Received: 19 May 2023 Revised: 29 August 2023 Accepted: 30 August 2023 DOI: 10.1002/rra.4211 RESEARCH ARTICLE What you hear may not be what you see: Potential of citizen science methods to use bats as riverine forest quality indicators David Lo pez-Bosch 1,2 | Estel Blanch 2 | Ferran Páramo 2 | Carles Flaquer-Sánchez 2 | Adrià Lo pez-Baucells 2 1 CREAF, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Catalonia, Spain Abstract 2 BiBio Research Group, Natural Sciences Mediterranean habitats will be one of the Eurasian ecosystems more strongly Museum of Granollers, Granollers, Catalonia, Spain affected by Climate Change, especially their riverine systems. Monitoring these eco- systems, which are endemism hotspots and extremely sensitive to changes in rain Correspondence David Lopez-Bosch, Natural Sciences Museum regimes and extreme weather events like droughts, is of crucial importance. Decades of Granollers, Carrer Francesc Macià of citizen science projects have proven their utility in highlighting ecological shifts 51, 08402 Granollers, Catalonia, Spain. Email: dlbosch@mcng.cat and conservation action priority areas. The Bat Monitoring Programme (www. batmonitoring.org), for instance, has already been used to develop ecological indica- Funding information  Barcelona Zoo; Agaur Industrial Fundacio tors to evaluate the evolution and conservation status of Mediterranean ecosystems. Doctorates, Grant/Award Number: However, using bats as ecological indicators for aquatic ecosystems has resulted in EMC/283/2020; Catalan Government, Grant/Award Number: DB201804 contradicting results, making its application a little controversial. In the present study, we compared two citizen science protocols (visual counting vs. passive acoustic mon- itoring) used in the Bat Monitoring Programme to test the utility of trawling bats as indicators of Mediterranean riverine habitat quality at both local and landscape scales. By doing so, we aimed to build a specific ecological indicator to determine habitat quality through visual and acoustic counts. Although both protocols pre- sented similar positive significant responses to riverine forest quality, visual counts are suggested as the best sampling approach due to their simplicity and potential within citizen science projects. Moreover, for the first time, we defined threshold values of trawling bat activity to assign different levels of habitat quality to the sam- pled rivers. We applied them in NE Iberia to exemplify the benefits of using them in a Mediterranean region and discussed the potential, pros and cons of these two citizen science methodologies to establish a pan-European river biomonitoring programme using trawling bats. KEYWORDS bioacoustics, bioindicator, chiroptera, Mediterranean river, trawling bat, waterway survey This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2023 The Authors. River Research and Applications published by John Wiley & Sons Ltd. 92 wileyonlinelibrary.com/journal/rra River Res Applic. 2024;40:92–106. 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 93 1 | I N T RO DU CT I O N In riverine ecosystems, using bats as bioindicators have shown fairly contradictory results in the past, as reported by Russo et al. The Mediterranean basin has been recently dubbed a ‘climate change’ (2021). In Europe, aquatic ecosystem quality assessment using bats hotspot (Newbold et al., 2020). While current rises in temperature will has been conducted mainly using trawling bat species (i.e., Myotis dau- increase precipitation rates in most of the planet, the Mediterranean bentonii, Myotis capaccinii or Myotis dasycneme), due to the solid tro- region will be one of the few that will lose most winter rains, up to phic relationship of these species with freshwater habitats (Almenar 40% in some regions (Newbold et al., 2020; Tuel & Eltahir, 2020). et al., 2006; Biscardi et al., 2007). In a study conducted in the North- Regarding their biodiversity, Mediterranean biomes will suffer pez-Baucells et al. (2017) found that visual east Iberian peninsula, Lo declines in species richness between 10% and 13% for each degree of counting of trawling bats was correlated with riverine forest cover planetary warming, which could rise to 30% in urbanised areas quality (Munné et al., 2003) and forest cover in a 2500 m buffer. (Newbold et al., 2020; Zittis et al., 2022). The consequences will be However, 1 year later, de Conno et al. (2018) assessed the effective- especially visible in forested and freshwater habitats, which will suffer ness of using acoustic data of multiple bat phonic groups as bioindica- changes at both landscape scale and microclimatic conditions and tors and found negative relationships between trawling bat activity structure (de Frenne et al., 2021). This is extremely worrying, consid- and different riverine habitat quality indices. All these studies have ering that the Mediterranean basin is one of the major endemism hot- used different methods and indices to assess the utility of trawling spots on the planet, especially for freshwater diversity (Filipe bats as indicators, highlighting the need to select the appropriate et al., 2013; Tierno de Figueroa et al., 2013). Mediterranean riverine ‘measures’ to develop indices of habitat integrity and quality, prefera- ecosystems are already found in a water-stressed region and present bly related to already-established indices like the index of riparian a wide range of taxa dependent on the water and microhabitats they quality (QBR from its Spanish acronyms) (Munné et al., 2003) or supply (Rocha et al., 2020; Stefanidis et al., 2018) being of particular macroinvertebrate community indices (Stefanidis et al., 2018). concern in any climate change scenario. Trawling bat activity can easily be monitored using citizen sci- In this context of anthropogenic-driven changes, having reliable ence. Waterway surveys using visual counts of trawling bats have bioindicators is of utmost importance to quantify the extent of these already been taking place in some north European territories. These changes' effects on wild animal populations, especially in sensitive initiatives have provided vast datasets used to monitor trawling bat ecosystems. Multiple taxa have been shown to reflect fluctuations in population trends (Aughney et al., 2009) and even assess habitat climatic conditions, landscape changes and habitat alterations, like selection and use by these species. Nevertheless, as has been butterflies (Dover & Settele, 2009; Lawson et al., 2012; Thomas & reported for other species (Díaz et al., 2013; Dunbar & Hanski, 2004; Torre et al., 2021), aquatic macroinvertebrates Brigham, 2010; Michaelsen et al., 2011), the biology and behaviour of (Hawkes, 1997), fish (Lo et al., 2021), amphibians (Ficetola & trawling bats may differ significantly between the Mediterranean Maiorano, 2016) and birds (Dutta, 2017). In order to use them prop- region and its northern counterparts. Therefore, establishing a erly as bioindicators, it is crucial to choose the right taxa (Bal large-scale citizen science monitoring project for aquatic bats would et al., 2018) and accurately assess what their population shifts or be necessary to understand these species' relation to changing Medi- responses are indicating (Gao et al., 2015), as some of them may terranean ecosystems. The Bat Monitoring Programme (www. reflect differential effects at macro and microscale environmental vari- batmonitoring.org), coordinated by the Natural Sciences Museum of ables (Götmark et al., 2008; Lawson et al., 2012). Granollers, adopted the waterway survey protocol already established Bats have been considered potential bioindicators for decades in the UK and Ireland, hereafter named ChiroRivers, adapted to the (Jones et al., 2009). Various studies have proven that bats are affected Mediterranean bats' phenology. In parallel, the same institution by a diverse range of ecosystem alterations like agricultural intensifi- launched the ChiroHabitats protocol, a citizen science acoustic moni-  pez- cation (Park, 2015), wildfire occurrence (Loeb & Blakey, 2021; Lo toring protocol based on automatic acoustic stations that, when  pez-Baucells Baucells et al., 2021), landscape composition (Lo applied in aquatic environments, can also easily detect and record the et al., 2017; Morris et al., 2010), light pollution (Hooker et al., 2022) activity of trawling bats. and urbanisation (Russo & Ancillotto, 2015). Being volant, highly vag- These last two protocols have great potential under the umbrella ile animals and potentially found in most habitats, but also with some  pez-Baucells et al. of bioindication and citizen science, as seen in Lo species having very specific requirements for their diurnal roosts and (2017) and Tuneu-Corral et al. (2020). However, to be able to use thus being very selective on the habitats they inhabit, make bats them for conservation on the ground, it is crucial to understand what potentially good bioindicators of habitat changes. Yet, it is possible shifts in these species' activity mean concerning both local and land- that the bioindicator potential of bats can differ depending on the scape habitat variables. Also, providing reference activity values and scale of the environmental characteristics we are assessing. For thresholds to be used in later environmental quality assessments instance, a study conducted in England and Scotland (Fuentes- would be of utmost importance to develop robust, consistent and Montemayor et al., 2017) showed that the acoustic activity of differ- easy-to-use indicators. In the present study, we compared the two ent bat species was affected differently by the landscape and local surveying protocols to elucidate how the monitoring of these species habitat characteristics, not only varying according to the scale of the could be integrated as riverine ecosystem bioindicators. This study's environmental variables assessed and the bat species but also by the specific aims were as follows: (1) Comparing the similarity of the sur- survey area (England vs. Scotland). vey results obtained through two different citizen science protocols 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 94 LÓPEZ-BOSCH ET AL. (visual vs. acoustics counts) for assessing trawling bat activity; Myotis capaccinii. A total of 60 sites were surveyed using the two (2) Assessing the effects of environmental conditions at local or micro- sampling protocols (see next section for more information) between habitat and landscape levels on trawling bat activity; (3) Summarising May and August 2021 (37 in the Segre basin and 23 in the Fluvià the pros and cons of each citizen science protocol for the establish- basin, Figure 1a,b respectively). In the landscape analysis, 58 other ment of a potential pan-European trawling bat biomonitoring project locations sampled visually by volunteers during the last year were also (4) Developing an ecological indicator based on trawling bat activity to added to the dataset, covering more or less evenly the rest of the infer habitat quality quickly and effectively in the field, based on pre- regions within the state, with sampling sites ranging approximately viously known and well-spread biological indicators. from 0 m to 1000 m.a.s.l., and covering a wide variety of habitats (from urban rivers to high mountain streams in the Pyrenees) (Figure 1c). 2 | MATERIALS AND METHODS 2.1 | Study area 2.2 | Visual sampling or ‘ChiroRivers’ The study was conducted in the north-eastern Iberian Peninsula, in a The visual surveys were conducted by adapting the ChiroRivers pro- region with a ‘dry-summer’ or ‘Mediterranean’ climate according to tocol from the Bat Monitoring Programme (see www.batmonitoring. the Köppen classification (Peel et al., 2007). The sampling sites were org/rivers). While in the ChiroRivers protocol, the surveyor selects selected across two main river basins: the Segre basin (inland) and the four sampling points to count trawling bat passes for 10 min each Fluvià basin (coastal). While the Segre area is characterised by dry using a torch or a headlamp pointing across the river (totalling 40 min), summers and cold winters and a landscape that combines extensive we sampled one point per survey, but for 1 h. Also, instead of count- agricultural lands and steep mountainous chains, the Fluvià basin pre- ing them using the light of manual torches, trawling bat activity was sents a mild climate and overall high humidity, with a mosaic land- recorded using infrared cameras at the river level, an equivalent scape combining agricultural lands and more extensive forests. Both method to that proposed in the Bat Monitoring Programme. By using areas have known breeding colonies of both Myotis daubentonii and video cameras, we could gather all the recordings for future reference. F I G U R E 1 Study area: (a) Segre and (b) Fluvià basins (Catalonia, Spain), surveyed from May to August 2021 using both visual counts and passive acoustic surveys (green circles) and (c) northeastern Iberia network of ChiroRiver transects, including all the locations surveyed by volunteers using visual counts (blue squares). [Color figure can be viewed at wileyonlinelibrary.com] 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 95 The camera was placed using a small tripod facing the river stream recorded acoustically corresponded to the trawling species perpendicularly with one external near-infrared (IR) focus. The camera M. daubentonii or M. capaccinii. started recording 1 h after sunset and recorded for one consecutive hour, coinciding with the peak activity period for trawling bats during the breeding period. The bat activity count was done manually by 2.4 | Local variables counting a ‘visual pass’ each time a trawling bat crossed the middle of the light beam of the IR focus. The habitat characteristics of each sampling site were assessed by In order to better cope with geographical variability, data from  adapting the ‘Projecte Rius’ citizen science protocol of the Associacio 58 other locations sampled visually by volunteers were also added to Habitats (www.projecterius.cat) to quantify the health status of a the dataset (only in the landscape analysis and in the models for visual 100-m-long river stretch. It includes different well-spread and used counts), totalling 118 locations (Table S1). Of these 58 points, 51 were measures and ecological indicators to assess the hydromorphological sampled during July 2022, while the other seven came from other and biological quality. years. The volunteers followed the ChiroRivers protocol, selecting For the hydromorphological quality, we assessed several categori- four sampling points and counting the visual bat activity for 10 min at cal physical characteristics of the rivers, such as river width, depth, each point. For the landscape analysis, our dataset (60 min) and the water speed and temperature. Hydromorphological quality also volunteer dataset (40 min) were standardised to the average activity included the QBR index (Munné et al., 2003), spanning from 0 to per 10 min. 100, which is calculated using scores from four numerical variables measured from a 100-m-long river stretch (each spanning from 0 to a maximum score of 25) and a categorical variable affecting the result- 2.3 | Acoustic sampling or ‘ChiroHabitats’ ing scores of the others. These measures are as follows: Acoustic sampling of bat activity was conducted following the citizen • Total riparian cover: percentage of vegetation cover in the river science protocol called ‘ChiroHabitats’ from the Bat Monitoring Pro- margin excluding annual herbaceous plants, and degree of connec- gramme (www.batmonitoring.org). Audiomoth ultrasound detectors tivity to the closest woodland, assessed visually and from satellite were used to passively record acoustic bat activity from 30 min images respectively. before sunset to 30 min after sunrise. The detectors were pro- • Cover quality: composition of native plant species (i.e., total num- grammed according to the ChiroHabitats protocol (sample rate of ber of native tree species and shrub species), degree of continuity 250 kHz and medium gain), generating 5-minute-long wav files. of the tree community in the river edge, and the structure of the AudioMoths were deployed using custom-made waterproof boxes at tree community (i.e., gallery forest). Man-made buildings, the pres- around 1 m height directly facing the river to maximise the recording ence of garbage and different levels of non-native tree species of trawling Myotis bats (see here the boxes' specificities: https:// lower the results of this value. www.batmonitoring.org/habitats/en/protocol/ ). • Cover structure: riverine forest structural complexity, with highest The 5-minute-long files were later processed using the Kaleido- values representing more structured forests with various vegeta- scope software (Wildlife Acoustics, Illinois, USA), dividing each file tion levels and covering larger areas of the river edge, calculated into 5-second-long files, using the following signal parameters in the from the percentages of tree cover within the total riparian cover. settings: minimum and maximum frequency range of 8–120 kHz, min- Shrub and helophyte covers increase the final score. Yet, the score imum and maximum length of detected pulses of 2–500 ms, maximum was lowered if trees and shrubs were found in separated patches inter-syllable gap of 500 ms and a minimum number of pulses of two. or if trees were planted in a linear fashion. These files were also automatically classified using the automatic clas- • Channel alterations: different scores according to artificial struc- sifier provided by Kaleidoscope Pro, which classified them at the spe- tures in the river, with unmodified river channels having the high- cies level. Finally, each resulting file was manually validated and, if est score, fully channelised rivers having the lowest. Other needed, reclassified to either a bat species or a phonic group (groups structures like wells or bridges further lowered the score. of species that can not be acoustically distinguished). • Geomorphological type: physical and geological characteristics of The measure for acoustic bat activity was a ‘bat pass’, defined as the substrate and the river margins. Geomorphological type has no a 5-second-long file containing at least two pulses of a bat species or score, and it only affects how the final score of cover quality is phonic group (see, e.g., Azam et al., 2015; Millon et al., 2015; Torrent measured. et al., 2018). The results of all the ChiroHabitats surveys are currently uploaded and publicly available on the online platform www. The biological quality was calculated with an adaptation of the batmonitoring.org. For the present study, only bat passes of the pho- Biological Monitoring Working Party (Hawkes, 1997), in which each nic group ‘Myotis 50’ (i.e., Myotis species with frequencies of maxi- macroinvertebrate taxon is given a score from 1 to 10 according to mum energy between 40 and 70 kHz) were used in the models. From the water quality where they are usually found. Macroinvertebrates previous and current bat trapping sessions conducted in the studied were sampled using the kick sampling technique and hand nets. All rivers (Blanch et al. 2023), in which 429 out of 434 trapped Myotis caught macroinvertebrates were classified to family level. For each bats were trawling species, we can confirm that most of the Myotis sampling point, two Biological Monitoring Working Party (BMWP) 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 96 LÓPEZ-BOSCH ET AL. indices were calculated: the absolute BMWP summing the scores of and Noguera Pallaresa) to account for the largest size of the Segre all the families found there (aBMWP), and the relative BMWP, sum- and the different water regimes of the different branches. The corre- ming the scores of all the families divided by the number of families lation between all the numerical explanatory variables was assessed found in the sampling point (rBMWP). The detailed variables of each using the corrplot function from the corrplot package (Wei & sampling point are presented in Table S2, and the field sheet used to Simko, 2021), and one variable within each pair with a correlation collect the data can be downloaded from Appendix 1. value above 0.7 was excluded. Multicollinearity of the resulting satu- rated model was assessed using variance inflation factors with the vif function from car R package (Fox & Weisberg, 2019), excluding those 2.5 | Landscape variables variables with a VIF value above five. For the final model selection, we used a multi-model approach, using a saturated model including all At the landscape level, different variables were extracted for each the selected numerical and categorical variables with the dredge func- sampling point using QGIS v.3.16.14-Hannover (QGIS Development tion of the MuMIn R package (Barton, 2022) to test all possible Team, 2020) from a vectorised version of the S2GLC 2017 Land models with the selected variables. In the dredge approach, the final Cover Map of Europe (Rybicki et al., 2020). We used buffers of model was selected as the model containing the highest number of 50, 100, 500, 1000 and 2000 m from which we extracted the per- variables and the lowest AICc value from the models with a delta- centage of 12 different land covers categories (i.e., artificial surfaces, AICc value lower than two. Non-converging models were excluded. natural material surfaces, broadleaf tree cover, coniferous tree cover, To compare the usefulness of the different pairs of models (acoustics herbaceous vegetation, moors and heathland, sclerophyllous vegeta- vs. visual), R2 values were compared to see how well each model deviance  tion, cultivated areas, vineyards, marshes, peatbogs and water bodies). adjusted to data variability using the formula 1 nulldeviance . Except for the forest habitats (broadleaf tree cover and coniferous Regarding the models with the local environmental variables, the tree cover), the rest were grouped into three categories: urban (artifi- overall QBR results and the four numerical QBR components cial surfaces), open (natural material surfaces, herbaceous vegetation, (i.e., total riparian cover, cover quality, cover structure and channel moors and heathland, sclerophyllous vegetation, cultivated areas and alteration scores) were highly correlated. With the aim of assessing vineyards) and aquatic (marshes, peatbogs and water bodies). Both small-scale forest characteristics, we performed two groups of models, forest types were kept separate because, in the Mediterranean region, one containing the four separated QBR components and another with riverine forests mostly correspond to broadleaf forests, while conifer- the total QBR score (each type of model with both acoustics and ous forests are usually more abundant. Thus, we obtained a total of visual counts as the response variables). No multicollinearity was five habitat categories. Also, for each buffer size, the Shannon diver- found for any variable. As the models with the separated QBR compo- sity index was calculated from the original 12 land cover categories to nents presented lower AICs and higher R2, these are the ones dis- measure the diversity of habitats. cussed in the present study (the results of the models with the final QBR score are included in Table S3). The final models for acoustics included cover quality, cover structure, channel alteration, relative 2.6 | Statistical analyses BMWP, water temperature and river basin. For the visual activity, the final model included cover quality, cover structure, water temperature First, we used simple linear models with acoustic and visual counts to and transparency, river basin and altitude above sea level. see if both protocols were correlated and provided comparable data. Regarding the models with landscape variables, due to the low Then, four models were made with acoustic and visual bat activity number of sampling points in the acoustics models, only the simplified (as the response variables) and local and landscape variables habitat variables (open, urban, aquatic, broadleaf forest and pine for- (as explanatory variables) separately. GLMs were performed with a ests) were kept. In contrast, the original cover variables were used for Negative Binomial distribution to account for the overdispersion of the visual models. In order to choose the proper buffer size, a satu- the data using the glm.nb function from the MASS R package rated model was run for every buffer size and the ones with the low- (Venables & Ripley, 2002). Explanatory variables included: QBR score, est AIC were chosen. None of the variables showed autocorrelation, the four numerical components of the QBR (i.e., total riparian cover, but broadleaf forest cover was excluded due to some issues with mul- cover structure, cover quality and channel alteration), river width, ticollinearity. The final model for acoustic data included open habitats, maximum depth, water speed, temperature and transparency, urban covers in a 50 m buffer and basin, while for visual data, it aBMWP and rBMWP indices, water pH and sampling point altitude. included herbaceous, natural material surfaces, peatbog, urban covers, All explanatory variables were scaled from 0 to 100 using the scale Shannon index in a 100 m buffer and basin. function. Other measured categorical variables (e.g., geomorphological type, different river substrate categories, shadow over the river and presence of different structures in the channel, see Table S2) were 2.7 | Development of a habitat quality ecological excluded from the final models for local environmental traits as they indicator using bat activity showed no relevant differences in bat activity between the different levels. The river basin was further divided into the Fluvià basin and To develop a user-friendly and practical ecological indicator using three branches of the Segre basin (i.e., Segre, Noguera Ribagorçana trawling bat activity, we needed to define a gradient of key threshold 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 97 values that could be easily understood and used to assign sampling localities to either good or bad habitat quality. The success of ecologi- cal indicators depends on their simplicity and practicality in the field to assess characteristics and habitat quality that cannot easily be cal- culated with georeferenced image systems and require other more complex methodologies. Amongst all our models, the environmental variables at the local scale surrounding the habitat (e.g., cover quality, cover structure, total riparian cover) were those that resulted in more highly significant results. Therefore, we selected this model to define the gradient of key threshold values to assign a quality value to a specific site. In order to design the ecological indicator, we modelled the effect of the environmental variable on bat activity, and extracted the regression F I G U R E 2 Correlation between acoustic bat passes (Myotis lines of said effects using the predictorEffect function from the effects 50 phonic group) per night and trawling bat counts using near Infrared package (Fox & Weisberg, 2019). We established the categorical limits video recordings per 10 minutes in the same sampling localities in the northeastern Iberian Peninsula. of different qualities using the different possible scores of the three selected variables, scaling them from 0 to 100 using the scale function and establishing four different categories: bad quality (0–25 scores), (p < 0.05). Water temperature and altitude above sea level also had deficient quality (25–50), good quality (50–75) and optimal quality significant negative effects ( p < 0.005 and 0.05 respectively). (75–100). Then, in order to determine the thresholds of bat activity, Between both models, the first one (with acoustics) presented a R2 we calculated the predicted bat activity at the limits of each habitat score of 0.41, while the second (with visual counts) had a score quality using the extracted regression line of the effect. of 0.26. Finally, we applied the resulting categories to all the ChiroRivers censuses registered in the Bat Monitoring Programme platform (www. batmonitoring.org) to exemplify the usefulness of such ecological 3.2 | Landscape-scale environmental effect models indicators. The resulting classifications can be found online and are all communicated to the citizen science project volunteers. At the landscape level, the models with the lowest AIC corresponded to the buffer at 50 m around the sampling point for acoustic activity models and at 100 m for visual counts (the results of all the models at 3 | RESULTS different buffer sizes are included in Table S4). The final models built with the acoustic data showed a negative effect of urban cover We recorded a total of 529,577 acoustic bat passes, from which (p < 0.005) on trawling bats' activity in a 50 m buffer (see Figure 3 103,612 corresponded to the Myotis 50 phonic group. We also and Table 1). However, the final model built with the visual counts counted a total of 5,815 visual bat passes of trawling bats in the video showed a significant negative effect of peatbog ( p < 0.05) and herba- recordings. Myotis nightly bat activity ranged from 0 to 6387 bat ceous (p < 0.01) habitat covers in a 100 m buffer, as well as a margin- passes per night (average of 1,726.87 ± 1,709.05) and ranged from ally negative effect of urban cover and Shannon's index score (both 0 to 519 visual bat passes in 10 min (average of 96.91 ± 133.29). We with p < 0.1). In both landscape models, basin was a significant vari- only had one night without acoustic bat passes and six nights when able for trawling bat activity accounting for regional differences. The we did not record any flying Myotis over the water. Although trawling R2 scores for landscape models were 0.29 and 0.36 for acoustics and Myotis' acoustic and visual activity were significantly correlated, with visual counts, respectively. 8 a p-value of 3.28e , their correlation value was not as high as expected, being R2 = 0.41 (Figure 2). 3.3 | Development of a habitat quality ecological indicator using trawling bat activity 3.1 | Local-scale environmental effect models Of all the significant variables from our local models, we chose cover At the local scale, for the models with the acoustic datasets and the quality to develop an ecological indicator based on trawling bat activ- four separated QBR scores, cover quality was the variable that ity that would provide easy-to-use threshold values to assign a spe- showed the strongest significant response (p < 0.005), positively cific habitat quality assessment to each sampled locality. We chose related to bat activity (see Figure 3 and Table 1). Cover structure, cover quality as this variable is strongly related to the presence of however, seemed to decrease it (p < 0.05), with rBMWP and water native and pristine riparian vegetation, and one of the main ecological temperature showing a similar negative effect ( p < 0.05). For the problems currently found in Mediterranean rivers is the increase in models using visual data, very similarly, cover quality and cover struc- allochthonous and invasive species, which are of major conservation ture had significant positive and negative effects respectively concern (Badalamenti et al., 2018; Bruno et al., 2019; Munné 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 98 LÓPEZ-BOSCH ET AL. F I G U R E 3 Estimates and modelled effects of the selected environmental variables on trawling bat activity, including the models at local (a) and landscape (b) scales, using acoustics (green circle) and visual data (blue square). The results at the landscape scale using visual bat activity include the full dataset with the extra sampling points from the volunteers. The different basin categories have been excluded from the figure for better visualisation of the effects of the environmental variables. [Color figure can be viewed at wileyonlinelibrary.com] et al., 2003; Salinas et al., 2000). Also, following the QBR index proto- 4 | DI SCU SSION col as a baseline, the cover quality score is the one that covers the most diverse characteristics of the habitat surrounding the sampling This study is the first to test the performance of two citizen-science point, as it takes into account the composition of the floral community protocols to use trawling bat activity as bioindicators and to develop in the sampling point, the coverage of the community and some an ecological indicator of riverine forest quality based on categorised aspects of the structure (i.e., riparian forest in gallery) and anthropo- bat activity levels. Our results show that both acoustic and visual bat genic alterations of the river (see Materials and Methods for more activity have the potential to reflect specific characteristics of the details). riparian forests affecting their activity, especially forest quality and We summarised the pros and cons of both citizen science proto- vegetation structure, but visual counts overperformed the acoustics in cols in Table 2 following the expert criteria of the authors. Due to the terms of ease of use in the field and data analysis workload. The local easiness and high potential of the ChiroRiver (visual counts) within cit- variables of the microhabitats were shown to relate better with trawl- izen science projects and its overperformance compared to the Chiro- ing bat activity than larger landscape-scale characteristics, suggesting Habitats in terms of workload, we selected this method to develop that the relative abundance of trawling Myotis should preferably be the ecological indicator. The effect of cover quality on visual bat activ- used to test or indicate habitat quality at the local scale or microhabi- ity followed an exponential regression line and thus an exponential tat. Below we further discuss considerations for establishing a Medi- model was used to develop the threshold values for our index. The terranean riverine biomonitoring protocol using trawling bats and the final threshold values in visual counts (bat passes/10 min) defining pros and cons of the different citizen science methods used in the the different cover quality categories were as follows: 0–5 for bad present work. scores, 6–7 for deficient, 8–11 for good and more than 12 for optimal quality (Figure 4). Finally, we applied this indicator to classify the 58 points sampled 4.1 | Acoustic versus visual counts sampling by the volunteers (Supplementary material 1). Of the 51 points, protocols 13 had no presence of trawling bat activity, 21 had a bad quality (both summing 68% as bad quality), four had a deficient quality (8%), two In our study, both sampling methodologies (visual counts and acous- had good quality scores (4%) and 10 had optimal quality scores (20%). tics) presented similar results regarding the effect of habitat 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 99 T A B L E 1 Results of the Negative Binomial GLM models at local and landscape levels and with acoustic and visual bat activity as response variable, with the variables included in the final models and the significance of each variable according to the p-value. Estimate Std. error t value p-value Local models Acoustic activity R2 = 0.41 (Intercept) *** 11.932 1.587 7.516 0.001 Cover quality *** 0.020 0.006 3.484 0.001 Cover structure * 0.016 0.007 2.243 0.029 Channel alteration  0.008 0.005 1.660 0.103 rBMWP * 0.017 0.007 2.357 0.022 Water temperature ** 0.051 0.018 2.819 0.007 Basin Noguera Pallaresa 1.160 0.669 1.733 0.089 Basin Noguera Ribagorçana *** 6.090 0.765 7.960 0.001 Basin Segre  0.657 0.385 1.707 0.094 Visual counts R2 = 0.26 (Intercept) *** 9.491 2.294 4.138 0.001 Cover quality * 0.016 0.008 2.135 0.038 Cover structure * 0.018 0.009 2.089 0.042 Water temperature ** 0.068 0.026 2.661 0.010 Water transparency 0.007 0.005 1.321 0.192 Altitude  0.024 0.016 1.491 0.142 Basin Noguera Pallaresa 0.565 1.128 0.501 0.619 Basin Noguera Ribagorçana *** 3.332 1.179 2.825 0.007 Basin Segre 0.172 0.630 0.272 0.787 Landscape models Acoustic activity (50 m buffer) R2 = 0.29 (Intercept) *** 7.501 0.292 25.703 0.000 Open 0.008 0.008 1.011 0.317 Urban *** 0.046 0.014 3.373 0.001 Basin Noguera Pallaresa 0.309 0.688 0.449 0.655 Basin Noguera Ribagorçana *** 2.741 0.513 5.339 0.000 Basin Segre 0.168 0.377 0.447 0.657 Visual counts (100 m buffer) R2 = 0.36 (Intercept) 1.369 0.969 1.413 0.161 Herbaceous * 0.011 0.006 1.850 0.067 Natural material surfaces  0.022 0.013 1.669 0.098 Peatbogs * 0.056 0.026 2.128 0.036 Shannon's index  0.015 0.009 1.732 0.086 Urban  0.029 0.016 1.740 0.085 Basin Besòs 0.465 0.978 0.476 0.635 Basin Ebre 0.301 0.939 0.321 0.749 Basin Fluvià ** 2.365 0.806 2.936 0.004 Basin Gaià 0.749 1.801 0.416 0.678 Basin Llobregat  1.516 0.848 1.788 0.077 Basin Noguera Pallaresa ** 2.719 0.915 2.970 0.004 Basin Noguera Ribagorçana * 2.200 1.007 2.185 0.031 Basin Segre *** 2.500 0.811 3.082 0.003 Basin Ter ** 2.280 0.882 2.586 0.011 Basin Tordera 1.322 0.958 1.379 0.171 Note: The significance of each variable corresponds to ***p < 0.005, **p < 0.01, *p < 0.05 and p < 0.1. 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 100 LÓPEZ-BOSCH ET AL. T A B L E 2 Comparison of acoustic and visual citizen science This constraint is a determining factor in all types of acoustic surveys. protocols pros (+) and cons ( ) for trawling bat monitoring in a citizen Most Myotis bat species present frequency-modulated calls that make science project. The first row includes the links to the respective it difficult to distinguish between different species acoustically (van protocols. de Sijpe, 2011). Thus, while we have confidence that most of the ChiroHabitats (acoustics) ChiroRivers (visual counts) acoustic bat passes in our models correspond to trawling bats (mist- www.batmonitoring.org/habitats www.batmonitoring.org/ netting was carried out in the same areas to confirm Myotis spp), we rivers cannot assure that no other Myotis species were included in our - Two days of fieldwork required + One day of fieldwork acoustic dataset. Similarly, while visual counts can certainly be required assigned to trawling bats, our study area presented two different - Need of purchasing specialised + No specialised equipment trawling bat species, Myotis daubentonii and Myotis capaccinii, (as seen equipment needed in Blanch et al., 2023), that cannot be distinguished through visual - Longer and more specialised + Almost no training detections. While these two species differ in roost selection, studies training required required on foraging habitat use have previously found that both species usu- - Large datasets, but less specific for + Only focusing on trawling trawling bats bats ally select forested riparian habitats (Almenar et al., 2006; de Conno et al., 2018; Todd & Williamson, 2019; Warren et al., 2000), rather - Much longer posterior data + Immediate results processing than non-vegetated river transects, so their potential as riverine forest + Higher statistical explanatory - Lower statistical bioindicator could be similar. However, more thorough research power explanatory power would still be needed to discern specific differences between them. characteristics on their activity levels, which is further confirmed by 4.2 | Scale- and protocol-dependent responses of the significant correlation between them (Figure 2). However, previ- trawling bats to environmental conditions ous studies have shown that these detection methods do not always present the same performance when assessing bat activity. In a study The sampling methodologies in our study performed differently on Hawaiian hoary bats (Gorresen et al., 2017), the authors found that according to the scales at which environmental variables were most flying bats recorded with video cameras were not detected using assessed. Overall, local models presented better performance (lower acoustics, which is particularly surprising regarding the relatively low- AICs) than models at the landscape level. However, different proto- frequency long-range calls of the species. Trawling Myotis bats pre- cols provided divergent results: the acoustics model presented higher sent frequency-modulated calls that are usually above 40 kHz of peak R2 at the local level, whereas the visual count model was better at the frequency (van de Sijpe, 2011), which makes the detection distance of landscape level, probably due to those including the volunteer-based their calls relatively low (Adams et al., 2012; Monadjem et al., 2017). censuses and, therefore, landscape variability. At the landscape level, Another study, aiming to find the correlation between acoustics and smaller buffer sizes resulted in better-adjusting models, presenting visual counts of emerging bats in caves (Revilla-Martín et al., 2020), lower AICs and higher R2 scores (Table S4). Buffers of 50 and 100 m found that, while you could correlate both variables, the correlation were chosen to explain our datasets. Landscape variables have been was very site-specific and also influenced by the distance the ultra- proven to be good predictors of bat presence–absence (Arthur sound recorder was placed from the cave entrance. This could poten- et al., 2014; Ducci et al., 2015) but performed very poorly when using tially happen with trawling bats as different environmental  pez-Baucells et al., 2017 also found that relative abundance effects. Lo characteristics like river width, vegetation clutter, or terrain around landscape variables only showed some effect on trawling bat the water could make bats more difficult to see and record (Wang presence–absence data, while they found no significant results for et al., 2014), affecting their detectability. This challenge was also sup- overall bat activity. Being volant mammals, bats present a relatively ported by the acoustics model that included the total QBR score as an high capacity to choose specific habitats or areas to forage, causing a explanatory variable, in which the geomorphological type of the river lot of variation in plots that are very close together. Moreover, visual bank seemed to affect the resulting bat activity (Table S3). Surpris- models probably performed better at the landscape level than the ingly, none of our final models included river width nor water speed, acoustics model because our dataset for visual landscape models was while other studies have shown that both variables are extremely larger and more geographically widespread (we included important in selecting the most appropriate hunting grounds (Almenar volunteer-collected data), presenting a higher diversity of pez-Baucells et al., 2017; Todd & Williamson, 2019). et al., 2009; Lo landscape and land-cover features. Because sampling sites were explicitly selected to detect trawling bats, it is possible that the hydromorphological characteristics of the rivers we sampled did not present enough variability to significantly 4.3 | Trawling bats as bioindicators influence their foraging activity. In terms of methodological limitations, neither of the protocols While both activity measures (visual counts and acoustics) were good allows the identification of trawling bats at the species level (Table 2). indicators of different parameters of the quality of riverine 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 101 F I G U R E 4 Cover quality categories (bad, deficient, good and optimal) included in the newly developed ecological indicator based on trawling bat activity. Examples of sampling points with the different indicator scores (a). Expected trawling bat activity (blue line) sampled using visual counts is shown using an exponential model (b). The values of visual bat activity thresholds are indicated to improve their utility and usefulness. [Color figure can be viewed at wileyonlinelibrary.com] ecosystems, the whole QBR score was finally excluded from the high trawling bat activity, these can be used to understand fine-scale selected variables on the models. This contradicts, to some extent, the variations in the structure of the vegetation around the fluvial area,  pez-Baucells et al. (2017), which found that high previous work by Lo ultimately related to a certain degree of quality and maturity of the visual trawling bat activity was indicative of high QBR scores. Our riparian forest. fieldwork was concentrated in areas with relatively high bat activity, On one hand, cover quality accounts for the total number of with almost no sampling points where trawling bats were absent, and native tree and shrub species, continuity and cover of the tree com- relatively high QBR scores. Therefore, it is likely that our dataset did munity along the river margin, riverine forest community structure not include enough variation to account for the total QBR index score (e.g., in gallery), presence or not of non-native trees and its prevalence variability. Nevertheless, as shown by our results (with both acoustic (e.g., presence of communities of non-native trees) and presence of and visual data), when the four components of the QBR index were human structures or garbage. Our models showed a significant posi- included separately, cover quality and cover structure showed signifi- tive relationship between trawling bat activity and river transects with cant effects. Our results indicate that even in areas with relatively more naturalised and native vegetation and in which the forest is 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 102 LÓPEZ-BOSCH ET AL. more structured in a gallery. We found no studies regarding cover herbaceous cover, which probably represented most of the habitat in quality and relating trawling bat activity specifically with the degree of more naturalised landscapes, and negatively affected by the amount naturalisation of the riverine forest cover. The presence of trees and of peatbogs. This may be due to the almost absence of peatbogs in shrubs near the river margin has already been proven to promote the the sampled points, only circumstantially appearing in areas with no activity of endangered trawling bat species like Myotis capaccinii in visual trawling bat activity. Almenar et al. (2009), which was present and abundant in our study area. The authors reported a strong preference for this species to for- age in rivers with forested margins than in rivers with reeds or no veg- 4.4 | Ecological indicator of riparian habitat quality etation. Yet, this study only showed preference using presence– absence data of forested vegetation but did not assess this effect at a In this study, we developed for the first time an ecological indicator of fine-scale habitat. Thus, considering the two species of trawling bats riparian cover quality based on the trawling bat activity levels (specifi- converging in our study area, this specific bat guild's activity seems to cally for visual count data). We provided specific thresholds to classify be effective in monitoring river forest quality in the Mediterranean surveyed rivers into four categories (bad, deficient, good and optimal). region. Additionally, we applied this indicator in NE Iberian rivers to test its On the other hand, cover structure is obtained from the percent- usefulness at the local scale (Figure S1). While we found it useful to age of tree, shrub and helophyte cover and their spatial distribution locate idoneous areas for trawling bat habitat conservation, we also (i.e., a proxy of forest structure complexity) as well as the presence of detected that it could be somehow misleading when applied in non- tree plantations. Our results showed that trawling bat activity was suitable sampling points for trawling bats. For instance, when we reduced in transects with a higher riverine forest structural complexity applied the indicator in a ChiroRivers transect carried out at high alti- (i.e., more continuous forest mixing various vegetation types within tudes with some turbulent waters (northwesternmost location the same forest clutches). Contrary to the cover quality score, this Figure S1), we got very inconsistent and conflicting results. We seems to contradict most studies showing that a more structured encourage other research teams, naturalists and conservationists to cover translates into higher Myotis bat activity scores (Almenar adopt and adapt the method and apply it in their countries in order pez-Baucells et al., 2017; Tuneu-Corral et al., 2020). et al., 2009; Lo to start a pan-European monitoring programme, as it has already been pez-Baucells et al. (2017) and de Yet, as we can observe comparing Lo done for other taxonomical groups like butterflies (van Swaay Conno et al. (2018), depending on the riverine habitat index used, the et al., 2019) or macroinvertebrates (Stefanidis et al., 2018). We effects of forest clutter on trawling bat activity vary drastically. Also, acknowledge that some regional variations might hamper the broad bat responses to forest clutter are very species-specific. Yates and application of the method, but slight adjustments would be enough to Muzika (2006), and Novella-Fernandez et al. (2022) found that the adapt it to any other European region. effect of various forest structural characteristics on bat occupancy models varied between different Myotis species. Our results also showed that acoustic trawling bat activity was 4.5 | Selection of a citizen science protocol for significantly lower with higher levels of urbanisation around the sam- biomonitoring pling plots. Other studies have shown the detrimental effects of urban and artificial constructions on bat species like Eptesicus serotinus One of the main drawbacks of using bats as bioindicators is that, in (Arthur et al., 2014), yet these results are often very species- and many cases, the true extent of the relationship between the variables trait-specific (Jung & Threlfall, 2018). Urbanisation may be related to and the bioindication index has not been fully assessed (Russo lower water quality and prey availability (Li & Kalcounis- et al., 2021). The development of the proposed categories of bat Rueppell, 2018) which probably makes trawling bats avoid these areas activity will help to relate trawling bat activity levels to specific habitat as foraging grounds. Also, urban areas usually show higher light pollu- quality. Establishing protocols and bat activity indicators has become tion, negatively altering bat activity patterns for light-sensitive bat a crucial need to be able to highlight conservation priority targets in a species like trawling bats (Haddock et al., 2019; Hooker et al., 2022; continental context (Ewers & Didham, 2006; Jones et al., 2009; Laforge et al., 2019; Russo et al., 2019). However, our results only Tuneu-Corral et al., 2020). Protocols to monitor trawling bat activity show a significant effect with acoustic methods, with visual activity using visual waterway surveys have been conducted for several only presenting a marginally significant response. decades in countries like the United Kingdom and Ireland, resulting in It is also relevant that acoustic bat activity appears to be nega- excellent long-term population trends for the species (Aughney tively affected by higher rBMWP scores. This goes in accordance with et al., 2009). The results of our models and reference values are prob- the results found by De Conno et al. (2018) with lower acoustic trawl- ably more representative of the Mediterranean area, but our methods ing bat activity with higher scores of macroinvertebrate indices. In this can easily be replicated and compared in any European territory. study, other bats appeared to lower their acoustic activity with higher Our results prove that, while the two citizen science protocols pez-Baucells et al. macroinvertebrate scores. Previous work by Lo can be used as relevant indicators of habitat quality in Mediterranean (2017) found no significant effects of macroinvertebrates indices on rivers, the ChiroRivers is the most appropriate due to the ease of use, visual trawling bat activity. Visual counts were positively affected by low data analyses workload and potential within a citizen science 15351467, 2024, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/rra.4211 by Readcube (Labtiva Inc.), Wiley Online Library on [18/01/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License LÓPEZ-BOSCH ET AL. 103 project. As explained above, acoustics models adjusted better for the Ferran Páramo and Eric Marín for their IT support. Finally, special variability of our dataset, at least at the local scale. However, when thanks to all the volunteers form the Bat Monitoring Programme that aiming to establish a potential network of citizen scientists to monitor sampled rivers throughout Catalonia using the ChiroRivers protocol, riverine habitat quality, there are other aspects to consider to choose whose data were used for the present work. the most suitable protocol for volunteers (e.g., certain aspects to increase participation and motivation). DATA AVAILABILITY STAT EMEN T While at prior, our results would suggest that ChiroHabitats The data that support the findings of this study are available on would be the most adequate method to be used as a bioindicator of request from the corresponding author. The data are not publicly river quality, other factors would suggest ChiroRivers as the most available due to privacy or ethical restrictions. appropriate (Table 2). When comparing the ChiroHabitats and Chir- oRivers protocols, while the former requires at least 2 days of rela- OR CID tively short fieldwork (one to deploy the detector and another to take David L opez-Bosch https://orcid.org/0000-0002-6185-4902 it back), the second can be conducted in a single night of visual sam- Estel Blanch https://orcid.org/0000-0002-7127-9294 pling in the river stretch (i.e., less than 2 h). Frensley et al. (2017) Ferran Páramo https://orcid.org/0000-0002-7534-3759 already found that one of the main factors regarding long-term volun- Carles Flaquer-Sánchez https://orcid.org/0000-0002-8618-9005 teer involvement in citizen science is the time needed to conduct the pez-Baucells Adrià Lo https://orcid.org/0000-0001-8446-0108 sampling, the differing abilities in using different tools for data gather- ing, and the time needed to analyse the raw data to obtain tangible RE FE RE NCE S results. While the ChiroRiver offers immediate results, the ChiroHabi- Adams, A. M., Jantzen, M. K., Hamilton, R. M., & Fenton, M. B. (2012). Do tat requires much more time and effort. The volunteer must learn you hear what I hear? Implications of detector selection for acoustic monitoring of bats. Methods in Ecology and Evolution, 3, 992–998. acoustic detector programming, bat call identification and have the https://doi.org/10.1111/j.2041-210X.2012.00244.x time to manually check thousands of recordings (Mas et al., 2022; Almenar, D., Aihartza, J., Goiti, U., Salsamendi, E., & Garin, I. (2006). Habi- Nelson & Gillam, 2020). Also, while the ChiroRivers requires higher tat selection and spatial use by the trawling bat Myotis capaccinii fieldwork time, this is usually seen as positive by most naturalist vol- (Bonaparte, 1837). Acta Chiropterologica, 8, 157–167. https://doi.org/ 10.3161/1733-5329(2006)8[157:HSASUB]2.0.CO;2 unteers, who are often more willing to spend time on the field than Almenar, D., Aihartza, J., Goiti, U., Salsamendi, E., & Garin, I. (2009). Forag- analysing data on a computer. That is why fieldwork time was left as ing behaviour of the long-fingered bat Myotis capaccinii: Implications neither positive nor negative in Table 2, as this matter can be highly for conservation and management. Endangered Species Research, 8, subjective depending on individual experiences. Finally, another criti- 69–78. Arthur, L., Lemaire, M., Dufrêne, L., Le Viol, I., Julien, J. F., & Kerbiriou, C. cal issue is the costs associated with participation in a citizen science (2014). Understanding bat-habitat associations and the effects of project. While the ChiroHabitats requires the volunteer to purchase monitoring on long-term roost success using a volunteer dataset. Acta an acoustic ultrasound recorder, the ChiroRivers only requires a hand Chiropterologica, 16, 397–411. https://doi.org/10.3161/ torch. Thus, we propose that the ChiroRivers waterway survey may 150811014X687350 be more adequate for establishing citizen-science river monitoring Aughney, T., Langton, S., & Roche, N. (2009). All Ireland Daubenton's bat waterway monitoring scheme 2006–2008. due to its simple methodology, affordability and immediate observable Azam, C., Kerbiriou, C., Vernet, A., Julien, J.-F., Bas, Y., Plichard, L., results. Maratrat, J., & Le Viol, I. (2015). Is part-night lighting an effective mea- Our work sets the base to use a citizen science project to estab- sure to limit the impacts of artificial lighting on bats? Global Change lish a network of trawling bat monitoring transects as indicators of riv- Biology, 21, 4333–4341. https://doi.org/10.1111/gcb.13036 Badalamenti, E., Cusimano, D., La Mantia, T., Pasta, S., Romano, S., erine quality in the Mediterranean region. The following decades will Troia, A., & Ilardi, V. (2018). The ongoing naturalisation of eucalyptus be of utmost importance as breaking points for climate change- spp. in the Mediterranean Basin: New threats to native species and induced habitat alterations (Pörtner et al., 2022). Networking between habitats. Australian Forestry, 81, 239–249. https://doi.org/10.1080/ different regions and monitoring at large scales will be crucial for data 00049158.2018.1533512 Bal, P., Tulloch, A. I. T., Addison, P. F. E., McDonald-Madden, E., & collection, assessing the effects of these changes, and developing Rhodes, J. R. (2018). Selecting indicator species for biodiversity man- proper mitigation measures in riparian areas, a conservation priority in agement. Frontiers in Ecology and the Environment, 16, 589–598. the Mediterranean basin and many other dry areas on the planet. https://doi.org/10.1002/fee.1972 Barton, K. (2022). MuMIn: Multi-Model Inference. Bat conservation trust, 2021. National Bat Monitoring Programme annual report. ACKNOWLEDGEMEN TS Biscardi, S., Russo, D., Casciani, V., Cesarini, D., Mei, M., & Boitani, L. We thank the Barcelona Zoo Foundation for their financial support of (2007). Foraging requirements of the endangered long-fingered bat: the project, as well as the Catalan Government. We also want to all The influence of micro-habitat structure, water quality and prey type. the city councils that facilitated our fieldwork on the river transects of Journal of Zoology, 273, 372–381. https://doi.org/10.1111/j.1469- 7998.2007.00337.x the respective towns, especially the city councils of Alfarràs, Balaguer, Blanch, E., Lo pez-Baucells, A., Mata, V. A., Flaquer, C., & Lo  pez-Bosch, Pont de Montanyana, Besalú and Camarassa. Also, special thanks to D. (2023). To share or not to share: DNA metabarcoding reveals trophic Xavier Puig-Montserrat for his logistical and technical support and for niche overlap between sympatric trawling bats. 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See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 106 LÓPEZ-BOSCH ET AL. Zittis, G., Almazroui, M., Alpert, P., Ciais, P., Cramer, W., Dahdal, Y., & Fnais, M. (2022). Climate change and weather extremes in the eastern  pez-Bosch, D., Blanch, E., Páramo, How to cite this article: Lo Mediterranean and Middle East. Reviews of Geophysics, 60, pez-Baucells, A. (2024). What you F., Flaquer-Sánchez, C., & Lo e2021RG000762. https://doi.org/10.1029/2021RG000762 hear may not be what you see: Potential of citizen science methods to use bats as riverine forest quality indicators. River SUPPORTING INFORMATION Research and Applications, 40(1), 92–106. https://doi.org/10. Additional supporting information can be found online in the Support- 1002/rra.4211 ing Information section at the end of this article.