# Assessing the consistency of control activities with priority zones for aquatic invasive rodents in Western France

Olivier Pays, Anne Mimet, Manon Bonnet, Antonin Grimault-Fremy, Xavier Grillo

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## To cite this version:

Olivier Pays, Anne Mimet, Manon Bonnet, Antonin Grimault-Fremy, Xavier Grillo. Assessing the consistency of control activities with priority zones for aquatic invasive rodents in Western France. European Journal of Wildlife Research, 2026, 72 (2), pp.22. 10.1007/s10344-026-02056-0. hal-05495172

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https://univ-angers.hal.science/hal-05495172v1

Submitted on 2 Mar 2026

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# Assessing the consistency of control activities with priority zones for aquatic invasive rodents in Western France

Olivier Pays<fnref n="1" />,<fnref n="2" /> ID · Anne Mimet<fnref n="1" /> · Manon Bonnet<fnref n="1" />,<fnref n="2" /> · Antonin Grimault-fremy<fnref n="3" /> · Xavier Grillo<fnref n="4" />

Received: 3 September 2025 / Revised: 24 December 2025 / Accepted: 16 January 2026
© The Author(s) 2026

## Abstract

Aquatic invasive alien rodents (AIAR) pose a serious threat to ecosystems leading to significant economic, environmental, and public health concerns. In France, AIAR populations are well established, and eradication is not possible. Thus, a permanent control program has been implemented in several regions to mitigate their impacts. However, comprehensive assessment of the current management strategies and field implementation remains limited. This study aimed to examine whether the control activities (i.e. trapping) among 2021 and 2023 targeting coypus and muskrats and coordinated by the local One Health entity for pest species aligned with the priority action zones defined in December 2020 by the regional governmental watershed agency. Our study shows that the estimated risk for municipalities of being impacted by AIAR did not translate into a higher density of volunteer trappers involved in control activities allocated to municipalities. On the contrary, an increase of the estimated risk in a municipality was associated with a decrease of removed AIAR per year per trapper. Finally, although trappers set their traps in municipalities with high amount of built-up areas, landscape factors such as the density of watercourses and area of riparian grasslands in the municipality do not contribute to explain the mismatch between the distribution of the efforts of control activities and the zonation of priority areas. To strengthen the strategic approach to AIAR management, our results highlight the need for a more robust collaborative framework among stakeholders, including local and regional governmental authorities, field operators and scientists.

**Keywords** Pest management · Permanent control programme · Invasive species · Coypu · Myocastor coypus · Muskrat · Ondatra zibethicus · Citizen engagement · Trapping · Captures · Wetlands

## Introduction

Among the numerous invasive alien species (IAS) that have colonized wetlands, (semi)aquatic invasive alien rodents (AIAR) such as the Coypu (*Myocastor coypus*) and Muskrat (*Ondatra zibethicus*), have become widely established in various riparian habitats, with no possibility of eradication in many areas (Bonnet et al. 2023). While they are listed in the Union Concern list (EU Regulation No 1143/2014),

the estimated worldwide costs associated with muskrats and coypus amount to approximately US$377.5 and US$ 90.9 million, respectively (Diagne et al. 2023), with the latter being largely underestimated. In Europe, few published studies have assessed costs, losses and population control of coypus and muskrats, although some research has been conducted in Italy (Bertolino et al. 2005; Panzacchi et al. 2007; Bertolino and Viterbi 2010) and United Kingdom (Gosling and Baker 1989) for the Coypu and the Netherlands (Ydenberg et al. 2019; Bos et al. 2020) for the Muskrat.

While research quantifying the ecological effects of AIAR on ecosystems remains limited, it has been documented that coypus and muskrats negatively impact native biodiversity (Boorman and Fuller 1981; Bertolino et al. 2005, 2011; Diggins and Stewart 2000; Nagayama et al. 2020), contribute to riverbank collapse, disrupt drainage systems, and alter water flow and quality through their burrowing activities (Panzacchi et al. 2007; Sofia et al. 2017). They also serve as significant carriers and transmitters of infectious diseases

<footnote>☑ Olivier Pays
olivier.pays@univ-angers.fr</footnote>

<footnote n="1">1 BIODIVAG, Univ Angers, Angers F-49000, France</footnote>

<footnote n="2">2 REHABS International Research Laboratory, CNRS-Nelson
Mandela University, George Campus, George, South Africa</footnote>

<footnote n="3">3 FDGDON 49, 23 rue Georges Morel, Beaucouzé
49070, France</footnote>

<footnote n="4">4 EPTB Sèvre Nantaise, Clisson F-44190, France</footnote>
and parasites that can impact humans, domestic animals, and wildlife (Ayral et al. 2020; Harran et al. 2024). As the impact of AIAR in many wetlands has increased over the last decades due to the lack of effective natural predation (Mori et al. 2020) including cold winters (Gosling 1981), control activities (e.g., trapping, shooting) are conducted in many areas to limit their populations. This is the case, for instance, in Netherlands (van Loon et al. 2017; Bos et al. 2019, 2020) and Great Britain (Gosling and Baker 1989) for muskrats, and for coypus, in France (Bonnet et al. 2021), Italy (Panzacchi et al. 2007), the Delmarva Peninsula, USA (Anderson et al. 2022), South Korea (Kim et al. 2019) and in Great Britain where they have been eradicated (Gosling and Baker 1989).

When eradication of IAS is not possible, the alternative approach is to set permanent control programmes in areas where their impact needs to be reduced (Braysher 1993; Simberloff et al. 2018). However, securing funding for the substantial costs associated with control activities poses a significant challenge to long-term planning. In many countries, the engagement of volunteers as field operators has become pivotal in IAS control efforts (Bryce et al. 2011; Dechoum et al. 2019). Since volunteers’ salaries are not covered, monetary reward systems have been introduced to maintain their engagement (Sheail 1999; Dedah et al. 2010, Kim et al. 2019; Bonnet et al. 2023). However, a bounty scheme alone does not guarantee the effectiveness of control activities (Sheail 1999). In this context, evaluating the effectiveness of control activities is crucial to convince decision-makers and funders to support permanent control programmes (Bradshaw et al. 2016).

When the objective is both to reduce the impacts of IAS on ecosystems and human well-being and to allocate limited funds effectively, it is essential to prioritize management efforts in high-risk areas to determine where control activities should be implemented first (Lehtomäki and Moilanen 2013; McGeoch et al. 2016; Stone and Andreu 2017; Potgieter et al. 2022). Given the potential socio-economic and environmental damage caused by AIAR, control activities against coypus and muskrats should initially focus on areas with (i) high conservation value (RAMSAR sites, Natura 2000 areas, and natural areas of ecological interest), (ii) high risk of disease transmission (e.g., bathing areas, water sports, water tourism, and fishing), (iii) hydraulic and engineering structures and (iv) production sites along watercourses (e.g., drinking water sources, crops,...) (Bonnet et al. 2023). However few studies have documented whether such spatial prioritization of control actions against AIAR has been applied.

In France, control activities against AIAR involve many local and regional entities, which provide training, equipment, network management, and financial support (Bonnet et al. 2023). While a national One Health entity against pest species exists (namely FREDON-France), regional or

local One Health entities coordinate field actions and groups of trappers (namely GDON) by (i) training and recruiting new volunteer trappers, (ii) rewarding captures, (iii) providing technical and legal support, and (iv) managing AIAR carcasses although local variations may exist. Other governmental entities such as inter-municipality cooperation organizations, regional and departmental councils, municipalities, and watershed associations, may be locally involved in funding control activities although it may vary across France. The number of volunteer trappers and their annual trapping success is unique in France. For instance, in the “Pays de la Loire” region with an approximative area of 32 000 km², about 3 000 trappers operate, reaching an average density of 1 trapper/10 km² (Bonnet et al. 2021). However, the large number of stakeholders and volunteers may create challenges in coordinating control activities in priority areas. Indeed, a mismatch may exist between the distribution of trappers and the spatial prioritization of control zones identified by regional or local entities especially if volunteers set up traps based on personal preferences, approaches and local knowledge, rather than following official priority sites. To our knowledge, no study has examined whether control activities in France are preferentially implemented in high-risky areas.

The aim of this study was to evaluate the effectiveness of the current strategy implemented by local authorities for the permanent control programme targeting coypus and muskrats, and to examine whether the control activities coordinated by the local One Health entity against pest species (FDGDON49) aligned with the priority action zonation established in December 2020 by the regional government watershed agency (EPTB-SN). Specifically, using the capture dataset provided by FDGDON49 from 2021 to 2023 covering 28 municipalities over 602.4 km², we tested whether the density of trappers and annual capture rates for each municipality correlated with the estimated risk of being impacted by AIAR. A high-risk assignment indicated priority action areas with lots of facilities and activities sensitive to damage that AIAR may cause, including sanitation infrastructures, drinking water sources, high-value ecosystems, recreational water activity zone, agriculture, fishing and tourism areas. If the permanent control programme considered the official priority zoning, our prediction was that control efforts per municipality, including trapper density and annual removal rates, should increase with higher estimated AIAR risk. Finally, we also examined whether five landscape variables (the amount of built-up areas, riparian grassland and water surface, and the density of watercourses or roads) that have been reported to affect the density of AIAR (Bertolino and Ingegno 2009; Ruys et al. 2011; Ecke et al. 2014; Pays et al. 2024) influenced the relationship between the estimated risk of being impacted by AIAR and control activity intensity.
# Materials and methods

## Study area

The study area covers 28 municipalities across 602.4 km² in the “Maine-et-Loire” department of the “Pays de la Loire” region in western France (Fig. 1a). The region has a typical oceanic climate, with an average annual temperature of 11.5 °C and 750 mm of rainfall. The terrain is predominantly flat with vast plains reaching a maximum elevation of 216 m. An extensive network of rivers and wetlands, including ponds, characterised the bocage landscape with meadows and crop fields enclosed by hedgerows and ditches (Fig. 1b).

## Territorial management of control activities

This study analysed the data set of control activities provided by the local One Health entity responsible for pest species management in the French department of “Maine-et-Loire” (i.e. FDGDON 49) in charge to manage the permanent

control programme against AIAIR. While the authors of this study were not involved in the captures, we address key ethical considerations regarding the control activities. All traps engaged in captures (i) were authorized by both public and private landowners to conduct control activities on their properties and (ii) granted an official permit with an identification number or an official authorization issued through a decree from the local authority, specifically the Prefecture of “Maine-et-Loire” (Decrees No. 2019/SEE/2194, No. DAPI-BCC 2007–1179, No. 2019308–001 C of February 4, 2019, No. 07/DDAF/263 of July 16, 2007, and the decree of February 17, 2016). The trapping procedure complied with the French regulation (Decree of the French Ministry of Ecology and Sustainable Development of the 29/1/2007 pursuant to the Article L. 427–8 and 2/9/2016 pursuant to the Article R. 427–6 from the French Environmental Code). All procedures complied with the ethical guidelines of the national and European regulations on the care and use of animals (French authority Decision 2007/04/06 and Directive 2010/63/EC, and the French Penal Code pursuant to Article 521–1 et R. 654–1).

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Description of the study area including 28 municipalities in western France (a), showing land cover and hydrological network (b), estimated risk associated to potential damage of Aquatic Invasive Alien Rodents (AIAR) (c), and control activities of AIAR per municipality (d)</figcaption></figure>
Over the studied area, trapping is the main control activity to remove AIAR from rivers and wetlands, as this method has proved its efficiency over the last twenty years in western France (Bonnet et al. 2021). The number of trappers varies from a single to tens per municipality. To capture AIAR, trappers use on one-door cage traps (45 × 45 × 90 cm), coni-bear traps, and two-door cage traps. A key limitation of the number of traps per trapper is the requirement to inspect all active traps before midday, which restricts the number they can set. The number of cages used per trapper typically ranges from one to ten, though occasionally, trappers may manage more. Baited traps are strategically placed on active animal paths near aquatic habitats such as rivers, ponds, ditches, channels, marshes, lakes, and dams. Trappers frequently relocate their traps several times during the session of control activities to enhance coverage, particularly in municipalities with fewer active trappers. While control activities occur throughout most of the year, trapping is stopped during July and August in areas with recreational activities and tourism.

## Control activities data

Trappers are required to provide biannual reports on the number of easily identifiable coypus and muskrats they have removed from their municipality during local meetings organised by the One Health entity responsible for pest species management in the French department of "Maine-et-Loire" (i.e. FDGDON 49). A monetary reward system has been implemented to encourage citizen engagement in control activities, whereby tappers receive financial compensation (about US$2.2 per removed animal) from the local authorities for each tail they present. The local One Health entity communicated control activities (i.e., the number of trappers and captures) at the municipal level between 2021 and 2023; however, these activities did not occur every year in each municipality. Since muskrat accounted for only 1.7% of the total number of captures over the three-year period, we pooled captures of coypus and muskrats together for each year. In total, 6341 AIAR were removed (Fig. 1d).

Trapping effort (e.g., the number of cages used per trapper, the number of nights cages remain active) is crucial for understanding spatial and temporal variations in capture rates. However, local authorities do not require trappers to document their trapping effort, only the number of coypus and muskrats they capture. As a result, a high capture rate may indicate either that a trapper deployed a large number of cages throughout the year and/or that the local AIAR population is particularly dense. Given that the only available proxy for trapping effort was the number of trappers operating in each municipality per year, we analysed both the total number of AIARs removed per year per municipality and

the number of captures per trapper per year per municipality. To account for variations in municipality size (ranging from 9.1 to 88.4 km²), both metrics were standardized by dividing them by the respective municipality area, thereby assessing AIAR removal density per year per km². The same approach was applied to the number of trappers to estimate the density of trappers engaged in control activities per km².

## Estimated risk of impact from AIAR

To assess the potential risk on the social-ecological watershed system caused by the presence of AIAR on a territory, we applied the method developed by the regional government watershed agency (EPTB-SN). This risk does not consider the local density of AIAR as, apart from captures, there is no standardized monitoring system of AIAR populations in the permanent programme. Eight components of the social-ecological watershed system (SES) that could be impacted by AIAR presence were identified. These components include sanitation, drinking water, equipment, infrastructure and works, ecosystem dynamics and functions, recreational water activities, agriculture, fishing and tourism (Table 1). In each 250 × 250 m pixel of the watershed, the watershed agency used its own SYSMA database (https://sysma.io/) to extract the percentage of the surface area covered by the eight SES components based on the attributes listed in the Table 1 (see details of SES components). A risk score was assigned to each to each SES components based on the type and strength of the impacts caused by AIAR. Then the percentage of the surface area of each SES component was multiplied by its associated risk score the overall risk level for a pixel (Estimated risk<sub>pixel</sub>) using the following equation:

$$ \text{Estimated risk}_{\text{pixel}} = \sum_{i=1}^{8} P_i \times S_i \quad (1) $$

with $P_i$ the percentage of the surface area of the i<sup>th</sup> SES component (8 components) in a 250 × 250 m pixel and $S_i$ the risk score assigned to that component. In the data set, the estimated risk for a pixel ranged from 0 to 188.45 as a pixel could encompass several SES components at risk (Fig. 1c). As (i) the aim of the article was to analyse the relationships between the estimated risk of being impacted by AIAR and the indicators of control activities (i.e., the density of trappers, removed AIAR per year and removed AIAR per trapper per year) and (ii) data of control activities were assessed at the municipality level (see previous section on capture data), all estimated risks per pixel within the same municipality were pooled (Estimated risk<sub>total</sub>) and divided by the surface area of the municipality in km² (A):

$$ \text{Estimated risk}_{\text{municipality}} = \frac{\text{Estimated risk}_{\text{total}}}{A} \quad (2) $$
<table><caption>**Table 1** Components of social-ecological system (SES) that the regional government watershed agency (EPTB SN) aims to protect in municipalities, guiding the assessment of the risk score associated with the presence of aquatic invasive alien rodents (AIAR) (see methods)</caption><thead><tr><th>Components of the SES to protect</th><th>Details of the SES components</th><th>Assigned risk score</th><th>Main risks associated with the presence of AIAR</th></tr></thead><tbody><tr><td>Sanitation</td><td>Wastewater treatment infrastructure</td><td>20</td><td>Damage on human made structure</td></tr><tr><td rowspan="3">Drinking water</td><td>Infrastructure around the catchment site (perimeter of protection)</td><td>20</td><td rowspan="3">Decrease of water quality and damage on human made structure</td></tr><tr><td>Infrastructure around the pipelines (perimeter of protection)</td><td>10</td></tr><tr><td>Other infrastructure</td><td>5</td></tr><tr><td rowspan="2">Equipment, infrastructure and works</td><td>Main hydraulic structure and drainage and main road bridge</td><td>20</td><td rowspan="2">Siltation, ice jam, collapse of riverbank and damage on human made and drainage structure</td></tr><tr><td>Local road bridge</td><td>10</td></tr><tr><td rowspan="5">Ecosystem dynamics and functions</td><td>Natura 2000 site and area with high natural-heritage value (ZNIEFF I)</td><td>20</td><td rowspan="5">Ecosystem degradation with negative effects on native biodiversity, wetlands and ecosystem services</td></tr><tr><td>Restoration and renaturing sites</td><td>20</td></tr><tr><td>Area with priority water bodies</td><td>10</td></tr><tr><td>Other area with natural-heritage value (ZNIEFF II)</td><td>5</td></tr><tr><td>Area with high density of ponds</td><td>5</td></tr><tr><td rowspan="2">Recreational water activities</td><td>Waterway, area with canoe-Kayak and other water activities</td><td>20</td><td rowspan="2">Disease transmission to human (e.g., leptospirosis)</td></tr><tr><td>Large water area on main watercourses</td><td>5</td></tr><tr><td rowspan="3">Agriculture</td><td>Grassland of livestock with a free access to watercourse</td><td>20</td><td rowspan="3">Disease transmission to livestock (e.g., leptospirosis)</td></tr><tr><td>Grassland of livestock with no access to watercourse</td><td>10</td></tr><tr><td>Winter and summer crops</td><td>5</td></tr><tr><td>Fishing</td><td>Fishing sites</td><td>20</td><td>Animal eating crops with potential effects on yields</td></tr><tr><td rowspan="3">Tourism</td><td>Area of high touristic value, city downtown, supervised swimming area and hiking tagged track along watercourse</td><td>20</td><td rowspan="3">Disease transmission to human (e.g., leptospirosis) and collapse of riverbank</td></tr><tr><td>Track along watercourse and camping site</td><td>10</td></tr><tr><td>Lodging area</td><td>5</td></tr></tbody></table>

This estimated risk per km² (Estimated risk<sub>municipality</sub>) was estimated at the same spatial scale than the control activities and ranged from 83.1 to 403.7.

## Landscape variables

As local density of AIAR is not available on the study area, we have extracted decisive landscape variables that that have been reported to affect the density of AIAR (Bertolino and Ingegno 2009; Ruys et al. 2011; Ecke et al. 2014; Pays et al. 2024). Thus, these variables can directly or indirectly contribute to explain a possible relationship between the data of control activities and the priority actions zonation and thus act as confounding factors. Five landscape variables that might affect the density of trappers and AIAR removed in each municipality were computed including the density of watercourses per km², the density of roads per km², the percentage of the municipality covered by waterbodies, the percentage of the municipality covered by built-up areas,

and the percentage of the municipality covered by riparian grasslands. The riparian grassland was defined as grassland located in a 10 m buffer zone around water bodies and watercourses in the study area (the watercourses and waterbodies being themselves defined by intersecting the water information from the land cover and hydrological network data). All five landscape variables were computed for the 28 municipalities. All spatial data were downloaded from the French government authority repositories (Table 2) and processed in QGIS (QGIS Development Team 2024) to obtain the landscapes variables.

## Data analysis

We fitted linear mixed effect models to investigate the relationship between the estimated risk of impact from AIAR for a municipality and the density of trappers involved in control activities. To fulfil statistical convenience of linear models, the two variables were log-transformed. We also
<table><caption>**Table 2** Description of the Spatial data from the French government authority repositories</caption><thead><tr><th>Spatial data</th><th>Name of the open access database</th><th>Type</th><th>Resolution</th><th>Open source</th></tr></thead><tbody><tr><td>Boundary of municipalities</td><td>ADMIN EXPRESS</td><td>Vector</td><td>Metric</td><td>https://geoservices.ign.fr/adminexpress</td></tr><tr><td>Land cover</td><td>COSIA</td><td>Raster</td><td>20 cm</td><td>https://geoservices.ign.fr/cosia</td></tr><tr><td>Hydrological network with watercourses and waterbodies</td><td>BDTOPAGE</td><td>Vector</td><td>Metric</td><td>https://www.data.gouv.fr/fr/datasets/bd-topage-r-metropole-2023/</td></tr><tr><td>Roads</td><td>BDTOPO</td><td>Vector</td><td>Metric</td><td>https://geoservices.ign.fr/bdtopo</td></tr></tbody></table>

included one of the landscape variables i.e. the amount of built-up areas, riparian grassland, water surface (%), the density of watercourses or roads (No. per km²) as a covariate. The test whether the slope of the relationship between the estimated risk of impact from AIAR for a municipality and the density of trappers involved in control activities (i.e. β₁ in the Eq. (3) below) was affected by the landscape variables, we included the two-way interactions between the two cited variables:

$$w(x) = \beta_0 + \beta_1 \log(\text{estimated risk}) + \beta_2 \text{landscape variable} + \beta_3 \log(\text{estimated risk}) \times \text{landscape variable} + \varepsilon$$

with w(x), the log-transformed response variable (e.g. density of trappers), β₀, the intercept and βₖ the coefficient estimated for each predictor xₖ and ε the error term.

As the density of trappers may vary across the three years (2021, 2022 and 2023), we included year as a cofactor. As a municipality could have three data (for the three studied years), we included the identity of the municipality as random factor. Considering the high number of potential predictors and the limited number of municipalities, we did not run a full comprehensive model that failed to converge and run distinct models for each landscape variable.

The same procedure was applied to the two other control activities variables i.e. the log-transformed density of removed AIAR per year and the log-transformed density of removed AIAR per trapper per year in a municipality. In all models, we performed diagnostics of residuals checking that residuals distribution fulfilled statistical convenience of linear model with the normality of residuals and the distribution of residuals against fitted values. Statistical analyses were performed using R 4.4.2 (R Development Core Team 2024), with the nlme package (Pinheiro et al. 2019).

## Results

The estimated risk of impact from aquatic invasive alien rodents (AIAR) did not affect the density of trappers involved in control activities in municipalities (F₁₋₃₀=0.001, P=0.975; Fig. 2A). However, that density

of trappers increased with the amount of built-up areas (β±SE=0.420±0.141, F₁₋₃₀=8.846, P=0.007; Fig. 2B). We did not detect any effect of year (F₂₋₃₀=1.344, P=0.255), amount of riparian grassland (F₁₋₃₀=0.441, P=0.513) and water surface (F₁₋₃₀=2.901, P=0.101), density of watercourses (F₁₋₃₀=0.531, P=0.473) or roads (F₁₋₃₀=1.585, P=0.218) in municipalities.

When the estimated risk of impact from AIAR increased, the density of removed AIAR per year in a municipality tended to decrease (β±SE=-0.720±0.334, F₁₋₃₀=4.491, P=0.044; Fig. 2C) so did the density of removed AIAR per trapper per year (β±SE=-0.642±0.282, F₁₋₃₀=5.092, P=0.033; Fig. 2D). The lack of significant effect of the two-way interactions between the estimated risk of being impacted by AIAR and the five landscape factors showed that these relationships were not affected by any landscape factors including the amount of built-up areas (F₁₋₃₀=1.163, P=0.292; F₁₋₃₀=1.339, P=0.251), riparian grassland (F₁₋₃₀=0.005, P=0.944; F₁₋₃₀=0.215, P=0.647) and water surface (F₁₋₃₀=1.204, P=0.282; F₁₋₃₀=2.765, P=0.109), the density of watercourses (F₁₋₃₀=0.140, P=0.711; F₁₋₃₀=2.059, P=0.165) and roads (F₁₋₃₀=0.198, P=0.660; F₁₋₃₀=2.153, P=0.152) in municipalities.

Finally, the density of removed AIAR per year and the density of removed AIAR per trapper and per year did not vary with year (F₂₋₃₀=0.238, P=0.629; F₂₋₃₀=0.092, P=0.763) and was not affected by any landscape factors including the amount of built-up areas (F₁₋₃₀=2.676, P=0.116; F₁₋₃₀=0.001, P=0.985), riparian grassland (F₁₋₃₀=0.591, P=0.449; F₁₋₃₀=0.111, P=0.743) and water surface (F₁₋₃₀=2.458, P=0.130; F₁₋₃₀=1.357, P=0.256), the density of watercourses (F₁₋₃₀=0.039, P=0.844; F₁₋₃₀=0.126, P=0.726) and roads (F₁₋₃₀=0.078, P=0.782; F₁₋₃₀=0.554, P=0.463) in municipalities.

## Discussion

Our study suggests that (i) the estimated risk of impact from AIAR for a municipality did not affect the density of trappers involved in control activities allocated to municipalities, and (ii) when the estimated risk increased, the density

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Variation in the density of trappers involved in control activities with the estimated risk of impact from aquatic invasive alien rodents (AIAR) for a municipality (A) and its amount of built-up area (B). Panels (C) and (D) show the variation in the density of removed AIAR</figcaption></figure>

per year and per trap, respectively, with the estimated risk of impact from AIAR for a municipality. Significant trends (solid lines) are presented with 95% confident intervals (dashed lines)

of removed AIAR per year and per year per trap in a municipality decreased. Our results seem to not support our prediction that the efforts allocated to control activities per municipality increase when the estimated risk of impact from AIAR for a municipality increases. This finding may highlight a mismatch between the distribution of control efforts managed by the local One Heath entity (FDG-DON49) and the zoning of priority areas identified by the regional government watershed agency (EPTB SN).

Many challenges can arise in the management plan of invasive species, affecting its efficiency. These challenges include conflicts between stakeholders (Crowley et al. 2017), the need for stewardship and coordination among a wide range of actors, land uses and collective actions (Lubell et al. 2017; Graham et al. 2019), as well as the extensive spread and reproduction patterns of IAS populations (Baker and Bode 2016). The observed mismatch between the control efforts against AIAR and the zoning of priority actions is
likely due to a lack of communication among key stakeholders including the regional government watershed agency, the region entity creating the risk zoning, the local One Health entity, the local entity managing the control activities and volunteer trappers who may not be fully aware of the spatial zoning of priority areas. To our knowledge, the map outlining these zones has not been shared with all trappers or groups of trappers. This communication may further be exacerbated by the complexity of the multiple-stakeholder network, which likely hampers coordination and, in turn, reduces the effectiveness of control activities. In France, the permanent control programme involves a wide range of entities (see Bonnet et al. (2023) for details), including both local and regional actors. Local entities coordinate and oversee field operations through their employees and groups of volunteers (e.g., local One Health entities, hunting associations, and of certified trapper associations) and provide financial supports for field expenses (e.g., municipalities, public entities for inter-municipality cooperation, and local government watershed agencies or watershed associations). Regional entities establish guidelines for the AIAR management plan, although some also contribute financially to control activities (e.g., the French Office for Biodiversity, the Conservatoire for Natural Areas, and regional government watershed agencies or watershed associations). Similar complex collaborative networks have been established in the nutria (i.e. coypu) eradication project in South Korea (Kim et al. 2019) or for other IAS management programme like the invasive wild pig (Sus scrofa) in Queensland, Australia (Abeysinghe et al., 2024a, b). Our study suggests that greater efforts should be made to strengthen the collaborative framework (Abeysinghe et al. 2023), particularly in defining objectives of captures among relevant regional and local entities, and the network of trappers. Enhancing this coordination is likely to significantly improve the long-term success of invasive species control programs (DiTomaso et al. 2017).

To significantly reduce the costs associated with IAS management, the engagement of volunteers as field operators becomes decisive to conducting control activities, particularly when IAS are widespread (Anderson et al. 2017; Melero et al. 2018; Pagès et al. 2019; Anđelković et al. 2022; van Wilgen et al. 2025). Volunteer involvement has proven essential in mitigating the impacts of IAS such as the American mink *Neovison vison* in Scotland (Bryce et al. 2011), the pine *Pinus elliottii* in coastal southern Brazil (Dechoum et al. 2019) and various *Acacia* trees in coastal South Africa (Jubase et al. 2021). Compared to other long-term IAS control programs, the scale of volunteer involvement and their activities across the entire French territory make AIAR control activities particularly distinctive. The bounty system implemented by local entities, which rewards volunteers for each AIAR trapped or shot, plays a key role

in maintaining their long-term engagement (Bonnet et al. 2023). However, the widespread reliance on volunteers also poses a significant challenge in coordinating their activities. Many volunteers may use traps primarily for recreational purposes rather than for ecosystem conservation, public health protection, or economic cost reduction. Unlike paid employees, who can be directly managed, volunteers tend to deploy traps based on personal preferences and methods, rather than following priority sites or strategies established by regional entities. From a societal perspective, such decentralized decision-making may be less effective in managing IAS if volunteers weigh the personal costs and benefits of control efforts and pay less attention to broader ecosystem impacts or the long-term risk of species spread across the landscape (Epanchin-Niell 2017).

Our results tend to show that the density of AIAR removed—both per year and per trapper per year—decreased as the estimated risk of impact from AIAR for a municipality increased. Although these patterns would benefit from confirmation on a larger scale (i.e., including more municipalities), those municipalities facing a high risk of impact often host a variety of critical infrastructure and activities, including sanitation and drinking water facilities, public works, ecologically valuable ecosystems, and human activities such as recreational water use, agriculture, fishing, and tourism. Given the high costs of control activities, the observed decline in AIAR captures in priority areas may be explained by municipalities or landowners in high-risk areas being unwilling to finance additional efforts. Additionally, trappers may limit their trapping efforts in these areas to minimize interactions between citizens and control activities. Indeed, it is widely acknowledged within the permanent control program that volunteer trappers tend to avoid setting traps in tourist areas with recreational activities, particularly during vacation periods. Volunteers may also face difficulties accessing sites with costly infrastructure (e.g., sanitation and drinking water facilities), where outsider access is often restricted. Previous studies have highlighted that property access is a key factor influencing IAS management. For instance, landowners may deny access to their properties, thereby constraining the removal of invasive species (Bertolino et al. 2011). This issue has been documented in the management of the invasive American bullfrog (*Lithobates catesbeianus*) in France (Piria et al. 2017), the guttural toad (*Sclerophrys gutturalis*) in South Africa (Vimercati et al. 2017), and invasive plant species (Gardener et al. 2010). Future research is needed to investigate the social and economic factors driving the decrease in captures as the estimated risk of AIAR impact on municipalities increases.

To assess the efficiency of control activities and improve IAS policy and management strategies, a management
program should establish relevant benchmark indices (Abeysinghe et al. 2023). Given that trappers successfully remove a substantial number of AIAR each year, local entities actively recruit a large number of volunteer trappers, and both local and regional entities provide funding for field operations, the permanent control program demonstrates a certain level of effectiveness in capturing AIAR (Bonnet et al. 2023). In the “Maine et Loire” department of France, trappers report the annual number of coypus and muskrats removed to local authorities when claiming rewards (Bonnet et al. 2021). However, this data alone is insufficient for a comprehensive evaluation of control activities. Most capture records are not standardized by trapping effort. As a result, assessing program effectiveness solely based on the number of animals removed per municipality per year is inadequate if trapping effort is not taken into account. We encourage each volunteer trapper to report the number of trapping days, cage traps used, and captures, the type of device used to trap animals, while also identifying all species trapped (i.e. not just AIAR). A more meaningful indicator would be the number of AIAR removed per trap-night, which provides a better measure of trapping efficiency. However, accurately assessing this metric requires precise trap location data, which is currently not recorded across the entire “Maine et Loire” department.

Our results show that landscape factors may not affect the decrease between the estimated risk of impact from AIAR for a municipality and the density of removed AIAR per trapper and per year. A previous analysis conducted over a 15-year period found that the number of AIAR captured per trapper per year increased with pond density and road density, while it decreased with the amount of woodland per municipality (Pays et al. 2024). Additionally, previous studies have reported that AIAR have colonised a wide range of riparian habitats in France (Ruys et al. 2011), are more likely to be present in lowlands in Italy (Bertolino and Ingegno 2009), in swamps in Sweden (Ecke et al. 2014) and tend to avoid woodlands (Bertolino and Ingegno 2009). The extensive colonisation of AIAIR across aquatic habitats in France (Schertler et al. 2020) may mask any potential landscape effects on control activities, particularly when the heterogeneity of landscape composition assessed at a municipality scale exhibits limited variability (Pays et al. 2024). Nevertheless, our study tend to show that the density of trappers is positively correlated with the extent of built-up areas. This pattern suggests that volunteer trappers may be more inclined to set traps near urban and peri-urban areas to protect human activities and infrastructure, although no significant relationship was detected between the number of trappers and the estimated risk per municipality. An alternative explanation is that municipalities with higher built-up areas, which often indicate higher human populations, may be more likely to

engage a larger number of volunteers, who primarily trap close to their places of residence. We encourage further studies to explore the motivations of volunteer trappers in control activities. Understanding people's motivations for participating in control activities against IAS is essential for recruiting and retaining volunteers (Anđelković et al. 2022).

Finally, the regional government watershed agency (EPTB-SN) assessed the risk of impact from aquatic invasive alien rodents (AIAR) considering the potential damage on the social-ecological watershed system but without taking into account the local density of AIAR. Although we do not detect any effect of landscape factors on the decrease between the estimated risk of impact from AIAR for a municipality and the density of removed AIAR per trapper and per year, it is reasonable to expect that the risk of a municipality being impacted by AIAR increases as AIAR population grows. Indeed, it has been reported that impacts of IAS generally increase with their abundance (Sofaer et al. 2018; Bradley et al. 2019) although the nature of the relationship can vary (Jackson et al. 2015). For instance, Shwiff et al. (2020) demonstrated that as feral swine populations expand, the extent and cost of agricultural and infrastructural damage also rise. Therefore, we recommend that local and regional authorities establish a standardized monitoring system for AIAR populations, and that the regional watershed agency (EPTB-SN) incorporate local AIAR density into their risk assessments.

To conclude, AIAR pose serious threats to wetlands and impose substantial economic costs on society (Diagne et al. 2020). To mitigate the substantial costs associated with implementing and maintaining control activities, as well as to minimize the impacts of IAS on social-ecological systems, prioritizing control efforts in targeted areas appears to be a prudent approach. Our results suggest that biological invasions are inherently interdisciplinary issues, in which ecological dynamics interact with governance structures, economic constraints, and human decision-making to shape management outcomes. Integrating ecological assessments with social, economic, and policy perspectives is therefore essential to improve the effectiveness of invasive species management strategies (Guareschi et al. 2024). To strengthen the strategic management of AIAR, our findings underscore the importance of fostering more collaborative discussions among stakeholders, including local and regional government authorities, field operators, and scientists. Finally, given that field operators are predominantly volunteers, future studies should focus on conducting interviews to better understand their motivations and perceptions regarding their participation in the ongoing control program. This would be particularly useful in the context of bounty system, to assess whether volunteers would be willing to accept a bonus aligned with priority zones.
**Acknowledgements** We would like to thank all local partners including volunteers who contribute to make this study possible and the references for their useful comments.

**Author contributions** OP conceived the ideas and designed methodology; AGF provided data of control activities; XG provided the zonation of priority areas; AM computed the landscape variables; OP, MB compiled the database and prepared the data for the analyses; OP analysed the data; OP wrote the manuscript; all authors have improved the manuscript with significant comments.

**Funding** Open access funding provided by Université d'Angers. This study did not receive funding.

**Data availability** As data of control activities per municipality can be sensitive, the dataset will be provided on request to the corresponding author.

## Declarations

**Compliance with ethical standards** This study examines data of captures provided by the permanent control programme against AIAR managed by FDGDON 49, which is the One Health entity responsible for pest species in the French department of "Maine-et-Loire". FDGDON 49 is the local entity recognised by the French Ministry of Agriculture and Fisheries and the French Ministry of Ecology and Sustainable Development, according to the decree of the 06/4/2007 (see methods). Thus, authors of this article were not directly involved in trapping or manipulating individuals. This study did not involve endangered or protected species.

**Competing interests** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

Abeysinghe N, O'Bryan CJ, Guerrero AM et al (2023) Unravelling how collaboration impacts success of invasive species management. People Nat 5:2093-2106. https://doi.org/10.1002/pan3.10544

Abeysinghe N, O'Bryan CJ, Rhodes JR et al (2024a) Diversity in invasive species management networks. J Environ Manag 365:121424. https://doi.org/10.1016/j.jenvman.2024.121424

Abeysinghe N, Guerrero AM, Rhodes JR et al (2024b) How success is evaluated in collaborative invasive species management: A systematic review. J Environ Manag 348:119272. https://doi.org/10.1016/j.jenvman.2023.119272

Anderson DP, Pepper MA, Travers S et al (2022) Confirming the broadscale eradication success of nutria (*Myocastor coypus*) from the Delmarva Peninsula, USA. Biol Invasions 24:3509-3521. https://doi.org/10.1007/s10530-022-02855-x

Anderson LG, Chapman JK, Es contrela D et al (2017) The role of conservation volunteers in the detection, monitoring and management of invasive alien lionfish. Manag Biol Invasions 8:589-598. https://doi.org/10.3391/mbi.2017.8.4.14

Andelković AA, Handley LL, Marchante E et al (2022) A review of volunteers' motivations to monitor and control invasive alien species. NeoBiota 73:153-175. https://doi.org/10.3897/neobiota.73.79636

Ayal F, Kodjo A, Guédon G et al (2020) Muskrats are greater carriers of pathogenic Leptospira than coypus in ecosystems with temperate climates. PLoS ONE 15:e0228577. https://doi.org/10.1371/journal.pone.0228577

Baker CM, Bode M (2016) Placing invasive species management in a spatiotemporal context. Ecol Appl 26:712-725. https://doi.org/10.1890/15-0095

Bertolino S, Angelici C, Monaco E et al (2011) Interactions between Coypu (*Myocastor coypus*) and bird nests in three mediterranean wetlands of central Italy. Hystrix It J Mamm 22:333-339. https://doi.org/10.4404/Hystrix-22.2.4595

Bertolino S, Ingegno B (2009) Modelling the distribution of an introduced species: the coypu *Myocastor Coypus* (Mammalia, Rodentia) in Piedmont region. Ital J Zool 76:340-346. https://doi.org/10.1080/11250000903155483

Bertolino S, Perrone A, Gola L (2005) Effectiveness of coypu control in small Italian wetland areas. Wildl Soc Bull 33:714-20. http://doi.org/10.2193/0091-7648(2005)33[714:EOCCIS]2.0.CO;2

Bertolino S, Viterbi R (2010) Long-term cost-effectiveness of coypu (*Myocastor coypus*) control in Piedmont (Italy). Biol Invasions 12:2549-2558. https://doi.org/10.1007/s10530-009-9664-4

Bonnet M, Guédon G, Bertolino S et al (2023) Improving the management of aquatic invasive alien rodents in France: appraisal and recommended actions. Manag Biol Invasions 14:625-640. https://doi.org/10.3391/mbi.2023.14.4.04

Bonnet M, Guédon G, Pondaven M et al (2021) Aquatic invasive alien rodents in Western France: where do we stand today after decades of control? PLoS ONE 16:e0249904. https://doi.org/10.1371/journal.pone.0249904

Boorman LA, Fuller RM (1981) The changing status of reedswamp in the Norfolk Broads, England, UK. J Appl Ecol 18:241-269. https://doi.org/10.2307/2402493

Bos D, Kentie R, La Haye M et al (2019) Evidence for the effectiveness of controlling muskrat (*Ondatra zibethicus* L.) populations by trapping. Eur J Wildl Res 65:45. https://doi.org/10.1007/s10344-019-1279-1

Bos D, van Loon EE, Klop E et al (2020) A large-scale experiment to evaluate control of invasive muskrats. Wildl Soc Bull 44:314-322. https://doi.org/10.1002/wsb.1099

Bradley BA, Laginhas BB, Whitlock R et al (2019) Disentangling the abundance-impact relationship for invasive species. Proc Natl Acad Sci USA 116:9919-9924. https://doi.org/10.1073/pnas.1818081116

Bradshaw CJ, Leroy B, Bellard C et al (2016) Massive yet grossly underestimated global costs of invasive insects. Nat Commun 7:1-8. https://doi.org/10.1038/ncomms12986

Braysher M (1993) Managing vertebrate pests: principles and Strategies. Bureau of resource sciences. Australian Government Publishing Service, Canberra

Bryce R, Oliver MK, Davies L et al (2011) Turning back the tide of American mink invasion at an unprecedented scale through community participation and adaptive management. Biol Conserv 144:575-583. https://doi.org/10.1016/j.biocon.2010.10.013

Crowley SL, Hinchliffe S, McDonald RA (2017) Conflict in invasive species management. Front Ecol Environ 17:133-141. https://doi.org/10.1002/fee.1471
Dechoum MS, Giehl ELH, Sühs BGR et al (2019) Citizen engagement in the management of non-native invasive pines: does it make a difference? *Biol Invasions* 21:175–188. https://doi.org/10.1007/s10530-018-1814-0

Dedah CO, Kazmierczak RF, Walter RK (2010) The role of bounties and human behavior on Louisiana nutria harvests. J Agric Appl Econ 42:133–142. https://doi.org/10.1017/S1074070800003345

Diagne C, Ballesteros-Mejia L, Cuthbert RN et al (2023) Economic costs of invasive rodents worldwide: the tip of the iceberg. PeerJ 11:e14935. https://doi.org/10.7717/peerj.14935

Diagne C, Leroy B, Gozlan R et al (2020) InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7:277. https://doi.org/10.1038/s41597-020-00586-z

Diggins TP, Stewart KM (2000) Evidence of large change in unionid mussel abundance from selective muskrat predation, as inferred by shell remains left on shore. Int Rev Hydrobiol 85:505–520. https://doi.org/10.1002/1522-2632(200008)85:4%3C;505::AID-IR0H505%3E;3.0.CO;2-W

DiTomaso JM, Van Steenwyk RA, Nowierski RM et al (2017) Enhancing the effectiveness of biological control programs of invasive species through a more comprehensive pest management approach. Pest Manag Sci 73:9–13. https://doi.org/10.1002/ps.4347

Ecke F, Henry A, Danell K (2014) Landscape-based prediction of the occurrence of the invasive muskrat (*Ondatra zibethicus*). Ann Zool Fenn 51:325–334. https://doi.org/10.5735/086.051.0304

Epanchin-Niell RS (2017) Economics of invasive species policy and management. Biol Invasions 19:3333–3354. https://doi.org/10.1078/s10530-017-1406-4

Gardener MR, Atkinson R, Rentería JL (2010) Eradications and people: lessons from the plant eradication program in Galapagos. Restor Ecol 18:20–29. https://doi.org/10.1111/j.1526-100X.2009.00614.x

Gosling LM (1981) Climatic determinants of spring littering by feral coypus, *Myocastor coypus*. J Zool 195:281–288

Gosling LM, Baker SJ (1989) The eradication of muskrats and coypus from Britain. Biol J Linn Soc 38:39–51. https://doi.org/10.1111/j.1095-8312.1989.tb01561.x

Graham S, Metcalf AL, Gill N et al (2019) Opportunities for better use of collective action theory in research and governance for invasive species management. Conserv Biol 33:275–287. https://doi.org/10.1111/cobi.13266

Guareschi S, Mathers KL, South J et al (2024) Framing challenges and polarized issues in invasion science: toward an interdisciplinary agenda. Bioscience 74:825–839

Harran E, Kuntz G, Decors A et al (2024) Tracking potential *Lep-tospira* sources following human cases of leptospirosis: a one health approach applied to an ecosystem in Brittany, France. One Health 18:100726. https://doi.org/10.1016/j.ohnehl.2024.100726

Jackson MC, Ruiz-Navarro A, Britton JR (2015) Population density modifies the ecological impacts of invasive species. Oikos 123:880–887. https://doi.org/10.1111/oik.01661

Jubase N, Shackleton RT, Measey J (2021) Motivations and contributions of volunteer groups in the management of invasive alien plants in South Africa’s Western Cape province. Bothalia 51:a3. https://doi.org/10.38201/btha.abc.v51.i2.3

Kim YC, Kim A, Lim J et al (2019) Distribution and management of nutria (*Myocastor coypus*) populations in South Korea. Sustainability 11:4169. https://doi.org/10.3390/su11154169

Lehtomäki J, Moilanen A (2013) Methods and workflow for spatial conservation prioritization using Zonation. Environ Model Softw 47:128–137. https://doi.org/10.1016/j.envsoft.2013.05.001

Lubell M, Jasny L, Hastings A (2017) Network governance for invasive species management. Conserv Lett 10:699–707. https://doi.org/10.1111/conl.12311

McGeoch MA, Genovesi P, Bellingham PJ et al (2016) Prioritizing species, pathways, and sites to achieve conservation targets for

biological invasion. Biol Invasions 18:299–314. https://doi.org/10.1007/s10530-015-1013-1

Melero Y, Cornulier T, Oliver MK et al (2018) Ecological traps for large-scale invasive species control: predicting settling rules by recolonising American mink post-culling. J Appl Ecol 55:1769–1779. https://doi.org/10.1111/1365-2664.13115

Mori E, Andreoni A, Cecere F et al (2020) Patterns of activity rhythms of invasive coypus *Myocastor coypus* inferred through camera-trapping. Mamm Biol 100:591–599. https://doi.org/10.1007/s42991-020-00052-8

Nagayama S, Kume M, Oota M et al (2020) Common Coypu predation on unionid mussels and terrestrial plants in an invaded Japanese river. Knowl Manag Aquat Ecosyst 421:37. https://doi.org/10.1016/j.kmae.2020.0029

Pagès M, Fischer A, van der Wal R et al (2019) Empowered communities or “cheap labour”? Engaging volunteers in the rationalised management of invasive alien species in Great Britain. J Environ Manage 229:102–111. https://doi.org/10.1016/j.jenvman.2018.06.053

Panzacchi M, Cocchi R, Genovesi P et al (2007) Population control of coypu *Myocastor coypus* in Italy compared to eradication in UK: a cost-benefit analysis. Wildl Biol 13:159–171. https://doi.org/10.2981/0909-6396(2007)13[159:PCOCMC]2.0.CO;2

Pays O, Bonnet M, Marchand E et al (2024) Landscape drivers influence the efficiency of management of aquatic invasive alien rodents in Western France. Sustainability 16:1970. https://doi.org/10.3390/su16051970

Pinheiro J, Bates D, DebRoy S et al (2019) R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3. 1. Available online: https://svn.r-project.org/R-packages/trunk/nlme/

Piria M, Copp GH, Dick JTA et al (2017) Tackling invasive alien species in Europe II: threats and opportunities until 2020. Manag Biol Invasión 8:273–286. https://doi.org/10.3391/mbi.2017.8.3.02

Potgieter LJ, Shrestha N, Cadotte MW (2022) Prioritizing terrestrial invasive alien plant species for management in urban ecosystems. J Appl Ecol 59:872–883. https://doi.org/10.1111/1365-2664.14103

QGIS Development Team (2024) QGIS Geographic information system. Open-Source Geospatial Foundation Project. Available online: http://qgis.osgeo.org/en/site/

R Development Core Team (2024) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Available online: http://www.R-project.org

Ruys T, Lorvelec O, Marre A et al (2011) River management and habitat characteristics of three sympatric aquatic rodents: common muskrat, coypu and European beaver. Eur J Wildl Res 57:851–864. https://doi.org/10.1007/s10344-011-0497-y

Schertler A, Rabitsch W, Moser D et al (2020) The potential current distribution of the coypu (*Myocastor coypus*) in Europe and climate change induced shifts in the near future. Neobiota 58:129–160. https://doi.org/10.3897/neobiota.58.33118

Sheail J (1999) The grey squirrel (*Sciurus carolinensis*) — a UK historical perspective on a vertebrate pest species. J Environ Manage 55:145–156. https://doi.org/10.1006/jema.1998.0246

Shwiff S, Pelham A, Shwiff S et al (2020) Framework for assessing vertebrate invasive species damage: the case of feral swine in the United States. Biol Invasions 22:3101–3117. https://doi.org/10.1007/s10530-020-02311-8

Simberloff D, Keitt B, Will D et al (2018) Yes we can! Exciting progress and prospects for controlling invasives on islands and beyond. West N Am Nat 78:942–958. https://doi.org/10.3398/064.078.0431

Sofaer HR, Jamevich CS, Pearse IS (2018) The relationship between invader abundance and impact. Ecosphere 9:e02415. https://doi.org/10.1002/ecs2.2415

Sofia G, Masin R, Tarolli P (2017) Prospects for crowdsourced information on the geomorphic ‘engineering’ by the invasive coypu
(Myocastor Coypus). Earth Surf Process Landf 42:365–377.
https://doi.org/10.1002/esp.4081

Stone D, Andreu M (2017) Direct application of invasive species prioritization: the spatial invasive infestation and priority analysis model. Ecol Restor 35:255–265. https://doi.org/10.3368/er.35.3.255

van Loon EE, Bos D, van Hellenberg Hubar CJ et al (2017) A historical perspective on the effects of trapping and controlling the muskrat (Ondatra zibethicus) in the Netherlands. Pest Manag Sci 73:305–312. https://doi.org/10.1002/ps.4270

van Wilgen BW, Cole NS, Baard J et al (2025) Progress towards the control of invasive alien species in the Cape Floristic Region’s protected areas. Biol Invasions 27:8. https://doi.org/10.1007/s10530-024-03459-3

Vimercati G, Davies SJ, Hui C et al (2017) Does restricted access limit management of invasive urban frogs? Biol Invasions 19:3659–3674. https://doi.org/10.1007/s10530-017-1599-6

Ydenberg RC, van Loon EE, Bos D et al (2019) Damage to dykes and levees in the Netherlands is extensive and increases with muskrat (Ondatra zibethicus) density. Lutra 62:39–53

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