1 INTRODUCTION

In the face of the current global biodiversity crisis, understanding species distributions and population sizes is increasingly critical (Hortal et al., 2015; Nori et al., 2023). Data on species distribution can elucidate the environmental requirements of a species by examining its fundamental niche (Kearney, 2019; Takola & Schielzeth, 2022). When a species is suspected to be declining, these data are key for informing conservation actions (White et al., 2023). Many studies have focused on this issue to inform decision-making in this area (Kondratyeva et al., 2019; Loiseau et al., 2020). The need to systematically assess the condition of wildlife populations and related threats on a global scale was first recognised in the 1960s (Mace, 1994), leading to the publication of the IUCN Red List of Threatened Species in 1991. This list is based on the systematic assessment of species extinction risk (Mace et al., 2008). Currently, taxa are assigned into one of eight categories (from Least Concern to Extinct) based on geographic range, population trend, size and structure, as well as their temporal trends (IUCN Standards and Petitions Committee, 2022). Several of these criteria are linked to population size and focus on aspects such as decline (criterion A), ongoing decline or extreme fluctuations (B and C), and range, including severe fragmentation (B) and the extent of occurrence or area of occupancy (B and C; IUCN Standards and Petitions Committee, 2022). Nevertheless, a large number of species worldwide have yet to be evaluated, particularly in megadiverse countries, due to the absence of data, restricted data access, and inaccuracies in datasets concerning population size and geographic distribution (Hochkirch et al., 2020). Furthermore, insufficient funding often drastically limits the rate of such assessments (Juffe-Bignoli et al., 2016; Rondinini et al., 2014). Conservationists need efficient analytical tools to provide evidence of high extinction risks and to prioritise actions that reduce the number of Not Evaluated and Data Deficient species. Species that are poorly understood are frequently overlooked when allocating conservation resources (Woinarski et al., 2021). Considering that species most vulnerable to extinction are often naturally rare (Harnik et al., 2012), identifying such species can serve as an indicator of potential threats and can flag species as conservation priorities (Gauthier et al., 2010; Veach et al., 2017).

Although rarity is often intuitively interpreted as a species having few individuals, it is, in reality, a complex, multidimensional concept. To address this complexity, Rabinowitz (1981) proposed seven types of rarity based on three properties: (1) geographic distribution (widespread vs. narrow-ranged species), (2) habitat specificity and (3) local population size (abundance-based rarity), along with their various combinations. For instance, a rare species might be characterised by a small population that is widely dispersed geographically or by an abundant population restricted to a limited habitat area (Rabinowitz, 1986). This classification framework is still frequently used, for instance, for plants at regional (Quiroga & Souto, 2022) and national levels (Choe et al., 2019) and for deep-sea bivalves (McClain, 2021), serving as a base for further theoretical work (Maciel, 2021). Under nonexperimental conditions, the uneven distribution of individuals among species is a common pattern observed in biological communities (Magurran, 2004). Typically, a few species are dominant while many others are rare, causing the abundance distribution curve for communities to fit a near-logarithmic shape (McGill et al., 2007). This abundance distribution pattern can be considered a natural power law for biodiversity datasets and can be explained by the different abilities of species to access resources in a given space (Marquet et al., 2007). However, inaccurate or incomplete knowledge of species rarity can lead to erroneous allocation of resources or impede conservation actions (Dibner et al., 2017; Katzner et al., 2011). This is particularly concerning in countries with high biodiversity that are often poorly inventoried. Therefore, there is a growing demand for increased biodiversity surveying, especially in nations that harbour global biodiversity hotspots facing threats from escalating deforestation pressure and the effects of climate change (Habel et al., 2019; Kong et al., 2021). In response to this urgent demand, more efficient methods need to be adapted to aid in the understanding of biodiversity distribution and trends across extensive geographic scales. Data produced by citizen science have been making substantial contributions to biodiversity monitoring at the global scale (Chandler et al., 2017; Johnston et al., 2023; Mesaglio et al., 2023). These efforts have also influenced public policy (Fritz et al., 2019; Roger et al., 2023) and raised awareness among both the public and policymakers (Danielsen et al., 2014). With the widespread adoption of internet-enabled smartphones and the development of user-friendly applications to record biodiversity, members of the public, working in a nonprofessional and unpaid capacity, have been documenting the location of species worldwide (Deacon et al., 2023; Pocock et al., 2024; Tulloch, Possingham, et al., 2013). These records are often accompanied by photographic and video documentation, providing valuable secondary information (Klinger et al., 2023; Pernat et al., 2024). Nevertheless, the spatial adequacy (Backstrom et al., 2024) and overall quality of citizen-science data are highly variable, influenced by the heterogeneous behaviour of the observers (Callaghan, Poore, Hofmann et al., 2021; Pocock et al., 2023) and the accuracy of observations (Gorleri & Areta, 2022; Gorleri et al., 2023). Spatiotemporal biases, particularly those related to observer behaviour, can be identified by comparing unstructured (or semi-structured) data with results from structured surveys (Balázs et al., 2021; Szabo et al., 2012). This comparison facilitates the calibration of different datasets, making them suitable for trend analysis and enhancing their reliability (Forti et al., 2024; Hertzog et al., 2021). In fact, the integration of citizen-science data and structured surveys has been shown to offer effective complementary insights (Dimson et al., 2023; Robinson et al., 2020; Tulloch, Mustin, et al., 2013).

The Global Biodiversity Information Facility (GBIF) collates georeferenced species occurrence data from a variety of sources or research institutions, including academic institutions, government research facilities, museums, herbaria, as well as various fauna and flora inventories (henceforth referred to as RI data). A second stream of data originates from citizen-science initiatives (henceforth referred to as CS data). GBIF currently hosts over 1.5 billion records for taxa from around the globe (gbif.org). Despite this extensive database, GBIF data are not without limitations, some of which are inherent to the database itself (i.e. biases originating from the amalgamation of datasets collected through different methods), and others that stem from the incoming data, such as taxonomic bias, identification errors and incorrect or missing geographic coordinates (Petersen et al., 2021; Troudet et al., 2017). For smaller datasets, some of these issues can be mitigated by manual data cleaning, while for larger ones, automated filtering techniques are applied (Zizka et al., 2020). Additionally, the quality of different taxonomic and geographical subsets varies significantly (Szabo et al., 2023). In spite of these limitations, GBIF provides a viable alternative to conducting surveys of ecological communities at large spatial scales, particularly in biodiverse countries with limited scientific knowledge and resources (Heberling et al., 2021; Ivanova & Shashkov, 2021).

In regions, such as Europe, the United States and Australia, the volume of data generated through citizen science has significantly contributed to the assessment of population trends across various taxa, including snakes (Santos et al., 2022), bats (Barlow et al., 2015) and birds (Fink et al., 2020; Szabo et al., 2010). On the other hand, in Brazil, the integration of citizen science into biodiversity research is still relatively nascent. Despite this, certain taxonomic groups, particularly birds and amphibians, have been receiving disproportionately high interest (Forti & Szabo, 2023). Citizen science has notably advanced our understanding of reptile distribution in Brazil, discovering previously unknown areas of occurrence (Oliveira et al., 2023). Yet, in spite of these advances, citizen science data have not been formally incorporated into decision-making processes in the country. An important initial step in leveraging these data for conservation and policy-making is to evaluate whether observations from citizen science can accurately reflect population sizes and species distributions and whether they can reliably classify species as ‘rare’ or ‘common’.

Reptiles represent one of the most diverse animal groups on the planet, and Brazil stands as a significant biodiversity hotspot, holding the third-highest diversity of reptiles globally with 856 species; a number that continues to rise as new species are described each year (Guedes, Entiauspe-Neto, et al., 2023). Within these species, there is a broad range of environmental tolerances. Some species exhibit wide environmental tolerance, enabling them to coexist in diverse habitats, while others, with specialised habits, can only survive within narrowly defined environmental conditions and thus have restricted distributions (Birskis-Barros et al., 2019). In spite of their diverse adaptations, reptiles are threatened worldwide due to habitat loss caused by expanding agriculture, deforestation and urban development, as well as illegal exploitation (Böhm et al., 2013). Consequently, 23.5% of reptile species are at some risk of extinction globally, with the figure standing at 14.4% in Brazil (IUCN, 2024). A high proportion (73%) of Near Threatened and threatened Brazilian species are classified under Criterion B concerning geographical range and population trends (IUCN, 2024).

Reptiles are integral to diverse trophic interactions within ecosystems. Through their activities as grazers, browsers, apex predators and scavengers, they play important roles in trophic networks, facilitating the balance and functioning of these systems (Pinto-Coelho et al., 2021). Beyond these roles, reptiles also contribute to other ecological processes, including seed dispersal, pollination, nutrient cycling and ecosystem engineering by creating habitats such as burrows and pools, which serve other species (Miranda, 2017). The socioeconomic importance of reptiles is equally notable. They contribute to tourism (Cohen, 2019), their bioactive compounds are used in pharmacological research (Mishra et al., 2020), and in Brazil, they also serve as a protein source for rural communities (Cajaiba et al., 2015). Considering their ecological and socioeconomic roles, coupled with the threats they face, the conservation of reptiles should be prioritised, particularly in tropical countries where biodiversity is rich, and the impacts of biodiversity loss can be profound (Miranda, 2017).

Data deposited in research institutions often originate from localised studies that focus on one particular species or on a small number of related species. Due to variations in the design and aims of these studies, certain species may be overrepresented, while many others remain neglected, leading to potential biases in the data collected (Meineke & Daru, 2021). In contrast, citizen science initiatives often employ gamified apps to motivate volunteers to collect observations of a diverse array of species across larger geographic scales (Callaghan, Poore, Mesaglio, et al., 2021; Sandbrook et al., 2015). As representation often reflects availability, rare or less detectable species will have fewer observations than dominant and conspicuous species (Johnston et al., 2018). Considering that RI and CS biodiversity data are generally collected using differing methodologies (aims, design, and scale), we hypothesised that the two datasets would yield different relative species abundance estimates. In particular, we predict that CS data can provide more accurate estimations of species relative abundance than data contributed by research institutions. We test this hypothesis through a community ecology approach, using the number of observations (records) as a proxy for species abundance in local communities to compare species abundance and rarity estimates based on reptile occurrence data from Brazil, as presented in GBIF, contributed by CS versus RI. We also discuss the limitations of GBIF data, particularly in relation to the biases associated with these two types of data contribution.

2 METHODS

3 RESULTS

3.1 Overview of reptile GBIF data from Brazil

Based on GBIF data, we identified 754 reptile species within Brazil's geographical boundaries. After the exclusion of duplicates and potential misidentifications, these species were represented by 42,580 observations, covering 82.6% of the total species recognised as native to Brazil. Among the 43 families recorded in the database, Colubridae had the highest representation (13,879 observations), while nonnative families (Acrochordidae, Boyeriidae, Chamaeleonidae, Phrynosomatidae, Platysternidae, Psammophiidae and Varanidae) were represented by a single observation each. The database was mainly composed of Squamata (91%), while Testudines and Crocodylia accounted for only 5% and 4%, respectively.

The number of observations has increased substantially since the 1980s, particularly in the last 20 years, reaching a peak in 2022 (Figure 1). Historical data revealed the oldest observation from a research institute dates back to 1880, featuring a South American ground lizard (Ameiva ameiva) in the city of São Paulo. The earliest record from citizen science was a House gecko (Hemidactylus mabouia) logged in 1970 in the city of Rio de Janeiro.

There were 24,828 observations (58.3%) and 17,756 observations (41.7%) in the RI and CS subsets, respectively. iNaturalist was the largest source of citizen-science data (99.2%). The Argentine black and white tegu (Salvator merianae) was the most observed species within the CS dataset (1464 observations), while the Amazon lava lizard (Tropidurus torquatus) had the most (1388) RI observations.

Native species accounted for 35,199 observations, including 5683 observations of 316 species endemic to Brazil. Among these endemic species, the Neotropical lava lizard (Tropidus hispidus) had the highest number of observations (644). We also identified 66 exotic species in the database, represented by 1702 observations. The most common exotic species was the House gecko, with 1324 observations.

Considering the full data set (i.e. 436 species that were reported in both subsets), we classified 279 species as rare (Table S1). Ridley's worm lizard (Amphisbaena ridleyi), Noronha skink (Trachylepis atlantica) and the Endangered Calango (Tropidurus psammonastes) had the highest rarity values with regard to geographical range criteria (GRI), while the general values of rarity (RR) were highest for the Endangered Dumeril's worm lizard (Leposternon octostegum), and two other lizard species: Caparaonia itaiquara and Tropidurus pinima. Ameivula mumbuca, a species of teiid lizard, had the lowest commonness index (C_i).

3.2 Comparing species rarity between the two subsets

Among the 1188 hexagonal units of the grid, 308 contained no recorded reptile observations in the GBIF database. We identified a strong negative correlation between rarity and commonness indices across the full data set (ρ = –0.7564968, p-value < 2.2 ∗ 10⁻¹⁶). While most threatened and Near Threatened species had higher RR and lower C_i values, some Near Threatened or threatened species were still classified as common based on the evaluation of the full data set (Figure 2a). Although the proportion of common species did not significantly differ between the two subsets (28% and 34%, respectively; χ² = 1.7603, p-value = 0.1859), 81 species were classified differently based on the two subsets (Table S1). Furthermore, the coefficients for both the correlation of the rarity index and the commonness index between the two subsets were <0.70, and the index values differed for many species between the subsets (Figure 2b–f). Additionally, correlation analyses of the indices that made up the Rabinowitz rareness dimensions (GRI, HSI, PSI and RR) between the subsets showed that the highest disparity was related to the population size index, which had the lowest correlation coefficient (ρ = 0.3324617; p-value = 9.626 ∗ 10⁻¹²), followed by the commonness index (ρ = 0.6491; p-value ≤ 2.2 ∗ 10⁻¹⁶), rarity index (ρ = 0.6656551; p-value ≤ 2.2e ∗ 10⁻¹⁶), habitat specificity index (ρ = 0.7155195; p-value ≤ 2.2 ∗ 10⁻¹⁶) and geographic range index (0.7700875; p-value ≤  2.2 ∗ 10⁻¹⁶).

While RI data did not fit the Gambin distribution (AIC = 2837.02; α = 1.250859 to 5.550947; χ² = 17.344; df = 5; p-value = 0.004), CS data had a good fit (AIC = 2224.811; α = 1.836133 to 4.833614; χ² = 8.594; df = 5; p-value = 0.126). The commonness index (C_i) for threatened and Near Threatened species was consistently lower than for Least Concern species in both subsets, with statistical significance in both the CS (AIC = –226.1, t-value = –2.246, p = 0.0252, n = 406) and RI data (AIC = –72.722, t-value = –3.083, p = 0.00219, n = 406).

3.3 Spatial bias and species completeness in the two subsets

Data contributed by RI contained 81% of the total number of species reported from Brazil, while CS data contained only 63%. The former provided more observations, particularly from the south and southeast of the country, while the latter had a larger contribution in the northeast (Figure 3). Both subsets provided good species coverage from the central-western region. Species richness calculated from the two subsets did not show a significant correlation at the grid cell level (ρ = 0.069; p-value = 0.086).

The Atlantic Forest was the best-represented biome, with 15,543 observations, while the Pantanal only had 2109 observations. A total of 3929 observations were submitted from oceanic islands. The distribution of observations per biome differed significantly from the expected values, which were calculated based on the size of the area (χ² = 318,459, df = 5, p-value < 2.2 ∗ 10⁻¹⁶). The Amazon, Caatinga and Cerrado were underrepresented, with standardised residues of –178.71847, –11.72771 and –36.50577, respectively, while the Atlantic Forest, Pampa and Pantanal were overrepresented, with standardised residues of 536.59507, 149.28487 and 51.20920, respectively.

Considering the two subsets, 212 species were exclusively observed in the RI subset, while 87 species were unique to the CS subset. Thus, with 695 species, species richness was estimated to be higher in the professional scientist subset compared to the citizen scientist subset (542 species). Neither of the rarefaction curves for the two subsets nor that of the full data set presented a well-defined asymptote (Figure 4).

4 DISCUSSION

While traditional surveys conducted by experts are indispensable for advancing knowledge of taxonomy, biology and geographical coverage of species, RI data, such as those available in natural history collections, usually do not suffice to determine species abundance patterns or rarity at larger scales. This limitation largely stems from inherent biases associated with particular research objectives of professional researchers or logistical constraints (Isaac & Pocock, 2015). For example, many taxonomists go to remote places to describe new species from poorly studied regions (Brito et al., 2021; Kennedy et al., 2019), focusing predominantly on collecting potentially new taxa. Similarly, population ecologists may concentrate on monitoring and sampling specific species, often overlooking others present at the study site. Such biases in RI data can lead to an overrepresentation of certain species while neglecting others. In the context of Brazilian reptiles, research attention is often skewed towards larger species, particularly those whose geographic ranges coincide with the locations of institutions housing experts (Guedes, Moura, et al., 2023). As a result, observations of specific species disproportionately contribute to natural history collections, independent of the actual abundance or distribution range of these species.

In contrast, CS data, despite being known to have spatiotemporal (Bowler et al., 2022; Di Cecco et al., 2021) and species traits biases (Callaghan, Poore, Mesaglio, et al., 2021; Marcenò et al., 2021), typically provide a more comprehensive picture of species abundance and distribution range. These factors significantly influence species detectability among observers, affecting the number of observations each species may have (Szabo et al., 2012). Our findings suggest that CS data provide more accurate estimates of species abundance or rarity than RI data. Nevertheless, neither data set reflects “real” abundances perfectly. The disparity in rarity classification between the two subsets is likely due to the limited capability of RI data to estimate population sizes accurately. This conclusion is supported by the observation that, unlike CS data, RI data did not fit the Gambin distribution, which represents the expected natural pattern for species abundance (Matthews et al., 2019). While geographic range data are well-documented for most terrestrial vertebrates, obtaining accurate population size data remains challenging for many reptile species (Ficetola et al., 2018). This issue is reflected in the relatively high number of reptile species classified under criterion B and in the relatively high correlation between the two datasets with regard to the geographic range index but not for the population size index.

Although the use of observations from CS appears to offer fewer constraints for inferring species abundance compared to data from RI, we need to highlight the potential risk of false negatives (species that were present but remained undetected) and false positives (misidentifications or recording species that were in fact absent) related to data from citizen science initiatives (Gorleri et al., 2023; McDonald & Hodgson, 2021). Despite these challenges, integrating data from various sources is known to improve data quality (Brown & Williams, 2019; McDonald & Hodgson, 2021). Nevertheless, more evidence from empirical tests using robust estimators of population size among species is necessary to validate whether integrating data sources can improve abundance data for Brazilian reptiles.

The integration of different data sources has improved our understanding of species richness and taxonomic coverage in our data set, highlighting the benefits of spatial complementarity. Despite these advances, the rarefaction curve for the full data set did not reach a satisfactory asymptote. The RI data set provided a more extensive list of species, with 212 (28% of all Brazilian reptile species in GBIF) species that were exclusive to this data set. For instance, the only records of the colubrid Zonateres lanei were seven museum specimens. Among these 212 species, only six were classified as common: the Spotted ground-snake (Adelphostigma occipitalis), Yellow head mussurana (Boiruna maculata), Keeled sepia snake (Dryophylax hypoconia), Amazon coastal house snake (Dryophylax nattereri), Dark blind snake (Liotyphlops beui) and Coastal house snake (Mesotes strigatus). Among the 203 rare species, 18 were classified as threatened and three as Near Threatened (Table S1).

Conversely, 87 reptile species (12% of the total species in GBIF) were only present in the CS subset, making it highly valuable. All of these species were classified as rare, with nine of them listed as globally threatened and one as Near Threatened. This underscores the significant role that volunteer observations can play in capturing data on rare and globally threatened species (Báthori et al., 2022; Fontaine et al., 2022; Tiralongo et al., 2020). Our study identified certain grid cells that had a larger contribution from the CS subset than from the RI subset, especially in the Northeast (Figure 3). While the combination of RI and CS data improved the total coverage of reptile species at the national scale (GBIF includes information on 82.6% of the reptile species in Brazil), there is still potential for improvement. The rarefaction curve has not yet reached a plateau, suggesting the species count could increase with further sampling efforts, especially in understudied regions. Spatial gaps are primarily located in the Amazon Basin, where increased research effort is required to improve our understanding of species abundance and distribution patterns. These regions should be prioritised for attention by academic experts, and the involvement of organised citizen-science initiatives could contribute to filling these gaps (Brooks et al., 2023). An integrated approach that combines the efforts of professionals and volunteers in structured projects (including a programme for training citizens) could result in a highly effective strategy for increasing data availability in these under-researched regions (Callaghan et al., 2019).

Citizen science initiatives typically produce more observations in urban landscapes (Tulloch & Szabo, 2012) and our results indicate that such sources are currently the main contributors of occurrence data on reptiles in Brazil, proportionally surpassing the representativeness of RI data since 2019 (Figure 1). The spatiotemporal differences in RI and CS data were also reflected in differences in the most observed species. We can also infer the distribution of exotic species, as the GBIF contains data on 61 exotic species. Exotic species can affect native biodiversity negatively, and citizen science is considered an effective tool to monitor their distribution and trends (Encarnação et al., 2021; Johnson et al., 2020; Phillips et al., 2021). The abundance of CS data supports macroecological research (Altwegg & Nichols, 2019). For example, many studies have used citizen science data to build species distribution models, particularly for birds and mammals (Feldman et al., 2021). In spite of the data available, other vertebrate groups have received less attention (Feldman et al., 2021). Based on GBIF data, citizen science can contribute to more accurate distribution models for some species (Robinson et al., 2020), such as the House gecko, which has over 1000 observations in Brazil.

The unprecedented global biodiversity crisis underscores the urgency of identifying and cataloguing (Tilker et al., 2020), a task made more challenging by the widespread lack of information on population sizes for most species (Kindsvater et al., 2018). In this context, our study has provided valuable information on biases present in large-scale public data concerning species abundance, which can inform their use for ecological science and informed conservation decision-making (Johnston et al., 2023). We have demonstrated that CS data can be an important source for obtaining reptile species abundance patterns and rarity in Brazil. Nevertheless, biases need to be recognised and accounted for. CS and RI data differ in estimating population size, which can affect the accuracy of classifying rare species. In spite of these challenges, we recommend the integration of these two types of data to study spatial and taxonomic coverage.

5 IMPLICATIONS AND RECOMMENDATIONS

While rare species are not necessarily threatened, a detailed evaluation of data sources and the underlying causes of rarity can serve as an early warning to trigger conservation actions. For example, the teiid lizard Glaucomastix cyanura, which displays high values across all rarity indices in both RI and CS data, is currently classified as Data Deficient by the IUCN. This classification positions it as a potential candidate for evaluation, with a high chance of being recognised as threatened. More concerning is the case of the Pantanal coral snake (Micrurus tricolor), currently evaluated as Least Concern. Yet, this species has reached the maximum values for all rarity indices in both subsets. The convergence of these rarity dimensions strongly suggests that this species needs to have its threat status revised, given its endemicity to the Pantanal, its limited geographical range, and appearently low densities. Furthermore, the populational trend of this species is currently unknown (https://www.iucnredlist.org/species/15202936/15202939). Given the ongoing large-scale land conversion and extreme fires in the Pantanal (Garcia et al., 2021) and future predicted threats (Lima et al., 2020), these environmental pressures intensify the urgency for reassessment. Another similar example is the Black-headed coral snake Micrurus averyi, also classified as Least Concern, yet exhibiting concerningly low rarity indices. Restricted to the northern Amazonia and a small area in Guyana (http://www.reptile-database.org/), its limited observations and small geographical range are cause for concern. Similarly, other species like the colubrid snake Helicops tapajonicus and the tropidurid lizard Tropidurus insulanus should also have their global threat status carefully re-evaluated. Despite certain limitations, our results indicate that CS data have reached a threshold, having accumulated a sufficient amount of data to inform species conservation in Brazil. This approach could also benefit other countries, particularly those that have traditionally lacked extensive population data but are currently experiencing increased volunteer activity. This methodology aids in filling critical data gaps, while also empowering local communities to actively partake in biodiversity conservation.

AUTHOR CONTRIBUTIONS

Lucas Rodriguez Forti: Conceptualization; data curation; formal analysis; investigation; methodology; supervision; writing—original draft; writing—review and editing. Jandson Lucas Camelo da Silva: Formal analysis; writing—original draft; writing—review and editing. Eveline Almeida Ferreira: Formal analysis; visualisation; writing—review and editing. Judit K. Szabo: Conceptualization; supervision; visualisation; writing—review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.