Wallacean shortfall precludes what? the relationship between landscape structure and primate diversity in the Atlantic Forest

Wallacean shortfall precludes what? the relationship between landscape structure and primate diversity in the Atlantic Forest | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Wallacean shortfall precludes what? the relationship between landscape structure and primate diversity in the Atlantic Forest Thairik Mateus Silva Marques, Nicolas Silva Bosco, Afonso Kempner, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6719344/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Currently, the ecological key-question is how deforestation affects biodiversity at different levels of landscape fragmentation and at different spatial scales. To answer this question, we need unbiased knowledge of biodiversity spatial distribution across all landscape configuration gradients. However, the availability of biodiversity data is still limited and space-temporally biased. Here, we assess spatial sampling bias in primates, and how this shortcoming can affect our ability to assess the relationships between deforestation and biodiversity decline in a global hotspot ecoregion, the Atlantic Forest. We predict that sampling of primates in Atlantic Forest is spatially biased towards large and connected forest remnants closer to cities and roads, consequently leading to knowledge shortcoming in small and disconnected forest fragments far from cities and roads. We used a dataset of primary occurrence records from the Atlantic Forest primates to test spatial sampling bias on a landscape perspective. Our findings show sampling spatial bias towards large and connected forest patches following our prediction. These findings highlight that the current primate biodiversity knowledge is insufficient to understand the relationships between Atlantic Forest landscape fragmentation and biodiversity loss at a landscape perspective. A greater sampling effort in small and disconnected fragments is necessary before making sound inferences about the effects of landscape modification and fragmentation in primates’ biodiversity, which can also extend to a large portion of the known biodiversity of the Atlantic Forest. Biodiversity shortfalls landscape changes landscape ecology macroecology spatial bias Atlantic Forest Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Human-induced landscape changes are causing species extinction (Dirzo et al., 2014 ) triggering the global biodiversity crisis (Barnosky et al., 2011 ). Understanding how landscape changes impact biodiversity at different spatial scales is still a challenge, because biodiversity shortfalls (Hortal et al., 2015 ) hamper untangling community assembly processes and the gaps of species distribution knowledge (Ladle and Hortal, 2013 ). Landscape modification and fragmentation affect biodiversity on multiscale processes (Netzel et al., 2024 ). While multiscale modeling approaches can be used to infer broad-scale biodiversity patterns, such as inventory completeness and integrated structure combining landscape ecology and macroecology for niche modeling applications (Sobral-Souza et al. 2021a ), such approaches are influenced by shortfalls in biodiversity knowledge (Whittaker et al., 2005 ; Hortal et al., 2015 ). Among the biodiversity shortfalls (see Hortal et al., 2015 ), the Wallacean shortfall, poor understanding of species’ spatial distribution (Whittaker et al., 2005 ), is particularly critical to infer the effect of landscape modifications on biodiversity (Sobral-Souza et al., 2021b ) because the relative roles of ecological mechanisms in biodiversity loss are spatially dependent (Levin 1992 ). The assembly of digitally accessible information on biodiversity can help to understand the drivers of species loss, and the relationships between species loss and habitat fragmentation (Sobral-Souza et al., 2021a ). However, the digitally accessible biodiversity datasets are also biased or incomplete (Hortal et al., 2015 ; Oliveira et al., 2016 ; Meyer et al., 2016 ; Lobo et al., 2018 ; Castro-Souza et al., 2024). The incompleteness of biodiversity data is mainly caused by the shortcomings of integration and availability, whether from small personal collections or large databases (Robertson et al., 2014 ; Soltis et al., 2016). Data compilation from papers, gray literature and unpublished data can reduce the spatial and temporal bias and increase the predictiveness of ecological patterns (Whittaker et al., 2005 ; Hortal et al., 2015 ). Inferring how biodiversity data shortcomings can influence the prediction of ecological and environmental relationships is a fundamental task, especially in highly impacted regions, such as global hotspots. The Atlantic Forest (AF) is a global hotspot (Myers et al., 2000 ) with a long history of landscape modification and fragmentation, leading to significant biodiversity loss (Cincotta, 2000 ; Tabarelli et al., 2010 ; Santos et al., 2019 ). AF has been the focus of extensive compilations of species occurrence records (Rodrigues et al., 2019), making it an ideal setting for assessing the impacts of biodiversity data shortcoming on predictions regarding the relationship between landscape modification, fragmentation and large-scale biodiversity loss. Numerous studies have explored sampling bias in the AF. Sobral-Souza et al., ( 2021a ) found that sampling bias in butterflies was influenced by the presence of large forest remnants, which shaped community structure and diversity patterns. Additionally, Sousa Da Silva et al., (2025) showed that the distribution of epiphytic bryophytes was strongly affected by under-sampled regions within biodiversity hotspots, indicating the need for more spatially balanced sampling. Similarly, Sobral-Souza et al. ( 2024 ) demonstrated that small mammal records were heavily concentrated in accessible areas, revealing a mismatch between biodiversity richness and sampling effort across the AF. These findings highlight how sampling bias can obscure ecological patterns, especially in forest-dependent taxa such as primates. Given their sensitivity to habitat loss and fragmentation, primates are ideal for assessing spatial shortfalls in biodiversity data from AF (Benchimol and Peres, 2013). Here, address spatial shortcomings in primate sampling in the AF landscape. We hypothesize that the available data on primate distribution are biased in relation to landscape structure larger forest patches tend to sustain primate populations for longer periods, increasing the probability of species records and, consequently, limiting the representation of small forest fragments in primate studies. Because of that, occurrence data are spatially clustered, leaving small and disconnected fragments undersampled, compromising inferences about biodiversity spatial patterns, and species-landscape relationships. Therefore, we predict that sampling effort is spatially biased (1) towards landscapes with greater forest cover, larger, and more connected fragments. Access and infrastructure availability are critical factors for logistical feasibility and cost reduction in biodiversity sampling (Fig. 1 ). This issue becomes even more relevant in developing countries, where research funding has historically been constrained. Thus, we predict that (2) sampling bias is also associated with proximity to roads and cities. As a consequence, knowledge shortcoming persists in landscapes with lower forest cover, small and disconnected fragments, and areas distant from cities and roads, ultimately resulting in an inability to predict the relationship between landscape modification and primate biodiversity loss on a landscape perspective. 2. METHODS 2.1 Study Area The study was carried out in the Atlantic Forest ecoregion, which originally covered eastern Brazil, Paraguay and Argentina (Ribeiro et al. 2009 ; Muylaert et al. 2018 ). Currently, the AF comprises 28% of its original forest cover, including native forest (26%) and non-forest (2%) formations (Rezende et al., 2018 ). Forest remnants are predominantly small (< 0.5 km 2 ) and disconnected from each other (Ribeiro et al. 2009 ). 2.2 Species occurrence datasets We compiled primate species occurrence records from four publicly accessible biodiversity databases: the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/pt/ ), speciesLink ( https://specieslink.net/ ), the Brazilian Biodiversity Information System (SiBBr, https://www.sibbr.gov.br/?lang=pt_BR ), and the Biodiversity Portal (PortalBio, https://portaldabiodiversidade.icmbio.gov.br ). Additionally, we incorporated records from Culot et al. ( 2019 ), which includes published and unpublished data from community-level surveys. The raw occurrence data retrieved from each source are listed in Supplementary Table 1. All records were consolidated into a single database and subsequently cleaned and standardized using the bdc clean R package (Ribeiro et al. 2022 ). This package offers automated procedures to detect and remove records with spatial inaccuracies, taxonomic inconsistencies, and duplication issues. Specifically, we excluded records without geographic coordinates, those not identified at the species level (e.g., labeled as “cf.”, “aff.”, or “sp.”), duplicates, and those with generic coordinates corresponding to municipality or district centroids. These steps were performed using the functions bdc_filter_based_on_coordinate_precision(), bdc_filter_name_not_identified(), and bdc_filter_duplicate(). Taxonomic names across all records were standardized using the Catalogue of Life ( https://www.catalogueoflife.org/ ), recognizing valid species names and their respective synonyms. 2.3 Landscape variables We used forest cover percentage, patches size, functional connectivity, landscape homogeneity, distance to roads, and distance to cities, because they represent different aspects of landscape structure and accessibility, influencing species distribution and dynamics. We assessed the primate sampling spatial shortcoming based on accessibility and landscape metrics. We calculated 1) forest cover (% of natural habitat); 2) patches size (ha); 3) Homogeneity; 4) Functional connectivity (m); 5) Euclidean distance to the nearest road (m) and 6) Euclidean distance to the nearest urban area (m). We calculated road distances based on the shapefile of federal and state paved roads from the National Department of Transport Infrastructure (DNIT, ). For urban areas, we used the shapefiles of urban centers from the Brazilian Foundation for Sustainable Development (FBDS, ), the Brazilian Institute of Geography and Statistics (IBGE, ), and the Atlantic Forest Foundation (SOS, ). Homogeneity was calculated based on the similarity of the Enhanced Vegetation Index (EVI) between adjacent pixels, using the textural features of the EVI. The images were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). Area of functionally connected forest, considering patches as connected when the distance between them is ≤ 200 m. Sampling points located up to 200 m from the forest edge were assigned the same value as if they were located inside the fragment. To calculate patches size (ha) and percentage of forest cover (%) to evaluate the relationship between landscape configuration and composition and spatial shortcoming in primates sampling. We used land cover shapefiles from FBDS, the SOS Atlantic Forest Foundation, the National Institute for Space Research (INPE, ), and the University of Maryland Global Forest Change Project (Hansen et al., 2013). Patch size was computed as the total forest area (in hectares) within each 1 km² grid cell. To estimate forest cover (%), we calculated the proportion of forest within a 30 m × 30 m square window centered on each focal cell (i.e., number of forested cells divided by the total number of cells within the window). As a result, forest cover values ranged from 0–100%. Each focal cell corresponded to a 1 km² grid cell used in the ecological analyses, systematically distributed across the study area to ensure spatial representativeness. All landscape metrics were calculated at a 30 × 30 m resolution and standardized to a 1 km² scale, following Sobral-Souza et al. ( 2021a , b ). 2.4. Sampling completeness We conducted a sampling completeness analysis (Lobo et al., 2018 ; Muylaert et al., 2018 a) to identify well-sampled sites at 1 km² resolution. This resolution was chosen because landscape structure and configuration has been shaped by land-use changes affecting ecological processes at smaller scales (Benítez-López et al., 2010 ; Sobral-Souza et al., 2021a ). To assess the impact of the minimum number of records and cell size on completeness index (Tessarolo et al., 2021; Bosco et al., 2022), we set a minimum of 20 occurrences for a cell to be considered well-sampled, balancing the risk of overestimating completeness in cells with low occurrences and ensuring adequate data representation. This threshold was deemed appropriate because primate groups in the Neotropics typically consist of 2 to 20 individuals and a foraging area of 1km² (Janson and Goldsmith, 1995 ). Thus, by adopting the 20-occurrence threshold, we aim to represent a significant number of occurrences per cell. For cells with occurrences > = 20, we calculated the completeness index derived from a species accumulation curve using the ‘Clench’ function from the KnowBr package (Lobo et al., 2018 ) in R 4.3.1 (R Development Core Team, 2021 ). Completeness ranges from 0 (poorly sampled) to 1 (well sampled) (Hortal and Lobo, 2005; Lobo et al., 2018 ). 2.5 Analysis of spatial distribution shortcomings We performed a PCA analysis to synthesize the six landscape variables to build an environmental gradient. We then inferred the frequency of each PCA axis condition in an environmental bi-dimensional space defined by 0.25 × 0.25 km grid cells. The two first PCA axis was standardized (z-score standardization) to range between 0 and 1, where values close to 1 indicate rare landscape conditions occurring in a unique or few grid cells or sites. In contrast, values near zero represent common landscape conditions found in many sites of the Atlantic Forest. To analyze whether high completeness sites provided a representative subset of the overall landscape variation and encompassed regions of rare landscape conditions (Sobral-Souza et al., 2021b ), we used Kolmogorov-Smirnov test to compare the frequencies of landscape conditions across all Atlantic Forest cells and the cells with high completeness, as well as the frequency distribution of patches sizes across the entire Atlantic Forest with that of the well-sampled sites. The same approach was applied to forest cover (%), distances to the nearest road and distances to the nearest urban center for the entire Atlantic Forest and the well-sampled sites. All analyses were performed in R 4.3.1 (R Development Core Team, 2021 ). 3. RESULTS The primate occurrence data compiled by Culot et al. ( 2019 ) initially included 7.363 sampling sites, comprising 1.415 community-based sampling sites and 5.948 individual occurrence records. Filtering the records obtained from GBIF, SpeciesLink, SiBBr and Portal Bio, a total of 2.077 occurrence records were retained for analysis. At a spatial resolution of 1 km², only 595 out of the 1,649,932 existing cells were considered highly sampled (completeness index > 0.7) (Fig. 2 ). We identified two main gradients in the two first PCA components: Axis 1, comprising mainly the variation in landscape structure such as patches size, functional connectivity and percentage of forest cover. The Axis 2, comprising distance from cities and roads (Fig. 3 ). Our analysis revealed a significant bias in the spatial distribution of well-sampled sites in relation to landscape structure within the Atlantic Forest. Specifically, well-sampled sites were disproportionately located in landscapes characterized by higher forest cover, larger and more connected patches, as summarized by the first principal component (PC1; Fig. 4 A). This pattern was statistically supported (Kolmogorov–Smirnov test: D = 0.700, df = 1, p < 0.002), indicating a clear preference for sampling in more intact forested areas. In addition, we found a significant tendency for well-sampled sites to be closer to urban centers and road networks, as captured by the second principal component (PC2; Fig. 4 B; D = 0.646, df = 1, p < 0.002). This suggests that landscapes located in more remote regions, which are typically harder to access, are systematically underrepresented in biodiversity surveys. A comparison between the environmental space occupied by all landscape cells in the Atlantic Forest (Fig. 4 C) and the distribution of well-sampled sites (Fig. 4 D) further highlights the extent of this bias. Well-sampled sites are clustered within a narrow portion of the landscape space, failing to represent the full spectrum of landscape heterogeneity. This underrepresentation is particularly evident in landscapes composed of small, isolated forest fragments located farther from human infrastructure. 4. DISCUSSION Our results indicate shortcoming in primate sampling, favoring landscapes with greater forest cover, larger and more connected forest remnants, and areas closer to cities and roads following our predictions. Our findings suggest that only part of the landscape environmental gradient has been well-sampled, with significant shortcoming in landscapes with small fragments, low forest cover, and greater distances from cities and roads. Due to the lack of sampling across the entire landscape gradient, the available data remain insufficient to accurately assess the relationship between landscape modifications and primate biodiversity in the Atlantic Forest on a landscape scale. We emphasize the urgent need for biodiversity sampling in small and more isolated forest fragments, particularly those far from cities and roads. Currently, the number of small forest fragments is increasing dramatically worldwide (Taubert et al., 2018 ). In the AF, landscapes predominantly consist of small forest remnants, with nearly 97% of forested areas occurring in fragments smaller than 50 ha (Sobral-Souza b 1 et al., 2024) Additionally, deforestation in the AF has intensified in recent years (INPE, 2022 ; SOS Mata Atlântica, 2022 ). Despite the ongoing biodiversity crisis, knowledge shortcoming concerning small fragments can severely hinder our ability to understand how biodiversity is responding to changes in landscape structure from a large-scale perspective. The spatial aggregation and scarcity of well-sampled sites in habitat fragments far from cities and major roads represent a common accessibility bias in most biodiversity data, especially in occurrence records of terrestrial vertebrates (such as mammals and birds) in forest ecoregions of the Amazon and Atlantic Forest (Tessarolo et al., 2014 ; Oliveira et al., 2016 ; Monsarrat et al., 2019 ; Stropp et al., 2020 ; Sobral-Souza et al., 2021b ). Despite the intuitive perception that cities and roads pose potential threats to biodiversity (Benítez-López et al., 2010 ), more accessible sites—usually located near urban centers or along roads—are frequently prioritized for sampling (Almeida et al., 2021 ). Therefore, to address knowledge gaps regarding the distribution of Neotropical primates, initiatives that expand sampling in remote and hard-to-access regions are still needed. Therefore, to mitigate primates' knowledge shortcoming, initiatives to increase sampling in locations distant from cities and roads are still needed. An example was presented by Da Silva et al., (2015), who evaluated the response of medium- and large-sized mammals in habitat remnants embedded in cultivated land matrices. The study revealed that small fragments can sustain a considerable level of species diversity, as habitat quality proved to be a more decisive factor for diversity patterns than traditional explanatory variables at local scales, such as fragment size and distance (Delciellos et al., 2016). The biases and shortcoming in well-sampled sites for primates reported here are also common for other taxonomic groups in Brazil (Oliveira et al., 2016 ), such as butterflies (Sobral-Souza et al., 2021b ), other invertebrates (Lewinsohn et al., 2005 ), and non-flying small mammals (Sobral-Souza & Bosco et al., 2024 ). These assessments offer a valuable opportunity to examine biases and gaps in biodiversity sampling, which is essential for identifying deficiencies associated with biodiversity data and for considering them when evaluating the impacts of habitat loss and fragmentation on biodiversity. Historically, large fragments have been prioritized for sampling different taxonomic groups in the AF, such as butterflies and mammals (Pardini, 2004 ; Sobral-Souza et al., 2021a ). As a result, small fragments have been consistently neglected, generating a historical sampling bias, where, for many organisms, sampling small fragments would address this issue (e.g., Sobral-Souza et al., 2021c). In the case of primates, the lack of sampling data in small fragments may also be attributed to the occurrence pattern of primates, which generally do not occupy small fragments (< 0.5 km²) (Chiarello & Melo, 2001 ) due to their large home ranges and territoriality (Pearce et al., 2013 ). In addition, low levels of research funding often lead researchers to sample locations with a higher historical probability of primate species records. However, this sampling pattern can affect our understanding of the ecological processes influencing occupancy when using occurrence models, in which presence and absence data need to be spatially well distributed. The lack of data in smaller fragments can create a confounding effect, making it impossible to distinguish the effects of habitat loss and fragment size on occupancy (Pardini, 2004 ). Furthermore, the bias favoring fragments near roads and cities may hinder our ability to establish a relationship between habitat loss and fragmentation and primate diversity. However, the analysis of large fragments distant from cities and roads remains a critical gap in research on Atlantic Forest primates, limiting our understanding of the large-scale effects of habitat loss and fragmentation on biodiversity. 5. CONCLUSION Our findings point to sampling shortcoming of primate in small disconnected fragments, far from cities and roads. The lack of well-sampled sites in small and disconnected patches that are far from cities and roads may provide an incomplete answer to the relationship between species loss due to landscape changes at large scale perspective. To overcome this challenge, sampling at smaller and larger fragments far from cities and roads should be prioritized for future sampling. Here, we highlight the need to take the spatial shortfalls into account when assessing species responses to habitat loss and fragmentation, as well as the need to expand sampling sites to include a broad range of landscape conditions, particularly small and disconnected ones. Declarations Competing interests The authors declare no competing interests Author Contribution T.M.S.M. conceived the study, wrote the original draft, and conducted the revisions and editing. N.S.B. assisted in writing and performed statistical analyses. T.S.-S., G.T., R.G.C., A.K., A.K.F.S., G.O.S., R.A.C.-S., R.R.M., and J.P.S. contributed to reviewing and editing the manuscript. L.C. provided the dataset and contributed to manuscript revisions and editing. All authors read and approved the final version of the manuscript. Acknowledgement We thank the participants of the Macroecology and Biodiversity Conservation Laboratory (MacrEco). NBS thanks the National Council for Scientific and Technological Development (CNPq) for the master's scholarship. TSS is grateful to PROAP/UFMT (coordination to support graduate studies at the Federal University of Mato Grosso) for the financial assistance. LC receives a Research Productivity Fellowship from CNPq (process #314964/2021-5) and is grateful to the São Paulo Research Foundation (FAPESP) for the Young Investigator grants (#2014/14739-0, #2021/06668-0). RGC was supported by a Visiting Research fellowship from São Paulo Research Foundation (FAPESP, no 2022/10760-1), and has continuously received productive grants from CNPq, which we gratefully acknowledge. Data Availability All data supporting the findings of this study are available within the manuscript, the supplementary information files, and at the following link: https://drive.google.com/drive/folders/1AIoukcSFcs4UJ7lPPk-nkSCCUO6ITC6q References Almeida T., Tessarolo G., Nabout J. et al. 2021. Dataset for: Nonstationary drivers on fish sampling efforts in Brazilian freshwaters. Divers. Distrib. , 1–11. Barnosky A. D., Matzke N., Tomiya S. et al . 2011. Has the Earth’s sixth mass extinction already arrived? Nature 47, 51–57. Benítez-López A., Alkemade R. & Verweij P. A. 2010. The impacts of roads and other infrastructure on mammal and bird populations: A meta-analysis. Biol. Conserv. 143, 1307–1316. Cavender-bares J., Gonzalez-rodriguez A., Pahlich A. et al. 2011. 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Uncertainty associated with survey design in species distribution models. Divers. Distrib. 20, 1258–1269. Whittaker R. J., Araújo M.B., Jepson P. et al. 2005. Conservation Biogeography: Assessment and Prospect. Divers. Distrib. 11, 3–23. Wright S. J., Stoner K. E., Beckman N. et al . 2007. The plight of large animals in tropical forests and the consequences for plant regeneration. Biotropica. 39 , 289–291. Additional Declarations No competing interests reported. Supplementary Files WallaceanshortfallSuplementar.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 21 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6719344","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493790608,"identity":"78d0137e-2e69-459c-9cd7-57bca1fe23d9","order_by":0,"name":"Thairik Mateus Silva Marques","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":true,"prefix":"","firstName":"Thairik","middleName":"Mateus Silva","lastName":"Marques","suffix":""},{"id":493790609,"identity":"5bbebac8-d711-4d7b-b96c-e4b03451b705","order_by":1,"name":"Nicolas Silva Bosco","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"Silva","lastName":"Bosco","suffix":""},{"id":493790610,"identity":"cf714127-796f-4730-9446-c775763e7bf7","order_by":2,"name":"Afonso Kempner","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Afonso","middleName":"","lastName":"Kempner","suffix":""},{"id":493790611,"identity":"3ae75015-74ef-4286-ad84-8caf5b40b826","order_by":3,"name":"Arielly Kerolly Ferraz Sousa","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Arielly","middleName":"Kerolly Ferraz","lastName":"Sousa","suffix":""},{"id":493790612,"identity":"fa60331c-b600-4ab6-a99f-7071db055e6b","order_by":4,"name":"Geovana Oliveira Silva","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Geovana","middleName":"Oliveira","lastName":"Silva","suffix":""},{"id":493790613,"identity":"072d4206-4801-4c19-a214-9db58daa1e5a","order_by":5,"name":"Rodrigo Antônio Castro-Souza","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"Antônio","lastName":"Castro-Souza","suffix":""},{"id":493790614,"identity":"ec89fbaa-0904-4c77-b9d0-2257c3229e24","order_by":6,"name":"Rafaela Rodrigues Moraes","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Rafaela","middleName":"Rodrigues","lastName":"Moraes","suffix":""},{"id":493790615,"identity":"3a3b50c7-c426-4bc3-ab67-d78c88a0fa19","order_by":7,"name":"Jessie Pereira Santos","email":"","orcid":"","institution":"Universidade Estadual de Campinas","correspondingAuthor":false,"prefix":"","firstName":"Jessie","middleName":"Pereira","lastName":"Santos","suffix":""},{"id":493790616,"identity":"219a63c9-6ed0-42b8-8521-300991647edb","order_by":8,"name":"Laurence Culot","email":"","orcid":"","institution":"Universidade Estadual Paulista","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Culot","suffix":""},{"id":493790617,"identity":"7e02cb07-2687-45fe-ba97-25ec843e6039","order_by":9,"name":"Geiziane Tessarolo","email":"","orcid":"","institution":"Universidade Estadual de Goiás","correspondingAuthor":false,"prefix":"","firstName":"Geiziane","middleName":"","lastName":"Tessarolo","suffix":""},{"id":493790618,"identity":"f154f7fb-0f41-4da4-a70a-2863babd9db0","order_by":10,"name":"Rosane Garcia Collevatti","email":"","orcid":"","institution":"Universidade Federal de Goiás - UFG","correspondingAuthor":false,"prefix":"","firstName":"Rosane","middleName":"Garcia","lastName":"Collevatti","suffix":""},{"id":493790619,"identity":"adb1eac1-f67a-467d-8850-1e589aef0f9f","order_by":11,"name":"Thadeu Sobral-Souza","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Thadeu","middleName":"","lastName":"Sobral-Souza","suffix":""}],"badges":[],"createdAt":"2025-05-21 20:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6719344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6719344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88271371,"identity":"1dacbd7a-664e-463f-811d-33b7bf75e752","added_by":"auto","created_at":"2025-08-04 17:13:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71633,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the hypothesis tested in this study, which proposes that knowledge of primate distribution in the Atlantic Forest is biased by landscape characteristics such as fragment size, connectivity, and proximity to roads. Large, connected fragments near roads have positive effects (green), while small, isolated fragments far from roads result in negative effects (red).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/e6c43d575c3a8b4af0ee7acb.jpg"},{"id":88272481,"identity":"66be5d88-6326-43f9-afba-fd735ae8e419","added_by":"auto","created_at":"2025-08-04 17:21:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64508,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical extent of the Atlantic Forest biome (blue area) and spatial distribution of well-sampled sites for primates (black dots), defined by sampling completeness values above 0.7 or sites with more than 20 occurrence records. The black dots additionally represent documented occurrences of primate species within the biome.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/0c10ba4feaafe2439fd72328.jpg"},{"id":88272482,"identity":"413e2885-e9fd-4f81-9945-4d1efac69e67","added_by":"auto","created_at":"2025-08-04 17:21:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59057,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Principal Component Analysis (PCA) showing the distribution of grids in relation to accessibility and landscape metrics in the Atlantic Forest. The colors represent a gradient of variation in environmental metrics, with the transition from purple to green indicating an increase in forest cover, patches size, and functional connectivity. (B) Map of environmental characteristics of each pixel.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/728c14c95c52f1d80a123736.jpg"},{"id":88271375,"identity":"c1c95f4f-46b8-4696-94f9-a1d81597b8ea","added_by":"auto","created_at":"2025-08-04 17:13:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66337,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the distributions of landscape conditions in the Brazilian Atlantic Forest.\u003cstrong\u003e \u003c/strong\u003e(A) and (B) represent the density of the first two principal components (PC1 and PC2), where well-sampled areas are highlighted in blue. (C) Density map of landscape cells in the principal component space of PC1 and PC2. (D) Density of well-sampled locations within the landscape cells, highlighting sampled areas (in green and red) in contrast to unsampled regions (in gray).\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/b640d4ae4015d07c9b8b2a99.jpg"},{"id":88505318,"identity":"9556fccf-3792-4d4b-80bd-3587ad19e1aa","added_by":"auto","created_at":"2025-08-07 07:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":863640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/1c45f6ff-e36f-4a55-ad5b-9949060a5844.pdf"},{"id":88271377,"identity":"2956f740-18ce-4c45-a47e-ca0f34b52385","added_by":"auto","created_at":"2025-08-04 17:13:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15359,"visible":true,"origin":"","legend":"","description":"","filename":"WallaceanshortfallSuplementar.docx","url":"https://assets-eu.researchsquare.com/files/rs-6719344/v1/2bfe751d465fde4f6a185452.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wallacean shortfall precludes what? the relationship between landscape structure and primate diversity in the Atlantic Forest","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHuman-induced landscape changes are causing species extinction (Dirzo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) triggering the global biodiversity crisis (Barnosky et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Understanding how landscape changes impact biodiversity at different spatial scales is still a challenge, because biodiversity shortfalls (Hortal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) hamper untangling community assembly processes and the gaps of species distribution knowledge (Ladle and Hortal, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLandscape modification and fragmentation affect biodiversity on multiscale processes (Netzel et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While multiscale modeling approaches can be used to infer broad-scale biodiversity patterns, such as inventory completeness and integrated structure combining landscape ecology and macroecology for niche modeling applications (Sobral-Souza et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e), such approaches are influenced by shortfalls in biodiversity knowledge (Whittaker et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hortal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among the biodiversity shortfalls (see Hortal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the Wallacean shortfall, poor understanding of species\u0026rsquo; spatial distribution (Whittaker et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), is particularly critical to infer the effect of landscape modifications on biodiversity (Sobral-Souza et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) because the relative roles of ecological mechanisms in biodiversity loss are spatially dependent (Levin \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe assembly of digitally accessible information on biodiversity can help to understand the drivers of species loss, and the relationships between species loss and habitat fragmentation (Sobral-Souza et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). However, the digitally accessible biodiversity datasets are also biased or incomplete (Hortal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Meyer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lobo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Castro-Souza et al., 2024). The incompleteness of biodiversity data is mainly caused by the shortcomings of integration and availability, whether from small personal collections or large databases (Robertson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Soltis et al., 2016). Data compilation from papers, gray literature and unpublished data can reduce the spatial and temporal bias and increase the predictiveness of ecological patterns (Whittaker et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hortal et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Inferring how biodiversity data shortcomings can influence the prediction of ecological and environmental relationships is a fundamental task, especially in highly impacted regions, such as global hotspots.\u003c/p\u003e\u003cp\u003eThe Atlantic Forest (AF) is a global hotspot (Myers et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) with a long history of landscape modification and fragmentation, leading to significant biodiversity loss (Cincotta, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Tabarelli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Santos et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AF has been the focus of extensive compilations of species occurrence records (Rodrigues et al., 2019), making it an ideal setting for assessing the impacts of biodiversity data shortcoming on predictions regarding the relationship between landscape modification, fragmentation and large-scale biodiversity loss.\u003c/p\u003e\u003cp\u003eNumerous studies have explored sampling bias in the AF. Sobral-Souza et al., (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) found that sampling bias in butterflies was influenced by the presence of large forest remnants, which shaped community structure and diversity patterns. Additionally, Sousa Da Silva et al., (2025) showed that the distribution of epiphytic bryophytes was strongly affected by under-sampled regions within biodiversity hotspots, indicating the need for more spatially balanced sampling. Similarly, Sobral-Souza et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated that small mammal records were heavily concentrated in accessible areas, revealing a mismatch between biodiversity richness and sampling effort across the AF. These findings highlight how sampling bias can obscure ecological patterns, especially in forest-dependent taxa such as primates. Given their sensitivity to habitat loss and fragmentation, primates are ideal for assessing spatial shortfalls in biodiversity data from AF (Benchimol and Peres, 2013).\u003c/p\u003e\u003cp\u003eHere, address spatial shortcomings in primate sampling in the AF landscape. We hypothesize that the available data on primate distribution are biased in relation to landscape structure larger forest patches tend to sustain primate populations for longer periods, increasing the probability of species records and, consequently, limiting the representation of small forest fragments in primate studies. Because of that, occurrence data are spatially clustered, leaving small and disconnected fragments undersampled, compromising inferences about biodiversity spatial patterns, and species-landscape relationships. Therefore, we predict that sampling effort is spatially biased (1) towards landscapes with greater forest cover, larger, and more connected fragments. Access and infrastructure availability are critical factors for logistical feasibility and cost reduction in biodiversity sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This issue becomes even more relevant in developing countries, where research funding has historically been constrained. Thus, we predict that (2) sampling bias is also associated with proximity to roads and cities. As a consequence, knowledge shortcoming persists in landscapes with lower forest cover, small and disconnected fragments, and areas distant from cities and roads, ultimately resulting in an inability to predict the relationship between landscape modification and primate biodiversity loss on a landscape perspective.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Area\u003c/h2\u003e\u003cp\u003eThe study was carried out in the Atlantic Forest ecoregion, which originally covered eastern Brazil, Paraguay and Argentina (Ribeiro et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Muylaert et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Currently, the AF comprises 28% of its original forest cover, including native forest (26%) and non-forest (2%) formations (Rezende et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Forest remnants are predominantly small (\u0026lt;\u0026thinsp;0.5 km\u003csup\u003e2\u003c/sup\u003e) and disconnected from each other (Ribeiro et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Species occurrence datasets\u003c/h2\u003e\u003cp\u003eWe compiled primate species occurrence records from four publicly accessible biodiversity databases: the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/pt/\u003c/span\u003e\u003cspan address=\"https://www.gbif.org/pt/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), speciesLink (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://specieslink.net/\u003c/span\u003e\u003cspan address=\"https://specieslink.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Brazilian Biodiversity Information System (SiBBr, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sibbr.gov.br/?lang=pt_BR\u003c/span\u003e\u003cspan address=\"https://www.sibbr.gov.br/?lang=pt_BR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Biodiversity Portal (PortalBio, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portaldabiodiversidade.icmbio.gov.br\u003c/span\u003e\u003cspan address=\"https://portaldabiodiversidade.icmbio.gov.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, we incorporated records from Culot et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which includes published and unpublished data from community-level surveys. The raw occurrence data retrieved from each source are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eAll records were consolidated into a single database and subsequently cleaned and standardized using the bdc clean R package (Ribeiro et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This package offers automated procedures to detect and remove records with spatial inaccuracies, taxonomic inconsistencies, and duplication issues. Specifically, we excluded records without geographic coordinates, those not identified at the species level (e.g., labeled as \u0026ldquo;cf.\u0026rdquo;, \u0026ldquo;aff.\u0026rdquo;, or \u0026ldquo;sp.\u0026rdquo;), duplicates, and those with generic coordinates corresponding to municipality or district centroids. These steps were performed using the functions bdc_filter_based_on_coordinate_precision(), bdc_filter_name_not_identified(), and bdc_filter_duplicate().\u003c/p\u003e\u003cp\u003eTaxonomic names across all records were standardized using the Catalogue of Life (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.catalogueoflife.org/\u003c/span\u003e\u003cspan address=\"https://www.catalogueoflife.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), recognizing valid species names and their respective synonyms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Landscape variables\u003c/h2\u003e\u003cp\u003eWe used forest cover percentage, patches size, functional connectivity, landscape homogeneity, distance to roads, and distance to cities, because they represent different aspects of landscape structure and accessibility, influencing species distribution and dynamics.\u003c/p\u003e\u003cp\u003eWe assessed the primate sampling spatial shortcoming based on accessibility and landscape metrics. We calculated 1) forest cover (% of natural habitat); 2) patches size (ha); 3) Homogeneity; 4) Functional connectivity (m); 5) Euclidean distance to the nearest road (m) and 6) Euclidean distance to the nearest urban area (m). We calculated road distances based on the shapefile of federal and state paved roads from the National Department of Transport Infrastructure (DNIT, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.br/dnit/pt-br\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.gov.br/dnit/pt-br%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For urban areas, we used the shapefiles of urban centers from the Brazilian Foundation for Sustainable Development (FBDS, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fbds.org.br/\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.fbds.org.br/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Brazilian Institute of Geography and Statistics (IBGE, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibge.gov.br/\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.ibge.gov.br/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Atlantic Forest Foundation (SOS, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atlanticforestassociation.com/pt/\u0026gt;\u003c/span\u003e\u003cspan address=\"https://atlanticforestassociation.com/pt/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHomogeneity was calculated based on the similarity of the Enhanced Vegetation Index (EVI) between adjacent pixels, using the textural features of the EVI. The images were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). Area of functionally connected forest, considering patches as connected when the distance between them is \u0026le;\u0026thinsp;200 m. Sampling points located up to 200 m from the forest edge were assigned the same value as if they were located inside the fragment.\u003c/p\u003e\u003cp\u003eTo calculate patches size (ha) and percentage of forest cover (%) to evaluate the relationship between landscape configuration and composition and spatial shortcoming in primates sampling. We used land cover shapefiles from FBDS, the SOS Atlantic Forest Foundation, the National Institute for Space Research (INPE, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.br/inpe/pt-br\u0026gt;\u003c/span\u003e\u003cspan address=\"https://www.gov.br/inpe/pt-br%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the University of Maryland Global Forest Change Project (Hansen et al., 2013). Patch size was computed as the total forest area (in hectares) within each 1 km\u0026sup2; grid cell. To estimate forest cover (%), we calculated the proportion of forest within a 30 m \u0026times; 30 m square window centered on each focal cell (i.e., number of forested cells divided by the total number of cells within the window). As a result, forest cover values ranged from 0\u0026ndash;100%.\u003c/p\u003e\u003cp\u003eEach focal cell corresponded to a 1 km\u0026sup2; grid cell used in the ecological analyses, systematically distributed across the study area to ensure spatial representativeness. All landscape metrics were calculated at a 30 \u0026times; 30 m resolution and standardized to a 1 km\u0026sup2; scale, following Sobral-Souza et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Sampling completeness\u003c/h2\u003e\u003cp\u003eWe conducted a sampling completeness analysis (Lobo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Muylaert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea) to identify well-sampled sites at 1 km\u0026sup2; resolution. This resolution was chosen because landscape structure and configuration has been shaped by land-use changes affecting ecological processes at smaller scales (Ben\u0026iacute;tez-L\u0026oacute;pez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sobral-Souza et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo assess the impact of the minimum number of records and cell size on completeness index (Tessarolo et al., 2021; Bosco et al., 2022), we set a minimum of 20 occurrences for a cell to be considered well-sampled, balancing the risk of overestimating completeness in cells with low occurrences and ensuring adequate data representation. This threshold was deemed appropriate because primate groups in the Neotropics typically consist of 2 to 20 individuals and a foraging area of 1km\u0026sup2; (Janson and Goldsmith, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Thus, by adopting the 20-occurrence threshold, we aim to represent a significant number of occurrences per cell.\u003c/p\u003e\u003cp\u003eFor cells with occurrences\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;20, we calculated the completeness index derived from a species accumulation curve using the \u0026lsquo;Clench\u0026rsquo; function from the KnowBr package (Lobo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in R 4.3.1 (R Development Core Team, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Completeness ranges from 0 (poorly sampled) to 1 (well sampled) (Hortal and Lobo, 2005; Lobo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Analysis of spatial distribution shortcomings\u003c/h2\u003e\u003cp\u003eWe performed a PCA analysis to synthesize the six landscape variables to build an environmental gradient. We then inferred the frequency of each PCA axis condition in an environmental bi-dimensional space defined by 0.25 \u0026times; 0.25 km grid cells. The two first PCA axis was standardized (z-score standardization) to range between 0 and 1, where values close to 1 indicate rare landscape conditions occurring in a unique or few grid cells or sites. In contrast, values near zero represent common landscape conditions found in many sites of the Atlantic Forest. To analyze whether high completeness sites provided a representative subset of the overall landscape variation and encompassed regions of rare landscape conditions (Sobral-Souza et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), we used Kolmogorov-Smirnov test to compare the frequencies of landscape conditions across all Atlantic Forest cells and the cells with high completeness, as well as the frequency distribution of patches sizes across the entire Atlantic Forest with that of the well-sampled sites. The same approach was applied to forest cover (%), distances to the nearest road and distances to the nearest urban center for the entire Atlantic Forest and the well-sampled sites. All analyses were performed in R 4.3.1 (R Development Core Team, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eThe primate occurrence data compiled by Culot et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) initially included 7.363 sampling sites, comprising 1.415 community-based sampling sites and 5.948 individual occurrence records. Filtering the records obtained from GBIF, SpeciesLink, SiBBr and Portal Bio, a total of 2.077 occurrence records were retained for analysis.\u003c/p\u003e\u003cp\u003eAt a spatial resolution of 1 km\u0026sup2;, only 595 out of the 1,649,932 existing cells were considered highly sampled (completeness index\u0026thinsp;\u0026gt;\u0026thinsp;0.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe identified two main gradients in the two first PCA components: Axis 1, comprising mainly the variation in landscape structure such as patches size, functional connectivity and percentage of forest cover. The Axis 2, comprising distance from cities and roads (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur analysis revealed a significant bias in the spatial distribution of well-sampled sites in relation to landscape structure within the Atlantic Forest. Specifically, well-sampled sites were disproportionately located in landscapes characterized by higher forest cover, larger and more connected patches, as summarized by the first principal component (PC1; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This pattern was statistically supported (Kolmogorov\u0026ndash;Smirnov test: D\u0026thinsp;=\u0026thinsp;0.700, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002), indicating a clear preference for sampling in more intact forested areas.\u003c/p\u003e\u003cp\u003eIn addition, we found a significant tendency for well-sampled sites to be closer to urban centers and road networks, as captured by the second principal component (PC2; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; D\u0026thinsp;=\u0026thinsp;0.646, df\u0026thinsp;=\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.002). This suggests that landscapes located in more remote regions, which are typically harder to access, are systematically underrepresented in biodiversity surveys.\u003c/p\u003e\u003cp\u003eA comparison between the environmental space occupied by all landscape cells in the Atlantic Forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and the distribution of well-sampled sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) further highlights the extent of this bias. Well-sampled sites are clustered within a narrow portion of the landscape space, failing to represent the full spectrum of landscape heterogeneity. This underrepresentation is particularly evident in landscapes composed of small, isolated forest fragments located farther from human infrastructure.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur results indicate shortcoming in primate sampling, favoring landscapes with greater forest cover, larger and more connected forest remnants, and areas closer to cities and roads following our predictions. Our findings suggest that only part of the landscape environmental gradient has been well-sampled, with significant shortcoming in landscapes with small fragments, low forest cover, and greater distances from cities and roads. Due to the lack of sampling across the entire landscape gradient, the available data remain insufficient to accurately assess the relationship between landscape modifications and primate biodiversity in the Atlantic Forest on a landscape scale.\u003c/p\u003e\u003cp\u003eWe emphasize the urgent need for biodiversity sampling in small and more isolated forest fragments, particularly those far from cities and roads. Currently, the number of small forest fragments is increasing dramatically worldwide (Taubert et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the AF, landscapes predominantly consist of small forest remnants, with nearly 97% of forested areas occurring in fragments smaller than 50 ha (Sobral-Souza b 1 et al., 2024) Additionally, deforestation in the AF has intensified in recent years (INPE, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; SOS Mata Atl\u0026acirc;ntica, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the ongoing biodiversity crisis, knowledge shortcoming concerning small fragments can severely hinder our ability to understand how biodiversity is responding to changes in landscape structure from a large-scale perspective.\u003c/p\u003e\u003cp\u003eThe spatial aggregation and scarcity of well-sampled sites in habitat fragments far from cities and major roads represent a common accessibility bias in most biodiversity data, especially in occurrence records of terrestrial vertebrates (such as mammals and birds) in forest ecoregions of the Amazon and Atlantic Forest (Tessarolo et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Monsarrat et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Stropp et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sobral-Souza et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Despite the intuitive perception that cities and roads pose potential threats to biodiversity (Ben\u0026iacute;tez-L\u0026oacute;pez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), more accessible sites\u0026mdash;usually located near urban centers or along roads\u0026mdash;are frequently prioritized for sampling (Almeida et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, to address knowledge gaps regarding the distribution of Neotropical primates, initiatives that expand sampling in remote and hard-to-access regions are still needed.\u003c/p\u003e\u003cp\u003eTherefore, to mitigate primates' knowledge shortcoming, initiatives to increase sampling in locations distant from cities and roads are still needed. An example was presented by Da Silva et al., (2015), who evaluated the response of medium- and large-sized mammals in habitat remnants embedded in cultivated land matrices. The study revealed that small fragments can sustain a considerable level of species diversity, as habitat quality proved to be a more decisive factor for diversity patterns than traditional explanatory variables at local scales, such as fragment size and distance (Delciellos et al., 2016).\u003c/p\u003e\u003cp\u003eThe biases and shortcoming in well-sampled sites for primates reported here are also common for other taxonomic groups in Brazil (Oliveira et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), such as butterflies (Sobral-Souza et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), other invertebrates (Lewinsohn et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and non-flying small mammals (Sobral-Souza \u0026amp; Bosco et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These assessments offer a valuable opportunity to examine biases and gaps in biodiversity sampling, which is essential for identifying deficiencies associated with biodiversity data and for considering them when evaluating the impacts of habitat loss and fragmentation on biodiversity.\u003c/p\u003e\u003cp\u003eHistorically, large fragments have been prioritized for sampling different taxonomic groups in the AF, such as butterflies and mammals (Pardini, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sobral-Souza et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). As a result, small fragments have been consistently neglected, generating a historical sampling bias, where, for many organisms, sampling small fragments would address this issue (e.g., Sobral-Souza et al., 2021c). In the case of primates, the lack of sampling data in small fragments may also be attributed to the occurrence pattern of primates, which generally do not occupy small fragments (\u0026lt;\u0026thinsp;0.5 km\u0026sup2;) (Chiarello \u0026amp; Melo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) due to their large home ranges and territoriality (Pearce et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, low levels of research funding often lead researchers to sample locations with a higher historical probability of primate species records. However, this sampling pattern can affect our understanding of the ecological processes influencing occupancy when using occurrence models, in which presence and absence data need to be spatially well distributed. The lack of data in smaller fragments can create a confounding effect, making it impossible to distinguish the effects of habitat loss and fragment size on occupancy (Pardini, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Furthermore, the bias favoring fragments near roads and cities may hinder our ability to establish a relationship between habitat loss and fragmentation and primate diversity. However, the analysis of large fragments distant from cities and roads remains a critical gap in research on Atlantic Forest primates, limiting our understanding of the large-scale effects of habitat loss and fragmentation on biodiversity.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur findings point to sampling shortcoming of primate in small disconnected fragments, far from cities and roads. The lack of well-sampled sites in small and disconnected patches that are far from cities and roads may provide an incomplete answer to the relationship between species loss due to landscape changes at large scale perspective. To overcome this challenge, sampling at smaller and larger fragments far from cities and roads should be prioritized for future sampling. Here, we highlight the need to take the spatial shortfalls into account when assessing species responses to habitat loss and fragmentation, as well as the need to expand sampling sites to include a broad range of landscape conditions, particularly small and disconnected ones.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.M.S.M. conceived the study, wrote the original draft, and conducted the revisions and editing. N.S.B. assisted in writing and performed statistical analyses. T.S.-S., G.T., R.G.C., A.K., A.K.F.S., G.O.S., R.A.C.-S., R.R.M., and J.P.S. contributed to reviewing and editing the manuscript. L.C. provided the dataset and contributed to manuscript revisions and editing. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants of the Macroecology and Biodiversity Conservation Laboratory (MacrEco). NBS thanks the National Council for Scientific and Technological Development (CNPq) for the master\u0026apos;s scholarship. TSS is grateful to PROAP/UFMT (coordination to support graduate studies at the Federal University of Mato Grosso) for the financial assistance. LC receives a Research Productivity Fellowship from CNPq (process #314964/2021-5) and is grateful to the S\u0026atilde;o Paulo Research Foundation (FAPESP) for the Young Investigator grants (#2014/14739-0, #2021/06668-0). RGC was supported by a Visiting Research fellowship from S\u0026atilde;o Paulo Research Foundation (FAPESP, no 2022/10760-1), and has continuously received productive grants from CNPq, which we gratefully acknowledge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the manuscript, the supplementary information files, and at the following link: https://drive.google.com/drive/folders/1AIoukcSFcs4UJ7lPPk-nkSCCUO6ITC6q\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida T., Tessarolo G., Nabout J. et al. 2021. Dataset for: Nonstationary drivers on fish sampling efforts in Brazilian freshwaters. \u003cem\u003eDivers. Distrib.\u003c/em\u003e, 1\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eBarnosky A. D., Matzke N., Tomiya S. et al\u003cem\u003e.\u003c/em\u003e 2011. 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The plight of large animals in tropical forests and the consequences for plant regeneration. \u003cem\u003eBiotropica.\u003c/em\u003e 39 , 289\u0026ndash;291.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biodiversity shortfalls, landscape changes, landscape ecology, macroecology, spatial bias, Atlantic Forest","lastPublishedDoi":"10.21203/rs.3.rs-6719344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6719344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrently, the ecological key-question is how deforestation affects biodiversity at different levels of landscape fragmentation and at different spatial scales. To answer this question, we need unbiased knowledge of biodiversity spatial distribution across all landscape configuration gradients. However, the availability of biodiversity data is still limited and space-temporally biased. Here, we assess spatial sampling bias in primates, and how this shortcoming can affect our ability to assess the relationships between deforestation and biodiversity decline in a global hotspot ecoregion, the Atlantic Forest. We predict that sampling of primates in Atlantic Forest is spatially biased towards large and connected forest remnants closer to cities and roads, consequently leading to knowledge shortcoming in small and disconnected forest fragments far from cities and roads. We used a dataset of primary occurrence records from the Atlantic Forest primates to test spatial sampling bias on a landscape perspective. Our findings show sampling spatial bias towards large and connected forest patches following our prediction. These findings highlight that the current primate biodiversity knowledge is insufficient to understand the relationships between Atlantic Forest landscape fragmentation and biodiversity loss at a landscape perspective. A greater sampling effort in small and disconnected fragments is necessary before making sound inferences about the effects of landscape modification and fragmentation in primates\u0026rsquo; biodiversity, which can also extend to a large portion of the known biodiversity of the Atlantic Forest.\u003c/p\u003e","manuscriptTitle":"Wallacean shortfall precludes what? the relationship between landscape structure and primate diversity in the Atlantic Forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 17:13:30","doi":"10.21203/rs.3.rs-6719344/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T03:44:51+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"132020412169623377238911063368756927629","date":"2026-02-09T08:22:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"124948453966464200205244744400610361015","date":"2026-02-04T00:48:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-21T20:51:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300790548432450076182224353796158061777","date":"2025-11-17T13:35:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25440722779833111665936371896738668194","date":"2025-11-14T10:11:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T21:33:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-09T13:27:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T02:41:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biodiversity and Conservation","date":"2025-05-21T20:17:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biodiversity-and-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bioc","sideBox":"Learn more about [Biodiversity and Conservation](https://www.springer.com/journal/10531)","snPcode":"10531","submissionUrl":"https://submission.nature.com/new-submission/10531/3","title":"Biodiversity and Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"14a28cb4-2250-4f6b-9c4e-eb6e27750558","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T03:24:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 17:13:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6719344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6719344","identity":"rs-6719344","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}