Discard butterfly local extinctions through untargeted citizen science: the interplay between species traits and user effort

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Citizen science has repeatedly shown its value in documenting species occurrences, mostly in very recent years. This study investigates the effectiveness of untargeted citizen science records in discarding the possibility of local extinctions in butterfly populations across all Italian National Parks. We addressed three research questions: i) the ability of citizen science data to supplement existing knowledge to complete occurrences time series, ii) the impact of functional traits determining species appearance on data collection, and iii) the interplay between participant engagement and species appearance in the amount of diversity recorded on the iNaturalist platform. Our analysis of 47,356 records (39,929 from literature and 7,427 from iNaturalist) shows that the addition of iNaturalist data fills many recent gaps in occurrence time series, thus reducing the likelihood of potential local extinctions. User effort strongly interacts with species size, distribution, and length of flight periods in determining the frequency of records for individual species. Notably, records from more engaged users encompass a higher fraction of local biodiversity and are more likely to discard local extinctions, and these users are less affected by species size. We also provide updated butterfly checklists for all Italian National Parks and a new R package to calculate potential extinction over time. These results offer guidance for protected areas, conservationists, policymakers, and citizen scientists to optimise monitoring of local populations. butterflies citizen science conservation strategies iNaturalist National Parks species traits Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The Anthropocene epoch is characterized by a severe crisis in biodiversity, leading to a rapid and unprecedented global loss of species and genetic diversity (Ceballos et al. 2015; Turvey and Crees 2019 ). Insects are especially affected, with up to 40% of species at risk of extinction (Riley 1986 ; Habel et al. 2019 ; Sánchez-Bayo and Wyckhuys 2019 ). In some protected areas of Central Europe, insects have lost 75% of their biomass (Hallmann et al. 2017 ) and the species included in the grassland butterfly indicator index declined by 39% in number of individuals in recent decades (Warren et al. 2021 ). This widespread decline could have significant impacts on ecosystems, as insects play a crucial role in ecological networks (Rosenberg et al. 1986 ; Weisser and Siemann 2008 ; Jankielsohn 2018 ; Yang and Gratton 2014 ; Noriega et al. 2018 ; Montgomery et al. 2020 ). Despite the urgency for monitoring local extinctions and declines, species trends across space and time are largely unavailable, especially for less visible and unpopular taxa like most insects (Rocha-Ortega et al. 2021 ; Montgomery et al. 2020 ; Lobo 2016 ). Insects are also characterized by a taxonomic complexity that is often solvable only by specialists (Roskov et al. 2019 ; Marshall 2008 ), so monitoring schemes are not available for most insect taxa or, if present, are limited to specific regions and run by professional taxonomists. Due to the lack of long-term monitoring data, detecting extinctions at local and regional scales in many southern European biodiversity hotspots must be based on time series of occurrence data. Published occurrences follow a typical pattern: there are a few large datasets published in dedicated faunistic papers, rarely replicated over time, and many single records published over the years. This pattern reduces the completeness of time series. In recent decades, citizen science, the involvement of the public in scientific projects (Heigl et al. 2019 ; Fontaine et al. 2021 ), has repeatedly demonstrated its contribution to documenting species occurrences and spread of invasive species (Gallo and Waitt 2011 ; Maistrello et al. 2016 ; Crall et al. 2010 ; Mannino and Balistreri 2018 ), monitoring protected species and entire communities, as in the case of the eButterfly Monitoring Scheme (BMS) (Zapponi et al. 2017 ; Campanaro et al. 2017 ; Oberhauser and Prysby 2008 , Warren et al. 2021 ). Citizen science activities provide both targeted and untargeted data. Targeted data come from organized initiatives that aim to involve the public in pre-defined objectives, such as monitoring a specific taxon (e.g. Callaghan et al. 2020 ; Krabbenhoft and Kashian 2020 ; Carpaneto et al. 2017 ). Untargeted data are provided randomly, without a predefined and taxon-specific (or area-specific) goal, and usually come from citizens uploading their observations on citizen science platforms (e.g. iNaturalist and eBird), without following any project directives. Records from untargeted citizen science are numerous and more distributed temporally and spatially than those from targeted citizen science. However, they are more influenced by operator biases since the type and amount of data depend on the user's personal preferences (Isaac and Pocock 2015 ; Callaghan et al. 2021 ). In this study, we assess the contribution of untargeted citizen science records in discarding local extinctions in all Italian National Parks. Specifically, we addressed three main research questions: 1) To what extent can untargeted citizen science data supplement existing knowledge on butterfly diversity obtained through literature to determine the persistence or local extinction of butterflies populations? Unlike literature data, citizen science records are more recent and have seen a significant rise in recent years (Fischer et al. 2021 ) offering the possibility of resolving concerns regarding local extinctions. 2) To what extent does the data collected through citizen science on Italian butterflies depends on species appearance? The number of records uploaded on citizen science platforms may be influenced by species ecological traits. Larger species with a wider geographical distribution and longer flight periods might receive more records (Callaghan et al. 2021 ; Barbato et al. 2021 ; Stoudt et al. 2021 ). 3) Does species appearance affect participant differently depending on their level of engagement in citizen science? A more dedicated involvement in citizen science activities may lead to the observation of less common and less noticeable species (Callaghan et al. 2021 ). We examined whether users who exhibit greater effort are more or less influenced by species appearance and whether they document a larger portion of butterfly diversity with a comparable number of records compared to users characterised by lower effort. To address these questions, we analysed 58,993 verified records of Italian butterflies on iNaturalist, one of the best-known and most used citizen science platforms (Aristeidou et al. 2021 ; Echeverria et al. 2021 ; Cambria et al. 2021 ; Nugent 2018 ; Sanderson et al. 2021 ). We selected butterflies as a model taxon due to their ecological relevance (Cruden and Hermann-Parker 1979 ; Courtney et al. 1982 ; Reddi and Bai 1984 ; Jennersten 1984 ; Ghazanfar et al. 2016 ), their concerning conservation status (Franzén and Johannesson 2007 ; Dirzo et al. 2014 ; McDermott Long et al. 2017 ; Schultz et al. 2019 ) and their ease of monitoring by citizen scientists (Wei et al. 2016 ; Prudic et al. 2017 ). Within Italy, National Parks are particularly suitable for our study. They are protected areas that boast a high diversity of fauna and flora (Capotorti et al. 2012 ). Additionally, they are the areas most extensively studied by researchers, ensuring a large quantity and quality of literature data on species occurrence that can serve as a basis for evaluating how untargeted citizen science data can complement traditional scientific research. Finally, National Parks are also where citizen science activities occur most frequently, both as independent and organised activities such bioblitzes (Lundmark et al 2003). Two novel and significant resources are also provided: a) updated butterfly checklists for all Italian National Parks (with time series of occurrence for each species starting from the year 1806 - see Danaus chrysippus presence in Vesuvio National Park) and b) a new R package ( https://github.com/leondap/pets ) that includes a suite of functions designed to the calculate the potential extinction upon time series index (PETS) introduced by Labadessa et al. ( 2021 ). This is used to evaluate the importance of untargeted citizen science records in discarding local butterfly extinctions. Material And Methods Data collection We collected butterfly occurrences from two sources within Italian National Parks (Fig. 1 ): Data collected in the Italian CkMap http://faunaitalia.it/documents/CkMap_ITA.pdf . This resource contains literature data and butterfly specimen records that are kept in the main national collections. The database was published in 2007 and it is continuously updated by EB. As of December 2021 it contains 335,499 records of Italian butterflies. The spatial resolution of the checklist is represented by 10 km x 10 km squares. We only included in our analysis the occurrences whose square centre is not more than 5 km away from a National Park perimeter. Out of the total of 39,929 records present in CkMap within the perimeter of Italian National Parks, 20,191 do not have a precise observation date, so they are unusable for our analyses. Therefore the CkMap has a total of 19,738 usable data. iNaturalist observations. We identified 58,993 records of Italian butterflies, of which 7,427 collected in National Parks and uploaded until December 2021. We obtained the accuracy of occurrence data from iNaturalist and we removed data with a location error higher than 1000m. All the collected data were organised in the Darwin Core format, a widely used standard format in biodiversity research applications (Groom et al. 2019 ; Wieczorek et al. 2012 ; Wieczorek et al. 2009 ). The main fields for each record are "occurrenceID", which contains the specimen reference; “Scientific name” in genus species format; “Locality”, where the occurrence localisation is located; "decimalLatitude" and "decimalLongitude" that are the locality coordinates in decimal degrees; “basisOfRecord” that specifies the source of the records, "literature" for CkMap records and "iNaturalist" for records obtained from that platform; "recordedby" is the source of bibliographic information, reference collection, or the iNaturalist user who uploaded the data; and finally "catalogNumber" contains the URL of iNaturalist observations. The Darwin Core file is available for each National Park in the repository of PETS package ( https://github.com/leondap/pets/tree/main/data ). Aim 1 - Evaluating untargeted citizen science contribution in butterfly diversity monitoring. We assessed the Potential Extinction upon Time Series (PETS) to evaluate the role of citizen science in dispelling doubts about local extinctions. The PETS formula introduced by Labadessa et al. ( 2021 ) (Eq. 1) assesses the evidence for species loss within a community. It represents the potential extinction in the past years due to effective species losses or the absence of recent occurrence data. Eq. 1. \(PETS= \frac{{\sum }_{i=1}^{n}last year - {last occ}_{i}}{\sum _{i=1}^{n}\left(last year - {first occ}_{i}\right) + 1}\) In PETS formula (Eq. 1) first occ and last occ are the years of the first and of the last observation of the species i , respectively; last year is the year of the assessment (end of the study, 2021 in this case) and is the same for all species; n is the number of species recorded in the local butterfly community. The potential extinction index for each species is calculated based on the difference between the last year and the last record date (represented by the red bar in Fig. 2 ), divided by the time since the first observation date (represented by the cyan bar in Fig. 2 ). If all the species observed in the past have been observed in the last year, the PETS index is equal to zero. The output of PETS analysis includes the PETS index, the species list ordered by last observation date, and a graphic representation. In the graphs, each row on the Y axis represents a species and observation years on the X axis are marked as coloured squares with the colour indicating the source type. The species with more recent observations are displayed at the top, while older records are shown at the bottom. For each National Park we calculated two PETS values: i) the PETS1 index includes all the available sources (CkMap and iNaturalist) and ii) PETS0 index was obtained by considering data from CkMap only. We used the “pets” R function of the newly created PETS R package which is freely available at: https://github.com/leondap/pets . The difference between PETS0 and PETS1 represents the contribution of iNaturalist records in dispelling the perception of local extinction in each National Park butterfly community. Aims 2 and 3 - The interplay between user effort, species traits and documented biodiversity. We assessed if users who put in different levels of effort produce records with a different value in assessing local diversity. We scored the effort as the number of records for each user in the studied National Parks and transformed it by square root. To assess the value of each report we calculated the contribution of each record in establishing the PETS values as follows: for each National Park, we iteratively removed one iNaturalist record from the dataset and recalculated the PETS1 index without that single datum. The absolute value of the difference between the PETS1 obtained for the park and the PETS1 obtained without the record gives the contribution of that datum in establishing the observed potential for extinction. A Generalised Linear Mixed Model (GLMM) was used to verify if single records produced by users who put in more effort have a higher contribution in dispelling doubts about local extinctions. National parks and users were included as random factors. The contribution to the observed PETS1 was analysed by using a tweedie family. We used the “glmmTMB” function of the glmmTMB R package (Brooks et al. 2017 ). We then analysed the relationship between butterfly species traits, user effort, and the frequency of species records in our Italian butterfly dataset. To do this, we used all 58,993 records for Italian butterflies identified by the authors. For each species we obtained the following traits from Middleton-Welling et al. ( 2020 ) : i) the wing index, a measure of wing size, and a proxy for species visual appearance; ii) a set of phenology traits (number flight months, first and last month of flight, number of generations) that were subjected to PCA to obtain a single factor (Dapporto et al. 2019 ), which represents species appearance due to the duration of adult flight period; iii) the number of 10 x 10 km squares UTM cells where the species have been recorded in Italy after CkMap (representing a measure of species appearance based on their distribution). User effort was calculated as the number of butterfly records uploaded by each user. A Generalised Linear Mixed Model (GLMM) was used to assess the effect of these three species traits in determining the number of records uploaded by each user. Interactions between species' traits and user effort were also included in the model. Species and users were included as random factors. Count data was analysed using a Poisson family. We used the “glmmTMB” function of the glmmTMB R package. The interactions have been visualised in plots that distinguish the trends of users with high and low effort using the “plot_model” function of the sjPlot R package (Lüdecke 2021 ) with default settings. Finally, we evaluated if comparable amounts of records from users with varying levels of effort result in different levels of species diversity. To do this, we arranged the users based on the increasing number of records they had. Then, we separated the data into ten quantiles by aggregating the observations from users who exhibit increasing levels of effort, until each quantile boundary was reached. This method ensured that each quantile contained the same number of records, but the first quantiles were comprised of data submitted by users who showed lower levels of effort compared to the latter ones. The species diversity for each quantile was calculated using Hill numbers: species richness (q = 0), Shannon index (q = 1), Simpson index (q = 2). These calculations were performed using the “hill_taxa” function of the hillR R Package (Li 2018 ). We used Spearman tests to identify possible correlations between diversity values and the different level of user effort across the ten quantiles. Results Aim 1 - Evaluating untargeted citizen science contribution in butterfly diversity monitoring. We obtained 47,356 records from the Italian National Parks, consisting of 39,929 occurrences from CkMap and 7,427 from iNaturalist. While all observations collected by iNaturalist had an indicated year of collection, 50.6% of the records in CkMap, a total of 20,191, were not provided with this information and therefore not useful for analysing time series. The results of the PETS indexes for each National Park are displayed in Table 1 and arranged based on the contribution of iNaturalist in reducing the perception of local extinction, as indicated by the difference between PETS0 and PETS1 (Δ (PETS0 - PETS1)). The results show significant variations among the National Parks in terms of potential extinction of butterfly communities and the contribution of untargeted citizen science in reducing it. Based on CkMap data only, six Parks showed that over half of the time series for local species occurrences were represented by unconfirmed presences (PETS0 > 0.5). Table 1 For each National Park, the following data are provided: PETS1 index, PETS0 index,Richness (number of butterfly species), Δ (difference between PETS0 and PETS1 index), and % NA CkMap (percentage of CkMap data not provided with a precise observation date). National Park PETS1 PETS0 Richness Δ (PETS0 - PETS1) NA CkMap Parco Nazionale delle Cinque Terre 0.476 0.966 35 0.490 67.92 Parco Nazionale dell'Arcipelago di La Maddalena 0.254 0.615 27 0.361 34.22 Parco Nazionale delle Foreste Casentinesi, Monte Falterona e Campigna 0.256 0.605 105 0.349 87.79 Parco Nazionale del Gargano 0.199 0.524 100 0.326 60.00 Parco Nazionale dell'Appennino Tosco-Emiliano 0.473 0.781 62 0.308 13.89 Parco Nazionale delle Dolomiti Bellunesi 0.233 0.471 121 0.237 16.13 Parco Nazionale dell'Asinara 0.622 0.841 25 0.219 29.03 Parco Nazionale del Gran Sasso e Monti della Laga 0.136 0.352 147 0.216 73.98 Parco Nazionale del Circeo 0.499 0.679 62 0.180 18.87 Parco Nazionale dell'Alta Murgia 0.161 0.336 80 0.176 38.96 Parco Nazionale dell'Appennino Lucano - Val d'Agri - Lagonegrese 0.575 0.733 116 0.158 45.50 Parco Nazionale dell'Aspromonte 0.398 0.548 99 0.150 69.79 Parco Nazionale della Maiella 0.205 0.352 145 0.147 78.76 Parco Nazionale dell'Arcipelago Toscano 0.188 0.318 65 0.130 43.02 Parco Nazionale dei Monti Sibillini 0.177 0.301 143 0.124 66.72 Parco Nazionale del Gran Paradiso 0.130 0.251 154 0.121 33.99 Parco Nazionale del Cilento e Vallo di Diano 0.369 0.480 118 0.112 85.42 Parco Nazionale dell'Abruzzo, Lazio e Molise 0.202 0.311 138 0.109 52.41 Parco Nazionale della Val Grande 0.268 0.341 121 0.073 23.77 Parco Nazionale del Vesuvio 0.360 0.425 66 0.065 45.83 Parco Nazionale del Golfo di Orosei e del Gennargentu 0.061 0.124 53 0.063 21.53 Parco Nazionale della Sila 0.314 0.373 109 0.059 27.96 Parco Nazionale dell'Isola di Pantelleria 0.084 0.111 14 0.027 1.32 Parco Nazionale dello Stelvio 0.124 0.149 171 0.025 27.41 Parco Nazionale del Pollino 0.205 0.229 132 0.024 22.35 When iNaturalist data was added to the occurrence datasets, the PETS index dropped from an average of 0.449 ± 0.227 (standard deviation) (for PETS0) to 0.279 ± 0.154 (standard deviation) (for PETS1), which indicates a marked reduction in the lack of knowledge about recent species occurrence (red bar of Fig. 2 ) of 0.170 ± 0.120 (st. dev). For istance, the Gargano National Park was found to have a rich butterfly fauna which was studied in two main campaigns during 1940s and 1950s, but less than 20 species were recorded between 2000 and 2010. Over half of species were unrecorded since 1980 (Fig. 3 a) resulting in a high PETS0 value of 0.524. Citizen science activities allowed for the confirmation of 56 species in the last 5 years of research (2017–2021) returning in a great difference between PETS0 0.524 and PETS1 0.199 with a difference (Δ) of 0.326 (Table 1 and Fig. 3 a, b). All park graphs, and the butterfly species list including the first and the last observations, can be found in the Supplementary Results document (Appendix 1). Aims 2 and 3 – The interplay between user effort, species traits and documented biodiversity. The GLMM analysing the effect of user effort on the contribution of each observation to determine the PETS1 values showed a significant positive relationship (Estimate = 0.099, Standard error = 0.035, z value = 2.869, P = 0.004). This demonstrates that single records from a more committed user have a higher likelihood of reducing the perception of local extinction. The Italian data on butterflies collected on iNaturalist also showed that species with a wider distribution and a longer flight period received a higher number of records (Table 2 ), while larger species were not recorded more frequently. The three species traits had significant interactions with user effort (Table 2 ). Users with high effort (Fig. 4 a) tended to record species with a wider distribution more frequently. This relationship was less evident for users with a low engagement (Fig. 4 a). Similarly, the relationship between flight period and number of records was steeper for users with high engagement (Fig. 4 b). Table 2 The effect of the three variables of species contactability on the number of observations per user in a GLMM. Interactions with user effort (number of records per observer) are also reported. Variable Chisq P Distribution 73.049 < 0.001 Flight period 2.177 0.140 Wingspan 3.490 0.062 Distribution*User effort 508.641 < 0.001 Flight period*User effort 47.809 < 0.001 wingspan*User effort 19.882 < 0.001 Wingspan showed a different trend since users with higher effort tended to record more frequently smaller species more frequently while less engaged users showed a steeper and opposite trend (Fig. 4 c). We found that a similar number of records uploaded by users with high effort encompass a higher diversity in terms of the number of detected species (richness, q = 0) and the evenness of recorded individuals among species (Shannon index, q = 1; and Simpson index, q = 2) (q = 0: Rho = 0.893, P < 0.001; q = 1: Rho = 0.939, P < 0.001; q = 2: Rho = 0.939, P < 0.001; Fig. 5 ). Discussion We evaluated the impact of citizen science records on reducing the perception of local extinctions in butterfly communities in Italian National Parks, which have an extraordinary diversity in the European and Mediterranean regions. The records by citizen scientists confirmed that some potential/supposed local extinctions were actually due to lack of recent records. Additionally, we found that observers with varying levels of effort on iNaturalist had varying contribution to this process, primarily because they recorded different levels of butterfly diversity and responded differently to various aspects of species appearance. These findings provide crucial information for National Parks to develop effective strategies for promoting citizen science initiatives to monitor butterfly populations over time. Aim 1 - The Potential Extinction upon Time Series approach The establishment of the targeted butterfly monitoring scheme (BMS) citizen science project ( https://butterfly-monitoring.net/ ) has allowed for a precise tracking of the overall decline in butterfly populations over the past decades (Warren et al. 2021 ). The BMS has also helped detecting the effects of climate change on butterfly distribution and community composition, as well as the correlation between population trends and functional traits and phylogeny (Parmesan et al. 1999 ; Devictor et al. 2012 ; Bonelli et al. 2022 ; Halsch et al. 2021 ; Melero et al. 2022 ). However, long-term data for the BMS is only available for a few European countries and parts of North America. Local butterfly extinctions have been documented globally, even in areas without monitoring schemes (Finland: van Bergen et al. 2020 ; Panama: Basset et al. 2015 ; California: Preston et al. 2012 ; Italy: Bonelli et al. 2011 , 2022 ). In most cases, this evidence was based on exceptional datasets, mostly from the past decades, which allowed for the evaluation of a few butterfly communities. The PETS index can combine knowledge from multiple sources such as literature, museum data, expert collections, standardized monitoring, and untargeted citizen science to integrate heterogeneous and extended data and evaluate the possibility of local extinctions, even in the absence of exceptional datasets. Our dataset shows that published records are scarce or outdated for most Italian National Parks, and the occurrence of butterflies is not confirmed more than one-third of the time since their first sighting. A main reason for this is that about one-third of literature data is provided without a collection date, not even for the year, which greatly hinders the possibility to obtain complete time series. In light of the current biodiversity crisis, we recommend that researchers include precise data in their observations, especially considering the lack of well-established rules for writing faunistic papers. In this regard, citizen science is less affected by the lack of collection data. Due to a general lack of data, the likelihood of local extinctions in PETS0 appears to be quite high, with many species remaining unrecorded in national parks for decades. This highlights the need for field investigations to confirm the presence of previously recorded species. Such efforts can be costly, but the contribution of citizen science can help reduce the costs. The PETS algorithm found that iNaturalist data can play an important role in monitoring butterfly populations, reducing the lack of knowledge about persistence to an average of 11%. Another significant finding is the considerable variability in PETS0 and PETS1, as well as the difference between them (delta), among different national parks. This variability is largely dependent on the time since the last faunistic study of each park, and to some extent on the level of citizen science activity. For example, the Cinque Terre National Park has the highest delta value, largely due to the limited research efforts for butterflies in the park and the lack of published studies on its fauna. The PETS0 indicates a potential erosion of up to 94% of the community data over time, but the PETS1, which incorporates citizen science data, shows a lower potential extinction rate of 48.2%. On the other hand, the Pantelleria National Park has the lowest delta value (2.2%) due to recent studies (Voda et al. 2016) that make the iNaturalist data less impactful, although still significant. The Casentino National Park, which has been well-studied by both professional and amateur entomologists, has a high PETS0 value of 0.615 (61.5% of incompleteness) because most of the main studies in the 20th century did not report collection dates, rendering 87.79% of the data useless to assess time series. Aim 2 and 3 - The effect of species traits and users effort on iNaturalist occurrences. The analysis of PETS1 showed that single observations uploaded on iNaturalist by users who put in more effort contribute more to evaluating the potential for local extensions in Italian National Parks. This is expected if more committed users tend to record a higher proportion of butterfly diversity, being more focussed on taking pictures of different species and differently affected by species appearance. In general, our findings align with previous research on birds by Callaghan et al. ( 2021 ), which demonstrated that the availability of untargeted citizen science data depends on species appearance. For butterflies, we found that larger species with a longer flight period and broader geographical distribution are more likely to have a greater number of records available on the iNaturalist platform. A higher number of records for species showing a wider distribution and a longer flight period cannot be considered as a bias but as a desirable property, since these trends are the basis of high quality data obtained from structured monitoring schemes (e.g., in transect counts). However, this property is only shown by iNaturalist users uploading a high number of observations, as documented by the strong interactions between phenology and species range with user effort. In the case of less engaged users, the correlation between phenology and species range with user effort is less strict than for highly engaged users. This result could be due to the fact that they do not use iNaturalist frequently and their observations constitute too small samples to be affected by phenology and distribution. While a positive relationship linking upload frequency with phenology and distribution is a desired property of data, the tendency to document more often the occurrence of large species is a typical bias of citizen science data (Kral-O'Brien et al. 2020; Isaac et al. 2011 ; Moranz 2010 , Dennis et al. 2006 ). This expected behaviour is not generally confirmed in our analysis because larger species did not score a higher number of records. However, the interaction between user engagement and butterfly size showed a significant effect. In fact, the decision to upload an observation does not only depend on the probability of encountering a given species, but also on other factors, such as the personal appreciation for that species (e.g. Callaghan et al. 2021 ; Isaac and Pocock 2015 ). Also in this case, highly committed users provide more accurate data, as they do not seem to be selectively attracted to bigger and more visible butterfly species. The preference for capturing pictures of both large and small butterflies by highly engaged users is likely to contribute to the higher diversity observed in their records, both in terms of species richness and evenness. It is possible that these users may learn more about the taxonomy of the butterfly group they are interested in and photograph rarer or less conspicuous species. Additionally, these users may also search more widely to find species with limited distributions, and document butterflies during different seasons. Furthermore, it is possible that a highly committed user may actively search for species not encountered yet, which may further contribute to a more diverse sample of species captured in photographs. Final remarks Protected areas play a critical role in conserving biodiversity, promoting sustainability, and raising public awareness of the importance of natural capital and ecosystem services (Cooke et al. 2023 ; Chowdhury et al. 2022; Bastian 2013 ; Geldmann et al. 2013 ; Kettunen and Ten Brink 2013 ; Millennium Ecosystem Assessment 2013 ; Stolton et al. 2015 ; Signorello et al. 2018 ). Involving citizens in biodiversity monitoring through citizen science has been shown to be an effective and efficient way to gather data and information (Fontaine et al. 2021 ; Mannino and Balistreri 2018 ; Dennis et al. 2017 ; Zapponi et al. 2017 ). Our study suggests that citizen science data can also be used to complement existing literature data to more accurately determine the likelihood of species extinction. This information can then be used by National Parks to prioritize their conservation efforts and save financial resources. National Parks should encourage citizens to participate in both targeted and untargeted projects to gather standard and opportunistic data. This can be done through events such as bioblitz, where people are educated about the importance of monitoring biodiversity and encouraged to upload their observations to platforms like iNaturalist. It is important to be aware of the limitations of citizen science data, including the unequal contributions and quality of data provided by highly engaged users. To address this, National Parks should also promote activities that educate and engage the general public, such as workshops focused on taxa identification with the help of expert taxonomists. In Italy, this has already begun with the hosting of the first Italian BMS workshop in the Sila National Park in 2019, which has since been replicated in four other National Parks. This increased knowledge is likely to result in higher quality data and less influence from aesthetic preferences (Callaghan et al. 2021 ; Barbato et al. 2021 ; Randler 2021 ). Additionally, individuals who are highly engaged in untargeted citizen science are more likely to participate in targeted projects, such as the globally successful Butterfly Monitoring Scheme (Warren et al. 2021 ). The Italian National Parks are also committed to carrying out pollinator monitoring, including butterfly counts, through Ministry funding with the involvement of volunteer. Improving taxonomy knowledge through citizen science can also help to address the shortage of taxonomists as outlined by the Red List of Taxonomists, a European Commission-funded initiative to increase awareness of the available expertise for preserving insect biodiversity. Declarations Acknowledgments We would like to thank the many thousands of citizen scientists who contributed butterfly records to iNaturalist. E.v.T. and L.D. acknowledge the support of NBFC to University of Florence, Department of Biology, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, “Dalla ricerca all’impresa”, Investimento 1.4, Project CN00000033. Funding E.v.T. and L.D. acknowledge the support of NBFC to University of Florence, Department of Biology, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, “Dalla ricerca all’impresa”, Investimento 1.4, Project CN00000033. M.M. was co-funded by “la Caixa” Foundation (ID 100010434) (grant LCF/BQ/DR20/11790020). L.P. was co-funded by the European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended and Ministerial Decree No. 1062 of August 10, 2021. L.D. was co-funded by the projects "Monitoraggio dei maggiori gruppi di impollinatori di sei Parchi dell'Appennino Centro-Settentrionale", “Ricerca e conservazione sugli Impollinatori dell’Arcipelago Toscano e divulgazione sui Lepidotteri del Parco” included with the Direttiva Biodiversità projects 2019-2022 of the Italian Ministero della Transizione Ecologica. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions EvT, AC, LD designed the experiment, EvT, GS, MB, MM, LP, VS, AC, LD identified images uploaded on iNaturalist, EB and SB collected literature data in the updated version of CkMap, LD wrote the R functions of the PETS package, LD and EvT carried out the analyses, all the authors discussed the preliminary results and contributed in the interpretation of the results and in writing the manuscript. 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Supplementary Files Appendix1.docx Cite Share Download PDF Status: Published Journal Publication published 05 Oct, 2023 Read the published version in Biodiversity and Conservation → Version 1 posted Editorial decision: Major revision 28 Aug, 2023 Reviews received at journal 13 Jul, 2023 Reviewers agreed at journal 19 Jun, 2023 Reviewers agreed at journal 06 Jun, 2023 Reviewers invited by journal 30 May, 2023 Editor assigned by journal 18 Feb, 2023 Submission checks completed at journal 18 Feb, 2023 First submitted to journal 17 Feb, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2600076","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":177051341,"identity":"e93544a8-dfcb-4cbc-a162-b29328a6aa0c","order_by":0,"name":"Elia van 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Florence","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Dapporto","suffix":""}],"badges":[],"createdAt":"2023-02-17 17:59:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2600076/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2600076/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10531-023-02721-9","type":"published","date":"2023-10-05T15:01:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":33230613,"identity":"6da7b273-c6a0-43e9-9623-b400b4dcb39f","added_by":"auto","created_at":"2023-02-21 14:30:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":365882,"visible":true,"origin":"","legend":"\u003cp\u003eThe Italian National Parks shown in a map with the collected data divided by CkMap (CK, unusable data in brackets) and iNaturalist (iNat) sources.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/02d8821acac8c009d59242bc.png"},{"id":33230612,"identity":"d93c19b4-2c0b-4e32-9d48-2288e95fafd9","added_by":"auto","created_at":"2023-02-21 14:30:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47056,"visible":true,"origin":"","legend":"\u003cp\u003eThe rationale of the PETS algorithm to compute potential extinction based on three records (1981, 1985, and 2011) from 1981 to 2021. Same abbreviations as in Eq 1.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/8e4ae11b7e6a4299ea48dcea.png"},{"id":33232033,"identity":"ea419ce1-9be6-4b18-aebf-1e599c469635","added_by":"auto","created_at":"2023-02-21 14:38:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":483551,"visible":true,"origin":"","legend":"\u003cp\u003eThe time series graphs produced by PETS analysis on the butterfly data of the Gargano National Park where each species is represented with a row with its records as in Figure 2. (a) The results for PETS0 where literature records (dark grey dots) are only used to assess the potential for extinction and (b) the result for PETS1 where data from iNaturalist (in red) are also added. The years with both kinds of records for any given species are reported in blue. Green and pink segments represent persistence and absence as defined in Figure 2.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/1e347871e796c008cab63c8e.png"},{"id":33230614,"identity":"ea955d26-3082-4c62-ac36-857e693e7e99","added_by":"auto","created_at":"2023-02-21 14:30:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88413,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between number of records per user and species range (A), phenology (B) and Wingspan (C). Interaction between species traits and user effort is visualised by dividing the dataset in low and high effort users as done by “plot_model” R function of the sjPlot R package.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/00fc722a4f6592286566c955.png"},{"id":33230615,"identity":"f96af72b-a5d2-49e6-9945-04f14f122d5f","added_by":"auto","created_at":"2023-02-21 14:30:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":48842,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between three indexes of diversity obtained after Hill’s numbers (q=0: richness, q=1: Shannon index, q=2: Simpson index) for ten quantiles of observation containing a similar number of observations by user showing an increasing effort.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/7668ecb7891fa69c9efb516b.png"},{"id":44302425,"identity":"71ecb11e-91d2-480f-937c-ae1e0c64d682","added_by":"auto","created_at":"2023-10-09 15:10:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1227239,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/021f4b96-a6ba-4548-b46c-40ebadb42f95.pdf"},{"id":33233011,"identity":"0ecfa150-270f-432a-93bf-3c8134529053","added_by":"auto","created_at":"2023-02-21 14:46:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":521508,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2600076/v1/8def5618d8e918732e67d2f0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discard butterfly local extinctions through untargeted citizen science: the interplay between species traits and user effort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Anthropocene epoch is characterized by a severe crisis in biodiversity, leading to a rapid and unprecedented global loss of species and genetic diversity (Ceballos et al. 2015; Turvey and Crees \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Insects are especially affected, with up to 40% of species at risk of extinction (Riley \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Habel et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; S\u0026aacute;nchez-Bayo and Wyckhuys \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In some protected areas of Central Europe, insects have lost 75% of their biomass (Hallmann et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the species included in the grassland butterfly indicator index declined by 39% in number of individuals in recent decades (Warren et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This widespread decline could have significant impacts on ecosystems, as insects play a crucial role in ecological networks (Rosenberg et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Weisser and Siemann \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jankielsohn \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yang and Gratton \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Noriega et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Montgomery et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the urgency for monitoring local extinctions and declines, species trends across space and time are largely unavailable, especially for less visible and unpopular taxa like most insects (Rocha-Ortega et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Montgomery et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lobo \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Insects are also characterized by a taxonomic complexity that is often solvable only by specialists (Roskov et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marshall \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), so monitoring schemes are not available for most insect taxa or, if present, are limited to specific regions and run by professional taxonomists.\u003c/p\u003e \u003cp\u003eDue to the lack of long-term monitoring data, detecting extinctions at local and regional scales in many southern European biodiversity hotspots must be based on time series of occurrence data. Published occurrences follow a typical pattern: there are a few large datasets published in dedicated faunistic papers, rarely replicated over time, and many single records published over the years. This pattern reduces the completeness of time series. In recent decades, citizen science, the involvement of the public in scientific projects (Heigl et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fontaine et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), has repeatedly demonstrated its contribution to documenting species occurrences and spread of invasive species (Gallo and Waitt \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Maistrello et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Crall et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mannino and Balistreri \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), monitoring protected species and entire communities, as in the case of the eButterfly Monitoring Scheme (BMS) (Zapponi et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Campanaro et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oberhauser and Prysby \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Warren et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Citizen science activities provide both targeted and untargeted data. Targeted data come from organized initiatives that aim to involve the public in pre-defined objectives, such as monitoring a specific taxon (e.g. Callaghan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Krabbenhoft and Kashian \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Carpaneto et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Untargeted data are provided randomly, without a predefined and taxon-specific (or area-specific) goal, and usually come from citizens uploading their observations on citizen science platforms (e.g. iNaturalist and eBird), without following any project directives. Records from untargeted citizen science are numerous and more distributed temporally and spatially than those from targeted citizen science. However, they are more influenced by operator biases since the type and amount of data depend on the user's personal preferences (Isaac and Pocock \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Callaghan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this study, we assess the contribution of untargeted citizen science records in discarding local extinctions in all Italian National Parks. Specifically, we addressed three main research questions:\u003c/p\u003e \u003cp\u003e1) To what extent can untargeted citizen science data supplement existing knowledge on butterfly diversity obtained through literature to determine the persistence or local extinction of butterflies populations? Unlike literature data, citizen science records are more recent and have seen a significant rise in recent years (Fischer et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) offering the possibility of resolving concerns regarding local extinctions.\u003c/p\u003e \u003cp\u003e2) To what extent does the data collected through citizen science on Italian butterflies depends on species appearance? The number of records uploaded on citizen science platforms may be influenced by species ecological traits. Larger species with a wider geographical distribution and longer flight periods might receive more records (Callaghan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Barbato et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stoudt et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e3) Does species appearance affect participant differently depending on their level of engagement in citizen science? A more dedicated involvement in citizen science activities may lead to the observation of less common and less noticeable species (Callaghan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We examined whether users who exhibit greater effort are more or less influenced by species appearance and whether they document a larger portion of butterfly diversity with a comparable number of records compared to users characterised by lower effort.\u003c/p\u003e \u003cp\u003eTo address these questions, we analysed 58,993 verified records of Italian butterflies on iNaturalist, one of the best-known and most used citizen science platforms (Aristeidou et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Echeverria et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cambria et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nugent \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sanderson et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We selected butterflies as a model taxon due to their ecological relevance (Cruden and Hermann-Parker \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Courtney et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Reddi and Bai \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Jennersten \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Ghazanfar et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), their concerning conservation status (Franz\u0026eacute;n and Johannesson \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Dirzo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McDermott Long et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schultz et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and their ease of monitoring by citizen scientists (Wei et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Prudic et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin Italy, National Parks are particularly suitable for our study. They are protected areas that boast a high diversity of fauna and flora (Capotorti et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, they are the areas most extensively studied by researchers, ensuring a large quantity and quality of literature data on species occurrence that can serve as a basis for evaluating how untargeted citizen science data can complement traditional scientific research. Finally, National Parks are also where citizen science activities occur most frequently, both as independent and organised activities such bioblitzes (Lundmark et al 2003).\u003c/p\u003e \u003cp\u003eTwo novel and significant resources are also provided: a) updated butterfly checklists for all Italian National Parks (with time series of occurrence for each species starting from the year 1806 - see \u003cem\u003eDanaus chrysippus\u003c/em\u003e presence in Vesuvio National Park) and b) a new R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/leondap/pets\u003c/span\u003e\u003cspan address=\"https://github.com/leondap/pets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that includes a suite of functions designed to the calculate the potential extinction upon time series index (PETS) introduced by Labadessa et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is used to evaluate the importance of untargeted citizen science records in discarding local butterfly extinctions.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eWe collected butterfly occurrences from two sources within Italian National Parks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eData collected in the Italian CkMap \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://faunaitalia.it/documents/CkMap_ITA.pdf\u003c/span\u003e\u003cspan address=\"http://faunaitalia.it/documents/CkMap_ITA.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This resource contains literature data and butterfly specimen records that are kept in the main national collections. The database was published in 2007 and it is continuously updated by EB. As of December 2021 it contains 335,499 records of Italian butterflies. The spatial resolution of the checklist is represented by 10 km x 10 km squares. We only included in our analysis the occurrences whose square centre is not more than 5 km away from a National Park perimeter. Out of the total of 39,929 records present in CkMap within the perimeter of Italian National Parks, 20,191 do not have a precise observation date, so they are unusable for our analyses. Therefore the CkMap has a total of 19,738 usable data.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eiNaturalist observations. We identified 58,993 records of Italian butterflies, of which 7,427 collected in National Parks and uploaded until December 2021. We obtained the accuracy of occurrence data from iNaturalist and we removed data with a location error higher than 1000m.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAll the collected data were organised in the Darwin Core format, a widely used standard format in biodiversity research applications (Groom et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wieczorek et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wieczorek et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The main fields for each record are \"occurrenceID\", which contains the specimen reference; \u0026ldquo;Scientific name\u0026rdquo; in genus species format; \u0026ldquo;Locality\u0026rdquo;, where the occurrence localisation is located; \"decimalLatitude\" and \"decimalLongitude\" that are the locality coordinates in decimal degrees; \u0026ldquo;basisOfRecord\u0026rdquo; that specifies the source of the records, \"literature\" for CkMap records and \"iNaturalist\" for records obtained from that platform; \"recordedby\" is the source of bibliographic information, reference collection, or the iNaturalist user who uploaded the data; and finally \"catalogNumber\" contains the URL of iNaturalist observations. The Darwin Core file is available for each National Park in the repository of PETS package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/leondap/pets/tree/main/data\u003c/span\u003e\u003cspan address=\"https://github.com/leondap/pets/tree/main/data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAim 1 - Evaluating untargeted citizen science contribution in butterfly diversity monitoring.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe assessed the Potential Extinction upon Time Series (PETS) to evaluate the role of citizen science in dispelling doubts about local extinctions. The PETS formula introduced by Labadessa et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Eq.\u0026nbsp;1) assesses the evidence for species loss within a community. It represents the potential extinction in the past years due to effective species losses or the absence of recent occurrence data.\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;1.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PETS= \\frac{{\\sum }_{i=1}^{n}last year - {last occ}_{i}}{\\sum _{i=1}^{n}\\left(last year - {first occ}_{i}\\right) + 1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn PETS formula (Eq.\u0026nbsp;1) \u003cem\u003efirst occ\u003c/em\u003e and \u003cem\u003elast occ\u003c/em\u003e are the years of the first and of the last observation of the species \u003cem\u003ei\u003c/em\u003e, respectively; \u003cem\u003elast year\u003c/em\u003e is the year of the assessment (end of the study, 2021 in this case) and is the same for all species; \u003cem\u003en\u003c/em\u003e is the number of species recorded in the local butterfly community. The potential extinction index for each species is calculated based on the difference between the last year and the last record date (represented by the red bar in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), divided by the time since the first observation date (represented by the cyan bar in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). If all the species observed in the past have been observed in the last year, the PETS index is equal to zero. The output of PETS analysis includes the PETS index, the species list ordered by last observation date, and a graphic representation. In the graphs, each row on the Y axis represents a species and observation years on the X axis are marked as coloured squares with the colour indicating the source type. The species with more recent observations are displayed at the top, while older records are shown at the bottom.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor each National Park we calculated two PETS values: i) the PETS1 index includes all the available sources (CkMap and iNaturalist) and ii) PETS0 index was obtained by considering data from CkMap only. We used the \u0026ldquo;pets\u0026rdquo; R function of the newly created PETS R package which is freely available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/leondap/pets\u003c/span\u003e\u003cspan address=\"https://github.com/leondap/pets\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe difference between PETS0 and PETS1 represents the contribution of iNaturalist records in dispelling the perception of local extinction in each National Park butterfly community.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAims 2 and 3 - The interplay between user effort, species traits and documented biodiversity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe assessed if users who put in different levels of effort produce records with a different value in assessing local diversity. We scored the effort as the number of records for each user in the studied National Parks and transformed it by square root. To assess the value of each report we calculated the contribution of each record in establishing the PETS values as follows: for each National Park, we iteratively removed one iNaturalist record from the dataset and recalculated the PETS1 index without that single datum. The absolute value of the difference between the PETS1 obtained for the park and the PETS1 obtained without the record gives the contribution of that datum in establishing the observed potential for extinction. A Generalised Linear Mixed Model (GLMM) was used to verify if single records produced by users who put in more effort have a higher contribution in dispelling doubts about local extinctions. National parks and users were included as random factors. The contribution to the observed PETS1 was analysed by using a tweedie family. We used the \u0026ldquo;glmmTMB\u0026rdquo; function of the glmmTMB R package (Brooks et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then analysed the relationship between butterfly species traits, user effort, and the frequency of species records in our Italian butterfly dataset. To do this, we used all 58,993 records for Italian butterflies identified by the authors. For each species we obtained the following traits from Middleton-Welling et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) : i) the wing index, a measure of wing size, and a proxy for species visual appearance; ii) a set of phenology traits (number flight months, first and last month of flight, number of generations) that were subjected to PCA to obtain a single factor (Dapporto et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which represents species appearance due to the duration of adult flight period; iii) the number of 10 x 10 km squares UTM cells where the species have been recorded in Italy after CkMap (representing a measure of species appearance based on their distribution). User effort was calculated as the number of butterfly records uploaded by each user.\u003c/p\u003e \u003cp\u003eA Generalised Linear Mixed Model (GLMM) was used to assess the effect of these three species traits in determining the number of records uploaded by each user. Interactions between species' traits and user effort were also included in the model. Species and users were included as random factors. Count data was analysed using a Poisson family. We used the \u0026ldquo;glmmTMB\u0026rdquo; function of the glmmTMB R package. The interactions have been visualised in plots that distinguish the trends of users with high and low effort using the \u0026ldquo;plot_model\u0026rdquo; function of the sjPlot R package (L\u0026uuml;decke \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with default settings.\u003c/p\u003e \u003cp\u003eFinally, we evaluated if comparable amounts of records from users with varying levels of effort result in different levels of species diversity. To do this, we arranged the users based on the increasing number of records they had. Then, we separated the data into ten quantiles by aggregating the observations from users who exhibit increasing levels of effort, until each quantile boundary was reached. This method ensured that each quantile contained the same number of records, but the first quantiles were comprised of data submitted by users who showed lower levels of effort compared to the latter ones. The species diversity for each quantile was calculated using Hill numbers: species richness (q\u0026thinsp;=\u0026thinsp;0), Shannon index (q\u0026thinsp;=\u0026thinsp;1), Simpson index (q\u0026thinsp;=\u0026thinsp;2). These calculations were performed using the \u0026ldquo;hill_taxa\u0026rdquo; function of the hillR R Package (Li \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We used Spearman tests to identify possible correlations between diversity values and the different level of user effort across the ten quantiles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eAim 1 - Evaluating untargeted citizen science contribution in butterfly diversity monitoring.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe obtained 47,356 records from the Italian National Parks, consisting of 39,929 occurrences from CkMap and 7,427 from iNaturalist. While all observations collected by iNaturalist had an indicated year of collection, 50.6% of the records in CkMap, a total of 20,191, were not provided with this information and therefore not useful for analysing time series.\u003c/p\u003e \u003cp\u003eThe results of the PETS indexes for each National Park are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and arranged based on the contribution of iNaturalist in reducing the perception of local extinction, as indicated by the difference between PETS0 and PETS1 (Δ (PETS0 - PETS1)). The results show significant variations among the National Parks in terms of potential extinction of butterfly communities and the contribution of untargeted citizen science in reducing it. Based on CkMap data only, six Parks showed that over half of the time series for local species occurrences were represented by unconfirmed presences (PETS0\u0026thinsp;\u0026gt;\u0026thinsp;0.5).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFor each National Park, the following data are provided: PETS1 index, PETS0 index,Richness (number of butterfly species), Δ (difference between PETS0 and PETS1 index), and % NA CkMap (percentage of CkMap data not provided with a precise observation date).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Park\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePETS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePETS0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRichness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔ (PETS0 - PETS1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA CkMap\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale delle Cinque Terre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Arcipelago di La Maddalena\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale delle Foreste Casentinesi, Monte Falterona e Campigna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e87.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Gargano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Appennino Tosco-Emiliano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale delle Dolomiti Bellunesi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Asinara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Gran Sasso e Monti della Laga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Circeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Alta Murgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Appennino Lucano - Val d'Agri - Lagonegrese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Aspromonte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale della Maiella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Arcipelago Toscano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dei Monti Sibillini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Gran Paradiso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Cilento e Vallo di Diano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Abruzzo, Lazio e Molise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale della Val Grande\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Vesuvio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Golfo di Orosei e del Gennargentu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale della Sila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dell'Isola di Pantelleria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale dello Stelvio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParco Nazionale del Pollino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen iNaturalist data was added to the occurrence datasets, the PETS index dropped from an average of 0.449\u0026thinsp;\u0026plusmn;\u0026thinsp;0.227 (standard deviation) (for PETS0) to 0.279\u0026thinsp;\u0026plusmn;\u0026thinsp;0.154 (standard deviation) (for PETS1), which indicates a marked reduction in the lack of knowledge about recent species occurrence (red bar of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) of 0.170\u0026thinsp;\u0026plusmn;\u0026thinsp;0.120 (st. dev).\u003c/p\u003e \u003cp\u003eFor istance, the Gargano National Park was found to have a rich butterfly fauna which was studied in two main campaigns during 1940s and 1950s, but less than 20 species were recorded between 2000 and 2010. Over half of species were unrecorded since 1980 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) resulting in a high PETS0 value of 0.524. Citizen science activities allowed for the confirmation of 56 species in the last 5 years of research (2017\u0026ndash;2021) returning in a great difference between PETS0 0.524 and PETS1 0.199 with a difference (Δ) of 0.326 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll park graphs, and the butterfly species list including the first and the last observations, can be found in the Supplementary Results document (Appendix 1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAims 2 and 3 \u0026ndash; The interplay between user effort, species traits and documented biodiversity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe GLMM analysing the effect of user effort on the contribution of each observation to determine the PETS1 values showed a significant positive relationship (Estimate\u0026thinsp;=\u0026thinsp;0.099, Standard error\u0026thinsp;=\u0026thinsp;0.035, z value\u0026thinsp;=\u0026thinsp;2.869, P\u0026thinsp;=\u0026thinsp;0.004). This demonstrates that single records from a more committed user have a higher likelihood of reducing the perception of local extinction.\u003c/p\u003e \u003cp\u003eThe Italian data on butterflies collected on iNaturalist also showed that species with a wider distribution and a longer flight period received a higher number of records (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), while larger species were not recorded more frequently. The three species traits had significant interactions with user effort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Users with high effort (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) tended to record species with a wider distribution more frequently. This relationship was less evident for users with a low engagement (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Similarly, the relationship between flight period and number of records was steeper for users with high engagement (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of the three variables of species contactability on the number of observations per user in a GLMM. Interactions with user effort (number of records per observer) are also reported.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChisq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlight period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWingspan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution*User effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e508.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlight period*User effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewingspan*User effort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWingspan showed a different trend since users with higher effort tended to record more frequently smaller species more frequently while less engaged users showed a steeper and opposite trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eWe found that a similar number of records uploaded by users with high effort encompass a higher diversity in terms of the number of detected species (richness, q\u0026thinsp;=\u0026thinsp;0) and the evenness of recorded individuals among species (Shannon index, q\u0026thinsp;=\u0026thinsp;1; and Simpson index, q\u0026thinsp;=\u0026thinsp;2) (q\u0026thinsp;=\u0026thinsp;0: Rho\u0026thinsp;=\u0026thinsp;0.893, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; q\u0026thinsp;=\u0026thinsp;1: Rho\u0026thinsp;=\u0026thinsp;0.939, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; q\u0026thinsp;=\u0026thinsp;2: Rho\u0026thinsp;=\u0026thinsp;0.939, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe evaluated the impact of citizen science records on reducing the perception of local extinctions in butterfly communities in Italian National Parks, which have an extraordinary diversity in the European and Mediterranean regions. The records by citizen scientists confirmed that some potential/supposed local extinctions were actually due to lack of recent records. Additionally, we found that observers with varying levels of effort on iNaturalist had varying contribution to this process, primarily because they recorded different levels of butterfly diversity and responded differently to various aspects of species appearance. These findings provide crucial information for National Parks to develop effective strategies for promoting citizen science initiatives to monitor butterfly populations over time.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAim 1 - The Potential Extinction upon Time Series approach\u003c/h2\u003e \u003cp\u003eThe establishment of the targeted butterfly monitoring scheme (BMS) citizen science project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://butterfly-monitoring.net/\u003c/span\u003e\u003cspan address=\"https://butterfly-monitoring.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) has allowed for a precise tracking of the overall decline in butterfly populations over the past decades (Warren et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The BMS has also helped detecting the effects of climate change on butterfly distribution and community composition, as well as the correlation between population trends and functional traits and phylogeny (Parmesan et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Devictor et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bonelli et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Halsch et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Melero et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, long-term data for the BMS is only available for a few European countries and parts of North America.\u003c/p\u003e \u003cp\u003eLocal butterfly extinctions have been documented globally, even in areas without monitoring schemes (Finland: van Bergen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Panama: Basset et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; California: Preston et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Italy: Bonelli et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In most cases, this evidence was based on exceptional datasets, mostly from the past decades, which allowed for the evaluation of a few butterfly communities. The PETS index can combine knowledge from multiple sources such as literature, museum data, expert collections, standardized monitoring, and untargeted citizen science to integrate heterogeneous and extended data and evaluate the possibility of local extinctions, even in the absence of exceptional datasets. Our dataset shows that published records are scarce or outdated for most Italian National Parks, and the occurrence of butterflies is not confirmed more than one-third of the time since their first sighting. A main reason for this is that about one-third of literature data is provided without a collection date, not even for the year, which greatly hinders the possibility to obtain complete time series. In light of the current biodiversity crisis, we recommend that researchers include precise data in their observations, especially considering the lack of well-established rules for writing faunistic papers. In this regard, citizen science is less affected by the lack of collection data.\u003c/p\u003e \u003cp\u003eDue to a general lack of data, the likelihood of local extinctions in PETS0 appears to be quite high, with many species remaining unrecorded in national parks for decades. This highlights the need for field investigations to confirm the presence of previously recorded species. Such efforts can be costly, but the contribution of citizen science can help reduce the costs. The PETS algorithm found that iNaturalist data can play an important role in monitoring butterfly populations, reducing the lack of knowledge about persistence to an average of 11%.\u003c/p\u003e \u003cp\u003eAnother significant finding is the considerable variability in PETS0 and PETS1, as well as the difference between them (delta), among different national parks. This variability is largely dependent on the time since the last faunistic study of each park, and to some extent on the level of citizen science activity. For example, the Cinque Terre National Park has the highest delta value, largely due to the limited research efforts for butterflies in the park and the lack of published studies on its fauna. The PETS0 indicates a potential erosion of up to 94% of the community data over time, but the PETS1, which incorporates citizen science data, shows a lower potential extinction rate of 48.2%. On the other hand, the Pantelleria National Park has the lowest delta value (2.2%) due to recent studies (Voda et al. 2016) that make the iNaturalist data less impactful, although still significant. The Casentino National Park, which has been well-studied by both professional and amateur entomologists, has a high PETS0 value of 0.615 (61.5% of incompleteness) because most of the main studies in the 20th century did not report collection dates, rendering 87.79% of the data useless to assess time series.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAim 2 and 3 - The effect of species traits and users effort on iNaturalist occurrences.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis of PETS1 showed that single observations uploaded on iNaturalist by users who put in more effort contribute more to evaluating the potential for local extensions in Italian National Parks. This is expected if more committed users tend to record a higher proportion of butterfly diversity, being more focussed on taking pictures of different species and differently affected by species appearance.\u003c/p\u003e \u003cp\u003eIn general, our findings align with previous research on birds by Callaghan et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which demonstrated that the availability of untargeted citizen science data depends on species appearance. For butterflies, we found that larger species with a longer flight period and broader geographical distribution are more likely to have a greater number of records available on the iNaturalist platform. A higher number of records for species showing a wider distribution and a longer flight period cannot be considered as a bias but as a desirable property, since these trends are the basis of high quality data obtained from structured monitoring schemes (e.g., in transect counts). However, this property is only shown by iNaturalist users uploading a high number of observations, as documented by the strong interactions between phenology and species range with user effort. In the case of less engaged users, the correlation between phenology and species range with user effort is less strict than for highly engaged users. This result could be due to the fact that they do not use iNaturalist frequently and their observations constitute too small samples to be affected by phenology and distribution.\u003c/p\u003e \u003cp\u003eWhile a positive relationship linking upload frequency with phenology and distribution is a desired property of data, the tendency to document more often the occurrence of large species is a typical bias of citizen science data (Kral-O'Brien et al. 2020; Isaac et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Moranz \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Dennis et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). This expected behaviour is not generally confirmed in our analysis because larger species did not score a higher number of records. However, the interaction between user engagement and butterfly size showed a significant effect. In fact, the decision to upload an observation does not only depend on the probability of encountering a given species, but also on other factors, such as the personal appreciation for that species (e.g. Callaghan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Isaac and Pocock \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Also in this case, highly committed users provide more accurate data, as they do not seem to be selectively attracted to bigger and more visible butterfly species.\u003c/p\u003e \u003cp\u003eThe preference for capturing pictures of both large and small butterflies by highly engaged users is likely to contribute to the higher diversity observed in their records, both in terms of species richness and evenness. It is possible that these users may learn more about the taxonomy of the butterfly group they are interested in and photograph rarer or less conspicuous species. Additionally, these users may also search more widely to find species with limited distributions, and document butterflies during different seasons. Furthermore, it is possible that a highly committed user may actively search for species not encountered yet, which may further contribute to a more diverse sample of species captured in photographs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFinal remarks\u003c/h2\u003e \u003cp\u003eProtected areas play a critical role in conserving biodiversity, promoting sustainability, and raising public awareness of the importance of natural capital and ecosystem services (Cooke et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chowdhury et al. 2022; Bastian \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Geldmann et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kettunen and Ten Brink \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Millennium Ecosystem Assessment \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stolton et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Signorello et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Involving citizens in biodiversity monitoring through citizen science has been shown to be an effective and efficient way to gather data and information (Fontaine et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mannino and Balistreri \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dennis et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zapponi et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our study suggests that citizen science data can also be used to complement existing literature data to more accurately determine the likelihood of species extinction. This information can then be used by National Parks to prioritize their conservation efforts and save financial resources. National Parks should encourage citizens to participate in both targeted and untargeted projects to gather standard and opportunistic data. This can be done through events such as bioblitz, where people are educated about the importance of monitoring biodiversity and encouraged to upload their observations to platforms like iNaturalist.\u003c/p\u003e \u003cp\u003eIt is important to be aware of the limitations of citizen science data, including the unequal contributions and quality of data provided by highly engaged users. To address this, National Parks should also promote activities that educate and engage the general public, such as workshops focused on taxa identification with the help of expert taxonomists. In Italy, this has already begun with the hosting of the first Italian BMS workshop in the Sila National Park in 2019, which has since been replicated in four other National Parks. This increased knowledge is likely to result in higher quality data and less influence from aesthetic preferences (Callaghan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Barbato et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Randler \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, individuals who are highly engaged in untargeted citizen science are more likely to participate in targeted projects, such as the globally successful Butterfly Monitoring Scheme (Warren et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Italian National Parks are also committed to carrying out pollinator monitoring, including butterfly counts, through Ministry funding with the involvement of volunteer. Improving taxonomy knowledge through citizen science can also help to address the shortage of taxonomists as outlined by the Red List of Taxonomists, a European Commission-funded initiative to increase awareness of the available expertise for preserving insect biodiversity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the many thousands of citizen scientists who contributed butterfly records to iNaturalist.\u003c/p\u003e\n\u003cp\u003eE.v.T. and L.D. acknowledge the support of NBFC to University of Florence, Department of Biology, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u0026ldquo;Dalla ricerca all\u0026rsquo;impresa\u0026rdquo;, Investimento 1.4, Project CN00000033.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.v.T. and L.D. acknowledge the support of NBFC to University of Florence, Department of Biology, funded by the Italian Ministry of University and Research, PNRR, Missione 4 Componente 2, \u0026ldquo;Dalla ricerca all\u0026rsquo;impresa\u0026rdquo;, Investimento 1.4, Project CN00000033.\u003c/p\u003e\n\u003cp\u003eM.M. was co-funded by \u0026ldquo;la Caixa\u0026rdquo; Foundation (ID 100010434) (grant LCF/BQ/DR20/11790020).\u003c/p\u003e\n\u003cp\u003eL.P. was co-funded by the European Union - PON Research and Innovation 2014-2020 in accordance with Article 24, paragraph 3a), of Law No. 240 of December 30, 2010, as amended and Ministerial Decree No. 1062 of August 10, 2021.\u003c/p\u003e\n\u003cp\u003eL.D. was co-funded by the projects \u0026quot;Monitoraggio dei maggiori gruppi di impollinatori di sei Parchi dell\u0026apos;Appennino Centro-Settentrionale\u0026quot;, \u0026ldquo;Ricerca e conservazione sugli Impollinatori dell\u0026rsquo;Arcipelago Toscano e divulgazione sui Lepidotteri del Parco\u0026rdquo; included with the Direttiva Biodiversit\u0026agrave; projects 2019-2022 of the Italian Ministero della Transizione Ecologica.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvT, AC, LD designed the experiment, EvT, GS, MB, MM, LP, VS, AC, LD identified images uploaded on iNaturalist, EB and SB collected literature data in the updated version of CkMap, LD wrote the R functions of the PETS package, LD and EvT carried out the analyses, all the authors discussed the preliminary results and contributed in the interpretation of the results and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the GitHub repository, https://github.com/leondap/pets.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAristeidou M, Herodotou C, Ballard HL, et al (2021) Exploring the participation of young citizen scientists in scientific research: The case of iNaturalist. 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Biological Conservation 208:139\u0026ndash;145. https://doi.org/10.1016/j.biocon.2016.04.035\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"butterflies, citizen science, conservation strategies, iNaturalist, National Parks, species traits","lastPublishedDoi":"10.21203/rs.3.rs-2600076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2600076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe detection of extinctions at local and regional scales in many biodiversity hotspots is often hindered by the lack of long-term monitoring data, and thus relies on time series of occurrence data. Citizen science has repeatedly shown its value in documenting species occurrences, mostly in very recent years. This study investigates the effectiveness of untargeted citizen science records in discarding the possibility of local extinctions in butterfly populations across all Italian National Parks. We addressed three research questions: i) the ability of citizen science data to supplement existing knowledge to complete occurrences time series, ii) the impact of functional traits determining species appearance on data collection, and iii) the interplay between participant engagement and species appearance in the amount of diversity recorded on the iNaturalist platform. Our analysis of 47,356 records (39,929 from literature and 7,427 from iNaturalist) shows that the addition of iNaturalist data fills many recent gaps in occurrence time series, thus reducing the likelihood of potential local extinctions. User effort strongly interacts with species size, distribution, and length of flight periods in determining the frequency of records for individual species. Notably, records from more engaged users encompass a higher fraction of local biodiversity and are more likely to discard local extinctions, and these users are less affected by species size. We also provide updated butterfly checklists for all Italian National Parks and a new R package to calculate potential extinction over time. These results offer guidance for protected areas, conservationists, policymakers, and citizen scientists to optimise monitoring of local populations.\u003c/p\u003e","manuscriptTitle":"Discard butterfly local extinctions through untargeted citizen science: the interplay between species traits and user effort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-21 14:30:35","doi":"10.21203/rs.3.rs-2600076/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-28T12:45:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-13T09:57:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86de1440-fac9-464b-8372-2d995768007b","date":"2023-06-19T07:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38e7cbb7-0e0c-4bc1-8449-3554ef1b2fd8","date":"2023-06-06T05:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-31T02:39:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-02-18T06:45:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-18T06:44:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2023-02-17T17:55:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eaecc7ff-faf4-4d9d-9227-a20352cb1ed0","owner":[],"postedDate":"February 21st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-09T15:09:13+00:00","versionOfRecord":{"articleIdentity":"rs-2600076","link":"https://doi.org/10.1007/s10531-023-02721-9","journal":{"identity":"biodiversity-and-conservation","isVorOnly":false,"title":"Biodiversity and Conservation"},"publishedOn":"2023-10-05 15:01:25","publishedOnDateReadable":"October 5th, 2023"},"versionCreatedAt":"2023-02-21 14:30:35","video":"","vorDoi":"10.1007/s10531-023-02721-9","vorDoiUrl":"https://doi.org/10.1007/s10531-023-02721-9","workflowStages":[]},"version":"v1","identity":"rs-2600076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2600076","identity":"rs-2600076","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}