Journal Information
Vol. 22. Issue 3.
Pages 277-287 (July - September 2024)
Share
Share
Download PDF
More article options
Visits
1314
Vol. 22. Issue 3.
Pages 277-287 (July - September 2024)
Research Letters
Full text access
Declining representation of imperiled Atlantic Forest birds in community-science datasets
Visits
1314
Lucas Rodriguez Fortia,b, Ana Passettib, Talita Oliveirac, Juan Limad, Arthur Queirosc, Maria Alice Dantas Ferreira Lopese, Judit K. Szabof,
Corresponding author
judit.szabo@cdu.edu.au

Corresponding author.
a Departamento de Biociências, Universidade Federal Rural do Semi-Árido, Av. Francisco Mota, 572 — Bairro Costa e Silva, 59625-900 Mossoró, Rio Grande do Norte, Brazil
b Programa de Pós-Graduação em Ecologia: Teoria, Aplicações e Valores, Instituto de Biologia, Universidade Federal da Bahia, Rua Barão de Jeremoabo, 668 — Campus de Ondina CEP: 40170-115 Salvador, Bahia, Brazil
c Undergraduate Program in Ecology, Universidade Federal Rural do Semi-Árido, Av. Francisco Mota, 572 — Bairro Costa e Silva, 59625-900 Mossoró, Rio Grande do Norte, Brazil
d Programa de Pós-Graduação em Ecologia e Conservação, Universidade Federal Rural do Semi-Árido, Av. Francisco Mota, 572 — Bairro Costa e Silva, 59625-900, Mossoró, Rio Grande do Norte, Brazil
e Undergraduate Program in Medicine Veterinary, Universidade Federal Rural do Semi-Árido, Av. Francisco Mota, 572 — Bairro Costa e Silva, 59625-900 Mossoró, Rio Grande do Norte, Brazil
f College of Engineering, IT and Environment, Charles Darwin University, Casuarina, Northern Territory 0909, Australia
Highlights

  • Bird species of the Atlantic Forest in Brazil are threatened and declining.

  • Declines can lead to decreased detectability and fewer observations.

  • We analyzed bird data from three citizen science platforms for 2000–2022.

  • The representation of threatened and Near Threatened species decreased through time.

  • We recommend future species-specific monitoring to fill survey gaps.

This item has received
Article information
Abstract
Full Text
Bibliography
Download PDF
Statistics
Figures (4)
Show moreShow less
Tables (2)
Table 1. Endemic bird species of the Brazilian Atlantic Forest based on Vale et al. (2018) with global threat status (IUCN 2022) and the number of observations (N) on three digital community science platforms based on data from 2000–2022; LC — Least Concern, NT — Near Threatened, VU — Vulnerable, EN — Endangered, CR — Critically Endangered, EW — Extinct in the Wild, EX — Extinct. The common name of declining species (165 species) is marked in bold and with unknown trend (11 species) in italics (BirdLife International, 2023). Note: we use BirdLife International taxonomy and nomenclature.
Table 2. Matrix with Spearman rank correlation coefficients (n = 23) for the correlations between threatened and Near Threatened species richness, number of observations of endemic species and the number of observers each year in the interval between 2000 and 2022. All correlations were p < 0.01, but exact p-values ​​could not be calculated.
Show moreShow less
Abstract

While monitoring is essential for effective conservation, obtaining occurrence data is often challenging, time consuming and expensive. The Brazilian Atlantic Forest has a high number of threatened and endemic species that need effective and urgent conservation actions informed by sound monitoring data. Community (or citizen) science surveys can provide cost-effective data for large areas over extended time and these geocoded and time-stamped observations can deliver information on species of conservation interest. We provide a spatio-temporal analysis of Least Concern, Near Threatened and globally threatened Atlantic Forest endemic bird species from iNaturalist, eBird and WikiAves and analyze species according to their global trends. Together, these three datasets contained 838,880 unique observations of 218 species in 2000–2022, including 95 threatened and Near Threatened species. While the absolute number of observations of threatened and Near Threatened species increased annually, their proportion decreased compared to the total number of observations. Similarly, the proportion of observations of declining species decreased. Through time, the number of non-specialist birdwatchers could have increased, with the higher survey effort resulting in a higher proportion of common (i.e., more easily observed) species. However, this pattern can also reflect real trends, as most threatened and Near Threatened species were declining, leading to decreased detectability and relatively fewer observations, even with the same effort and skills. Decreasing and threatened species need special attention and targeted monitoring. In spite of the biases inherent in non-structured datasets and the difficulties of surveying rare species, community science can provide an effective warning system, and can improve monitoring of species at high risk of extinction.

Keywords:
Brazil
Community science
Extinction risk
Species occurrence data
Spatial distribution
Temporal patterns
Full Text
Introduction

Datasets of species occurrences provide the basis for studies of spatial distribution and assessments of population trends (Chapman, 2005; Soberón and Townsend Peterson, 2009), fundamental to inform conservation actions (Robinson et al., 2018). Unfortunately, individual researchers are constrained by logistics and funding, which prevent data collection at broad spatial scales and longer time frames. As rare or threatened species usually occur at low densities and require large sampling effort (Green and Young, 1993), they are often underrepresented in datasets compared to locally abundant or widely distributed species (Martikainen and Kouki, 2003). Unlike traditional research, community (or citizen) science (CS) has produced large amounts of data at large scales, occasionally also providing information on threatened species (Bonney, 2021; Lloyd et al., 2020; Wilson et al., 2020). The effort devoted by CS has reduced the Wallacean shortfall, i.e., the lack of knowledge with regard to species distribution (Deacon et al., 2023; Hortal et al., 2015).

Worldwide, many datasets collected by CS feed into the largest global biodiversity database, the Global Biodiversity Information Facility (GBIF; Bonney, 2021; Callaghan et al., 2021). While spatial, temporal, and taxonomic biases are inherent to most CS datasets and they often only record presences (Di Cecco et al., 2021; Szabo et al., 2012a,b), statistical methods are becoming available to handle most of these issues (Bird et al., 2014; Jiménez et al., 2019; Szabo et al., 2010) and after cleaning and adjustments, the data can inform conservation decision making (Newman et al., 2017).

Birds are the best-represented taxonomic group in global biodiversity databases (Troudet et al., 2017). For instance, in October 2023, GBIF included over 1.7 million bird records, representing approximately 6% of all biodiversity data (https://www.gbif.org/). To illustrate the volume of information available with regard to birds, in the same month, eBird had over 630,000 occurrence lists of 1800 species for Brazil (https://ebird.org/) and the Brazilian WikiAves hosted over 4.5 million photos and nearly 270,000 sound recordings of 1961 species (https://www.wikiaves.com.br/). At the forefront of producing unstructured data, iNaturalist brings together a global community of over 2.8 million observers. This platform, formerly managed by the California Academy of Sciences and National Geographic and now a non-profit organization, has been serving as a barometer of CS activity virtually around the globe and at the time of writing this article, contained 323,000 observations of 1702 bird species for Brazil (https://www.inaturalist.org/). However, we need to mention here that these platforms use somewhat different taxonomy that need to be reconciled before directly comparing species diversity and numbers.

As a result of this high interest in birds, in countries that have a relatively high number of birdwatchers and low avian species richness, the coverage of bird species on these CS platforms is nearly complete. For example, for the United States, almost 90% of extant bird species reported from the country are represented by at least one record on iNaturalist (Di Cecco et al., 2021). However, the species most frequently recorded on iNaturalist tend to be common in urbanized and other human-modified habitats, as well as large and easily observed (Di Cecco et al., 2021). Overall, CS has produced contrasting data with regard to the representativeness of threatened species observations (Sánchez-Clavijo et al., 2021). For instance, in Western Australia, volunteer surveyors were more interested in recording rare bird species compared to common species, practically oversampling rare and “interesting” or “birdwatching trophy” species (Tulloch and Szabo, 2012). On the other hand, in Colombia, during the Covid-19 pandemic the number of visits to less disturbed areas decreased, leading to a decreasing number of observations of species of conservation interest compared to Least Concern species (Sánchez-Clavijo et al., 2021). Nevertheless, the decreasing number of rare or threatened species observations in remote and protected areas can be a worrying indicator of species declines (Barnes et al., 2015). Therefore, these patterns need to be assessed, if possible, using an independent dataset collected using structured surveys, as they can indicate real declines (Szabo et al., 2011).

Following BirdLife International’s (2023) taxonomy, with 1816 species, Brazil has the third highest avian species diversity in the world, of which 257 species are endemic to the country. However, Brazil also ranks extremely high (second in the world) with regard to the number of species in danger of extinction (BirdLife International, 2023). The Atlantic Forest is the second largest tropical moist forest domain in South America after the Amazon and is considered a global biodiversity hotspot (Myers et al., 2000). This biome is home to over 800 bird species, 223 of which are endemic, a number that can further increase due to ongoing taxonomic revisions (Pizo and Tonetti, 2020). The three Endemic Bird Areas (Atlantic Forest lowlands and Atlantic Forest mountains and the Atlantic slope of Alagoas and Pernambuco) and 163 Important Bird and Biodiversity Areas in particular, hosts several endemic, restricted range and threatened taxa (Bencke et al., 2006). Most of these birds are threatened by habitat fragmentation and habitat loss, both ongoing and current (BirdLife International, 2023). In fact, over 92% of the original vegetation has been lost due to deforestation (Marques and Grelle, 2021). This level of habitat loss has led to a substantial extinction debt (Uezu and Metzger, 2016), which makes biodiversity surveys and the appropriate conservation actions particularly urgent (Szabo et al., 2011). The Atlantic Forest covers nearly 11,200,000 km2 of ​​the Brazilian territory, spreading from sea level to above 3000 m and containing a mosaic of ombrophilous, deciduous and semideciduous forests, mangroves, dunes and high-altitude meadows (Ribeiro et al., 2011). The Atlantic Forest displays complex vertical stratification, offering a variety of substrates and microhabitats for its highly diversified biota (Morellato et al., 2000). Unfortunately, its remaining biodiversity continues to face challenges related not only to habitat fragmentation but also global climate change (de Lima et al., 2020; SOS Mata Atlântica/INPE, 2018).

In Brazil, CS-collected data have been used to study migratory patterns of birds (Cunha et al., 2022; Guaraldo et al., 2022; Lees and Martin, 2015; Lopes and Schunck, 2022; Schubert et al., 2019), as well as general species distribution (Santos et al., 2021; Zulian et al., 2021), habitat use (Barbosa et al., 2021; Devenish et al., 2021), feeding behavior (de Souza et al., 2022), novel nesting behavior (Alexandrino et al., 2022) and species interactions (Bosenbecker et al., 2023). However, there is no specific information on the spatio-temporal patterns of imperiled bird species in CS-collected data in Brazil, which can restrict decision making for species protection. Considering the importance of participatory monitoring in inform conservation actions, we aim to describe spatio-temporal patterns of globally threatened and Near Threatened Atlantic Forest endemic bird species based on observations collected by CS in the three most popular digital platforms hosting bird observations from Brazil. Based on the spatial data, we evaluate the representativeness of observations in urban, as well as protected areas. Using temporal data, we quantify the proportion of observations of threatened and Near Threatened (thereafter imperiled) species in relation to all species observed in 2000–2022. We also describe the monthly distribution of observations and observers. Finally, we discuss the representativeness of imperiled Atlantic Forest bird species in the dataset, providing recommendations and guidelines for future surveying efforts in partnership with the public to generate more robust datasets, in order to support conservation actions and to evaluate the effectiveness of past actions in the biome.

Materials and methods

Based on the list of Atlantic Forest endemic bird species in Vale et al. (2018), we compiled data from three CS platforms: (1) eBird (https://ebird.org/home), (2) WikiAves (https://www.wikiaves.com.br/index.php) and iNaturalist (https://www.inaturalist.org/). As eBird data are curated by experts, we used all observations, including species lists, while from the other two we used observations with audio and photo evidence. In order to use global threat status and trends, we adopted BirdLife International’s taxonomy (BirdLife International, 2023), obtaining trends from BirdLife Data Zone manually (http://datazone.birdlife.org/) and global threat status from IUCN (https://www.iucnredlist.org/; which in turn is based on the Red List assessment conducted by BirdLife) through the rredlist package (Chamberlain, 2020) of the program R version 4.0.5. (R Core Development Team, 2020). We used global threat status and trends from the assessment of 2020, as considering and recalculating the status and trends of these species at previous assessments (2000, 2004, 2008, 2012 and 2016) was outside the scope of this paper (see for instance Szabo et al. (2012a). Nevertheless, since 1988, only 93 species have been downlisted globally to a lower Red List category due to a genuine improvement in status, while and 436 species have been uplisted (BirdLife International, 2022). In addition, status is calculated for 10 years or three generations, whichever is the longer (IUCN Standards and Petitions Committee, 2022). Therefore, we can calculate 10 years for passerines and a 4-to-7-year generation length is realistic for the non-passerine endemic species (e.g., 4.1 years for Solitary Tinamou Tinamus solitarius and Long-trained Nightjar Macropsalis forcipata, 4.6 years for Rufous-capped Motmot Baryphthengus ruficapillus, 4.7 years for Red-breasted Toucan Ramphastos dicolorus 5.9 years for White-collared Kite Leptodon forbesi and 7.4 years for Black-fronted Piping-Guan Pipile jacutinga; Bird et al., 2020). Considering the evaluation period and the fact that habitat loss and fragmentation continue in the Atlantic Forest (Pizo and Tonetti, 2020; Schnell et al., 2013), we are confident that our methods are conservative.

We formally requested raw data for the area of interest from eBird and iNaturalist and received them in csv format. As bulk download request was not available for WikiAves, we used the instant data scraper app (https://webrobots.io/instantdata/), adhering to the “fair use” principle. We only considered research grade observations from iNaturalist, i.e., those that were identified at the species level and reached a 2/3 consensus among the identifiers on the suggested species.

The raw dataset contained 1,204,210 observations, with 58.8% of the observations in eBird, 39.8% in WikiAves and 1.4% in iNaturalist. Based on the species, date and location, we removed data points duplicated within and among the datasets and filtered observations to 2000–2022 using the distinct and filter functions in the dplyr R package (Wickham et al., 2022). Therefore, we excluded repeat observations of the same species from the same day and the same location, but treated observations of the same individual bird on different days as separate data points. We restricted the data to observations collected after January 1, 2000, because digital CS platforms became popular after this year (Mandeville et al., 2022), and to be able to use current threat and trend data, as discussed above. We also cleaned the data by deleting observations with missing coordinates and locations outside the limits of the Atlantic Forest using the clip tool in QGIS software version 3.16.9 (QGIS Development Team, 2021).

After removing duplicates and pre-2000 observations (approximately 30% of the data), we obtained a joint clean dataset of 838,880 observations of all Atlantic Forest endemic bird species for 2000–2022. Approximately 13.8% of these observations (115,582 data points) were of threatened and Near Threatened species. We used a fit test for Benford’s Law to evaluate the distribution of digits on the number of observations among species to check data quality of the joint dataset based on sampling heterogeneity (Szabo et al., 2023). For this test, we adopted the Benford function of the Benford analysis package (Cinelli, 2014), and following standard practice, we used only species with more than 100 observations (Nigrini, 2012). We tested the fit of the data to the unimodal gamma binomial distribution model, which is considered the best model of species abundance distributions for real communities with many rare species (Ugland et al., 2007) and used the fit abundance function in the gambin package to estimate statistical parameters and to test the fit using the maximum likelihood method (Matthews et al., 2014).

We identified 493,186 bird observations (about 40% of the raw data) that had exact geographic coordinates, i.e., we excluded the entire WikiAves dataset from this analysis, as locations are only provided at the scale of municipality level, as well as observations with incorrect or missing location information from the other two datasets. Using the subset with valid coordinates, we overlayed bird observations with urban and non-urban areas (IBGE, 2015), as well as protected areas (Centro de Estudos da Metrópole, 2021) using QGIS. We defined protected areas as conservation units that are strictly protected (IUCN Categories I–IV) and sustainable use (IUCN Categories V and VI). The remaining vegetation cover (about 70% of the vegetation cover) is protected by other area-based conservation measures, which may allow intervention and deforestation in specific situations (Rezende et al., 2018). We created distribution maps of observations of Atlantic Forest bird species using QGIS.

We compared observed and expected numbers of observations by region based on the area of each region using a χ2 goodness of fit through the chisq. test function in R. Considering that we had four regions (no relevant habitat in the north; Fig. 1), we adjusted the p-value for 0.0125, as a result of Bonferroni correction (0.05/4). We repeated the same test for observations inside and outside protected areas.

Fig. 1.

(a) The distribution of endemic Atlantic Forest bird species observations from two popular community science platforms (eBird and iNaturalist) from 2000 to 2022; and (b) observations of threatened and Near Threatened species inside (red), and outside (blue) protected areas. The inset shows the five major regions of Brazil: N: north, NE: northeast, CW: central west, SE: southeast and S: south. Species threat status and taxonomy follows BirdLife International (2023).

(0.54MB).

We tested whether the numbers of observations of imperiled species and contributing observers on the platforms changed along years applying generalized linear models (glm) using the glm function in R. For this, we chose quasipoisson distribution, to avoid false positives due to overdispersion in the count data. We also tested whether the proportion of observations of imperiled species changed along the years in relation to the number of all species observations. Given the normal distribution of the data, here we applied glm of the Gaussian family. Similarly, we fitted two Gaussian glm to compare the proportion of observations of decreasing and stable species along the years (i.e., by dividing the number of observations of decreasing species by the sum of decreasing and stable for each year).

To understand whether the diversity and abundance of imperiled species correlate with the annual number of observers on the platform, after visually checking the linearity of the data, we applied Spearman’s correlation through the corr. test function of the psych package (Revelle, 2022). To test the seasonality in the number of observations of Atlantic Forest endemic birds, we used the Rayleigh test of uniformity based on the ρ-value using the circular package (Agostinelli and Lund, 2023) and visualized monthly and annual observation patterns using the ggplot2 package (Wickham, 2016).

ResultsGeneral data description

The average number of observations per species was 3779, ranging from two sightings of the Alagoas Curassow (Mitu mitu) to 35,583 observations of the Ruby-crowned Tanager (Tachyphonus coronatus), which is a common, Least Concern species (Table 1). Eleven species had fewer than 100 observations, two of them classified as Least Concern. Among imperiled species, the ten most observed species accounted for 41% of the observations, and all of them were Near Threatened. Near Threatened species were represented in 9.4% of all (threatened + non-threatened species) observations, Vulnerable in 2.9%, Endangered in 1.3% and Critically Endangered species in 0.2% of observations.

Table 1.

Endemic bird species of the Brazilian Atlantic Forest based on Vale et al. (2018) with global threat status (IUCN 2022) and the number of observations (N) on three digital community science platforms based on data from 2000–2022; LC — Least Concern, NT — Near Threatened, VU — Vulnerable, EN — Endangered, CR — Critically Endangered, EW — Extinct in the Wild, EX — Extinct. The common name of declining species (165 species) is marked in bold and with unknown trend (11 species) in italics (BirdLife International, 2023). Note: we use BirdLife International taxonomy and nomenclature.

ID  Family  Scientific name  Common name  Threat status 
Tinamidae  Tinamus solitarius  Solitary tinamou  NT  3327 
Cracidae  Pipile jacutinga  Black-fronted piping-guan  EN  1408 
Cracidae  Ortalis araucuan  White-bellied chachalaca  LC  1971 
Cracidae  Ortalis squamata  Escaled chachalaca  LC  4437 
Cracidae  Crax blumenbachii  Red-billed curassow  EN  554 
Cracidae  Mitu mitu  Alagoas curassow  EW 
Odontophoridae  Odontophorus capueira  Spot-winged wood-quail  LC  3168 
Caprimulgidae  Macropsalis forcipata  Long-trained nightjar  LC  844 
Trochilidae  Ramphodon naevius  Saw-billed hermit  LC  7657 
10  Trochilidae  Glaucis dohrnii  Hook-billed hermit  VU  468 
11  Trochilidae  Phaethornis squalidus  Dusky-throated hermit  LC  1984 
12  Trochilidae  Phaethornis idaliae  Minute hermit  LC  800 
13  Trochilidae  Phaethornis eurynome  Scale-throated hermit  LC  9546 
14  Trochilidae  Phaethornis malaris  Great-billed hermit  LC  104 
15  Trochilidae  Lophornis chalybeus  Festive coquette  NT  3993 
16  Trochilidae  Clytolaema rubricauda  Brazilian ruby  LC  4023 
17  Trochilidae  Stephanoxis lalandi  Green-crowned plovercrest  LC  2489 
18  Trochilidae  Stephanoxis loddigesii  Violet-crowned plovercrest  LC  2487 
19  Trochilidae  Thalurania watertonii  Long-tailed woodnymph  EN  716 
20  Trochilidae  Thalurania glaucopis  Violet-capped woodnymph  LC  30,893 
21  Rallidae  Aramides saracura  Slaty-breasted wood-rail  LC  21,249 
22  Accipitridae  Leptodon forbesi  White-collared kite  EN  333 
23  Accipitridae  Buteogallus lacernulatus  White-necked hawk  VU  1841 
24  Accipitridae  Pseudastur polionotus  Mantled hawk  NT  1720 
25  Strigidae  Megascops sanctaecatarinae  Long-tufted screech-owl  LC  1296 
26  Strigidae  Megascops atricapilla  Black-capped screech-owl  LC  1521 
27  Strigidae  Pulsatrix koeniswaldiana  Tawny-browed Owl  LC  3827 
28  Strigidae  Strix hylophila  Rusty-barred Owl  LC  2202 
29  Strigidae  Glaucidium minutissimum  Least pygmy-owl  LC  1735 
30  Trogonidae  Trogon surrucura  Southern Surucua Trogon  LC  8433 
31  Momotidae  Baryphthengus ruficapillus  Rufous-capped motmot  LC  5809 
32  Galbulidae  Jacamaralcyon tridactyla  Three-toed jacamar  NT  1161 
33  Bucconidae  Notharchus swainsoni  Buff-belied puffbird  LC  1365 
34  Bucconidae  Malacoptila striata  Greater crescent-chested puffbird  LC  6572 
35  Ramphastidae  Ramphastos dicolorus  Red-breasted toucan  LC  17,762 
36  Ramphastidae  Selenidera maculirostris  Spot-billed toucanet  LC  4223 
37  Ramphastidae  Pteroglossus bailloni  Saffron toucanet  NT  1755 
38  Picidae  Picumnus exilis  Golden-spangled piculet  LC  1273 
39  Picidae  Picumnus temminckii  Ochre-collared piculet  LC  10,963 
40  Picidae  Melanerpes flavifrons  Yellow-fronted woodpecker  LC  9249 
41  Picidae  Veniliornis maculifrons  Yellow-eared woodpecker  LC  1703 
42  Picidae  Campephilus robustus  Robust woodpecker  LC  4911 
43  Picidae  Celeus galeatus  Helmeted woodpecker  VU  335 
44  Picidae  Celeus tinnunculus  Atlantic black-breasted woodpecker  VU  159 
45  Picidae  Piculus chrysochloros  Golden-green woodpecker  LC  59 
46  Picidae  Piculus aurulentus  Yellow-browed woodpecker  NT  5092 
47  Psittacidae  Touit melanonotus  Brown-backed parrotlet  NT  640 
48  Psittacidae  Touit surdus  Golden-tailed parrotlet  VU  860 
49  Psittacidae  Brotogeris tirica  Plain parakeet  LC  34,619 
50  Psittacidae  Pionopsitta pileata  Pileated parrot  LC  4045 
51  Psittacidae  Triclaria malachitacea  Blue-bellied parrot  LC  1533 
52  Psittacidae  Pionus reichenowi  Blue-breasted parrot  VU  267 
53  Psittacidae  Amazona vinacea  Vinaceous-breasted amazon  EN  2863 
54  Psittacidae  Amazona pretrei  Red-spectacled amazon  VU  882 
55  Psittacidae  Amazona rhodocorytha  Red-browed amazon  VU  1313 
56  Psittacidae  Amazona brasiliensis  Red-tailed amazon  NT  1204 
57  Psittacidae  Pyrrhura cruentata  Ochre-marked parakeet  VU  887 
58  Psittacidae  Pyrrhura frontalis  Maroon-bellied parakeet  LC  24,417 
59  Psittacidae  Pyrrhura griseipectus  Grey-breasted parakeet  EN  635 
60  Psittacidae  Pyrrhura leucotis  White-eared parakeet  VU  899 
61  Thamnophilidae  Hypoedaleus guttatus  Spot-backed antshrike  LC  6559 
62  Thamnophilidae  Mackenziaena leachii  Large-tailed antshrike  LC  3546 
63  Thamnophilidae  Mackenziaena severa  Tufted antshrike  LC  4471 
64  Thamnophilidae  Biatas nigropectus  White-bearded antshrike  VU  1245 
65  Thamnophilidae  Thamnophilus ambiguus  Sooretama slaty antshrike  LC  1873 
66  Thamnophilidae  Rhopias gularis  Star-throated antwren  LC  5203 
67  Thamnophilidae  Dysithamnus stictothorax  Spot-breasted antvireo  NT  3938 
68  Thamnophilidae  Dysithamnus xanthopterus  Rufous-backed antvireo  LC  1434 
69  Thamnophilidae  Dysithamnus plumbeus  Plumbeous antvireo  VU  353 
70  Thamnophilidae  Myrmotherula axillaris  White-flanked antwren  LC  1213 
71  Thamnophilidae  Myrmotherula minor  Salvadori’s antwren  VU  659 
72  Thamnophilidae  Myrmotherula urosticta  Band-tailed antwren  VU  446 
73  Thamnophilidae  Myrmotherula unicolor  Unicolored antwren  NT  3364 
74  Thamnophilidae  Myrmotherula snowi  Alagoas antwren  CR  102 
75  Thamnophilidae  Herpsilochmus pileatus  Bahia antwren  VU  624 
76  Thamnophilidae  Formicivora erythronotos  Black-hooded antwren  EN  488 
77  Thamnophilidae  Formicivora serrana  Serra antwren  LC  1487 
78  Thamnophilidae  Formicivora paludicola  Marsh antwren  CR  350 
79  Thamnophilidae  Formicivora acutirostris  Parana antwren  NT  898 
80  Thamnophilidae  Drymophila ferruginea  Ferruginous antbird  LC  7324 
81  Thamnophilidae  Drymophila rubricollis  Bertoni’s antbird  LC  2242 
82  Thamnophilidae  Drymophila genei  Rufous-tailed antbird  LC  1667 
83  Thamnophilidae  Drymophila ochropyga  Ochre-rumped antbird  NT  1879 
84  Thamnophilidae  Drymophila malura  Dusky-tailed antbird  LC  3715 
85  Thamnophilidae  Drymophila squamata  Scaled antbird  LC  5064 
86  Thamnophilidae  Terenura sicki  Orange-bellied antwren  CR  289 
87  Thamnophilidae  Terenura maculata  Streak-capped antwren  LC  4982 
88  Thamnophilidae  Cercomacra brasiliana  Rio de Janeiro antbird  NT  598 
89  Thamnophilidae  Pyriglena atra  Fringe-backed fire-eye  EN  243 
90  Thamnophilidae  Pyriglena leucoptera  White-shouldered fire-eye  LC  11,675 
91  Thamnophilidae  Rhopornis ardesiacus  Slender antbird  EN  528 
92  Thamnophilidae  Myrmoderus ruficauda  Scalloped antbird  EN  571 
93  Thamnophilidae  Myrmoderus loricatus  White-bibbed antbird  LC  1720 
94  Thamnophilidae  Myrmoderus squamosus  Squamate Antbird  LC  5380 
95  Conopophagidae  Conopophaga melanops  Black-cheeked gnateater  LC  6585 
96  Conopophagidae  Conopophaga cearae  Ceara gnateater  NT  710 
97  Grallariidae  Hylopezus nattereri  Speckle-breasted antpitta  LC  681 
98  Rhinocryptidae  Psilorhamphus guttatus  Spotted bamboowren  LC  1535 
99  Rhinocryptidae  Merulaxis ater  Slaty bristlefront  LC  2188 
100  Rhinocryptidae  Merulaxis stresemanni  Stresemann’s bristlefront  CR  57 
101  Rhinocryptidae  Eleoscytalopus psychopompus  Bahia tapaculo  EN  127 
102  Rhinocryptidae  Eleoscytalopus indigoticus  White-breasted tapaculo  LC  3571 
103  Rhinocryptidae  Scytalopus gonzagai  Boa Nova tapaculo  EN 
104  Rhinocryptidae  Scytalopus speluncae  Mouse-colored tapaculo  LC  3057 
105  Rhinocryptidae  Scytalopus diamantinensis  Diamantina tapaculo  EN  105 
106  Rhinocryptidae  Scytalopus petrophilus  Rock tapaculo  LC  537 
107  Rhinocryptidae  Scytalopus pachecoi  Planalto tapaculo  LC  510 
108  Rhinocryptidae  Scytalopus iraiensis  Marsh tapaculo  EN  198 
109  Formicariidae  Chamaeza meruloides  Cryptic antthrush  LC  2424 
110  Formicariidae  Chamaeza ruficauda  Rufous-tailed antthrush  LC  1780 
111  Furnariidae  Sclerurus cearensis  Ceara leaftosser  VU  165 
112  Furnariidae  Sclerurus scansor  Rufous-breasted leaftosser  LC  4279 
113  Furnariidae  Dendrocincla turdina  Plain-winged woodcreeper  LC  6540 
114  Furnariidae  Xiphorhynchus fuscus  Lesser woodcreeper  LC  10,401 
115  Furnariidae  Xiphorhynchus atlanticus  Atlantic woodcreeper  VU  790 
116  Furnariidae  Campylorhamphus falcularius  Black-billed Scythebill  LC  2579 
117  Furnariidae  Lepidocolaptes squamatus  Scaled woodcreeper  LC  3940 
118  Furnariidae  Lepidocolaptes falcinellus  Scalloped woodcreeper  LC  4479 
119  Furnariidae  Cinclodes pabsti  Long-tailed cinclodes  NT  1275 
120  Furnariidae  Anabazenops fuscus  White-collared foliage-gleaner  LC  3976 
121  Furnariidae  Cichlocolaptes leucophrus  Pale-browed treehunter  LC  2710 
122  Furnariidae  Heliobletus contaminatus  Sharp-billed treehunter  LC  1512 
123  Furnariidae  Philydor atricapillus  Black-capped foliage-gleaner  LC  5525 
124  Furnariidae  Anabacerthia amaurotis  White-browed foliage-gleaner  NT  1539 
125  Furnariidae  Anabacerthia lichtensteini  Ochre-breasted foliage-gleaner  LC  2804 
126  Furnariidae  Clibanornis dendrocolaptoides  Canebrake groundcreeper  LC  855 
127  Furnariidae  Automolus lammi  Pernambuco Foliage-gleaner  EN  230 
128  Furnariidae  Automolus leucophthalmus  White-eyed foliage-gleaner  LC  8375 
129  Furnariidae  Leptasthenura striolata  Striolated tit-spinetail  LC  993 
130  Furnariidae  Leptasthenura setaria  Araucaria tit-spinetail  NT  4866 
131  Furnariidae  Phacellodomus erythrophthalmus  Orange-eyed thornbird  LC  4006 
132  Furnariidae  Phacellodomus ferrugineigula  Orange-breasted thornbird  LC  3410 
133  Furnariidae  Asthenes moreirae  Itatiaia spinetail  LC  1031 
134  Furnariidae  Acrobatornis fonsecai  Pink-legged graveteiro  VU  234 
135  Furnariidae  Thripophaga macroura  Striated softtail  VU  583 
136  Furnariidae  Cranioleuca obsoleta  Olive spinetail  LC  3288 
137  Furnariidae  Cranioleuca pallida  Pallid spinetail  LC  10,153 
138  Furnariidae  Synallaxis cinerea  Bahia spinetail  NT  448 
139  Furnariidae  Synallaxis infuscata  Pinto’s spinetail  EN  109 
140  Furnariidae  Synallaxis ruficapilla  Rufous-capped spinetail  LC  13,720 
141  Tyrannidae  Phyllomyias virescens  Greenish tyrannulet  LC  1946 
142  Tyrannidae  Phyllomyias griseocapilla  Grey-capped tyrannulet  NT  4138 
143  Tyrannidae  Phylloscartes kronei  Restinga tyrannulet  LC  1751 
144  Tyrannidae  Phylloscartes beckeri  Bahia tyrannulet  EN  237 
145  Tyrannidae  Phylloscartes ceciliae  Alagoas tyrannulet  CR  339 
146  Tyrannidae  Phylloscartes paulista  Sao Paulo tyrannulet  NT  1441 
147  Tyrannidae  Phylloscartes oustaleti  Oustalet’s tyrannulet  NT  1890 
148  Tyrannidae  Phylloscartes difficilis  Serra do mar tyrannulet  LC  700 
149  Tyrannidae  Phylloscartes sylviolus  Bay-ringed tyrannulet  NT  1001 
150  Tyrannidae  Myiornis auricularis  Eared pygmy-tyrant  LC  7675 
151  Tyrannidae  Hemitriccus diops  Drab-breasted bamboo-tyrant  LC  3138 
152  Tyrannidae  Hemitriccus obsoletus  Brown-breasted bamboo-tyrant  LC  1603 
153  Tyrannidae  Hemitriccus orbitatus  Eye-ringed tody-tyrant  NT  3256 
154  Tyrannidae  Hemitriccus nidipendulus  Hangnest tody-tyrant  LC  4478 
155  Tyrannidae  Hemitriccus mirandae  Buff-breasted tody-tyrant  VU  541 
156  Tyrannidae  Hemitriccus kaempferi  Kaempfer’s tody-tyrant  VU  683 
157  Tyrannidae  Hemitriccus furcatus  Fork-tailed tody-tyrant  VU  1431 
158  Tyrannidae  Todirostrum poliocephalum  Yellow-lored tody-flycatcher  LC  18,110 
159  Tyrannidae  Platyrinchus leucoryphus  Russet-winged spadebill  VU  487 
160  Tyrannidae  Onychorhynchus swainsoni  Atlantic royal flycatcher  VU  765 
161  Tyrannidae  Knipolegus nigerrimus  Velvet black-tyrant  LC  5281 
162  Tyrannidae  Muscipipra vetula  Shear-tailed grey tyrant  LC  3475 
163  Tyrannidae  Attila rufus  Grey-hooded attila  LC  10,287 
164  Tyrannidae  Piprites pileata  Black-capped piprites  NT  1312 
165  Cotingidae  Carpornis cucullata  Hooded berryeater  LC  4600 
166  Cotingidae  Carpornis melanocephala  Black-headed berryeater  NT  1223 
167  Cotingidae  Phibalura flavirostris  Swallow-tailed cotinga  LC  1715 
168  Cotingidae  Cotinga maculata  Banded cotinga  CR  257 
169  Cotingidae  Lipaugus lanioides  Cinnamon-vented piha  LC  1669 
170  Cotingidae  Lipaugus ater  Black-and-gold cotinga  LC  1642 
171  Cotingidae  Lipaugus conditus  Grey-winged cotinga  VU  262 
172  Cotingidae  Procnias nudicollis  Bare-throated bellbird  NT  7788 
173  Cotingidae  Xipholena atropurpurea  White-winged cotinga  VU  701 
174  Pipridae  Neopelma aurifrons  Wied’s tyrant-manakin  NT  327 
175  Pipridae  Neopelma chrysolophum  Serra do Mar tyrant-manakin  LC  2188 
176  Pipridae  Antilophia bokermanni  Araripe manakin  CR 
177  Pipridae  Chiroxiphia caudata  Blue manakin  LC  26,382 
178  Pipridae  Ilicura militaris  Pin-tailed manakin  LC  7292 
179  Pipridae  Machaeropterus regulus  Kinglet manakin  LC  997 
180  Tityridae  Schiffornis virescens  Greenish schiffornis  LC  8586 
181  Tityridae  Iodopleura pipra  Buff-throated purpletuft  EN  1341 
182  Tityridae  Laniisoma elegans  Elegant mourner  NT  330 
183  Vireonidae  Hylophilus poicilotis  Rufous-crowned greenlet  LC  7960 
184  Corvidae  Cyanocorax coeruleus  Azure jay  NT  7582 
185  Polioptilidae  Polioptila lactea  Creamy-bellied gnatcatcher  NT  331 
186  Thraupidae  Nemosia rourei  Cherry-throated tanager  CR  147 
187  Thraupidae  Orchesticus abeillei  Brown tanager  NT  1804 
188  Thraupidae  Hemithraupis ruficapilla  Rufous-headed tanager  LC  10,180 
189  Thraupidae  Haplospiza unicolor  Uniform Finch  LC  3948 
190  Thraupidae  Tachyphonus coronatus  Ruby-crowned tanager  LC  35,583 
191  Thraupidae  Ramphocelus bresilius  Brazilian tanager  LC  21,613 
192  Thraupidae  Dacnis nigripes  Black-legged dacnis  NT  1239 
193  Thraupidae  Sporophila falcirostris  Temminck’s seedeater  VU  1157 
194  Thraupidae  Sporophila frontalis  Buffy-fronted seedeater  VU  2049 
195  Thraupidae  Saltator maxillosus  Thick-billed saltator  LC  2935 
196  Thraupidae  Saltator fuliginosus  Black-throated grosbeak  LC  4827 
197  Thraupidae  Castanozoster thoracicus  Bay-chested warbling-finch  LC  1978 
198  Thraupidae  Thlypopsis pyrrhocoma  Chestnut-headed tanager  LC  2298 
199  Thraupidae  Microspingus lateralis  Buff-breasted warbling-finch  LC  3726 
200  Thraupidae  Tangara cyanoptera  Azure-shouldered tanager  NT  3316 
201  Thraupidae  Tangara brasiliensis  White-bellied tanager  LC  827 
202  Thraupidae  Tangara peruviana  Black-backed tanager  VU  1912 
203  Thraupidae  Tangara cyanomelas  Silvery-breasted tanager  LC  465 
204  Thraupidae  Tangara seledon  Green-headed tanager  LC  21,755 
205  Thraupidae  Tangara fastuosa  Seven-colored tanager  VU  964 
206  Thraupidae  Tangara cyanocephala  Red-necked tanager  LC  13,062 
207  Thraupidae  Tangara desmaresti  Brassy-breasted tanager  LC  8705 
208  Thraupidae  Tangara cyanoventris  Gilt-edged tanager  LC  6928 
209  Thraupidae  Tangara ornata  Golden-chevroned tanager  LC  6297 
210  Mitrospingidae  Orthogonys chloricterus  Olive-green tanager  LC  5418 
211  Passerellidae  Arremon semitorquatus  Half-collared sparrow  LC  2873 
212  Icteridae  Anumara forbesi  Forbes’s blackbird  VU  288 
213  Fringillidae  Euphonia chalybea  Green-throated euphonia  NT  2811 
214  Fringillidae  Euphonia pectoralis  Chestnut-bellied euphonia  LC  14,946 
215  Tyrannidae  Pogonotriccus eximius  Southern bristle-tyrant  NT  893 
216  Tyrannidae  Mionectes rufiventris  Grey-hooded flycatcher  LC  7262 

The joint dataset had satisfactory heterogeneity, as it achieved marginally acceptable conformity to Benford’s Law with regard to digit distribution of the number of observations per species (Mean Absolute Deviation (MAD) = 0.01468667; Distortion Factor: −1.158198; Mantissa Arc Test: L2 = 0.0002087; df = 2; p = 0.9567; n = 212). Even though over 44% of the species were imperiled (presumably rare), the data did not fit the gamma binomial model (α = 29.789; X2 = 439.851; df = 13; p = 0.0000).

Spatial distribution

Most of the observations of Atlantic Forest bird species were collected in the southern and south-eastern regions of the Atlantic Forest (Fig. 1a), while observations in the northeast were sparse. The number of observations was not proportionally distributed as expected based on the size of each region (χ2 = 385,324, df = 3, p < 10−16). Similarly, imperiled species were not evenly distributed (Fig. 1b). Even though most observations (74.5%) were located outside protected areas, it was lower than expected based on the area of non-protected areas (91.7% of all Atlantic Forest) and this difference was significant (χ2 = 382,532, df = 1, p < 2.2 × 10−16).

Temporal distribution

Even though the observations of imperiled species increased annually (estimate = 0.1579, t = 10.86, p = 4.52 × 10−10, n = 23), their proportion in relation to all species observations (imperiled/all species records) decreased (estimate = −0.18379, t = −5.348, p = 2.65 × 10−5, n = 23), remaining below 15% (average proportion between 2000 and 2022) in the last five years (Fig. 2a and b). After 2017, this proportion was below 13.8%, and in 2022 the proportion of imperiled species reached its lowest (12%) in the 21 years of our study. This pattern was maintained in spite of the increase in the number of birdwatchers on the platforms in the later years (estimate = 0.1469, t = 9.942, p = 2.15 × 10−9, n = 23; Fig. 2c). Species richness and the number of observations of Atlantic Forest bird species recorded annually on the platform was strongly correlated with the number of observers (Table 2). We also found that while the proportion of observations of declining species was decreasing through the years (estimate = −0.34384, t = − 7.173, p = 4.52 × 10−16, N = 23), the proportion of stable species observations was increasing (estimate = 0.34384, t = 7.173, p = 4.52 × 10−7, n = 23).

Fig. 2.

The number (a) and the proportion (b) of observations of threatened and Near Threatened bird species in the Brazilian Atlantic Forest compiled by community scientists and (c) number of observers that submitted observations of birds from the Brazilian Atlantic Forest in 2000–2022; the dashed line in b is the trend line form a smooth linear model.

(0.34MB).
Table 2.

Matrix with Spearman rank correlation coefficients (n = 23) for the correlations between threatened and Near Threatened species richness, number of observations of endemic species and the number of observers each year in the interval between 2000 and 2022. All correlations were p < 0.01, but exact p-values ​​could not be calculated.

  Richness  Number of observers each year  Number of observations of endemic species 
Richness  –  0.84  0.87 
Number of observers each year  –  –  0.95 
Number of observations of endemic species  –  –  – 

Bird species were observed more in October and November, with smaller peaks in observation in January, May and July (Fig. 3). This pattern on the number of observations was considered synchronic (ρ = 0.0369, and p < 0.01; n = 838,880).

Fig. 3.

Monthly distribution of observations of endemic species from the Brazilian Atlantic Forest based on three community science platforms in 2000–2022.

(0.1MB).
Discussion

We saw a general increase in the number of bird observers and bird observation in 2000–2022, reflecting an increasing interest in citizen science in Brazil. The marginally acceptable conformity to Benford’s Law of the joint dataset suggests that the number of observations had satisfactory heterogeneity, which is evidence for the proportional real abundance of each species. In spite of this, the data did not fit the gamma binomial distribution model, potentially because of the relatively high number of observations of imperiled (and therefore presumably rare) species. In total, 79 species had 2000–10,000 observations, while only eight species, all Least Concern, had over 20,000 observations (Table 1). Given this pattern, our results reveal that imperiled Atlantic Forest bird species with decreasing populations are relatively often recorded by CS.

Five species, including the Near Threatened Bare-throated Bellbird (Procnias nudicollis) and Azure Jay (Cyanocorax coeruleus) had over 4000 observations, both of them being relatively large, attractive birds that are easy to detect when present and vocalizing and are sought after by birdwatchers. These characteristics are similar to those preferred by birdwatchers in South Australia (Szabo et al., 2011). We assume that on all three platforms, many observers are experienced birdwatchers who focus on finding desired species to complete their personal list of observed birds, similar to a type of observer identified in Western Australia by Tulloch and Szabo (2012). Nevertheless, populations of Critically Endangered species, which are by definition extremely rare, are restricted to small remnant patches of native vegetation. These remote habitats are usually beyond the reach of most observers using these CS platforms. Two Critically Endangered species, Stresemann’s Bristlefront (Merulaxis stresemanni) and Alagoas Antwren (Myrmotherula snowi), were represented by a total of 57 and 102 observations, respectively, on the three platforms in 2000–2022. In fact, Stresemann’s Bristlefront is only known to persist in one forest patch (Lees and Pimm, 2015). Similar to the results of Barbosa et al. (2021), most observations were concentrated in areas of high human population density, i.e., in southern and south-eastern Brazil. In 2020 and 2021, restrictions imposed by the pandemic also limited the movement of bird observers to areas closer to urban centers (Sánchez-Clavijo et al., 2021) and the number of observations in protected areas declined globally compared to pre-pandemic levels (Qiao et al., 2023). This convenience sampling can inflate the number of observations of Least Concern and urban-tolerant species compared to those restricted to particular habitats, usually found within protected areas. Our results confirm this pattern and demonstrate that the probability of observing a threatened or Near Threatened species is much higher (98.5%) outside urban areas. Unfortunately, strictly protected areas only cover 9% of the Atlantic Forest (Rezende et al., 2018), which could explain the relatively high number of observations of imperiled bird outside protected areas. This pattern does not confirm with bird observations made by CS in Australia (Barnes et al., 2015). In addition, many threatened and Near Threatened species use habitats outside protected areas for feeding and reproduction, which raises an important question about prioritizing areas for monitoring and conservation.

Even more worrying is the proportional decline of imperiled species observations in 2000–2022 compared to all bird observations in the Atlantic Forest. We assume that the number of generalist observers (i.e., non-bird specialist in the case of iNaturalist or non-threatened species specialist on the other two platforms) has grown throughout the years, leading to a relatively large increase in the number of observations of common species and simultaneously decreasing the relative number of observations of rare species, some of which are threatened or Near Threatened. This hypothesis is supported by the correlations between species richness, the number of observations of imperiled species and the number of observers that are active on the platforms each year. However, real population declines also result in lower reporting rates, which is a cause for concern, particularly in the case of globally threatened species. These kinds of declines are more pronounced when we consider the increase in the number of observers on all three CS platforms. In fact, 67% of threatened and Near Threatened bird species observed on the platform show declining population trends and a further 5% have unknown trends (BirdLife International, 2023).

Two endemic species listed as Critically Endangered that are present on the list of Vale et al. (2018) and were absent from the CS dataset: Purple-winged Ground-Dove (Claravis geoffroyi; Columbidae) last seen in 2007 and Kinglet Calyptura (Calyptura cristata; Tyrannidae) seen only once in 1996 within the last century — both had a high probability of being globally extinct (Butchart et al., 2018; Lees et al., 2021; Lees and Pimm, 2015). The third missing species, Pernambuco Pygmy-Owl (Glaucidium mooreorum; Strigidae) was last seen in 2004 and is listed as Critically Endangered (Possibly Extinct) by BirdLife International (2023). On the other hand, there were a few records of two Furnaridae species in the CS datasets, one observation of the Cryptic Treehunter (Cichlocolaptes mazarbarnetti) in 2001 and 26 observations of the Alagoas Foliage-gleaner (Philydor novaesi), which was last seen in 2011, and is also possibly extinct (Butchart et al., 2018; Lees and Pimm, 2015). The joint dataset also contained two observations of the Alagoas curassow, a species listed as Extinct in the Wild by IUCN, possibly of individuals reintroduced in 2019 (Francisco et al., 2021).

The negative trend in the proportion of observations of threatened and Near Threatened species is worrying and needs to be confirmed by focused analysis of targeted studies. Most observations of imperiled species occurred before the beginning of the austral summer (Jones and Carvalho, 2002). Arthropod abundance in the Atlantic Forest is highest during the wet season, in December, while fruit availability is highest in April–June (Develey and Peres, 2000), which in turn increases bird activity and thereby detection rates. For many forest birds, the breeding season ends in December–January (Sick, 1997), when the number of individuals is augmented by fledglings, and this can explain a small peak of observations in January. The Atlantic Forest also receives a handful of Neotropical migrants during this period (Moreira-Lima, 2013) and some austral migrants (Lees and Martin, 2015). Another explanation is that the general public is also on holiday during this time, so they have more time to observe birds. The other peak in sightings of imperiled species occurred in May and July, possibly explained by the weather, as conditions are more favorable for observers (i.e., dryer and cooler) to visit more remote areas of the Atlantic Forest. Observers in unstructured surveys are known to have a preference for weekends, holidays and fair weather (Knape et al., 2022).

The interannual variability in climatic conditions can also affect the number of birds present and CS effort, as El Niño years bring extreme conditions that manifest in longer periods of drought in some regions and higher temperature with torrential rainfalls in others (Costa et al., 2021). The 2015–16 El Niño event resulted in extended drought in the Atlantic Forest of the northeast (Gateau-Rey et al., 2018), possibly affecting the number of birds observed in this region as well as CS effort. Measures to combat the Covid-19 pandemic may have affected the activities of observers, which can explain the lower number of observations of imperiled species in 2020 and 2021 compared to the previous two years. Even though the pandemics has brought some positive changes to nature conservation (Forti et al., 2020), most of its effects were negative (Gibbons et al., 2022). However, the relatively high number of observers in 2020–21 (Fig. 2c) demonstrates the resilience of community scientists that engaged in birdwatching even during these difficult times. Even if the number of surveys remained the same during these two years, the spatial patterns could have changed. In Colombia, observers submitted more data from modified than from natural landscapes as a result of lockdowns and other travel restrictions (Sánchez-Clavijo et al., 2021). Thus, hard-to-reach sites, such as remote protected areas, where there is a higher probability of observing certain rare species, have presumably received fewer CS surveys. This could explain the high number of records we identified outside protected areas, since many protected areas limited the number of visitors and, therefore tourist activities related to birdwatching were negatively affected in 2020–22 (Spenceley et al., 2021). Nevertheless, protected areas represent only 9% of the remaining natural vegetation of the Atlantic Forest in Brazil, with over 90% being on private land (Rezende et al., 2018).

Based on our results, more effort should be directed to remote areas of the Atlantic Forest to improve species coverage and thereby provide more data to inform biodiversity conservation. Expeditions to specific locations targeting species of conservation concern should be encouraged by CS platforms, protocols should be provided to minimize the risks presented to birds by tourist activities. We recommend targeted surveys for imperiled species to fill knowledge gaps for decision making. The central corridor of the Atlantic Forest is one of the areas that potentially needs a larger sampling effort to increase its representation on the platforms, as this alone could help to create a more robust dataset of Atlantic Forest biodiversity.

Conclusions

Considering the increasing pressures for landscape modification and the consequences of climate change on the Atlantic Forest (de Lima et al., 2020; SOS Mata Atlântica/INPE, 2018), there is an ever-increasing need to understand the biodiversity of this threatened biome. There are also ongoing restoration (Rezende et al., 2018) and refaunation (Galetti et al., 2017) efforts and further opportunities to be taken advantage of in the future in this global biodiversity hotspot. Community science can offer an ideal tool to monitor and document (hopefully positive) changes in the number and abundance of threatened species and to fully understand changes in functional diversity and species-specific responses to active restoration (Melo et al., 2020; Uezu and Metzger, 2016).

Developing a reliable monitoring system is a crucial challenge in the conservation of Atlantic Forest biodiversity, as conservation prioritization depends on the availability of such data. While CS data have influenced conservation decisions in other parts of the world (Fontaine et al., 2022; Fraisl et al., 2020), the lack of understanding of biases often prevents the use of this approach to derive reliable population trends (Bayraktarov et al., 2019). Here we provided the first exploration of CS data for a large number of Atlantic Forest endemic bird species considering two decades and multiple data sources. Even considering the biases inherent in CS data and the difficulties of surveying, it has proved to be an exceptional tool to reduce the Wallacean shortfall (La Sorte and Somveille, 2019; Lees and Martin, 2015), and to bring a new perspective to monitor species facing extinction and their responses to management actions.

Conflict of interest

The authors declare no conflicts of interest.

Availability of data

The raw data for this study are available at https://zenodo.org/records/10044588.

Acknowledgements

We appreciate the work of thousands of community scientists who share species occurrence data on digital platforms and therefore made this study possible. We thank Anderson Sandro and Gabriel Bonfa, two community scientists from iNaturalist who gave us permission to use their photographs in the graphical abstract. Arthur Queiros (grant number 127626/2022-0) and Talita Oliveira, (grant number 135165/2022-9) were financed by the Institutional Scientific Initiation Scholarship Program (Programa Institucional de Bolsas de Iniciação Cientifica; PIBIC), provided by the Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; CAPES), while Juan Lima received financial support (grant number 88887.687023/2022-00) from the Postgraduate Program in Ecology and Conservation (Programa de Pos Graduação em Ecologia e Conservação) supported by CAPES. We are grateful to Darius Pukenis Tubelis, two anonymous reviewers and the handling editor for their constructive comments on previous versions of this manuscript.

References
[Agostinelli and Lund, 2023]
C. Agostinelli, U. Lund.
Circular Statistics (Version 0.5-0).
[Alexandrino et al., 2022]
Eduardo R. Alexandrino, et al.
Urban southern house wren (Troglodytes aedon musculus) nesting in apparently unsuitable human-made structures: Is it worth it?.
Ornitol. Neotrop., 33 (2022), pp. 44-52
[Barbosa et al., 2021]
Karlla Vanessade Camargo Barbosa, et al.
The contribution of citizen science to research on migratory and urban birds in Brazil.
Ornithol. Res., 29 (2021), pp. 1-11
[Barnes et al., 2015]
Megan Barnes, et al.
Evaluating protected area effectiveness using bird lists in the Australian Wet Tropics.
Divers. Distrib., 21 (2015), pp. 368-378
[Bayraktarov et al., 2019]
E. Bayraktarov, G. Ehmke, J. O’Connor, E.L. Burns, H.A. Nguyen, L. McRae, H.P. Possingham, D.B. Lindenmayer.
Do big unstructured biodiversity data mean more knowledge?.
Front. Ecol. Evol., 6 (2019), pp. 239
[Bencke et al., 2006]
Glayson A. Bencke, et al.
Áreas Importantes para a Conservação das Aves no Brasil, Parte I – Estados do Domínio da Mata Atlântica. SAVE Brasil.
(2006),
[Bird et al., 2014]
Tomas J. Bird, et al.
Statistical solutions for error and bias in global citizen science datasets.
Biol. Conserv., 173 (2014), pp. 144-154
[Bird et al., 2020]
J.P. Bird, R. Martin, H.R. Akçakaya, J.J. Gilroy, I.J. Burfield, S.T. Garnett, A.J. Symes, J. Taylor, Ç.H. Şekercioğlu, S.H.M. Butchart.
Generation lengths of the world’s birds and their implications for extinction risk.
Conserv. Biol., 34 (2020), pp. 1252-1261
[BirdLife International, 2022]
BirdLife International, 2022. The Status of the World’s Birds has Deteriorated in Recent Decades. (Accessed 26 October 2023) http://www.birdlife.org/.
[BirdLife International, 2023]
BirdLife International, 2023. Birdlife Data Zone. (Accessed 26 October 2023) http://datazone.birdlife.org/.
[Bonney, 2021]
Rick Bonney.
Expanding the impact of citizen science.
BioScience, 71 (2021), pp. 448-451
[Bosenbecker et al., 2023]
C. Bosenbecker, P.A. Anselmo, R.Z. Andreoli, G.H. Shimizu, P.E. Oliveira, P.K. Maruyama.
Contrasting nation-wide citizen science and expert collected data on hummingbird–plant interactions.
Perspect. Ecol. Conserv., 21 (2023), pp. 164-171
[Butchart et al., 2018]
Stuart H.M. Butchart, et al.
Which bird species have gone extinct? A novel quantitative classification approach.
Biol. Conserv., 227 (2018), pp. 9-18
[Callaghan et al., 2021]
Corey T. Callaghan, et al.
Three frontiers for the future of biodiversity research using citizen science data.
BioScience, 71 (2021), pp. 55-63
[Chamberlain, 2020]
Scott Chamberlain.
Rredlist: ‘IUCN’ Red List Client. R Package Version 0.7.0.
[Chapman, 2005]
A.D. Chapman.
Uses of Primary Species-Occurrence Data, Version 1.0. Report for the Global Biodiversity Information Facility, Copenhagen.
(2005),
[Cinelli, 2014]
C. Cinelli.
Benford Analysis: Benford Analysis for Data Validation and Forensic Analytics. R Package Version 0.1.5.
[Costa et al., 2021]
M.d.S. Costa, J.F.d. Oliveira-Júnior, P.J. dos Santos, W.L.F. Correia Filho, G.D. Gois, C.J. Cavalcante Blanco, P.E. Teodoro, C.A.d. Silva Junior, D.d. Barros Santiago, E.d.O. Souza, A.M.d.R.F. Jardim.
Rainfall extremes and drought in northeast Brazil and its relationship with El Niño–Southern Oscillation.
Int. J. Climatol., 41 (2021), pp. E2111-E2135
[Cunha et al., 2022]
Filipe C.R. Cunha, Leonardo Esteves Lopes, Antonina Selezneva.
Revealing migration schedule and potential breeding grounds of lined seedeaters using citizen science data.
Emu, 122 (2022), pp. 167-175
[de Lima et al., 2020]
Renato A.F. de Lima, et al.
The erosion of biodiversity and biomass in the Atlantic Forest biodiversity hotspot.
Nat. Commun., 11 (2020), pp. 1-16
[de Souza et al., 2022]
Eletra de Souza, et al.
Ophiophagy in Brazilian birds: a contribution from a collaborative platform of citizen science.
Ornithol. Res., 30 (2022), pp. 15-24
[Deacon et al., 2023]
C. Deacon, S. Govender, M.J. Samways.
Overcoming biases and identifying opportunities for citizen science to contribute more to global macroinvertebrate conservation.
Biodivers. Conserv., 32 (2023), pp. 1789-1806
[Develey and Peres, 2000]
Pedro Ferreira Develey, Carlos Augusto Peres.
Resource seasonality and the structure of mixed species bird flocks in a coastal Atlantic forest of southeastern Brazil.
J. Trop. Ecology, 16 (2000), pp. 33-53
[Devenish et al., 2021]
Christian Devenish, et al.
Multi-decadal land use impacts across the vast range of an iconic threatened species.
Divers. Distrib., 27 (2021), pp. 2218-2230
[Di Cecco et al., 2021]
Grace J. Di Cecco, et al.
Observing the observers: how participants contribute data to iNaturalist and implications for biodiversity science.
BioScience, 71 (2021), pp. 1179-1188
[Fontaine et al., 2022]
A. Fontaine, A. Simard, N. Brunet, K.H. Elliott.
Scientific contributions of citizen science applied to rare or threatened animals.
Conserv. Biol., (2022), pp. e13976
[Forti et al., 2020]
Lucas R. Forti, et al.
Ecological inheritance for a post COVID-19 world.
Biodivers. Conserv., 29 (2020), pp. 3491-3494
[Fraisl et al., 2020]
D. Fraisl, J. Campbell, L. See, U. Wehn, J. Wardlaw, M. Gold, I. Moorthy, R. Arias, J. Piera, J.L. Oliver, J. Masó, M. Penker, S. Fritz.
Mapping citizen science contributions to the UN sustainable development goals.
Sustainability Sci., 15 (2020), pp. 1735-1751
[Francisco et al., 2021]
Mercival Roberto Francisco, et al.
Recovered after an extreme bottleneck and saved by ex situ management: lessons from the Alagoas curassow (Pauxi mitu [Linnaeus, 1766]; Aves, Galliformes, Cracidae).
Zoo Biol., 40 (2021), pp. 76-78
[Galetti et al., 2017]
Mauro Galetti, et al.
Reversing defaunation by trophic rewilding in empty forests.
Biotropica, 1 (2017), pp. 5-8
[Gateau-Rey et al., 2018]
L. Gateau-Rey, E.V.J. Tanner, B. Rapidel, J. Marelli, S. Royaert.
Climate change could threaten cocoa production: effects of 2015-16 El niño-related drought on cocoa agroforests in Bahia, Brazil.
PLoS One, 13 (2018), pp. e0200454
[Gibbons et al., 2022]
David W. Gibbons, et al.
The relative importance of COVID-19 pandemic impacts on biodiversity conservation globally.
Conserv. Biol., 36 (2022), pp. e13781
[Green and Young, 1993]
Roger H. Green, Richard C. Young.
Sampling to detect rare species.
Ecol. Appl., 3 (1993), pp. 351-356
[Guaraldo et al., 2022]
André De Camargo Guaraldo, Juliane Coimbra Bczuska, Lilian Tonelli Manica.
Turdus flavipes altitudinal migration in the Atlantic Forest the yellow-legged thrush is a partial altitudinal migrant in the Atlantic Forest.
Avian Biol. Res., 15 (2022), pp. 117-124
[Hortal et al., 2015]
J. Hortal, F. de Bello, J.A.F. Diniz-Filho, T.M. Lewinsohn, J.M. Lobo, R.J. Ladle.
Seven shortfalls that beset large-scale knowledge of biodiversity.
Annu. Rev. Ecol. Evol. Syst., 46 (2015), pp. 523-549
[IBGE, 2015]
IBGE.
Serie Relatos Metodológicos: Areas Urbanizadas do Brasil. Vector Data (SIG).
[IUCN, 2022]
IUCN.
The IUCN Red List of Threatened Species. Version 2022-1.
[IUCN Standards and Petitions Committee, 2022]
IUCN Standards and Petitions Committee.
Guidelines for Using the IUCN Red List Categories and Criteria. Version 15.1. Prepared by the Standards and Petitions Committee.
[Jiménez et al., 2019]
Manuel Jiménez, Isaac Triguero, Robert John.
Handling uncertainty in citizen science data: towards an improved amateur-based large-scale classification.
Inf. Sci., 479 (2019), pp. 301-320
[Jones and Carvalho, 2002]
Charles Jones, Leila M.V. Carvalho.
Active and break phases in the South American monsoon system.
J. Clim., 15 (2002), pp. 905-914
[Knape et al., 2022]
J. Knape, S.J. Coulson, R. van der Wal, D. Arlt.
Temporal trends in opportunistic citizen science reports across multiple taxa.
[La Sorte and Somveille, 2019]
Frank A. La Sorte, Marius Somveille.
Survey completeness of a global citizen-science database of bird occurrence.
Ecography, 42 (2019), pp. 1-10
[Lees and Martin, 2015]
Alexander C. Lees, Robert W. Martin.
Exposing hidden endemism in a neotropical forest raptor using citizen science.
Ibis, 157 (2015), pp. 103-114
[Lees and Pimm, 2015]
Alexander C. Lees, Stuart L. Pimm.
Species, extinct before we know them?.
Curr. Biol., 25 (2015), pp. 177-180
[Lees et al., 2021]
Alexander C. Lees, et al.
Assessing the extinction probability of the purple-winged ground dove, an enigmatic bamboo specialist.
Front. Ecol. Evol., 9 (2021), pp. 624959
[Lloyd et al., 2020]
Thomas J. Lloyd, et al.
Estimating the spatial coverage of citizen science for monitoring threatened species.
Global Ecol. Conserv., 23 (2020), pp. e01048
[Lopes and Schunck, 2022]
Leonardo Esteves Lopes, Fabio Schunck.
Unravelling the migratory patterns of the rufous-tailed attila within the neotropics using citizen science and traditional data sources.
Ornithol. Res., 30 (2022), pp. 87-98
[Mandeville et al., 2022]
Caitlin P. Mandeville, Erlend B. Nilsen, Anders G. Finstad.
Spatial distribution of biodiversity citizen science in a natural area depends on area accessibility and differs from other recreational area use.
Ecol. Solut. Evid., 3 (2022), pp. e12185
[Marques and Grelle, 2021]
Marcia C.M. Marques, Carlos Eduardo Viveiros Grelle.
The Atlantic Forest: History, Biodiversity, Threats and Opportunities of the Mega-Diverse Forest.
Springer Nature, (2021),
[Martikainen and Kouki, 2003]
Petri Martikainen, Jari Kouki.
Sampling the rarest: threatened beetles in boreal forest biodiversity inventories.
Biodivers. Conserv., 12 (2003), pp. 1815-1831
[Matthews et al., 2014]
Thomas J. Matthews, et al.
The gambin model provides a superior fit to species abundance distributions with a single free parameter: evidence, implementation and interpretation.
Ecography, 37 (2014), pp. 1002-1011
[Melo et al., 2020]
Marcos Antônio Melo, Marco Aurélio G. da Silva, Augusto João Piratelli.
Improvement of vegetation structure enhances bird functional traits and habitat resilience in an area of ongoing restoration in the Atlantic Forest.
An. Acad. Bras. Cienc., 92 (2020), pp. e20191241
[Moreira-Lima, 2013]
Luciano Moreira-Lima.
Aves Da Mata Atlântica: Riqueza, Composição, Status, Endemismos E Conservação.
Universidade de São Paulo, (2013),
[Morellato et al., 2000]
L. Morellato, C. Patricia, C.élio Fernando Baptista Haddad.
Introduction: the Brazilian Atlantic Forest.
Biotropica, 32 (2000), pp. 786-792
[Myers et al., 2000]
N. Myers, R.A. Mittermeier, C.G. Mittermeier, G.A.B. Da Fonseca, J. Kent.
Biodiversity hotspots for conservation priorities.
Nature, 403 (2000), pp. 853-858
[Newman et al., 2017]
Gregory J. Newman, et al.
Leveraging the power of place in citizen science for effective conservation decision making.
Biol. Conserv., 208 (2017), pp. 55-64
[Nigrini, 2012]
M.J. Nigrini.
Benford’S Law: Applications for Forensic Accounting, Auditing, and Fraud Detection.
John Wiley & Sons, (2012),
[Pizo and Tonetti, 2020]
M.A. Pizo, V.R. Tonetti.
Living in a fragmented world: birds in the Atlantic Forest.
Ornithol Appl, 122 (2020), pp. 1-14
[QGIS Development Team, 2021]
QGIS Development Team, 2021. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org.
[Qiao et al., 2023]
Huijie Qiao, Michael Orr, Qinmin Yang, Xiangjiang Zhan, Fumin Lei, Alice C. Hughes.
Global birdwatching data reveal uneven consequences of the COVID-19 pandemic.
Biol. Conserv., 288 (2023), pp. 110351
[R Core Development Team, 2020]
R Core Development Team.
R: A Language and Environment for Statistical Computing.
[Revelle, 2022]
W. Revelle.
Psych: Procedures for Personality and Psychological Research.
[Rezende et al., 2018]
C.L. Rezende, et al.
From hotspot to hopespot: an opportunity for the Brazilian Atlantic Forest.
Perspect. Ecol. Conserv., 16 (2018), pp. 208-214
[Ribeiro et al., 2011]
Milton Cezar Ribeiro, et al.
The Brazilian Atlantic Forest: a shrinking biodiversity hotspot.
Biodiversity Hotspots, pp. 405-434
[Robinson et al., 2018]
Natasha M. Robinson, et al.
How to ensure threatened species monitoring leads to threatened species conservation.
Ecol. Manage. Restor., 19 (2018), pp. 222-229
[Sánchez-Clavijo et al., 2021]
Lina María Sánchez-Clavijo, et al.
Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia.
Biol. Conserv., 256 (2021), pp. 109077
[Santos et al., 2021]
Clarissa O. Santos, et al.
Distribution and migration phenology of purple martins (Progne subis) in Brazil.
Ornithol. Res., 29 (2021), pp. 213-222
[Schnell et al., 2013]
J.K. Schnell, G.M. Harris, S.L. Pimm, G.J. Russell.
Quantitative analysis of forest fragmentation in the Atlantic Forest reveals more threatened bird species than the current red list.
[Schubert et al., 2019]
Stephanie Caroline Schubert, Lilian Tonelli Manica, André De Camargo Guaraldo.
Revealing the potential of a huge citizen-science platform to study bird migration.
Emu, 119 (2019), pp. 364-373
[Sick, 1997]
H. Sick.
Ornitologia Brasileira.
Editora Nova Fronteira, (1997),
[Soberón and Townsend Peterson, 2009]
Jorge Soberón, A. Townsend Peterson.
Monitoring biodiversity loss with primary species-occurrence data: toward national-level indicators for the 2010 target of the convention on biological diversity.
[SOS Mata Atlântica/INPE, 2018]
SOS Mata Atlântica/INPE.
Atlas Dos Remanescentes Florestais Da Mata Atlântica: Período 2016-2017 – Relatório Técnico.
SOS Mata Atlântica/INPE, (2018),
[Spenceley et al., 2021]
Anna Spenceley, et al.
Tourism in protected and conserved areas amid the COVID-19 pandemic.
PARKS, 27 (2021), pp. 103-118
[Szabo et al., 2010]
Judit K. Szabo, et al.
Regional avian species declines estimated from volunteer-collected long-term data using list length analysis.
Ecol. Appl., 20 (2010), pp. 2157-2169
[Szabo et al., 2011]
Judit K. Szabo, et al.
Paying the extinction debt: woodland birds in the Mount Lofty Ranges, South Australia.
Emu, 111 (2011), pp. 59-70
[Szabo et al., 2012a]
J.K. Szabo, S.H.M. Butchart, H.P. Possingham, S.T. Garnett.
Adapting global biodiversity indicators to the national scale: a Red List Index for Australian birds.
Biol. Conserv., 148 (2012), pp. 61-68
[Szabo et al., 2012b]
Judit K. Szabo, Richard A. Fuller, Hugh P. Possingham.
A comparison of estimates of relative abundance from a weakly structured mass-participation bird atlas survey and a robustly designed monitoring scheme.
Ibis, 154 (2012), pp. 468-479
[Szabo et al., 2023]
Judit K. Szabo, Lucas Rodriguez Forti, Corey T. Callaghan.
Large biodiversity datasets conform to Benford’s law: implications for assessing sampling heterogeneity.
Biol. Conserv., 280 (2023), pp. 109982
[Troudet et al., 2017]
Julien Troudet, et al.
Taxonomic bias in biodiversity data and societal preferences.
[Tulloch and Szabo, 2012]
Ayesha Tulloch, Judit K. Szabo.
A behavioural ecology approach to understand volunteer surveying for citizen science datasets.
Emu, 112 (2012), pp. 313-325
[Uezu and Metzger, 2016]
Alexandre Uezu, Jean Paul Metzger.
Time-lag in responses of birds to Atlantic Forest fragmentation: restoration opportunity and urgency.
PLoS ONE, 11 (2016), pp. e0147909
[Ugland et al., 2007]
Karl I. Ugland, et al.
Modelling dimensionality in species abundance distributions: description and evaluation of the Gambin model.
Evol. Ecol. Res., 9 (2007), pp. 313-324
[Vale et al., 2018]
Mariana M. Vale, et al.
Endemic birds of the Atlantic Forest: traits, conservation status, and patterns of biodiversity.
J. Field Ornithol., 89 (2018), pp. 193-206
[Wickham, 2016]
Hadley Wickham.
ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag, (2016),
[Wickham et al., 2022]
Hadley Wickham, et al.
Dplyr: A Grammar of Data Manipulation. R Package Version 1.0.9.
[Wilson et al., 2020]
Joseph S. Wilson, et al.
More eyes on the prize: an observation of a very rare, threatened species of philippine bumble bee, Bombus irisanensis, on iNaturalist and the importance of citizen science in conservation biology.
J. Insect Conserv., 24 (2020), pp. 727-729
[Zulian et al., 2021]
Viviane Zulian, David A.W. Miller, Gonçalo Ferraz.
Integrating citizen-science and planned-survey data improves species distribution estimates.
Divers. Distrib., 27 (2021), pp. 2498-2509
Copyright © 2024. Associação Brasileira de Ciência Ecológica e Conservação
Download PDF
Perspectives in Ecology and Conservation
Article options
Tools