimensions and playback. f) Audio analysis: Typically, species identification is performed using a machine learning model and validated by experts, as demonstrated here with an annotated field recording from an agricultural landscape in Germany.</figcaption></figure>

compensation hinges on achieving these objectives (Keenleyside et al., 2014). Currently, RBP in Europe predominantly target vascular plants as indicators of species-rich grasslands (Elmiger et al., 2023). In contrast, PAM broadens the range of taxa that can be monitored by including soniferous animals, many of which are mobile, elusive, or nocturnal and thus difficult to detect using conventional methods (Sugai et al., 2019).

At the **species level**, PAM-derived recordings can be used to reliably identify most European bird as well as many bat species (and species groups otherwise) (Sugai et al., 2019). These include endangered large farmland birds with declining populations in Europe, such as the Little Bustard (*Tetrax tetrax* L.) or the Northern Lapwing (*Vanellus vanellus* L.), as well as once widespread and now declining typical farmland

passerines such as the Eurasian Skylark (*Alauda arvensis* L.) or the Ortolan Bunting (*Emberiza hortulana* L.). However, detectability by PAM is not sufficient alone. Effective RBP design requires evidence that the target species respond to specific agricultural management at either the field or the farm level. Birds with large home ranges therefore should be cautiously considered as target species in RBP, but not necessarily excluded. For bats, PAM is a well-established assessment method (Brinkløv et al., 2023). For example, it has been used to monitor the activity of endangered mouse-eared bats (*Myotis* spp.) and long-eared bats (*Plecotus* spp.) in relation to agricultural field margins (Blary et al., 2021) and field hedgerows (Biffi et al., 2024). Methods for the automated and reliable identification of soniferous insects (van Klink
et al., 2022) and anurans (Caouette et al., 2024) using PAM are currently under development (Funasas et al., accepted).

In Europe, RBP rarely target **ecological communities**, instead focusing primarily on indicator species specific to each RBP (Elmiger et al., 2023). However, using community-level diversity indicators (e.g. species richness) could better incentivise practices that enhance overall biodiversity of agricultural land. In fact, the diversity of and interactions within ecological communities, rather than the mere presence of individual species, offer a more comprehensive indicator of ecosystem condition and health (Goodale and Magrath, 2024; Hartig et al., 2024). Biodiversity-enhancing agricultural practices can improve multi-trophic diversity, along with the associated ecosystem functions and services (Buzhdygan and Petermann, 2023; Lohaus et al., 2013; Rehman et al., 2022), with multi-species co-occurrences of soniferous species identifiable through PAM. For example, abundant food supplies can increase the abundance of orthopterans, leading to more frequent acoustic emissions. These sounds, in turn, may attract sound-locating parasitoids as well as insectivorous bats and birds (Ronacher, 2019). Therefore, choosing acoustic communities as biodiversity targets in RBP can be a useful approach to reward differences between biodiversity-enhancing and conventional agricultural practices, both in terms of agro-ecosystem functioning and conservation value.

The third level that can be monitored with PAM are **soundscapes**. While this is an important and active field of research in itself, to our knowledge, no studies have explored soundscapes as a biodiversity target within the context of RBP. This means we can only speculate about which soundscapes are suitable targets, the appropriate indicators to measure them, the potential monitoring costs, their acceptability to farmers, and, most critically, whether the public would support funding soundscapes in agricultural landscapes through RBP. Therefore, we only briefly address this topic in Box 3.

### 3. Identifying result indicators

RBP require result indicators that correspond to their biodiversity targets and are measurable through a concise and transparent evaluation system, including defined thresholds farmers must meet to qualify for payments (Maher et al., 2018). In many current RBP, result indicators

are based on the presence of a minimum number of indicator species. More complex metrics such as species abundance (the number of individuals of a species within a given area) or proportional cover (the extent to which a species covers the available space) are rarely considered due to the complexities and efforts involved in assessment, even for plants (Elmiger et al., 2023). PAM is particularly well-suited for deriving result indicators for RBP, as many animals produce sounds during critical life phases such as mating, foraging, reproduction, and rearing their young – activities that RBP should aim to support and reward. PAM can provide multiple species-level and community-level result indicators for soniferous taxa (see Table 1 for an overview).

As with most monitoring methods, species presence is the most straightforward species-level result indicator for PAM-based RBP. Established workflows for automated species detection using machine learning exist for European birds (Kahl et al., 2021) and bats (Brinkløv et al., 2023) and the feasibility of the approach has been demonstrated in case studies for orthopterans (Caouette et al., 2024) and anurans (Bota et al., 2024). When estimating species presence for RBP, it is essential to consider interspecific variability in seasonal and diel vocal activity patterns (Singer et al., 2024) and to appropriately interpret machine learning model outputs such as confidence scores (Wood and Kahl, 2024). Additionally, long-term PAM facilitates the confirmation of species absence, which could support RBP that reward the absence of invasive species – an approach already implemented in some RBP targeting plants or certain habitats (Hagemann et al., 2025).

A major challenge with PAM remains that the location and number of sound sources is generally unavailable. Indeed, sound detection spaces – the area over which a recorder captures particular sounds, depending on habitat structure, sound source and its location, as well as sound propagation (Darras et al., 2016) – are rarely measured. Similarly, sound pressure levels are not available yet for most species, so that their effective detection distances are unknown (Yip et al., 2017). Alternative solutions, such as sound source localisation techniques using multiple microphones in the field (Buchmann and Schurr, 2024; Rhinehart et al., 2020), are still under development. As a result, the estimation of species abundances and densities (Pérez-Granados and Traba, 2021) is possible, but not trivial and not yet fully established. Vocal activity – which is often related to abundance (Pérez-Granados et al., 2019) – has been

#### Box 3

**Soundscapes as targets in future RBP?**

The soundscape represents the most comprehensive level of acoustic monitoring, encompassing biophony (sounds of vocalising animals), geophony (natural sounds like wind or rain), and anthropophony (human-generated sounds) across landscapes (Pijanowski et al., 2011). On the one hand, soundscapes can affect species and community-level indicators. For example, anthropogenic noise can alter vocal patterns in birds (Shannon et al., 2016), imposing additional energy costs and compromising vocal traits important for mate selection, ultimately reducing fitness (Crates et al., 2021). It can also impact the habitat selection of sound-dependent species, as described by the Acoustic Habitat Hypothesis (Mullet et al., 2017). PAM could inform about noise pollution from agricultural activities detrimental to wildlife, as farming operations, particularly those involving machinery, generate different noise levels (Karamousantas et al., 2009). On the other hand, the soundscape itself may be a conservation target (Dumyahn and Pijanowski, 2011), as its measurable composition has been linked to human well-being and mental health (Smith and Pijanowski, 2014). In agricultural landscapes, land use, landscape structure, and agricultural management practices can influence acoustic communities as well as soundscapes (Doohan et al., 2019; Müller et al., 2022; Myers et al., 2019). These effects can extend up to several kilometres around recorders (Barbaro et al., 2022), but vary in their strength depending on the spatial scale considered (Quinn et al., 2024). This suggests that farmers, to some extent, could play an active role in shaping the soundscapes on their land, for instance through diversification practices. However, the relationship between agricultural management and soundscapes is complex and remains insufficiently understood, making it challenging to propose appropriate indicators for RBP. Computing integrative acoustic diversity indices will likely not be informative enough, since their relationship with soundscape composition and biodiversity indicators is highly variable across different environmental contexts (Alcocer et al., 2022). Further, farmers can only be held partially accountable for achieving a certain level of acoustic diversity. The concept of rewarding farmers based on soundscapes is novel to our knowledge and raises the question whether society has a willingness-to-pay for soundscapes in (partly remote) agricultural landscapes and, if so, which types of soundscapes society may prioritise as RBP targets. Convincing farmers of the participation in soundscape-targeting RBP s will likely prove challenging as well. However, it is noteworthy that other AES focusing on landscape aesthetics, together with biodiversity benefits, have been widely accepted by farmers (Fleury et al., 2015). Introducing such new conservation targets would necessitate innovative measures, a topic beyond the scope of this study and requiring further investigation. All of these aspects require further research before the idea of using soundscapes as targets in RBP can be made more concrete.
<table><caption>**Table 1**
Potential result indicators for species-level and community-level biodiversity targets that may be used in the context of RBP, including exemplary references. Indicators marked with (X) indicate taxa for which PAM could theoretically be used to generate relevant data; however, to our knowledge, no published evidence currently exists.</caption><thead><tr><th>PAM-based result indicator</th><th>Unit used in RBP scoring system</th><th>Birds</th><th>Bats</th><th>Orthoptera and other insects</th><th>Anurans</th></tr></thead><tbody><tr><td colspan="6"><strong>Soniferous species</strong></td></tr><tr><td>Species presence/absence</td><td>Yes/no</td><td>Kahl et al. (2021)<br>Abrahams and Geary (2020)</td><td>Brinkløv et al. (2023)<br>Mac Aodha et al. (2022)<br>Newson et al. (2015)</td><td>van Klink et al. (2022)<br>Caouette et al. (2024)</td><td>Bota et al. (2024)</td></tr><tr><td>Species abundance or density</td><td>Number of vocalising individuals per PAM recorder or unit area</td><td>Pérez-Granados and Traba (2021)</td><td>Revilla-Martín et al. (2021)<br>Udell et al. (2024)</td><td>Fischer et al. (1997)</td><td>Crump et al. (2017)</td></tr><tr><td>Vocal activity (of a certain species)</td><td>Duration or number of acoustic detections per unit area and/or unit of time</td><td>Pérez-Granados et al. (2019)<br>Darras et al. (2018)</td><td>(X)</td><td>Thibault et al. (2024)</td><td>Xie et al. (2016)</td></tr><tr><td>Reproductive activity</td><td>Number of vocalisations indicating mating or breeding behaviour</td><td>Docker et al. (2020)<br>Jarrett and Willis (2024)</td><td>Schmidbauer and Denzinger (2019)<br>Charbonnier et al. (2014)<br>Vogelbacher et al. (2023)</td><td>Caouette et al. (2024)</td><td>Lapp et al. (2024)</td></tr><tr><td>Foraging or feeding activity</td><td>Feeding buzzes per unit of time</td><td>Furnas (2020)</td><td>Kotila et al. (2023)</td><td>Jeliazkov et al. (2016)</td><td>de Solla et al. (2006)</td></tr><tr><td colspan="6"><strong>Acoustic community</strong></td></tr><tr><td>Species richness</td><td>Number of soniferous species</td><td>Kahl et al. (2021)<br>Darras et al. (2019)</td><td>Biffi et al. (2024)</td><td>Newson et al. (2017)<br>Symes et al. (2024)</td><td>Melo et al. (2021)<br>Anunciação et al. (2022)</td></tr><tr><td>Species diversity</td><td>Combined measure of the number of soniferous species and their relative abundance<br>(e.g. Shannon diversity, Hill numbers)</td><td>Schillé et al. (2024)</td><td>Frey-Ehrenbold et al. (2013)<br>de Aguiar Silva et al. (2023)</td><td>Penone et al. (2013)</td><td>(X)</td></tr><tr><td>Vocal activity (of a community or guild)</td><td>Duration or number of acoustic detections per unit area and/or unit of time</td><td>Biffi et al. (2024)</td><td>Krings et al. (2022)<br>Biffi et al. (2024)<br>Charbonnier et al. (2014)<br>Weier et al. (2018)<br>Krings et al. (2022)<br>Jameson (2024)</td><td>Thibault et al. (2024)</td><td>Xie et al. (2016)<br>Lapp et al. (2024)</td></tr><tr><td>Foraging or feeding activity</td><td>Feeding buzzes per unit of time</td><td></td><td></td><td></td><td></td></tr><tr><td>Acoustic indices as indicators of biodiversity</td><td>Mathematical formulas that summarise acoustic features</td><td>Allen-Ankins et al. (2023)<br>Quinn et al. (2023)</td><td>de Aguiar Silva et al. (2023)</td><td>Martini et al. (2024)<br>Ross et al. (2021)</td><td>Brodie et al. (2020)<br>Botero-Canola et al. (2024)</td></tr></tbody></table>
effectively measured using PAM in numerous studies on birds (see references in Table 1) and can serve as a valuable indicator for RBP.

As bird songs indicate territorial defence of potential or used breeding sites, indicators of reproductive activity (incl. modeled nest counts; Larson and Gawlik, 2023) for birds have been successfully derived from PAM. For example, Jarrett and Willis (2024) mapped relative breeding densities of four wader species using PAM, though with different accuracies among species. Some bird species exhibit different song types throughout the breeding season, for example the European Nightjar (Caprimulgus europaeus L.) during the first clutch initiation period. As such, using sounds allows assessing the number of breeding pairs and not just singing males (Docker et al., 2020). The sounds produced by birds, as well as anurans and orthopterans, play an important role in mate choice and attraction as well as resource defence (Hall and Robinson, 2021; Wilkins et al., 2013), indicating high-quality habitats (Buxton et al., 2020). As a result, PAM can provide insights into the reproductive potential of a certain location (Calsbeek et al., 2022; Hall and Robinson, 2021), which may be rewarded through RBP. Similarly, foraging and feeding activity of bat species can be quantified using PAM by estimating the duration of bat passes and the number of feeding buzzes (Charbonnier et al., 2014; Vogelbacher et al., 2023).

Positive population trends or the maintenance of stable populations are key indicators for the ecological success in RBP programs (Hagemann et al., 2025). These trends can, in principle, be systematically monitored over time using replicable sampling methods and protocols based on PAM (Brinkløv et al., 2023; Kotila et al., 2023). However, significant challenges remain in developing robust datasets, workflows, and appropriate thresholds for RBP. These challenges stem from the time lags inherent in population trend changes, which often take years to become evident, as well as natural population variability driven by factors beyond farmers' control, such as weather, diseases, or resource availability.

At the community level, PAM facilitates the assessment of acoustic communities on agricultural land and their responses to management. The opportunities and challenges associated with species-level result indicators largely apply to community-level indicators as well. PAM has proven effective for monitoring species richness of birds (Darras et al., 2019) and bats (Fialas et al., 2023; Krings et al., 2022), with a growing body of research focusing on richness of orthopterans (Newson et al., 2017; Symes et al., 2024) and anurans (Anunciação et al., 2022; Dröge et al., 2024; Melo et al., 2021). When species richness data are combined with measures of their prevalence, such as abundance or activity, a variety of other species diversity indices can be estimated and used as indicators in RBP. In this context, if detailed knowledge of species-specific vocalising behavior is available (Jarrett and Willis, 2024), vocal activity can serve as proxy for relative abundance. PAM also offers opportunities for result indicators focused on trophic interactions or ecological functions: Certain orthopteran communities are associated with specific plant communities due to their diet and comparatively low mobility, making them potential indicators for which farmers can be rewarded. Similar to the species level, foraging and feeding activity indicators can be estimated for bat communities (Charbonnier et al., 2014; Jameson, 2024; Krings et al., 2022; Weier et al., 2018). The frequency of such bat calls reflects the availability of insects as food resources, which can be improved by various biodiversity-enhancing agricultural practices (Olimpi and Philpott, 2018). Finally, acoustic indices – mathematical formulas that summarise certain acoustic features – have been used as biodiversity indicators across different taxonomic groups (see Table 1 for references). However, the same challenges that arise in soundscape analysis (see Box 3), such as their significant sensitivity to confounding environmental and sonic conditions (Alcocer et al., 2022; Ross et al., 2021), also apply when using these indices to study ecological communities. This highlights the need for further investigation into their robustness and reliability.

In conclusion, PAM-derived result indicators that can currently be most effectively implemented in RBP, with varying payment levels

based on specified thresholds, include: (1) the presence of single or multiple target species of high conservation value, potentially across all soniferous taxa; (2) species richness of birds and/or bats; (3) vocal activity of birds and bats, either for individual species or ecological communities, as indicators of reproduction or foraging.

## 4. Costs and payment levels

Within an RBP framework, farmers incur various costs when implementing biodiversity-friendly management measures. These include opportunity costs related to land use change (e.g., forgone income as land is used for a hedge rather than agricultural production), investment costs (e.g., planting a hedge), and management costs for ongoing maintenance tasks (e.g., pruning the hedge). Additional costs arise from time spent on administrative tasks, such as completing participation forms, communicating with relevant authorities, gathering information on suitable management measures (Mettepenningen et al., 2009), and monitoring target species if this responsibility falls to farmers under the scheme (Hagemann et al., 2025). Farmers are likely to participate in an RBP only if the expected payment for achieving the biodiversity target surpasses their costs plus a certain "risk premium" (Drechsler, 2017). The magnitude of this risk premium depends on the farmer's level of risk aversion and the extent of risk involved, with greater risk and higher risk aversion necessitating a larger risk premium (Menapace et al., 2013; Tanaka et al., 2022).

Choosing PAM over alternative monitoring methods in RBP typically does not affect a farmer's costs for management measures. However, PAM is likely to alter the monitoring costs for the farmer or the monitoring agency (depending on who is responsible for monitoring), as well as the associated risk premium. Farmers can, in principle, handle, set up, and retrieve PAM recorders themselves (Dixon et al., 2023). This approach can reduce monitoring costs if farmers can integrate these tasks into regular visits to their land for agricultural production purposes. The annual cost of storing audio recordings on institutional servers may become prohibitive as data accumulates. However, once audio analysis workflows reach a level of satisfactory performance, storing already analysed data may no longer be necessary, thereby reducing these costs. Markova-Nenova et al. (2023a) compared the monitoring costs of PAM and ornithologist monitoring, including the costs of analysing and storing the monitoring data, for an exemplary public administration. They found that, depending on parameters such as the number and the timing (day/night) of observations required, PAM is generally more expensive than human observation for daytime monitoring. However, PAM offers a cost advantage for nighttime monitoring and in situations where multiple visits by human observers would be required (see Markova-Nenova et al., 2023a for details). PAM also seems to provide a comparative cost advantage if human monitoring is time consuming or difficult such as for rare species, cryptic or strictly nocturnal taxa, and mobile species with unpredictable activity periods (see also Williams et al. (2018) and Darras et al. (2019)). Generally, it is likely that the more hard-to-detect species within a community, the greater the benefit of using PAM in comparison with human monitoring, although PAM surveys in these cases can also be logistically demanding (cf. Wood et al., 2021).

If a target species inhabits the farmer's land but remains undetected during monitoring, the farmer will not receive a payment. Consequently, the risk premium needs to be higher when monitoring is prone to such "false negatives" (i.e., the species is present but not detected). Since PAM can be utilised over extended time periods without a substantial increase in monitoring costs (compared to human observers), it generally reduces the risk of non-detection and thus the risk premium, enhancing the cost-effectiveness of the scheme. However, species detection algorithms using machine learning still face challenges in simultaneously minimising both the rate of false negatives (missed detections) and false positives (incorrect detections of species presence) at the same time (Funosas et al., 2024). Detecting false positives is also a
risk when guidelines for the use and interpretation of results generated by machine learning algorithms are not strictly followed (Wood and Kahl, 2024). When accounting for these limitations, PAM may be equally or even more effective than point counts in detecting birds, especially for species requiring specific survey methods over large areas and during specific time periods (Darras et al., 2019; Singer et al., 2024; Toenies and Rich, 2021). To our knowledge, no systematic and comprehensive cost comparison of PAM with human monitoring exists for potential novel biodiversity targets at community level within an RBP. Darras et al. (2019) found a cost advantage of human observers for monitoring communities for short surveys, but the outcome depended on recorder price, so that recent hardware cost reductions may change this finding (Markova-Nenova et al., 2023a). However, developing expertise among the permanent or freelance staff of the authorities responsible for the data analysis workflow is crucial, and this effort will inevitably incur initial additional costs.

## 5. Acceptance by farmers

Given the voluntary nature of AES, the success of RBP with PAM monitoring hinges on farmers' acceptance (Bertke et al., 2008), as with any AES. Given the generally high acceptance of RBP among farmers (Massfeller et al., 2022), we focus here on the acceptance of ecological communities as novel biodiversity target and of PAM technology. As there is little prior research on these two topics, we draw on the general work on both farmers' perceptions of AES and of their acceptance of new technologies, while acknowledging the speculative nature of some of our thoughts.

Research indicates that farmers' willingness to participate in AES is influenced by the information they receive about the scheme's content (Boz, 2018; Zanella et al., 2014) and whether they are convinced of the usefulness of the corresponding management measures (Krämer and Wätzold, 2018). This implies that, with respect to communities as biodiversity targets, it is vital to inform farmers about the acoustic communities' value as biodiversity target and the importance of conserving them. Given the complexity of the relationship between management measures and the occurrence of specific communities, farmers need to be provided with information on potentially suitable management measures that promote such communities (Maher et al., 2018), even more than in the case of individual species as biodiversity targets. This will prepare farmers to develop their own strategies and

measures tailored to their land.

The willingness to participate in an RBP scheme with communities as biodiversity targets may also depend on individual farmers' attitudes towards biodiversity. Klebl et al. (2024) found that some farmers value biodiversity specifically for the ecosystem services it provides to agricultural production, while others see inherent value in nature, independent of its benefits to humans. Future research could explore whether the holistic approach of communities as biodiversity targets is more appealing to farmers who are interested in the inherent value of nature, as opposed to those who view biodiversity primarily as a production input. However, regardless of their attitudes towards biodiversity, farmers' perception of their ability to influence the occurrence of ecological communities on their land will ultimately affect their willingness to participate in such RBP schemes.

Farmers' adoption of new technologies is generally influenced by various factors, including farm-related aspects such as size and labour intensity (Mohr and Kühl, 2021), societal influences (Shang et al., 2021), the characteristics of the technology itself (Knierim et al., 2018), and farmer-related aspects. In the context of precision farming, farmer-related aspects include age, inclination towards innovation, and environmental awareness coupled with a willingness to implement environmental measures (Blasch et al., 2022). Farmers' experiences with technologies generally, and how the technology's purpose aligns with their personal goals, also play a role (Kernecker et al., 2020).

To our knowledge, only two surveys exist which explicitly address farmers' perceptions of novel monitoring technologies for RBP. Villanueva et al. (2024) surveyed olive farmers and their willingness to accept satellite-based monitoring as an alternative to field monitoring by personnel from the responsible administration for an RBP. They found that, on average, farmers demanded higher payments for satellite-based monitoring compared to field monitoring. This may indicate a general scepticism of farmers towards novel monitoring technologies. However, it is important to note that the farmers in this study were informed that satellite-based monitoring, in this case, is somewhat less accurate than field monitoring. The second survey (Gerling et al., 2023) was carried out among 70 farmers in the Mulde region (Saxony, Germany). Farmers were interviewed on agri-environmental issues and, in this context, asked about their willingness to participate in RBP with PAM. The survey revealed that most respondents (58 %) expressed a clear interest in participating, while 31 % indicated a possibility of participation, and only 11 % would not consider it at all. The main reasons were questions

### Box 4

Design of a pilot scheme for the whinchat.

A pilot study of a PAM-based RBP could target the whinchat (Saxicola rubetra L.), a species experiencing severe population declines in Europe (PECBMS, 2024), classified as vulnerable or endangered in several French regions, and critically endangered in Germany (Ryslavy et al., 2020). The whinchat is a trans-Saharan migrant, an obligate insectivore and ground-breeding bird that relies on grassland – especially wet oligotrophic mown meadows – mountain pastures, and fallow farmland. It prefers structurally rich grassland and uses vertical structures like shrubs, thistles, umbellifers, or fences for singing and perching (Bastian and Feulner, 2015). Key conservation measures for the whinchat include late/delayed mowing (Drechsler et al., 2007) and leaving unmown grass strips to provide tall vegetation structures and high insect prey availability (Grüebler et al., 2015; Oppermann et al., 2020). Gerling et al. (2023) propose an RBP approach for the whinchat conservation in Germany designed with a base payment for reducing management intensity on grasslands (e.g., fewer cuts and no nitrogen fertiliser application) and an additional success-based payment if whinchat presence is confirmed on the farmer's land (see also Biffi et al., 2024 for a similar approach). The base payment would mitigate the financial risk for farmers, which could increase their motivation to engage in the RBP. An indicator of whinchat presence and breeding success on a farmer's land measurable with PAM could be male singing activity during the breeding season, typically from late April to mid-to-late June (Südbeck et al., 2005), and potentially also the call of fledglings at or around the nest in late June to early July. PAM recorders could be deployed twice during the breeding period for a duration of seven days each to record the species' song. The detection of whinchat songs on at least three consecutive days within a designated area or on the same recording device(s) could serve as sufficient evidence of the species' presence. While cost calculations by Markova-Nenova et al. (2023a) suggest that monitoring costs for this schedule using AudioMoth recorders (currently one of the cheapest available products) are still substantial, these costs may decrease in the future. Authorities could provide farmers with recorders free-of-charge for monitoring, with farmers responsible for deploying and retrieving the devices. Conservation biologists could assist in these efforts, informing farmers about the ecology of the whinchat and ensuring selection of and placement in suitable whinchat habitats, reducing the risk to the farmers that whinchats are not detected.
around whether PAM could be integrated into existing farm workflows, the amount of effort required, and concerns about potential additional bureaucratic burden. These findings underscore the importance of involving farmers in defining the requirements for PAM implementation in RBP early on, including aspects such as timing and frequency of monitoring, placement of devices, etc.

In existing RBP, biodiversity targets are typically monitored either by farmers themselves or by the regulatory agency (Hagemann et al., 2025). If future RBP were to incorporate PAM, farmers' responsibilities would likely have to be limited to installing and collecting the recorders. Calibration, data retrieval, and maintenance of equipment would have to be handled by personnel of the regulatory agency. Analyses of recordings would likely have to rely on automated machine learning algorithms and be externally validated by experts. This approach implies, on one hand, a minimal workload for farmers. On the other hand, farmers would have to place significant trust in the accuracy and comprehensiveness of PAM to correctly record and identify the biodiversity target on their land. Additionally, it would demand a willingness from farmers to engage with technology and automated workflows. Audio recordings and expert annotations could be stored and made available as evidence of the (non-)achievement of biodiversity targets, ensuring transparency and accountability. This sharing of data would require automated screening of recordings to remove human voices (Cretois et al., 2022). In addition, trustworthy mechanisms would be needed to allow farmers to file an objection in case they believe a biodiversity target has been achieved but is not reflected by the PAM results. In contrast to human observation, PAM further carries risks such as device failure or theft of monitoring devices, which could be mitigated through real-time monitoring of sound recorder status (Sethi et al., 2021). Perceived risk has been identified as an important factor influencing RBP adoption in general (Massfeller et al., 2022), as well as the size of the risk premium (Drechsler, 2017). Therefore, these risks should be considered in the design of PAM-based RBP.

The installation of PAM on farmland might offer potential co-benefits for farmers in the future. These co-benefits may be related to the detection of endangered species which require a tailored management regime and for which farmers are rewarded (see Wätzold et al., 2024 for the idea of virtual fencing around meadow bird nests to protect them from trampling by grazing animals). Moreover, recent research (Ali et al., 2024) suggests that PAM has potential for the early detection of farmland pest species.

## 6. Towards pilot schemes

As the next step, we propose developing and testing PAM-based RBP through pilot studies (see Box 4). These studies should investigate the opportunities and challenges outlined conceptually in this paper. The pilot studies should be comprehensive, addressing not only biodiversity conservation but also cost and acceptance among farmers, conservation non-governmental organisations and authorities, to ensure the long-term viability and success of the approach. To enhance farmers' acceptance of innovative approaches, their proactive involvement in the design phase is crucial (Hölting et al., 2022) and the collaborative development of pilot studies with farmers on demonstration farms (Maher et al., 2018) could be a practical starting point. Farmers who successfully participated in these pilot schemes could then serve as role models for other farmers, fostering knowledge-sharing and adoption through farmer-to-farmer learning dynamics (Lewis et al., 2011).

PAM offers many advantages but also poses methodological challenges: unlike plant surveys, PAM surveys cover variable sound detection spaces, and the sound sources are challenging to localise within. However, this information is critical for accurately attributing detected animals to a specific site or field in the context of RBP, especially when field parcels are small. Acoustic localisation techniques, which can pinpoint the precise location of animals as sound sources in space, are

under development (Buchmann and Schurr, 2024; Rhinehart et al., 2020), with recent technological advances further facilitating this process (Heath et al., 2024). These methods, however, can be costly and are currently too complex for application in RBP. Therefore, at this stage, PAM-based RBP should be implemented on fields that are larger than the sound detection space of the chosen biodiversity target and recording device. Ultimately, the costs and challenges associated with a PAM-based RBP will vary greatly depending on the targeted result indicator.

## 7. Conclusion

Technological advances in biodiversity monitoring and their associated workflows, encompassing the collection, storage, and processing of data to extract ecological knowledge, offer significant potential to improve the design of RBP (Elmiger et al., 2023). PAM can broaden the scope of RBP to include a wider range of taxa, especially farmland birds (including nocturnal species), bats and potentially, at a later stage, orthonterans and anurans as new biodiversity targets. Its suitability for monitoring complex biodiversity targets, including rare and elusive species, as well as entire acoustic communities highlights its versatility in conservation efforts. PAM has now reached a level of technical maturity where it is increasingly becoming cost-competitive with traditional monitoring by human observers, though it still requires human calibration. Addressing remaining challenges, such as minimising simultaneously false positive and false negative species detections, is essential for ensuring the acceptance of PAM-based RBP. To transition the use of PAM as a monitoring approach in the context of RBP from the conceptual level to practical implementation, pilot schemes need to be developed and tested in close cooperation with farmers and authorities.

### CRediT authorship contribution statement

**Anna F. Cord:** Conceptualisation, Investigation, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition.
**Kevin Darras:** Conceptualisation, Writing – original draft, Writing – review & editing, Visualisation.
**Ryo Ogawa:** Investigation, Writing – original draft, Writing – review & editing.
**Luc Barbaro:** Writing – review & editing.
**Charlotte Gerling:** Investigation, Writing – original draft, Writing – review & editing.
**Maria Kernecker:** Writing – review & editing.
**Nonka Markova-Nenova:** Investigation, Writing – original draft, Writing – review & editing.
**Gabriela Rodriguez-Barrera:** Investigation, Writing – original draft, Writing – review & editing.
**Felix Zichner:** Investigation, Writing – review & editing.
**Frank Wätzold:** Conceptualisation, Investigation, Writing – original draft, Writing – review & editing, Supervision.

### Declaration of competing interest

None.

### Acknowledgements

This research was made possible by funding from the German Federal Ministry of Education and Research (BMBF) within the Research Initiative for the Conservation of Biodiversity (FEdA) for the ECO²SCAPE project (Funding codes: 03LW0079K, 03LW0080, 03LW0082). AFC and RO were also supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070–390732324. LB was supported by the project INRAE Biofair - PARMENIDE. The responsibility for the content of this publication lies with the authors. We would like to thank Katharina Schneider (University of Bonn) for her help with the literature search and management, and Ruben Remelgado (University of Bonn) for his design of the figure.
# Data availability

No data was used for the research described in the article.

## References

Abrahams, C., Geary, M., 2020. Combining bioacoustics and occupancy modelling for improved monitoring of rare breeding bird populations. Ecol. Indic. 112, 106131. https://doi.org/10.1016/j.ecolind.2020.106131.

Alcocer, I., Lima, H., Sugai, L.S.M., Llusia, D., 2022. Acoustic indices as proxies for biodiversity: a meta-analysis. Biol. Rev. 97, 2209-2236. https://doi.org/10.1111/brv.12890.

Ali, Md.A., Dhanaraj, R.K., Kadry, S., 2024. AI-enabled IoT-based pest prevention and controlling system using sound analytics in large agricultural field. Comput. Electron. Agric. 220, 108844. https://doi.org/10.1016/j.compag.2024.108844.

Allen-Ankins, S., McKnight, D.T., Nordberg, E.J., Hoefer, S., Roe, P., Watson, D.M., McDonald, P.G., Fuller, R.A., Schwarzkopf, L., 2023. Effectiveness of acoustic indices as indicators of vertebrate biodiversity. Ecol. Indic. 147, 109937. https://doi.org/10.1016/j.ecolind.2023.109937.

Ansell, D., Freudenberg, D., Munro, N., Gibbons, P., 2016. The cost-effectiveness of agri-environment schemes for biodiversity conservation: A quantitative review. Agric. Ecosyst. Environ. 225, 184-191. https://doi.org/10.1016/j.agee.2016.04.008.

Anunciação, P., Sugai, L.S.M., Martello, F., de Carvalho, L.M.T., Ribeiro, M.C., 2022. Estimating the diversity of tropical anurans in fragmented landscapes with acoustic monitoring: lessons from a sampling sufficiency perspective. Biodivers. Conserv. 31, 3055-3074. https://doi.org/10.1007/s10531-022-02475-w.

Armsworth, P.R., Acs, S., Dallimer, M., Gaston, K.J., Hanley, N., Wilson, P., 2012. The cost of policy simplification in conservation incentive programs. Ecol. Lett. 15, 406-414. https://doi.org/10.1111/j.1461-0248.2012.01747.x.

Barbara, L., Sourdril, A., Froidevaux, J.S.P., Cauchoy, M., Calatayud, F., Deconchat, M., Gasc, A., 2022. Linking acoustic diversity to compositional and configurational heterogeneity in mosaic landscapes. Landsc. Ecol. 37, 1125-1143. https://doi.org/10.1007/s10980-021-03191-8.

Bartkowski, B., Beckmann, M., Bednár, M., Biffi, S., Domingo-Marimon, C., Mesaroš, M., Schüßler, C., Sárapatka, B., Tarčak, S., Václavík, T., Ziv, G., Wittstock, F., 2023. Adoption and potential of agri-environmental schemes in Europe: cross-regional evidence from interviews with farmers. People and Nature 5, 1610-1621. https://doi.org/10.1002/pan3.10526.

Bastian, H.-V., Feulner, J. (Eds.), 2015. Living on the edge of extinction in Europe: Proceedings of the 1st European Winchat Symposium - Tagungsband zum 1. Europäischen Braunkehlchen Symposium. Landesbund für Vogelschutz, Kreisgruppe Hof, Helmbrechts. https://media-natur.com/Bastian-Feulner-Hrsg-Living-on-the-Edge-of-Extinction-in-Europe.

Batáry, P., Dicks, L.V., Kleijn, D., Sutherland, W.J., 2015. The role of agri-environment schemes in conservation and environmental management. Conserv. Biol. 29, 1006-1016. https://doi.org/10.1111/cobi.12536.

Bertke, E., Klimek, S., Wittig, B., 2008. Developing result-orientated payment schemes for environmental services in grasslands: results from two case studies in North-Western Germany. Biodiversity 9, 91-95. https://doi.org/10.1080/1488386.2008.9712893.

Besson, M., Alison, J., Bjerge, K., Gorochowski, T.E., Høye, T.T., Jucker, T., Mann, H.M. R., Clements, C.F., 2022. Towards the fully automated monitoring of ecological communities. Ecol. Lett. 25, 2753-2775. https://doi.org/10.1111/ele.14123.

Biffi, S., Chapman, P.J., Engler, J.O., Kunin, W.E., Ziv, G., 2024. Using automated passive acoustic monitoring to measure changes in bird and bat vocal activity around hedgerows of different ages. Biol. Conserv. 296, 110722. https://doi.org/10.1016/j.biocon.2024.110722.

Birge, T., Toivonen, M., Kaljonen, M., Herzon, I., 2017. Probing the grounds: developing a payment-by-results agri-environment scheme in Finland. Land Use Policy 61, 302-315. https://doi.org/10.1016/j.landusepol.2016.11.028.

Blary, C., Kerbiriou, C., Le Viol, I., Barré, K., 2021. Assessing the importance of field margins for bat species and communities in intensive agricultural landscapes. Agric. Ecosyst. Environ. 319, 107494. https://doi.org/10.1016/j.agee.2021.107494.

Blasch, J., Van Der Kroon, B., Van Beukering, P., Munster, R., Fabiani, S., Nino, P., Vanino, S., 2022. Farmer preferences for adopting precision farming technologies: a case study from Italy. Eur. Rev. Agric. Econ. 49, 33-81. https://doi.org/10.1093/erae/jbaa031.

Bota, G., Manzano-Rubio, R., Fanlo, H., Franch, N., Brotons, L., Villero, D., Devisscher, S., Pavesi, A., Cavaletti, E., Pérez-Granados, C., 2024. Passive acoustic monitoring and automated detection of the American bullfrog. Biol. Invasions 26, 1269-1279. https://doi.org/10.1007/s10530-023-03244-8.

Botero-Canola, S., Wilson, K., Garcia, E., Heisey, M., Reeves, L.E., Burkett-Cadena, N.D., Romagosa, C., Sieving, K.E., Wisely, S.M., 2024. Acoustic indices track local vertebrate biodiversity in a subtropical landscape. Ecol. Indic. 166, 112292. https://doi.org/10.1016/j.ecolind.2024.112292.

Boz, I., 2018. Determinants of farmers' enrollment in voluntary environmental programs: evidence from the Eregli reed bed area of Turkey. Environ. Dev. Sustain. 20, 2643-2661. https://doi.org/10.1007/s10668-017-0009-7.

Brinklov, S.M.M., Macaulay, J., Bergler, C., Tougaard, J., Beedholm, K., Elmeros, M., Madsen, P.T., 2023. Open-source workflow approaches to passive acoustic monitoring of bats. Methods Ecol. Evol. 14, 1747-1763. https://doi.org/10.1111/2041-210X.14131.

Brodie, S., Allen-Ankins, S., Towsey, M., Roe, P., Schwarzkopf, L., 2020. Automated species identification of frog choruses in environmental recordings using acoustic indices. Ecol. Indic. 119, 106852. https://doi.org/10.1016/j.ecolind.2020.106852.

Buchmann, C.M., Schurr, F.M., 2024. A ready-to use workflow for automated, sound-based bird identification and localization. bioRxiv. https://doi.org/10.1101/2024.07.02.601711v1.

Bullock, J.M., McCracken, M.E., Bowes, M.J., Chapman, R.E., Graves, A.R., Hinsley, S.A., Hutchins, M.G., Nowakowski, M., Nicholls, D.J.E., Oakley, S., Old, G.H., Ostle, N.J., Redhead, J.W., Woodcock, B.A., Bedwell, T., Mayes, S., Robinson, V.S., Pywell, R.F., 2021. Does agri-environmental management enhance biodiversity and multiple ecosystem services?: A farm-scale experiment. Agric. Ecosyst. Environ. 320, 107582. https://doi.org/10.1016/j.agee.2021.107582.

Burton, R.J.F., Schwarz, G., 2013. Result-oriented agri-environmental schemes in Europe and their potential for promoting behavioural change. Land Use Policy 30, 628-641. https://doi.org/10.1016/j.landusepol.2012.05.002.

Buxton, V.L., Enos, J.K., Sperry, J.H., Ward, M.P., 2020. A review of conspecific attraction for habitat selection across taxa. Ecol. Evol. 10, 12690-12699. https://doi.org/10.1002/ece3.6922.

Buzhydyan, O.Y., Petermann, J.S., 2023. Multitrophic biodiversity enhances ecosystem functions, services and ecological intensification in agriculture. J. Plant Ecol. 16, rtd019. https://doi.org/10.1093/jpe/rtd019.

Calsbeek, P., Zamora-Camacho, F.J., Symes, L.B., 2022. Individual contributions to group chorus dynamics influence access to mating opportunities in wood frogs. Ecol. Lett. 25, 1401-1409. https://doi.org/10.1111/ele.14002.

Caouette, A.P., Bayne, E.M., Judge, K.A., 2024. Large-scale bioacoustic monitoring to elucidate the distribution of a non-native katydid. Ecol. Entomol. 49, 119-127. https://doi.org/10.1111/een.13285.

Charbonnier, Y., Barbaro, L., Theillout, A., Jactel, H., 2014. Numerical and functional responses of Forest bats to a major insect Pest in pine plantations. PLoS One 9, e109488. https://doi.org/10.1371/journal.pone.0109488.

Crates, R., Langmore, N., Ranjard, L., Stojanovic, D., Rayner, L., Ingwersen, D., Heinson, R., 2021. Loss of vocal culture and fitness costs in a critically endangered songbird. Proc. R. Soc. B. 288, 20210225. https://doi.org/10.1098/rspb.2021.0225.

Cretois, B., Rosten, C.M., Sethi, S.S., 2022. Voice activity detection in eco-acoustic data enables privacy protection and is a proxy for human disturbance. Methods Ecol. Evol. 13, 2865-2874. https://doi.org/10.1111/2041-210X.14005.

Crump, P., Berven, K., Youker-Smith, T.E., Skelly, D., Thomas, S., Houlahan, J., 2017. Predicting anuran abundance using an automated acoustics approach, 51 (4), 582-589. https://doi.org/10.1670/16-174.

Darras, K., Pütz, P., Fahrurozi K., Rembold, Tscharntke, T., 2016. Measuring sound detection spaces for acoustic animal sampling and monitoring. Biol. Conserv. 201, 29-37. https://doi.org/10.1016/j.biocon.2016.06.021.

Darras, K., Furnas, B., Fitriawan, I., Mulyani, Y., Tscharntke, T., 2018. Estimating bird detection distances in sound recordings for standardizing detection ranges and distance sampling. Methods Ecol. Evol. 9, 1928-1938. https://doi.org/10.1111/2041-210X.13031.

Darras, K., Batáry, P., Furnas, B.J., Grass, I., Mulyani, Y.A., Tscharntke, T., 2019. Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide. Ecol. Appl. 29, e01954. https://doi.org/10.1002/eap.1954.

Darras, K.F.A., Pérez, N., Dilong, L., Hanf-Dressler, T., Markolf, M., Wanger, T.C., Cord, A.F., 2024a. ecoSound-web: an open-source, online platform for ecoacoustics. F1000Res 9, 1224. https://f1000research.com/articles/9-1224/v3.

Darras, K.F.A., Rountree, R., Wilgenburg, S.V., Cord, A.F., Chen, Y., Dong, L., Rocquencourt, A., Desjonquères, C., Diaz, P.M., Lin, T.-H., et al., 2024b. Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms. bioRxiv. https://doi.org/10.1101/2024.04.10.588860.

de Aguiar Silva, C., Machado, R.B., Silveira, M., Aguiar, L.M.S., 2023. Listening in the dark: acoustics indices reveal bat species diversity in a tropical savannah. Bioacoustics 32, 17-32. https://doi.org/10.1080/09524622.2022.2053741.

de Solla, S.R., Fernie, K.J., Barrett, G.C., Bishop, C.A., 2006. Population trends and calling phenology of anuran populations surveyed in Ontario estimated using acoustic surveys. In: Hawksworth, D.L., Bull, A.T. (Eds.), Marine, Freshwater, and Wetlands Biodiversity Conservation. Springer, Netherlands, Dordrecht, pp. 113-129. https://doi.org/10.1007/978-1-4020-5734-2-9.

Dixon, A.P., Baker, M.E., Ellis, E.C., 2023. Passive monitoring of avian habitat on working lands. Ecol. Appl. 33, e2860. https://doi.org/10.1002/eap.2860.

Docker, S., Lowe, A., Abrahams, C., 2020. Identification of different song types in the European nightjar *Caprimulgus europaeus*. Bird Study 67, 119-127. https://doi.org/10.1080/00063657.2020.1780414.

Doohan, B., Fuller, S., Parsons, S., Peterson, E.E., 2019. The sound of management: acoustic monitoring for agricultural industries. Ecol. Indic. 96, 739-746. https://doi.org/10.1016/j.ecolind.2018.09.029.

Drechsler, M., Johst, K., Ohl, C., Wätzold, F., 2007. Designing cost-effective payments for conservation measures to generate spatiotemporal habitat heterogeneity. Conserv. Biol. 21, 1475-1486. https://doi.org/10.1111/j.1523-1739.2007.00829.x.

Drechsler, M., 2017. Performance of input- and output-based payments for the conservation of Mobile species. Ecol. Econ. 134, 49-56. https://doi.org/10.1016/j.ecolecon.2016.12.022.

Dröge, S., Fulgence, T.R., Osen, K., Rakotomalala, A.A.N.A., Raveloaritiana, E., Schwab, D., Soazafy, M.R., Wurz, A., Kreft, H., Martin, D.A., 2024. Understanding acoustic indices as multi-taxa biodiversity and habitat quality indicators. Ecol. Indic. 169, 112909. https://doi.org/10.1016/j.ecolind.2024.112909.

Dumyahn, S.L., Pijanowski, B.C., 2011. Soundscape conservation. Landsc. Ecol. 26, 1327-1344. https://doi.org/10.1007/s10980-011-9635-x.
Elmiger, B.N., Finger, R., Ghazoul, J., Schaub, S., 2023. Biodiversity indicators for result-
based agri-environmental schemes – current state and future prospects. Agric. Syst.
244, 103538. https://doi.org/10.1016/j.agsy.2022.103538.

Farina, A., James, P., 2016. The acoustic communities: definition, description and
ecological role. Biosystems 147, 11–20. https://doi.org/10.1016/j.
biosystems.2016.05.011.

Farina, A., 2018. Perspectives in ecoacoustics: A contribution to defining a discipline.
Journal of Ecoacoustics 2, 10. https://doi.org/10.22261/jea.trzd5i.

Fialas, P.C., Froidevaux, J.S.P., Jones, G., Batáry, P., 2023. Transition to organic farming negatively affects bat activity. J. Appl. Ecol. 60, 2167–2176. https://doi.org/10.1111/1365-2664.14468.

Fischer, F.P., Schulz, U., Schubert, H., Knapp, P., Schmöger, M., 1997. Quantitative assessment of grassland quality: acoustic determination of population sizes of orthopteran Indicator species. Ecol. Appl. 7, 909–920. https://doi.org/10.1890/1051-0761(1997)007[0909:QAOGQA]2.0.CO;2.

Fleury, P., Seres, C., Dobremez, L., Nettier, B., Pauthenet, Y., 2015. "Flowering meadows", a result-oriented agri-environmental measure: technical and value changes in favour of biodiversity. Land Use Policy 46, 103–114. https://doi.org/10.1016/j.landusepol.2015.02.007.

Fraixedes, S., Lindén, A., Piha, M., Cabeza, M., Gregory, R., Lehikoinen, A., 2020. A state-of-the-art review on birds as indicators of biodiversity: advances, challenges, and future directions. Ecol. Indic. 118, 106728. https://doi.org/10.1016/j.ecolind.2020.106728.

Frey-Ehrenbold, A., Bontadina, F., Arlettaz, R., Obrist, M.K., 2013. Landscape connectivity, habitat structure and activity of bat guilds in farmland-dominated matrices. J. Appl. Ecol. 50, 252–261. https://doi.org/10.1111/1365-2664.12034.

Froidevaux, J.S.P., Boughey, K.L., Hawkins, C.L., Broyles, M., Jones, G., 2019. Managing hedgerows for nocturnal wildlife: do bats and their insect prey benefit from targeted agri-environmental schemes? J. Appl. Ecol. 56, 1610–1623. https://doi.org/10.1111/1365-2664.13412.

Funosas, D., Barbaro, L., Schillé, L., Elger, A., Castagneyrol, B., Cauchois, M., 2024. Assessing the potential of BirdNET to infer European bird communities from large-scale ecoacoustic data. Ecol. Ind. 164, 112146. https://doi.org/10.1016/j.ecolind.2024.112146.

Funosas et al., accepted. InsectSet: a finely annotated dataset for the automated acoustic identification of French Orthoptera and Cicadidae.

Furnas, J.B., 2020. Rapid and varied responses of songbirds to climate change in California coniferous forests. Biol. Conserv. 241, 108347. https://doi.org/10.1016/j.biocon.2019.108347.

Geppert, C., Hass, A., Földesi, R., Donkó, B., Akter, A., Tscharntke, T., Batáry, P., 2020. Agri-environment schemes enhance pollinator richness and abundance but bumblebee reproduction depends on field size. J. Appl. Ecol. 57, 1818–1828. https://doi.org/10.1111/1365-2664.13682.

Gerling, C., Cord, A., Drechsler, M., Höltig, L., Markova-Nenova, N., Ogawa, R., Sturm, A., Wätzold, F., 2023. Pay for Effort or Pay for Effect? Cost-effectiveness of action-based and result-based conservation payments with several conservation options. In: XXIV Annual BIOECON Conference.

Goodale, E., Magrath, R.D., 2024. Species diversity and interspecific information flow. Biol. Rev. 99, 999–1014. https://doi.org/10.1111/brv.13055.

Grüebler, M.U., Schuler, H., Spaar, R., Naef-Daenzer, B., 2015. Behavioural response to anthropogenic habitat disturbance: indirect impact of harvesting on whinchat populations in Switzerland. Biol. Conserv. 186, 52–59. https://doi.org/10.1016/j.biocon.2015.02.031.

Hagemann, N., Gerling, C., Höltig, L., Kernecker, M., Markova-Nenova, N.N., Wätzold, F., Wendler, J., Cord, A.F., 2025. Improving result-based schemes for nature conservation in agricultural landscapes—challenges and best practices from selected European countries. Reg. Environ. Chang. 25, 12. https://doi.org/10.1007/s10113-024-02324-2.

Hall, M., Robinson, D., 2021. Acoustic signalling in Orthoptera. Advances in Insect Physiology 61, 1–99. https://doi.org/10.1016/bs.aiip.2021.09.001.

Hartig, F., Abrego, N., Bush, A., Chase, J.M., Guillerla-Arroita, G., Leibold, M.A., Ovaskainen, O., Pellissier, L., Pichler, M., Poggiato, G., Pollock, L., Si-Moussi, S., Thuiller, W., Viana, D.S., Warton, D.I., Zurell, D., Yu, D.W., 2024. Novel community data in ecology-properties and prospects. Trends Ecol. Evol. 39, 280–293. https://doi.org/10.1016/j.tree.2023.09.017.

Haupert, S., Sébe, F., Sueur, J., 2023. Physics-based model to predict the acoustic detection distance of terrestrial autonomous recording units over the diel cycle and across seasons: insights from an alpine and a Neotropical forest. Methods Ecol. Evol. 14, 614–630. https://doi.org/10.1111/2041-210X.14020.

Heath, B.E., Penru, N.P.L., Skinner, J., Orme, C.D.L., Sethi, S.S., Ewers, R.M., Picinali, L., 2024. Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU). Methods. Ecol. Evol. 15, 1568–1579. https://doi.org/10.1111/2041-210X.14390.

Hecker, L.P., Sturm, A., Querhammer, L., Wätzold, F., 2024. Cost-effectiveness of state-
dependent versus state-independent agri-environment schemes for biodiversity
conservation. Ecol. Econ. 217, 108088. https://doi.org/10.1016/j.ecocon.2023.108088.

Hill, A.P., Prince, P., Snaddon, J.L., Doncaster, C.P., Rogers, A., 2019. AudioMoth: A low-cost acoustic device for monitoring biodiversity and the environment. HardwareX 6, e00073. https://doi.org/10.1016/j.ohx.2019.e00073.

Höltig, L., Busse, M., Bülow, S., Engler, J.O., Hagemann, N., Joornmann, I., Kernecker, M.L., Larondelle, N., Sturm, A., Turkelboom, F., Wätzold, F., Cord, A.F., 2022. Co-design: working with farmers in Europe to halt the loss of biological diversity. Ecol. Solut. Evid. 3, e12169. https://doi.org/10.1002/2688-8319.12169.

Image, M., Gardner, E., Clough, Y., Kunin, W.E., Potts, S.G., Smith, H.G., Stone, G.N., Westbury, D.B., Breeze, T.D., 2023. Which interventions contribute most to the net

effect of England's agri-environment schemes on pollination services? Landsc. Ecol.
38, 271–291. https://doi.org/10.1007/s10980-022-01559-w.

Jameson, J.W., 2024. Buzzfindr: automating the detection of feeding buzzes in bat echolocation recordings. PLoS ONE 19 (8), e0306063. https://doi.org/10.1371/journal.pone.0306063.

Jarrett, D., Willis, S.G., 2024. Acoustic detection rate can outperform traditional survey approaches in estimating relative densities of breeding waders. Ibis. https://doi.org/10.1111/ibi.13375.

Jielazkov, A., Bas, Y., Kerbiriou, C., Julien, J.-F., Penone, C., Le Viol, I., 2016. Large-scale scale-automated acoustic monitoring allows to detect temporal decline of bush-crickets. Global Ecol. Conserv. 6, 208–218. https://doi.org/10.1016/j.gecco.2016.02.008.

Jongeneel, R., Gonzalez-Martinez, A., 2023. Implementing the EU eco-scheme in the Netherlands: A results-based points system approach. EuroChoices 22, 20–27. https://doi.org/10.1111/1746-692X.12388.

Kahl, S., Wood, C.M., Eibl, M., Klinck, H., 2021. BirdNET: A deep learning solution for avian diversity monitoring. Eco. Inform. 61, 101236. https://doi.org/10.1016/j.ecoinf.2021.101236.

Karamousantas, D., Varzakas, T., Kanakis, A., Dalamagas, B.C., 2009. Noise levels produced by agricultural machinery and different farming processes. Int. J. Acoust. Vib 14 (4), 220–225. https://doi.org/10.20855/ijav.2009.14.4252.

Keenleyside, C., Radley, G., Tucker, G., Underwood, E., Hart, K., Allen, B., Menadue, H., 2014. Results-Based Payments for Biodiversity Guidance Handbook: Designing and Implementing Results-Based Agri-Environment Schemes 2014–20. Prepared for the European Commission. DG Environment, Contract No ENV.B.2/ETU/2013/0046. Institute for European Environmental Policy, London. https://www.rbpnetwork.eu/media/rbaps-handbook.pdf.

Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., Borges, F., 2020. Experience versus expectation: farmers' perceptions of smart farming technologies for cropping systems across Europe. Precis. Agric. 21, 34–50. https://doi.org/10.1007/s11119-019-09651-z.

Klebl, F., Parisi, A., Häfner, K., Adler, A., Barreiro, S., Bodea, F.V., Brönnimann, V., De Vries, J.P.R., Dos Santos, A., Hood, A.S.C., Melts, I., Popa, R., Vajna, F., Velado-Alonso, E., Kernecker, M.L., 2024. How values and perceptions shape farmers' biodiversity management: insights from ten European countries. Biol. Conserv. 291, 110496. https://doi.org/10.1016/j.biocon.2024.110496.

Knierim, A., Borges, F., Kernecker, M.L., Kraus, T., Wurbs, A., 2018. What drives adoption of smart farming technologies? Evidence from a cross-country study. In: 13th European IFSA Symposium, 1-5 July 2018, Chania (Greece). In: https://ifsa.boku.ac.at/cms/fileadmin/Proceeding2018/Theme4_Knierim.pdf.

Kotila, M., Suominen, K.M., Vasko, V.V., Blomberg, A.S., Lehikoinen, A., Andersson, T., Aspi, J., Cederberg, T., Hänninen, J., Inkinen, J., Koskinen, J., Lundberg, G., Mäkinen, K., Rontti, M., Snickars, M., Solbakken, J., Sundell, J., Syväperä, I., Vuoromaa, S., Ylönen, J., Vesterinen, E.J., Lilley, T.M., 2023. Large-scale long-term passive-acoustic monitoring reveals spatio-temporal activity patterns of boreal bats. Ecography 6, e06617. https://doi.org/10.1111/ecog.06617.

Krämer, J.E., Wätzold, F., 2018. The agglomeration bonus in practice—an exploratory assessment of the Swiss network bonus. J. Nat. Conserv. 43, 126–135. https://doi.org/10.1016/j.jnc.2018.03.002.

Krings, C.H., Darras, K., Hass, A., Batáry, P., Fabian, Y., 2022. Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes. Basic Appl. Ecol. 63, 23–35. https://doi.org/10.1016/j.baae.2022.05.002.

Lapp, S., Smith, T.C., Knapp, R.A., Lindauer, A., Kitzes, J., 2024. Aquatic soundscape recordings reveal diverse vocalizations and nocturnal activity of an endangered frog. Am. Nat. 203, 618–627. https://doi.org/10.1086/729422.

Larson, R.C., Gawlik, D.E., 2023. Call rate as an index of nest count in wading bird colonies. Ibis 165, 504–516. https://doi.org/10.1111/ibi.13163.

Lewis, D.J., Barham, B.L., Robinson, B., 2011. Are there spatial spillovers in the adoption of clean technology? The case of organic dairy farming. Land Econ. 87, 250–267. https://doi.org/10.3368/e.je.87.2.250.

Lohaus, K., Vidal, S., Thies, C., 2013. Farming practices change food web structures in cereal aphid–parasitoid–hyperparasitoid communities. Oecologia 171, 249–259. https://doi.org/10.1007/s00442-012-2387-8.

Mac Aodha, O., Martínez Balvanera, S., Damstra, E., Cooke, M., Eichinski, P., Browning, E., Barataud, M., Boughey, K., Coles, R., Giacomini, G., Swiney, G., Obrist, M.K., Parsons, S., Sattler, T., Jones, K.E., 2022. Towards a general approach for bat echolocation detection and classification. bioRxiv. https://doi.org/10.1101/2022.12.14.520490.

Maher, C., Moran, J., Beaufoy, G., Berastegi Garciandia, A., Bleasdale, A., Byrne, D., Copland, A., Dunford, B., Edge, R., Iragui Yoldi, U., Jones, G., Lopez Rodriguez, F., McLoughlin, D., Donoghue, B.O., 2018. Results-based agri-environmental payments general guidance handbook. Step-by-step guide to designing a results-based payments scheme: lessons from Ireland and Spain. Report prepared for the European Commission, DG Environment, Agreement No. 07.027722/2014/697042/SUB/B2. https://www.rbpnetwork.eu/media/rbaps_gh01_general_guidance.pdf.

Markova-Nenova, N., Engler, J.O., Cord, A.F., Wätzold, F., 2023a. Will passive acoustic monitoring make result-based payments more attractive? A cost comparison with human observation for farmland bird monitoring. Conserv. Sci. Pract. 5, e13003. https://doi.org/10.1111/csp.13003.

Markova-Nenova, N., Wätzold, F., Sturm, A., 2023b. Optimizing agri-environment schemes for cost-effectiveness, fairness or both? Q Open 3, 1–22. https://doi.org/10.1093/qopen/qoad005.

Martini, F., Chen, Y.-F., Mammides, C., Goodale, E., Goodale, U.M., 2024. Exploring potential relationships between acoustic indices and ecosystem functions: a test on
insect herbivory. Oecologia 204, 875–883. https://doi.org/10.1007/s00442-024-05536-9.

Massfeller, A., Meraner, M., Hüttel, S., Uehleke, R., 2022. Farmers' acceptance of results-based agri-environmental schemes: A German perspective. Land Use Policy 120, 106281. https://doi.org/10.1016/j.landusepol.2022.106281.

Melo, I., Llusia, D., Bastos, R.P., Signorelli, L., 2021. Active or passive acoustic monitoring? Assessing methods to track aranur communities in tropical savanna wetlands. Ecol. Indic. 132, 108305. https://doi.org/10.1016/j.ecolind.2021.108305.

Menapace, L., Colson, G., Raffaelli, R., 2013. Risk Aversion, Subjective Beliefs, and Farmer Risk Management Strategies. American. J. Agric. Economics 95, 384–389. https://doi.org/10.1093/ajae/aas107.

Metcalf, O., Abrahams, C., Ashington, B., Baker, E., Bradfer-Lawrence, T., Browning, E., Carruthers-Jones, J., Darby, J., Dick, J., Eldridge, A., Elliott, D., Heath, B., Howden-Leach, P., Johnston, A., Lees, A., Meyer, C., Ruiz Arana, U., Smyth, S., 2023. Good practice guidelines for long-term ecoacoustic monitoring in the UK. Acoustics Network, UK. https://nora.nerc.ac.uk/id/eprint/534646/.

Mettepenningen, E., Verspecht, A., Van Huylenbroeck, G., 2009. Measuring private transaction costs of European agri-environmental schemes. J. Environ. Plan. Manag. 52, 649–667. https://doi.org/10.1080/09640560902958206.

Mohr, S., Kühl, R., 2021. Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precis. Agric. 22, 1816–1844. https://doi.org/10.1007/s11119-021-09814-x.

Müller, S., Gossner, M.M., Penone, C., Jung, K., Renner, S.C., Farina, A., Anhäuser, L., Ayasse, M., Boch, S., Haensel, F., Heitzmann, J., Kleinn, C., Magdon, P., Perović, D. J., Pieretti, N., Shaw, T., Steckel, J., Tschapka, M., Vogt, J., Westphal, C., Scherer-Lorenzen, M., 2022. Land-use intensity and landscape structure drive the acoustic composition of grasslands. Agric. Ecosyst. Environ. 328, 107845. https://doi.org/10.1016/j.agecon.2021.107845.

Mullet, T.C., Farina, A., Gage, S.H., 2017. The acoustic habitat hypothesis: an ecoacoustics perspective on species habitat selection. Biosemiotics 10, 319–336. https://doi.org/10.1007/s12304-017-9288-5.

Myers, D., Berg, H., Maneas, G., 2019. Comparing the soundscapes of organic and conventional olive groves: A potential method for bird diversity monitoring. Ecol. Indic. 103, 642–649. https://doi.org/10.1016/j.ecolind.2019.04.030.

Newson, S.E., Bas, Y., Murray, A., Gillings, S., 2017. Potential for coupling the monitoring of bush-crickets with established large-scale acoustic monitoring of bats. Methods Ecol. Evol. 8, 1051–1062. https://doi.org/10.1111/2041-210X.12720.

Newson, S.E., Evans, H.E., Gillings, S., 2015. A novel citizen science approach for large-scale standardised monitoring of bat activity and distribution, evaluated in eastern England. Biol. Conserv. 191, 38–49. https://doi.org/10.1016/j.biocon.2015.06.009.

Nieto-Mora, D.A., Rodríguez-Buritica, S., Rodríguez-Marín, P., Martínez-Vargaz, J.D., Isaza-Narváez, C., 2023. Systematic review of machine learning methods applied to ecoacoustics and soundscapes monitoring. Heliyon 9, e20275. https://doi.org/10.1016/j.heliyon.2023.e20275.

Olimpi, E.M., Philpott, S.M., 2018. Agroecological farming practices promote bats. Agric. Ecosyst. Environ. 265, 282–291. https://doi.org/10.1016/j.agecon.2018.06.008.

Oppermann, R., Pfister, S.C., Eirich, A., 2020. Sicherung der Biodiversität in der Agrarlandschaft - Quantifizierung des Maßnahmenbedarfs und Empfehlungen zur Umsetzung. Institut für Agrarökologie und Biodiversität (IFAB), Mannheim, 191pages.

PECBMS, 2024. Species Trends. Pan-European Common Bird Monitoring Scheme. https://pecbms.info/trends-and-indicators/species-trends/.

Penone, C., Le Viol, I., Pellissier, V., Julien, J., Bas, Y., Kerbiriou, C., 2013. Use of large-scale acoustic monitoring to assess anthropogenic pressures on Orthoptera communities. Conserv. Biol. 27, 979–987. https://doi.org/10.1111/cobi.12083.

Pérez-Granados, C., Bota, G., Giralt, D., Barrero, A., Gómez-Catasús, J., Bustillo-De La Rosa, D., Traba, J., 2019. Vocal activity rate index: a useful method to infer terrestrial bird abundance with acoustic monitoring. Ibis 161, 901–907. https://doi.org/10.1111/ibi.12728.

Pérez-Granados, C., Traba, J., 2021. Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research. Ibis 163, 765–783. https://doi.org/10.1111/ibi.12944.

Pérez-Granados, C., 2023. BIRDNET: applications, performance, pitfalls and future opportunities. Ibis 165, 1068–1075. https://doi.org/10.1111/ibi.13193.

Pijanowski, B.C., Villanueva-Rivera, L.J., Dumyahn, S.L., Farina, A., Krause, B.L., Napoletano, B.M., Gage, S.H., Pieretti, N., 2011. Soundscape ecology: the science of sound in the landscape. BioScience 61, 203–216. https://doi.org/10.1525/bio.2011.61.3.6.

Pijanowski, B.C., Brown, C.J., 2022. Grand challenges in acoustic remote sensing: discoveries to support a better understanding of our changing planet. Front. Remote Sens. 2, 824848. https://doi.org/10.3389/frsen.2021.824848.

Quinn, C.A., Burns, P., Hakkenberg, C.R., Salas, L., Pasch, B., Goetz, S.J., Clark, M.L., 2023. Soundscape components inform acoustic index patterns and refine estimates of bird species richness. Front. Remote Sens. 4, 111868. https://doi.org/10.3389/frsen.2023.1156837.

Quinn, J.E., Pederson, C., Vickery, C.E., Hyland, E., Eckberg, J.O., 2024. Acoustic indices are responsive to local-scale but not landscape-scale agricultural land use. Ecol. Ind. 160, 111868. https://doi.org/10.1016/j.ecolind.2024.111868.

Raven, P.H., Wagner, D.L., 2021. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. USA 118, e2002548117. https://doi.org/10.1073/pnas.2002548117.

Rehman, A., Farooq, M., Lee, D.-J., Siddique, K.H.M., 2022. Sustainable agricultural practices for food security and ecosystem services. Environ. Sci. Pollut. Res. 29, 84076–84095. https://doi.org/10.1007/s11356-022-23635-z.

Revilla-Martín, N., Budinski, I., Puig-Montserrat, X., Flaqueer, C., López-Baucells, A., 2021. Monitoring cave-dwelling bats using remote passive acoustic detectors: A new approach for cave monitoring. Bioacoustics 30, 527–542. https://doi.org/10.1080/09524622.2020.1816492.

Rhinehart, T.A., Chronister, L.M., Devlin, T., Kitzes, J., 2020. Acoustic localization of terrestrial wildlife: current practices and future opportunities. Ecol. Evol. 10, 6794–6818. https://doi.org/10.1002/ece3.6216.

Rigal, S., Dakos, V., Alonso, H., Auniņš, A., Benkö, Z., Brotons, L., Chodkiewicz, T., Chylarecki, P., De Carli, E., Del Moral, J.C., et al., 2023. Farmland practices are driving bird population decline across Europe. Proc. Natl. Acad. Sci. USA 120, e2216573120. https://doi.org/10.1073/pnas.2216573120.

Roilo, S., Engler, J.O., Václavík, T., Cord, A.F., 2023. Landscape-level heterogeneity of agri-environment measures improves habitat suitability for farmland birds. Ecol. Appl. 33, e2720. https://doi.org/10.1002/eap.2720.

Ronacher, B., 2019. Innate releasing mechanisms and fixed action patterns: basic ethological concepts as drivers for neuroethological studies on acoustic communication in Orthoptera. J. Comp. Physiol. A. 205, 33–50. https://doi.org/10.1007/s00359-018-01311-3.

Ross, S.R.P.-J., Friedman, N.R., Yoshimura, M., Yoshida, T., Donohue, I., Economo, E.P., 2021. Utility of acoustic indices for ecological monitoring in complex sonic environments. Ecol. Indic. 121, 107114. https://doi.org/10.1016/j.ecolind.2020.107114.

Ryslavy, T., Bauer, H.G., Gerlach, B., Hüppop, O., Stahmer, J., Südbeck, P., Sudfeldt, C., 2020. Rote Liste der Brutvögel Deutschlands. 6. Fassung. Berichte zum. Vogelschutz 57, 13–112.

Schaub, S., Ghazoul, J., Huber, R., Zhang, W., Sander, A., Rees, C., Banerjee, S., Finger, R., 2023. The role of behavioural factors and opportunity costs in farmers' participation in voluntary agri-environmental schemes: A systematic review. Aust. J. Agric. Econ. 74, 617–660. https://doi.org/10.1111/1477-9552.12538.

Schillé, L., Valdés-Correcher, E., Archaux, F., Bälácenoiu, F., Bjørn, M.C., Bogdziewicz, M., Boivin, T., Branco, M., Domestoy, T., De Groot, M., Dobrosavljević, J., Duduman, M., et al., 2024. Decomposing drivers in avian insectivory: large-scale effects of climate, habitat and bird diversity. J. Biogeogr. 51, 1079–1094. https://doi.org/10.1111/jbi.14808.

Schmidbauer, P., Denzinger, A., 2019. Social calls of Myotis nattereri during swarming: call structure mirrors the different behavioral context. PLoS One 14, e0221792. https://doi.org/10.1371/journal.pone.0221792.

Schöttker, O., Hütt, C., Jauker, F., Witt, J., Bareth, G., Wätzold, F., 2023. Monitoring costs of result-based payments for biodiversity conservation: will UAV-assisted remote sensing be the game-changer? J. Nat. Conserv. 76, 126494. https://doi.org/10.1016/j.jnc.2023.126494.

Sethi, S.S., Fossey, F., Cretois, B., Rosten, C.M., 2021. Management relevant applications of acoustic monitoring for Norwegian Nature – The Sound of Norway. NINA Report 2064. Norwegian Institute for Nature Research. https://brage.nina.no/nina-xmlui/handle/11250/2832294.

Shang, L., Heckelei, T., Gerullis, M.K., Börner, J., Rasch, S., 2021. Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. Agric. Syst. 190, 103074. https://doi.org/10.1016/j.agsy.2021.103074.

Shannon, G., McKenna, M.F., Angeloni, L.M., Crooks, K.R., Fristrup, K.M., Brown, E., Warner, K.A., Nelson, M.D., White, C., Briggs, J., McFarland, S., Wittemeyer, G., 2016. A synthesis of two decades of research documenting the effects of noise on wildlife. Biol. Rev. 91, 982–1005. https://doi.org/10.1111/brv.12207.

Sidemo-Holm, W., Smith, H.G., Brady, M.V., 2018. Improving agricultural pollution abatement through result-based payment schemes. Land Use Policy 77, 209–219.

Singer, D., Kamp, J., Hondong, H., Schuldt, A., Hagge, J., 2024. Diel and seasonal vocal activity patterns revealed by passive acoustic monitoring suggest expert recommendations for breeding bird surveys need adjustment. Preprint. https://doi.org/10.13140/RG.2.2.34491.84003.

Smith, J.W., Pijanowski, B.C., 2014. Human and policy dimensions of soundscape ecology. Glob. Environ. Chang. 28, 63–74. https://doi.org/10.1016/j.gloenvcha.2014.05.007.

Südbeck, P., Andretzke, H., Fischer, S., Gedeon, K., Schikore, T., Schröder, K., Sudfeldt, C., 2005. Methodenstandards zur Erfassung der Brutvögel Deutschlands.

Sugai, L.S.M., Silva, T.S.F., Ribeiro, J.W., Lusia, D., 2019. Terrestrial passive acoustic monitoring: review and perspectives. BioScience 69, 15–25. https://doi.org/10.1093/biosci/biy147.

Sugai, L.S.M., Desjonquères, C., Silva, T.S.F., Lusia, D., 2020. A roadmap for survey designs in terrestrial acoustic monitoring. Remote Sens. Ecol. Conserv. 6, 220–235. https://doi.org/10.1002/rse.2131.

Symes, L.B., Madhusudhana, S., Martinson, S.J., Geipel, I., Ter Hofstede, H.M., 2024. Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids. Philos. Trans. R. Soc. B 379, 20230110. https://doi.org/10.1098/rstb.2023.0110.

Tanaka, K., Hanley, N., Kuhfuss, L., 2022. Farmers' preferences toward an outcome-based payment for ecosystem service scheme in Japan. Aust. J. Agric. Econ. 73, 720–738. https://doi.org/10.1111/1477-9552.12478.

Thibault, M., Garnier, L.K.M., Kauffmann, C., Bas, Y., Kerbiriou, C., 2024. Listening to the response of bat and bush-cricket communities to management regimes of powerline clearings. Conserv. Sci. Pract. 6, e13127. https://doi.org/10.1111/csp2.13127.

Toenies, M., Rich, L., 2021. Advancing bird survey efforts through novel recorder technology and automated species identification. Calif. Fish W wildl. J. 107, 56–70. https://doi.org/10.51492/cfwj.107.5.

Ubach, A., Páramo, F., Gutiérrez, C., Stefanescu, C., 2020. Vegetation encroachment drives changes in the composition of butterfly assemblages and species loss in
Mediterranean ecosystems. Insect Conserv. Divers. 13, 151–161. https://doi.org/10.1111/icad.12397.

Uchida, K., Ushimaru, A., 2014. Biodiversity declines due to abandonment and intensification of agricultural lands: patterns and mechanisms. Ecol. Monogr. 84, 637–658. https://doi.org/10.1890/13-2170.1.

Udell, B.J., Straw, B.R., Loeb, S.C., Irvine, K.M., Thogmartin, W.E., Lausen, C.L., Reichard, J.D., Coleman, J.T.H., Cryan, P.M., Frick, W.F., Reichert, B.E., 2024. Using mobile acoustic monitoring and false-positive N-mixture models to estimate bat abundance and population trends. Ecol. Monogr. 94, e1617. https://doi.org/10.1002/ecm.1617.

van Klink, R., August, T., Bas, Y., Bodesheim, P., Bonn, A., Fossøy, F., Høye, T.T., Jongejans, E., Menz, M.H.M., Miraldo, A., Roslin, T., Roy, H.E., Ruczynski, I., Schigel, D., Schäffler, L., Sheard, J.K., Svenningsen, C., Tschan, G.F., Wäldchen, J., Zizka, V.M.A., Aström, J., Bowler, D.E., 2022. Emerging technologies revolutionise insect ecology and monitoring. Trends Ecol. Evol. 37, 872–885. https://doi.org/10.1016/j.tree.2022.06.001.

Villanueva, A.J., Granado-Díaz, R., Colombo, S., 2024. Comparing practice- and results-based agri-environmental schemes controlled by remote sensing: an application to olive groves in Spain. Aust. J. Agric. Econ. 75, 524–545. https://doi.org/10.1111/1477-9552.12573.

Vogelbacher, M., Bellafkir, H., Gottwald, J., Schneider, D., Muhling, M., Freisleben, B., 2023. Deep learning for recognizing bat species and bat behavior in audio recordings. *10th IEEE Swiss conference on data science (SDS)*. Zurich, Switzerland 50–57. https://doi.org/10.1109/SDS57534.2023.00014.

Wätzold, F., Drechsler, M., 2005. Spatially uniform versus spatially heterogeneous compensation payments for biodiversity-enhancing land-use measures. Environ. Resour. Econ. 31, 73–93. https://doi.org/10.1007/s10640-004-6979-6.

Wätzold, F., Schwerdtner, K., 2005. Why be wasteful when preserving a valuable resource? A review article on the cost-effectiveness of European biodiversity conservation policy. Biol. Conserv. 123, 327–338. https://doi.org/10.1016/j.biocon.2004.12.001.

Wätzold, F., Drechsler, M., Johst, K., Mewes, M., Sturm, A., 2016. A novel, spatiotemporally explicit ecological-economic modeling procedure for the design of cost-effective agri-environment schemes to conserve biodiversity. American J. Agri. Economics 98, 489–512. https://doi.org/10.1093/ajae/aav058.

Wätzold, F., Jauker, F., Komainda, M., Schöttker, O., Horn, J., Sturm, A., Isselstein, J., 2024. Harnessing virtual fencing for more effective and adaptive agri-environment schemes to conserve grassland biodiversity. Biol. Conserv. 297, 110736. https://doi.org/10.1016/j.biocon.2024.110736.

Weier, S.M., Grass, I., Linden, V.M.G., Tscharntke, T., Taylor, P.J., 2018. Natural vegetation and bug abundance promote insectivorous bat activity in macadamia orchards, South Africa. Biol. Conserv. 226, 16–23. https://doi.org/10.1016/j.biocon.2018.07.017.

Wilkins, M.R., Seddon, N., Safran, R.J., 2013. Evolutionary divergence in acoustic signals: causes and consequences. Trends Ecol. Evol. 28, 156–166. https://doi.org/10.1016/j.tree.2012.10.002.

Williams, E.M., O'Donnell, C.F.J., Armstrong, D.P., 2018. Cost-benefit analysis of acoustic recorders as a solution to sampling challenges experienced monitoring cryptic species. Ecol. Evol. 8, 6839–6848. https://doi.org/10.1002/ece3.4199.

Wood, C.M., Kahl, S., Chaon, P., Peery, M.Z., Klinck, H., 2021. Survey coverage, recording duration and community composition affect observed species richness in passive acoustic surveys. Methods Ecol. Evol. 12, 885–896. https://doi.org/10.1111/2041-210X.13571.

Wood, C.M., Kahl, S., 2024. Guidelines for appropriate use of BirdNET scores and other detector outputs. J. Ornithol. 165, 777–782. https://doi.org/10.1007/s10336-024-02144-5.

Wood, C.M., Socolar, J., Kahl, S., Peery, M.Z., Chaon, P., Kelly, K., Koch, R.A., Sawyer, S. C., Klinck, H., 2024. A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances. J. Appl. Ecol. 61, 797–808. https://doi.org/10.1111/1365-2664.14579.

Wuepper, D., Huber, R., 2022. Comparing effectiveness and return on investment of action- and results-based agri-environmental payments in Switzerland. American J. Agri. Economics 104, 1585–1604. https://doi.org/10.1111/ajae.12284.

Xie, J., Michael, T., Zhang, J., Roe, P., 2016. Detecting frog calling activity based on acoustic event detection and multi-label learning. In: Procedia Computer Science, International Conference on Computational Science (ICCS), San Diego, California, USA 80, pp. 627–638. https://doi.org/10.1016/j.procs.2016.05.352.

Yip, D., Leston, L., Bayne, E., Sólymos, P., Grover, A., 2017. Experimentally derived detection distances from audio recordings and human observers enable integrated analysis of point count data. Avian Conserv. Ecol. 12 (1), 11. https://doi.org/10.5751/ACE-00997-120111.

Zabel, A., Bostedt, G., Engel, S., 2014. Performance payments for groups: the case of carnivore conservation in northern Sweden. Environ. Resour. Econ. 59, 613–631. https://doi.org/10.1007/s10640-013-9752-x.

Zanella, M.A., Schleyer, C., Speelman, S., 2014. Why do farmers join payments for ecosystem services (PES) schemes? An assessment of PES water scheme participation in Brazil. Ecol. Econ. 105, 166–176. https://doi.org/10.1016/j.ecolecon.2014.06.004.