Species richness variation in marine and terrestrial fauna across wide-spread, fragmented territories: assessing inherent challenges of data scarcity at local and regional scales

Species richness variation in marine and terrestrial fauna across wide-spread, fragmented territories: assessing inherent challenges of data scarcity at local and regional scales | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Species richness variation in marine and terrestrial fauna across wide-spread, fragmented territories: assessing inherent challenges of data scarcity at local and regional scales Authors : Kilian Barreiro , Laura Benestan , Charlotte Moritz , Simon Ducatez , Jérémy Le Luyer 0000-0001-9409-3196 , and Cristián Monaco 0000-0003-4057-5290 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173640768.88486210/v1 633 views 293 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The ongoing biodiversity crisis calls for a complete biodiversity inventory of marine and terrestrial ecosystems. The task is particularly challenging for fragmented island territories, where baseline biodiversity information is often difficult to procure. By centralising information from different sources (museums, research institutions, citizen scientists), ‘big-data’ platforms provide an opportunity to evaluate species biodiversity information of understudied regions. Using data from the Global Biodiversity Information Facility (GBIF), we curated the first biogeographic dataset for both marine and terrestrial animal species in French Polynesia, a large territory composed of 124 islands and atolls that belongs to the Central Pacific region, a marine biodiversity hotspot facing conservation challenges. The dataset revealed heterogeneous species richness across archipelagos and islands, prompting an investigation into potential sampling biases (institutional, taxonomic, spatial) as well as an assessment of island-specific accessibility biases. We estimated that the archipelagos and islands had an inventory completeness rate that ranges from 12 to 85%, suggesting that a large proportion of the studied area remains poorly documented. Spatial and temporal sampling biases were partly explained by accessibility constraints (proximity to airports, roads or ports), and inventory completeness was higher for marine than terrestrial species. The biases quantified here challenge our ability to conduct biogeographic analyses that integrate the land-sea meta-ecosystem. Our database allows identifying taxa and sampling locations that require urgent attention, as well as comprehensively recorded species that can serve as indicators for environmental degradation. Explicitly acknowledging the inherent biases of biodiversity datasets is the first step towards a more comprehensive characterization of species diversity across fragmented territories. This information is crucial for guiding sound adaptive-management and conservation planning strategies. Introduction Humans are driving an unprecedented erosion of marine and terrestrial biodiversity, fundamentally altering the structure and functioning of ecosystems, and in return threatening the beneficial contributions that nature provides (IPBES, 2019; Ceballos & Ehrlich, 2023; Gorman et al. , 2023). Implementing conservation actions to confront this crisis requires comprehensive and spatially explicit baseline information on species diversity across the planet (Singh, 2002). Ultimately, these data are essential for guiding conservation management based on a sound understanding of the ecological and evolutionary processes that drive spatial and temporal patterns of species distribution across ecosystems (Newmark et al. , 2017; Pilowsky et al. , 2022). Thanks to the concerted efforts from museums, research institutions, citizen scientists, and ‘big-data’ platforms facilitating the integration of information, biodiversity records are increasingly available (Farley et al. , 2018; Kays et al. , 2020; Heberling et al. , 2021). Over the last two decades, many initiatives to centralise species occurrence data have emerged, notably some online repositories including the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/) and the Ocean Biodiversity Information System (OBIS, https://obis.org/). By adhering to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) and the metadata-sharing standards, such as the Darwin Core (DwC) (Wieczorek et al. , 2012), EML (Fegraus et al. , 2005), and BioCASe (Güntsch et al. , 2007), these intergovernmental research infrastructures promise to expedite the study of biodiversity across ecosystems. GBIF and OBIS are the largest open-access occurrence data portals for terrestrial and marine species, both being routinely used to inform resource management and conservation programs (e.g., Levin et al. , 2014; Amano et al. , 2016; Underwood et al. , 2018; Lin et al. , 2022; Takashina & Kusumoto, 2023). Despite their growing popularity, open-access biodiversity databases have been criticised on the grounds of poor data quality, potentially limiting their scope and applicability (Hortal et al. , 2015). Important shortfalls that are often cited include standardisation issues during sampling (Troia & McManamay, 2016; Zizka et al. , 2020), incomplete and/or incorrect records (e.g., species misidentification) and sampling biases, either spatial/temporal (i.e., unbalanced sampling efforts across space/time), taxonomic (i.e., skewed sampling favouring certain taxa), or both (Troudet et al. , 2017; Zizka et al. , 2020; García-Roselló et al. , 2023; Rocchini et al. , 2023). While cleaning and filtering methods allow readily correcting for incomplete and/or incorrect entries, sampling biases are difficult to diagnose and require special attention (Schiesari et al. , 2007). The spatial sampling bias, considered one of the main challenges limiting our comprehensive understanding of large-scale biodiversity patterns (Wüest et al. , 2020), can be partly explained by socio-economic reasons (e.g., wealthy zones are more likely to be surveyed Beck et al. , 2014), a scientific bias towards certain taxa (Troudet et al. , 2017), differences in sampling standards (König et al. , 2019), and/or by logistical difficulties to access certain locations (Kadmon et al. , 2004; Engemann et al. , 2015). Remote oceanic islands are likely to show sampling gaps due to their geographical isolation, which ultimately results in patchy and poorly representative data for the study region. The difficulties and high costs associated with organising monitoring campaigns further exacerbate these biases. As a result, some islands are underrepresented in long-term monitoring schemes (Stephenson et al. , 2017), and, aside from a few exceptions (e.g., Hachich et al. , 2015), comprehensive biodiversity studies across widespread archipelagos remain rare. This paucity of information for islands and atolls is particularly detrimental because they are a priori highly vulnerable ecosystems that potentially harbor high levels of endemism due to their isolation (Simberloff, 2000; Russell & Kueffer, 2019). Additionally, fragmented archipelagos are unique natural laboratories that provide opportunities for studying the ecological and evolutionary processes driving biodiversity patterns, dispersal potential, endemism and extinction rates, for both marine and terrestrial organisms. However, a proper understanding of these biogeographical processes first requires robust baseline information on species distribution (Warren et al. , 2015; Whittaker et al. , 2017). With 124 high islands and atolls spread across five archipelagos covering 4.8 million km 2 (Andréfouët & Adjeroud, 2019; Galzin & Meyer, 2024), French Polynesia represents the epitome of a fragmented territory. The large number of islands, their relative isolation, and the sheer variation in geomorphological characteristics they exhibit complicate efforts to survey the entire region or avoid sampling biases. Indeed, the marine and terrestrial biogeography of French Polynesia has only been partly studied, with a remarkable skew towards specific taxonomic groups. In the marine realm, targeted investigations have mainly focused on marine molluscs, brown seaweeds ( Phaeophyceae ) and reef fishes (Kulbicki, 2007; Salvat, 2009; Tröndlé & Boutet, 2009; Delrieu-Trottin et al. , 2015, 2019; Salvat & Tröndlé, 2017; Boutet et al. , 2020; Vieira et al. , 2021, 2023; see references therein). In the terrestrial realm, data compilations include a checklist of the recorded land and fresh-water arthropods (Ramage, 2017), a biogeographic atlas of birds (Thibault & Cibois, 2017), and an inventory of the vascular flora (Florence, 1997, 2004; Chevillotte et al. , 2019), as well as some rare studies focusing on the phylogeographic origins of specific terrestrial biota (e.g., Gillespie et al. , 2008; Hembry, 2018). Overall, the lack of a centralised, complete, and unbiased dataset for the region prevents an exhaustive analysis of the biogeographical status of marine and terrestrial species across French Polynesia. As a model of a highly fragmented island system, improving our fundamental understanding of French Polynesian biogeography is not only critical for cataloguing the existing fauna of the region, but also for contributing to our general comprehension of the ecological processes driving the current biodiversity crisis in isolated systems (Russell & Kueffer, 2019; Fernandez-Palacios et al. , 2021). Using data originally downloaded from open-access portals (GBIF, OBIS), we compiled and curated the first biogeographic dataset for both marine and terrestrial animal species in French Polynesia. We used these data to: (1) provide a baseline characterization of the number of species in the region; (2) identify taxonomic groups that might require further investigation, as well as comprehensively recorded species that can serve as indicators for environmental degradation; (3) identify poorly- and well-surveyed islands; and (4) quantify island-specific accessibility biases leading to heterogeneous sampling efforts. Materials and Methods Data collection We downloaded occurrence data from the GBIF portal (http//gbif.org; https://doi.org/10.15468/dl.gaxgr7) on May 24, 2023, covering French Polynesia (polygon spanning between 5°S and 30°S, and 134°W and 155°W). Species occurrences are defined as records of a particular species (or other taxonomic rank), with a geographic location and timestamp. These raw data were treated following the Darwin Core (Wieczorek et al. , 2012), Ecological Metadata Language (Fegraus et al. , 2005), and BioCASe (Güntsch et al. , 2007) standards. A pre-filtration of the data was done to exclude records missing geographic location and/or taxonomic classification (e.g., not available or zeros), yielding 297,789 occurrences (Fig. 1). Because GBIF and OBIS signed a data-sharing agreement which was effective at the time we downloaded the data, the marine data from OBIS was also contained in our GBIF data. The coastline shapefiles used to analyse the region included 120 geographical structures, most of which were atolls and high islands. Hereafter, we refer to all geographical structures as “islands”. Each record retrieved from the GBIF dataset was assigned to its nearest island based on geographic distances estimated using the function st_nearest_feature available in the sf package v.1.0-15 (Pebesma & Bivand, 2023) in R (R Core Team, 2024). Validation of the taxonomic information To clean, homogenise, and validate the taxonomic information in the dataset, we assumed that misidentifications would occur at the species level. To ensure taxonomic reliability we validated each species name using ad hoc taxonomic data repositories. We first validated the species name of each recognized taxon with WoRMS ( World Register of Marine Species, https://www.marinespecies.org/) using the wm_records_name function from the R package worrms (Chamberlain & Vanhoorne, 2023). We then assigned a taxonomic status (i.e., “accepted”, “doubtful”, “synonym”) to each record following the criteria outlined by the GBIF Backbone taxonomy (see https://doi.org/10.15468/39omei, https://hosted-datasets.gbif.org/datasets/backbone/). Taxa that were assigned as either “doubtful” or “synonym” were replaced by the updated taxonomic name provided by WoRMS. Taxa not recognized by the WoRMS repository were further examined using the gnr_resolve function from the R package taxize (Chamberlain et al. , 2020), which provides a means to validate species names by accessing several additional repositories via specific Application Programming Interfaces (e.g., ITIS: Integrated Taxonomic Information System , https://www.itis.gov/; CoL : Catalogue of Life, https://www.catalogueoflife.org/; bold: Barcode of Living Data, https://www.boldsystems.org/). Taxa that were not recognized neither by WoRMS nor Taxize were submitted to TAXREF (taxonomic reference curated by the French National Museum of Natural History, https://inpn.mnhn.fr/programme/referentiel-taxonomique-taxref) using the rt_taxa_search function from the rtaxref R package (Grenié & Gruson, 2022). A final manual check was done for records that could not be identified in the aforementioned taxonomic repositories. Habitat classification and biogeographical status Habitat classification for marine and terrestrial species were verified using WoRMS and Taxref, respectively. Habitat information was split into four categories (i.e., marine, brackish, freshwater and terrestrial) according to the classification scheme favoured by WoRMS. Missing habitat information was completed using the TaxRef database. For our analyses of terrestrial and marine ecosystems, we focused on species that were classified as exclusively “marine” or exclusively “terrestrial”. Species classified as amphibious or those inhabiting both terrestrial and marine environments at different life stages (e.g., seabirds like Gygis alba or Sula sula ) or during specific phases of their life cycle (e.g., insects with aquatic larval stages) were included in the cleaned dataset (labeled as ”Mixed” in Fig. 1) but excluded from further analyses. Data filtration sequence Because geographic, taxonomic and accuracy standards have changed over time (Maldonado et al. , 2015; Zizka et al. , 2020), and notorious errors were detected in older records, we retained entries dating from 1950 onwards and excluded those without timestamps (Fig. 1). Subsequently, we removed all absence data to rule out potential biases due to false negatives and no-observation data (Bonnet-Lebrun et al. , 2023). We then restricted occurrences to those described in the basisOfRecords column as: “human observation”, “machine observation”, “material sample”, “material citation” and “preserved specimen” according to recommendations by Smith et al. 2018. Cross-checking values corrected real duplicates in decimalLatitude, decimalLongitude, ScientificName, Year, Month and Day categories. Finally, species records lacking Habitat information were removed from the dataset (Fig. 1). Taxonomic biases: Identifying under- and over-represented groups We estimated the taxonomic bias at the Class level based on its over- or under-representation, relative to an “ideal sampling effort index”. The ideal number of records for a given class was estimated based on the hypothetical scenario where each species received the same number of records, and therefore each class received a number of records directly proportional to its number of species (Troudet et al. , 2017), according to: Ideal = N rec * (N sp_group / N sp_tot ) where N rec = total number of records, N sp_group = number of distinct species within the taxonomic group, and N sp_tot = total number of species present in the whole dataset. Taxonomic bias was assessed based on the difference between the ideal and observed sampling efforts, calculated for each class with more than 100 records in marine habitats and more than ten records in terrestrial habitats. To highlight values that deviated significantly from the ideal, we applied an inverse hyperbolic sine transformation to the data. We also identified the top ten most representative species for each habitat. Spatial and temporal heterogeneity in the sampling effort To examine spatial heterogeneity in the sampling effort, we first mapped the number of records and species estimated for each island within each archipelago. The agreement between the number of records and species per island (log 10 -transformed) was evaluated based on the Pearson correlation coefficients and its statistical significance. Additionally, to quantify the prevalence of heterogeneous sampling effort across space, we assessed the proportion of species recorded only once at each island, a common method in biodiversity studies to evaluate sampling completeness and detect potential under-sampling biases (Lim et al. , 2012). The parameter uniqueness—species that have only been collected once—is widely recognized as an indicator of incomplete sampling (Chao et al. , 2020; Montes et al. , 2021), allowing researchers to infer the adequacy of the sampling effort and identify areas that may require further investigation. We considered q k as the number of species documented in k sampling-effort units, so that the number of species observed in a single sampling-effort unit is q 1 (i.e., unique), the number of duplicates is q 2 , and so on. Inventory completeness To investigate the degree of inventory completeness in the dataset for both marine and terrestrial ecosystems at the scale of the archipelago and the island, we estimated the species inventory completeness percentage ( C ), calculated as: C ( i ) = ( Sobs ( i ) / Sest ( i ) ) * 100 where i = each island or archipelago, Sobs = number of species observed, and Sest = number of species estimated at each archipelago and island (Soberón et al. , 2007). To estimate Sest , we used the Species Accumulation Curve (SAC) approach that describes the relationship between species richness and sampling effort, i.e., the number of records available in a grid cell (Deng et al. , 2015). To derive the SACs, we split the area in 0.05° (~25 km 2 ) grid cells. We described the SACs using the specaccuml function (method = “exact”) available in the R package vegan v.2.6-4 (Oksanen et al. , 2024). We fitted the Michaelis-Menten model with the fitspecaccum function (method = “michaelis-menten”) to provide estimates of the number of species likely to be present (i.e., Sest, which corresponds to the asymptotic richness, or parameter Vm in the Michaelis-Menten equation) and the number of records required to capture 50% ( K ) of the estimated number of species predicted by the model (Chao, 1984, 1987; Colwell & Coddington, 1994). Because biodiversity assessments can be biased by grid cells with extremely low species records, we considered a minimum threshold of ten observations to run the SACs, as was done in previous studies (Soberón et al. , 2007; De Araujo et al. , 2022). While we aimed to calculate the SACs for each island and archipelago based on grid cells, as recently done in several studies on macroecology using GBIF datasets (De Araujo et al. , 2022; Ramírez et al. , 2022; Chanachai et al. , 2024), only seven islands (i.e., Anaa, Huahine, Moorea, Nuku Hiva, Raiatea-Tahaa, Rangiroa and Tahiti) were sufficiently large to yield enough cells (greater or equal to 10) to fit the Michaelis-Menten model. We therefore generated archipelago-scale models based on 5-km grid cells, while for the island-scale models we used the geographic coordinates associated with the species records. A preliminary comparison between these two approaches (0.05-degree resolution grid cells vs. records) revealed a significant correlation between them for the archipelago scale ( R 2 = 0.64). Therefore, we only presented SACs based on records for both archipelagos and islands. To evaluate inventory completeness, we determined the total number of islands with more than 100 records and C greater or equal to 80%, meaning that at least 80% of the species have been sampled (Soberón & Peterson, 2004; Chanachai et al. , 2024). We then examined the correlation between the number of records and C to test whether these proxies of sampling effort and reliability were associated. We used a Spearman correlation test for non-parametric data. Statistical significance was evaluated based on ⍺ = 0.05. Sampling bias due to accessibility To explore the influence of accessibility constraints on these sampling biases, we used a Bayesian approach to estimate how sampling rates vary with proximity to several common anthropic accessibility factors (i.e., rivers, roads, cities, airports, and ports). Using the calculate_bias function from the sampbias R package v. 2.0.0 (Zizka et al. , 2021), we estimated the bias weights ( w ), which quantify the impact of each accessibility factor on sampling rates. These weights are calculated assuming an exponential decline in sampling rates as distance from accessibility factors increases. This package also provides spatially explicit estimates of the number of records (i.e., expected records) using a Poisson sampling process while accounting for the influence of the accessibility factors. Because the geospatial data contained by default in the sampbias package is incomplete for French Polynesia (Natural Earth Data, https://www.naturalearthdata.com/), we manually inputted vector data for rivers, roads, cities with > 1,000 inhabitants, airports, and ports. These data were provided by the French Polynesian agency for marine resources, the Direction des Ressources Marines . We defined a grid ( inp_raster parameter) contained within the same polygon used for downloading the GBIF data, with 0.05 degrees resolution (~ 5.5 km). This was done for consistency with the SAC analyses. Each grid cell was assigned to the nearest island based on geographic distances estimated using the function st_nearest_feature available in the sf R package v.1.0-15 (Pebesma & Bivand, 2023). Code availability Analyses were done in R version 4.4.3 (R Core Team, 2024). The analyses scripts are available in GitHub (https://github.com/KilianBARREIRO/biogeography_datadiv). The data are available in SEANOE (https://www.seanoe.org/data/00878/99018/). Curated GBIF dataset for French Polynesian marine and terrestrial species From the original 297,789 records included in the GBIF dataset, we removed 20,967 records that were either dated before 1950, or which did not have a time stamp (Fig. 1). A total of 107,200 records (35.9%) were identified as duplicated, 24.7% of which originated from citizen science sources (e.g., iNaturalist research-grade observations). We excluded 12,825 records with no or unclear habitat information, 45.4% of which were sourced from research institutions or peer-reviewed datasets. The number of records accessible via GBIF per year has increased over time since 1950, reaching maximum values in 2011, 2006, and 2009, with 19,770, 11,056, and 9,397 records, respectively (Fig. S1). This increase in records was mainly explained by the punctual contribution of two out of 121 publishers: OBIS-SEAMAP and UMS PatriNat (OFB-CNRS-MNHN, Paris). The mean number of records per species was 22 (median = 3), ranging from 1 to 12,339. The records produced by citizen scientists represented 20.6 % (32,155) of those records. Data collected by citizen scientists were also the main source of data for 17 islands, and the only source (100%) for six islands (Fangatau, Hiti, Marutea nord, Motu Nao, Pinaki, and Vairaatea). Human observation , including institutional and citizen science publishers, was the most frequently used recording method, with 71.1% (111,260 records) of total records. Preserved specimen and material sample categories accounted for 23.8% and 4.6% of records, respectively. WoRMS validated the taxonomy of 82.3% of total records and 97% of non-terrestrial records. Only 268 species lacked information on their habitat, which we completed manually. The resulting cleaned dataset was composed of 156,380 records including 111,889 marine, 15,979 terrestrial and 28,512 mixed records for 5,863 marine, 1,045 terrestrial and 201 mixed species, collected from 1950 to 2022 (Figs. 1 and S1). Taxonomic composition and biases The number of recorded species was ~5.6 times higher for marine than terrestrial ecosystems, with 5,863 marine and 1,045 terrestrial species, respectively. For marine taxa, the dataset included 18 phyla, with three major groups: Mollusca (2,303 species), Chordata (1,713 species), and Arthropoda (1,144 species), accounting for over 94.5% of marine records (105,778 records). Five classes alone accounted for 91.8% of the observations: Teleostei (75,313 records, 1,522 species), Gastropoda (15,416 records, 1,999 species), Malacostraca (5,678 records, 1,070 species), Bivalvia (3,470 records, 270 species) and Mammalia (2,807 records, 25 species; Fig. 2). The most represented marine species were Tridacna maxima (small giant clam, 1,310 records), Megaptera novaeangliae (humpback whale, 1,011 records), and Ctenochaetus striatus (striated surgeonfish, 941 records; Fig. 2). A total of 90% of the marine species had 27 or fewer records, and 30% were unique records. The terrestrial taxa comprised five phyla, including Arthropods (766 species), Mollusca (202 species), Chordata (73 species), Platyhelminthes (3 species) and Nematoda (1 species). The five most recorded classes were Aves (8,921 records, 53 species), Insecta (3,569 records, 688 species), Gastropoda (2,295 records, 202 species), Arachnida (528 records, 61 species), and Squamata (442 records, 10 species), representing 98.6% of all terrestrial species records. A total of 90% of the terrestrial species had 23 records or fewer, and 30% were unique records. Three introduced bird species, Geopelia striata (zebra dove), Acridotheres tristis (common myna), Pycnonotus cafer (red-vented bulbul), were the most recorded terrestrial species, with 1,174, 1,140 and 957 occurrences (Fig. 2), of which 93.1% were provided by the “Cornell Lab of Ornithology”. Spatial and temporal heterogeneity in sampling effort and the number of recorded species We observed a significant and strong correlation between the log-10 number of records per island (i.e., a proxy for sampling effort) and the number of species per island for both marine (ρ = 0.729, P < 0.001) and terrestrial (ρ = 0.890, P < 0.001) ecosystems (Fig. S2). This analysis excluded islands that lacked records in both marine and terrestrial habitats. Our dataset included marine species records for 119 out of 120 islands. The number of records per island was heterogeneous (Fig. 3), ranging from 1 to 54,761, with a mean of 940 records (median = 70). The number of species present was also highly heterogeneous across space, ranging from 1 to 2,759 species per island, with a mean of 194 species (median = 56) per island. The Society Archipelago (13 islands) held 63.3% of all marine-species records, 92.2% of which were observed in Moorea (54,761 records), Tahiti (6,721 records), and Raiatea-Tahaa (3,829 records; Fig. 4). Considering the other four archipelagos, the islands that exhibited the highest number of records were Rapa (3,731 records) in the Austral islands (11 islands), Fakarava (3,739 records) in the Tuamotu (69 islands), Nuka Hiva (2,741 records) in the Marquesas (17 islands), and Mangareva (2,490 records) in the Gambier (11 islands; Fig. 3). Gambier was the least sampled archipelago, accounting for 3.6% of all marine records, and for only 13.4% of all marine species identified. Considering the terrestrial habitat, our dataset identified 64 islands with at least one species record, and 56 islands with no records. As for the marine habitat, the number of terrestrial species records per island was heterogeneous (Fig 3), ranging from 1 to 4,700, with a mean of 250 records per island (median = 11.5). The number of species identified per island ranged from 1 to 388, with a mean of 36 species per island (median = 5). The Society Archipelago held 74.3% of all terrestrial species records, 85.2% of which were registered in the trio Moorea (4,341 records, 388 species), Tahiti (4,700 records, 306 species), and Raiatea-Tahaa (1,070 records, 204 species; Fig. 4). Considering the other four archipelagos, the islands showing the highest number of records were Anaa (694 records) in the Tuamotu, Rurutu (470 records) in the Austral, Nuku Hiva (528 records) in the Marquesas, and Mangareva (113 records) in the Gambier (Fig. 4). As for the marine database, the Gambier archipelago had the lowest number of terrestrial records, representing only 4.7% of all terrestrial species identified. Inventory completeness Considering the archipelago scale, the SAC analysis showed that the number of species recorded increased with sampling effort. Although the curves for both marine and terrestrial datasets exhibited a plateau, they did not reach a clear saturation point (Fig. S3). Our calculations suggest that marine inventory completeness was comparable among archipelagos, with 75.59%, 76.99%, 71.42%, 80.41% and 79.01% for the Austral, Gambier, Marquesas, Society and Tuamotu Archipelagos, respectively, indicating that at least 70% of the species were detected overall. According to the asymptote values based on the Michaelis-Menten model ( Vm ), marine species richness was lowest at the Gambier (1,150 species) and the highest at the Society (5,916 species) archipelagos. The Austral, Marquesas and Tuamotu Archipelagos showed similar asymptote values of 2,901, 2,860 and 2,270 expected species, respectively (Table 1). Inventory completeness for terrestrial species was highly heterogeneous across archipelagos, ranging from 48.10% for the Marquesas, the northernmost and most remote archipelago, to 84.50% in the Tuamotu, the largest archipelago. Inventory completeness for terrestrial species was higher than for marine species in the Society ( C = 84.04%) and Tuamotu Archipelagos ( C = 84.50%), but lower in the Gambier ( C = 64.06%) and Marquesas ( C = 48.10%). In the Austral Islands, inventory completeness was similar between marine and terrestrial species ( C = 72.65%). For terrestrial species, the asymptote values based on the Michaelis-Menten model ( Vm ) ranged from 89 (Gambier) to 839 species (Society), with 283, 275, 102 species estimated for the Austral islands, Marquesas and Tuamotu, respectively. At the island scale and for the marine dataset, we fitted SACs for 84 out of 119 islands having at least 10 records (Table 2). Inventory completeness was highly heterogeneous, ranging from 10.89% (Faaite Atoll, Tuamotu, 104 records) to 85.85% (Tenararo Atoll, Tuamotu, 17 records), with an average (± SD) of 49.50 % (±16.91%). Assuming a threshold of C ≥ 80% and at least 100 records, only two islands were classified as well-sampled: Moorea (54,761 records, C = 84.13%) and Fakarava (3,739 records, C = 83.20%). Among the islands with the highest number of records, we identified low to moderate inventory completeness for Tahiti (1,337 records, C = 69.40%) and Raiatea-Tahaa (3,829 records, C = 34.30%) in the Society, Rapa (3,731 records, C = 67.83%) and Rimatara (1,664 records, C = 31.80%) in the Austral Islands, and Nuku Hiva (2,741 records, C = 61.96%) and Fatu Hiva (1,103 records, C = 27.01%) in the Marquesas. The correlation between inventory completeness and the number of records per island was low (R² = 0.28; P-value 10 records) to fit SACs (Table 3). Inventory completeness ranged from 8.54% for the Tikey Atoll (13 records, Tuamotu) to 97.39% for Anaa Atoll (694 records, Tuamotu). Other well-sampled islands ( C ≥ 80% and 100 records) included Ua Huka (114 records, C = 89.59%) in the Marquesas, Raivavae (201 records, C = 84.64%) and Rimatara (225 records, C = 80 . 04%) in the Austral. Islands with the highest number of records, including Moorea (4,341 records, C = 70.56%) and Tahiti (4,699 records, C = 63.23%, respectively) were nearly well-sampled. Terrestrial species inventory completeness and sampling effort were not correlated across these islands (R² = 0.07; P-value > 0.05). Some islands exhibited contrasting patterns between terrestrial and marine inventories. For instance, Rimatara was well sampled for terrestrial species (225 records, C = 80.04%) but only moderately sampled for marine species (1,664 records, C = 31.80%). Fakarava showed the opposite trend, with an almost complete marine inventory ( C = 83.20%), while its terrestrial inventory was sparse (23 records , C = 25.07%). Islands for which we were unable to fit SACs were classified as either “neglected islands” (i.e., with no data at all) or “poorly-documented islands” (i.e., with not enough data). For the marine and terrestrial data, we identified two and 56 neglected islands, respectively. The problem of missing data was prevalent across archipelagos, but less important in the Society and Austral archipelagos (Fig. 5). We found 36 and 32 poorly-documented islands for marine and terrestrial ecosystems, respectively. The data scarcity was particularly pronounced in the largest archipelago, the Tuamotu, as well as in the southernmost archipelago, the Gambier (Fig. S4). Sampling bias due to human accessibility For marine species, we found that sampling effort was strongly associated with the presence of roads ( w = 0.047), and moderately affected by the presence of ports or airports ( w = 0.020). The presence of cities and rivers (waterbodies) contributed very little to the sampling bias of marine species (cities’ w = 0.002, waterbodies’ w = 0.001) (Fig. 6). Similar results were found for the terrestrial data, where the presence of roads contributed the most to the accessibility bias ( w = 0.062). The effect of airports and ports was moderate ( w = 0.031) while the influence of cities and water bodies was negligible (cities’ w = 0.004, waterbodies’ w = 0.001) (Fig. 6). The model also revealed a low number of marine and terrestrial records (Table S1), even after correcting for accessibility biases, in the Tuamotu and Gambier Archipelagos, except for Mangareva, Hao, and Arutua Islands. In contrast, most islands in the Society Archipelago were oversampled relative to the overall sampling effort across French Polynesia. Discussion Our study compiles the most comprehensive open-source database on animal biodiversity in French Polynesia, illuminating regional- and island-scale biodiversity patterns of marine and terrestrial fauna across this vast and fragmented territory. While our results highlight significant disparities in sampling effort across islands, this work offers valuable quantitative insights into completeness of taxonomic and spatial data throughout French Polynesia. This work also highlights understudied areas and taxonomic groups, providing a practical tool for conservation planners to guide future sampling strategies and enhance biodiversity representativeness. We argue that this integrative approach is essential for explicitly addressing the inherent biases often present in large-scale biodiversity studies (Rocchini et al. , 2023). Building an accurate GBIF dataset While open-source biodiversity datasets offer unique opportunities for studying macroecological processes, global repositories face criticism due to significant variation in data quality and quantity, depending on geographic, temporal, and taxonomic factors (García-Roselló et al. , 2023). Ignoring these caveats can lead to erroneous conclusions. However, when carefully considered, they can enhance the utility of open-source data by highlighting critical biodiversity knowledge gaps (e.g., Meyer et al. , 2016; Cornwell et al. , 2019; Moudrý & Devillers, 2020). Addressing uncertainties in the data first requires acknowledging that open-source biogeographic datasets are likely to be incomplete (Wüest et al. , 2020), especially in vast and fragmented regions and for specific groups of organisms. Secondly, standardised taxonomic repositories (e.g., WORMS) offer workflows for cleaning data retrieved from open-source platforms while adhering to FAIR data-sharing principles. Here, by applying previously validated filtering protocols (Bonnet-Lebrun et al. , 2023), we enhanced the geographic and taxonomic accuracy of GBIF records for French Polynesia, closely matching recent expert taxonomic assessments.Our database contains a total of 7,109 species, including 1,876 vertebrates and 5,233 invertebrates. Regarding vertebrates, we found that every known marine mammal (26 out of 26 species) and a large number of birds (129 out of 175 species) previously documented in the region are represented (Clements et al. 2024). Our database includes 2,303 marine molluscs out of 3,022 referenced in a recently published checklist and identification guide (Boutet et al. , 2020) and the Teleostei class included 1,523 species, which is more than the 1,310 reported in the most complete identification guides for the region (Bacchet et al. , 2017; Siu et al. , 2017). While the taxonomic coverage is reassuring for marine species, it remains relatively limited for terrestrial species. For example, our records include only 688 out of 2,497 insect species (Insecta) and 61 out of 365 spider species (Arachnida) described in the region (Ramage, 2017). Data scarcity for insects is a global issue, and in some regions, it is partly driven by species extinction rates that outpace discovery rates (Porch et al. , 2020; Rocha-Ortega et al. , 2021). Islands, which harbour approximately 20% of the world’s terrestrial biodiversity, are critical reservoirs of fragile and threatened biodiversity (Fernandez-Palacios et al. , 2021). This highlights the urgent need to document the exceptional biodiversity of insular countries like French Polynesia, where some taxonomic groups, such as ground beetles, contribute significantly to global biodiversity (Liebherr, 2012; Fernandez-Palacios et al. , 2021). Our study provides an efficient framework for identifying poorly sampled species, which can be extended to other taxonomic groups in French Polynesia (e.g., plants or algae) and applied more broadly to other regions. Linnean shortfall The Linnean shortfall—i.e., only a fraction of the planet’s species has been described—is a major gap in our understanding of biodiversity (Hortal et al. , 2015), limiting our ability to effectively address the ongoing extinction crisis (Ceballos & Ehrlich, 2023). The Linnean shortfall is partly driven by taxonomic sampling biases, where societal preferences influence which groups are more frequently recorded (Troudet et al. , 2017). This explains why patterns of sampling efforts are often represented by homogeneously-sampled taxonomic groups such as marine mammals (Moudrý & Devillers, 2020), fishes (Mora et al. , 2008) or insects (Sánchez-Fernández et al. , 2021). Notably, our taxonomic bias analysis revealed a significant under-representation of non-charismatic invertebrate species such as Gastropoda, Malacostraca, Anthozoa, Bivalvia, Polychaeta in the marine environment, as well as Insecta, Gastropoda, Arachnida, Malacostraca, in terrestrial ecosystems. This finding aligns with Troudet et al. (2017) who also identified biases against these classes at the global scale. Conversely, vertebrates were well-represented, with the humpback whale ( Megaptera novaeangliae ) being one of the most frequently recorded species. This discrepancy often stems from the aesthetic appeal of certain species, which influences both public interest and scientific focus (Stokes, 2007; Ducarme et al. , 2013; De Pinho et al. , 2014). Furthermore, studies have effectively shown that visual appeal shapes the perception and prioritisation of species in research and conservation (Langlois et al. , 2022). To address these biases and enhance biodiversity inventories in French Polynesia, our dataset can help guide future research priorities, focusing on the underrepresented invertebrates and terrestrial species identified. By addressing these gaps, we can move towards a more comprehensive and balanced understanding of biodiversity, which is crucial for developing effective conservation strategies. Wallacean shortfall Another significant gap in our understanding of biodiversity is the incomplete knowledge of species’ geographic distribution, also known as the Wallacean shortfall (Lomolino, 2004; Wüest et al. , 2020). Despite extensive efforts, biodiversity sampling remains a resource-intensive, time-consuming and costly process, often resulting in substantial gaps in the spatial coverage of species records. Short-term projects frequently fail to capture the full spectrum of species within an assemblage because many species can be cryptic, rare or elusive, ultimately leading to incomplete assessments of global biodiversity patterns. However, these data gaps and uncertainties can be gauged and possibly mitigated through robust modelling approaches (Rocchini et al. , 2023). In our study, marine inventory completeness was consistently moderate across French Polynesia’s archipelagos, being up to 71% of known species at the regional scale. Furthermore, none of the species accumulation curves for the archipelagos reached saturation, indicating that species richness predictions require more sampling to improve accuracy. Statistical methods to correct these biases (e.g., Chao et al. , 2020) could be used for comparing community assemblages among archipelagos, as has been recently done with woody plants (Kusumoto et al. , 2023). Another strategy is to focus on well-documented groups, with complete inventories, enabling the description of their spatial distribution patterns (Shirey et al. , 2021). For terrestrial species, we found that inventory completeness was more variable than that of marine species. The Marquesas archipelago was especially under-surveyed, as only half of the total estimated animal species have been documented. Owing to their geographical isolation and intricate topography, the Marquesas Islands harbour a high level of floral and faunal endemism, with many native and endemic arthropod species probably yet to be discovered (Hembry, 2018). Indeed, many studies have highlighted the uniqueness of this archipelago in terms of species assemblages (Delrieu-Trottin et al. , 2015; Galzin et al. , 2016) and genetic diversity (Reisser et al. , 2019). This biological distinctiveness, combined with the underrepresentation of terrestrial studies compared to marine ones, likely accounts for the discrepancy with other archipelagos, despite the strong interest that scientists have expressed for this biodiversity hotspot (Mittermeier et al. , 2005). Prioritising terrestrial biodiversity research in the Marquesas is crucial for establishing reliable comparisons across the land-to-sea continuum in this archipelago. Similarly, a more sustained sampling effort is much needed in the Gambier and Tuamotu Archipelagos, where a significant number of islands remain insufficiently inventoried. This is an urgent call because, while scientific expeditions could potentially discover new species (e.g., Williams et al. , 2012), other species could become extinct before being documented (e.g., Zimmerman et al. , 2009; Richling & Bouchet, 2013). Sampling-effort biases can obscure the true spatial distribution of biodiversity, complicating the identification of biodiversity hotspots and the quantification of biodiversity loss (Hughes et al. , 2021). This study contributes to addressing this gap by pinpointing overlooked locations of the Polynesia-Micronesia biodiversity hotspot. For instance, Raiatea and Tahaa, which together form the largest lagoon in the Society Archipelago, may host a particularly high level of biodiversity not fully reflected in current GBIF data. This hypothesis is supported by research showing that 26 of the 32 marine sponges recorded across French Polynesia were found in Raiatea-Tahaa (Hall et al. , 2013). Similarly, our findings confirmed that the island of Rapa harbours remarkable marine diversity, as evidenced by studies on coral-reef and terrestrial communities, including taxa unique to this island (Meyer & Claridge, 2014; Adjeroud et al. , 2016). However, despite being one of the best documented islands in the archipelago, Rapa’s inventory completeness remains just behind the global threshold of 80%, suggesting that further sampling efforts are necessary to fully capture this island’s biodiversity. Conservation science is often compelled to assist in decision-making based on limited and incomplete data (Soulé, 1985). The spatial heterogeneity in sampling effort that we identified for both marine and terrestrial fauna in French Polynesia is considerable, with up to 70% of islands lacking data on their terrestrial environments. This striking data deficiency was also evidenced by another study using GBIF data to analyse species diversity in a remote region (Bonnet-Lebrun et al. , 2023). An additional challenge, particularly for vast and fragmented territories such as French Polynesia, is the need for data at a sufficiently high spatial resolution to capture island-wide variation. We identified 56 islands that either lacked digital data entirely or were poorly documented, likely due to their remoteness. To fill the spatial gaps in biodiversity data for French Polynesia, we recommend that future sampling efforts prioritise these islands, while also considering the disparity in data coverage between marine and terrestrial ecosystems. The marine-terrestrial sampling bias Marine and terrestrial ecosystems are often studied separately, partly due to historical, cultural, or practical reasons (Raffaelli et al. , 2005; Munguia & Ojanguren, 2015). However, because the land-sea continuum operates as an integrated meta-ecosystem, this research divide hampers our ability to fully understand and effectively protect interconnected ecosystems (Álvarez-Romero et al. , 2011; Hughes et al. , 2021). Maintaining a healthy land-sea ecosystem is particularly crucial in small-island territories, where biodiversity is vulnerable to human activities (Russell & Kueffer, 2019; Fernandez-Palacios et al. , 2021), and where the wellbeing of local populations heavily depends on local natural resources, especially through fishing and tourism. French Polynesia is no exception, with tourism as its primary economic activity and fish and invertebrates as staples in the local diet (Gillett & Tauati, 2018). Unlike the global trend (Hughes et al. , 2021), our data show that French Polynesian biodiversity is better documented in marine ecosystems than in terrestrial ones. This discrepancy is partly due to the focus of scientific research and exploration on marine environments (e.g., the oldest of the two major ecology research units in French Polynesia, the CRIOBE, is entirely focused on marine environments) and to the inaccessibility of the mountainous regions (Gillespie et al. , 2008) and seamounts (Hanafi-Portier & Samedi, 2024). The gap is also likely influenced by the huge difference in surface area between land (4,167 km²) and sea (2.5*10 6 km²), which may also explain why the marine habitats host 20 times more species than terrestrial ones. While surface-area differences are a factor to consider, our records indicate that the disparity is also driven by a lack of terrestrial data for over 56 islands, compared to just two islands with missing marine data. The observed imbalance in marine versus terrestrial data coverage is not only due to the inherent differences between these ecosystems but also reflects underlying biases in sampling practices, exacerbated by the accessibility factors. Sampling bias is partly influenced by accessibility factors The accessibility bias hypothesis posits that more accessible areas tend to be surveyed more frequently than less accessible zones (Zizka et al. , 2021). This can significantly impact the global understanding of natural communities (Mangiacotti et al. , 2013; Hughes et al. , 2021). Our database revealed a pronounced geographic bias in species records, with the most accessible islands (i.e., Tahiti and Moorea in the Society Archipelago, Fakarava in the Tuamotu) being heavily sampled. In contrast, less accessible islands (e.g., Tureia, Napuka and Tenarunga in the Tuamotu, Motu One and Motu Nao in the Marquesas) are poorly documented. However, Rapa Island stands out as an exception, having attracted significant attention from the scientific community due to its hosting of several threatened endemic plant and animal species (Gillespie et al. , 2008; Meyer & Claridge, 2014; Adjeroud et al. , 2016; Barrett et al. , 2021). The sampling bias in Tahiti and Moorea is also likely related to the presence of local research institutions (e.g., CNRS-EPHE-Université de Perpignan CRIOBE station, Ifremer, IRD, UC-Berkeley Gump station, University of French Polynesia) there. While Tahiti’s international airport contributes to the sampling bias observed in the Society Islands, our accessibility bias analysis indicated that the distance from ‘airports and ports’ was not the main anthropogenic factor explaining the variance in sampling effort at the scale of French Polynesia. Overall, our accessibility bias analysis showed that sampling efforts in both marine and terrestrial datasets are predominantly skewed towards areas near roads and, to a lesser extent, airports/ports. This aggregation pattern around roads is well-documented in the literature for both terrestrial and marine species (Reddy & Dávalos, 2003; Hughes et al. , 2021), particularly in studies based on citizen-science data (Mair & Ruete, 2016).Accessibility biases can vary depending on geographic and taxonomic contexts (Mair & Ruete, 2016), highlighting the importance of considering situations in a case-by-case basis. For instance, Freitag et al. (1998) found that records of smaller species in African terrestrial ecosystems were minimally affected by accessibility biases, whereas larger species were disproportionately represented in protected areas. Similarly, Cardoso et al. (2024) identified various accessibility-bias factors for marine species in the western Atlantic Ocean, including proximity to the coastline, research institutions, ports, protected areas, and urban centres. Recognizing and understanding the nuances underlying these various biases is crucial for enhancing the accuracy and comprehensiveness of biodiversity datasets. Institutional bias in open-source databases While accessibility factors provide important insights into sampling patterns, they are not the sole source of bias impacting our biodiversity records. Institutional biases, particularly those associated with open-source databases, might also play a crucial role. The unevenness in data contributions often stems from disparities in funding, data-sharing policies, and digitization efforts across different regions and institutions. The soaring popularity of GBIF data worldwide is reflected in our dataset for French Polynesia, where the number of records per year increased from 10 in 1950 to 1,878 in 2022. We anticipate that the dataset will continue to grow with the engagement of additional contributors, thereby enhancing its reliability (Ivanova & Shashkov, 2021), if institutions continue to adhere to standardisation protocols (Wieczorek et al. , 2012). Interestingly, the surge in data during 2006, 2009, and 2011, which constitutes the bulk of the dataset, was driven by the digitization of the French Museum of Natural History dataset (managed by PatriNat) and a major field sampling campaign by Cornell University (USA). The patchiness in data contributions to global open-source databases can be attributed to differences in funding and data-sharing policies across countries, inadequate efforts in digitalising local and national databases, and the sporadic and spatially heterogeneous nature of formal research campaigns (Beck et al. , 2014). However, combining GBIF records with national databases can yield more complete inventories, as demonstrated by de Araujo et al. (2022) for Amazonian epiphytes. In the case of French Polynesia, engaging local research institutions, private entities, government agencies and developing a citizen science network to compile and share existing (but often inaccessible) information would significantly reduce biases and strengthen the database. The use and adaptation of existing portals such as FauneFrance (https://www.faune-france.org/) or iNaturalist (https://www.inaturalist.org/) to local flora and fauna could for example be advocated to further centralise and favour the collection and compilation of local naturalist data. Capitalising from citizen science while reducing biases in GBIF datasets Addressing biases and shortfalls in GBIF datasets is crucial to ensure their efficiency and accuracy in describing species distribution patterns. Citizen science has been increasingly recognized as an effective method for filling gaps in biodiversity information, especially in areas where formal scientific campaigns are limited or sporadic (Isaac et al. , 2014; Amano et al. , 2016). In our database for French Polynesia, we observed an increase in species records driven by citizen science initiatives, in agreement with the global trend (Heberling et al. , 2021). Indeed, a substantial 23% of records originated from participatory science efforts. While citizen scientists may not always adhere to standard scientific protocols, their contributions provide valuable insights into broader trends, which can then be rigorously analysed. To minimise taxonomic and geographic biases, the involvement of taxonomic experts remains crucial (Maldonado et al. , 2015). Conclusions and perspectives Centralising biodiversity information from museums, research institutions, and citizen scientists into big-data platforms offers a transformative opportunity for evaluating species biodiversity in understudied regions. These platforms enable comprehensive data analysis, facilitate global collaboration, engage the public in science, and ultimately contribute to more informed conservation strategies and biodiversity management. Our study provides significant insights into the biodiversity patterns of both marine and terrestrial fauna across the vast and fragmented territory of French Polynesia. We found that while marine inventory completeness is relatively high, averaging up to 70% of known species at the regional scale, terrestrial biogeography remains underexplored, particularly in the Marquesas and Gambier Archipelagos. The analysis indicates a notable skew in the data toward specific taxonomic groups, highlighting the urgent need for comprehensive surveys to fill these gaps. Furthermore, our findings underscore the value of citizen science initiatives, which have contributed to 23% of species records in our database, demonstrating their potential to enhance biodiversity knowledge in regions where formal scientific efforts are limited. 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(A) Major class representation in sampling effort (i.e., observed - ideal). Over- and under-representation of each class are illustrated by the green and orange bars respectively. An inverse hyperbolic-sine transformation was used for the x-axis. (B) Number of records for the top 10 most-sampled marine (left) and terrestrial (right) species. Figure 3. Number of GBIF database records (log-10 transformed) across French Polynesia for marine ( A ) and terrestrial ( B ) species. Blue and red colours indicate a low and high number of occurrences (proxy of sampling rate) respectively, for a 0.05-degree resolution. Figure 4 . Number of marine ( A ) and terrestrial ( B ) records per island and archipelago. Islands without records are excluded. The x-axis is presented on a logarithmic scale. Figure 5 . Map of French Polynesian “neglected” islands (i.e., without species records) in marine (left panel) and terrestrial (right panel) datasets. The islands are color-coded according to their respective archipelagos. Figure 6. Accessibility bias. Results from the calculate bias function ( Sampbias package) which estimates marine (a) and terrestrial (b) expected occurrences based on accessibility, illustrating the impact of various infrastructure types on species sampling efforts. (Left) The posterior weight shows the relative importance of anthropic accessibility factors: -roads, ports/airports, cities, and waterbodies—on sampling efforts in both ecosystems. (Right) The sampling rate, i.e., the expected number of occurrences, as a function of distance from accessibility factors is depicted for marine (a) and terrestrial (b) environments. Table 1 . Archipelago-scale Michaelis-Menten model output parameters based on a records approach ( N records as sampling units). Analyses are per archipelago and for each ecosystem. Parameters provided are the number of observed species ( Sobs ), the maximum number of species estimated ( Sest ), the number of cells/records required to capture 50% of the maximum number of species estimated ( K ), and the inventory completeness in percentage ( C ). Ecosystem Archipelago Sobs Sobs K C N Marine Austral 1,881 2615.52 4036.62 75.59 11,208 Gambier 788 1023.53 1324.89 76.99 4,012 Marquesas 1,929 2744.47 4294.65 71.42 9,636 Society 4,160 5174.48 20207.04 80.41 70800 Tuamotu 1,602 2027.48 5042.25 79.01 16234 Terrestrial Austral 283 389.51 561.55 72.65 1,398 Gambier 49 76.49 98.18 64.06 172 Marquesas 275 571.69 1402.90 48.10 1,271 Society 613 728.29 2684.62 84.04 11,868 Tuamotu 102 120.71 301.91 84.50 1,267 Table 2 . Island-scale Michaelis-Menten model output parameters based on a records approach (N records as sampling units) for the marine ecosystem. Parameters provided are the number of observed species ( Sobs ), the maximum number of species estimated ( Sest ), the number of records required to capture 50% of the maximum number of species estimated ( K ), and the inventory completeness in percentage ( C ). AHE 103 379.44 784.20 27.14 116 AMANU 17 27.50 155.50 61.81 23 ANAA 112 146.67 429.35 76.36 87 APATAKI 175 483.86 571.29 36.17 213 BANC CLARK 84 270.50 484.44 31.05 96 BANC LAWSON 32 53.13 139.61 60.23 30 BANC MAC DONALD 29 62.29 97.23 46.55 34 BANC NEILSON 85 162.55 161.77 52.29 148 BANC PRESIDENT THIERS 122 250.41 261.07 48.72 200 BORA BORA 635 995.59 2098.04 63.78 1657 EIAO 349 1007.21 1078.27 34.65 532 FAAITE 116 1065.12 1192.85 10.89 104 FAKAHINA 12 16.83 57.23 71.30 19 FAKARAVA 489 587.76 1075.76 83.20 3739 FANGATAUFA 53 126.73 179.44 41.82 70 FATU HIVA 1243 4601.19 6291.63 27.01 1504 FATU HUKU 73 191.57 418.02 38.11 74 HAO 207 514.68 949.77 40.22 399 HARAIKI 69 216.66 296.69 31.85 53 HATU ITI 51 145.37 178.43 35.08 58 HATUTAA 206 761.01 950.68 27.07 198 HEREHERETUE 17 72.91 76.28 23.32 13 HIVA OA 660 1244.07 1676.62 53.05 1181 HUAHINE 849 1591.33 2462.25 53.35 1381 KAUEHI 39 66.16 134.54 58.95 65 KAUKURA 169 361.51 495.58 46.75 272 MAIAO 154 335.14 781.80 45.95 292 MAKATEA 131 221.45 495.28 59.15 95 MAKEMO 199 332.88 421.34 59.78 442 MANGAREVA 734 1048.09 1410.89 70.03 2490 MANIHI 142 328.86 455.88 43.18 223 MANUAE 149 365.42 341.95 40.77 237 MARIA 219 395.93 407.31 55.31 491 MARIA EST 29 91.69 192.61 31.63 26 MAROTIRI 109 209.67 230.64 51.99 165 MARUTEA SUD 180 260.99 303.74 68.97 502 MATAIVA 107 282.83 442.22 37.83 132 MAUPIHAA 264 575.39 776.64 45.88 430 MAUPITI 157 400.49 612.72 39.20 129 MEHETIA 45 64.77 132.15 69.47 33 MOHOTANI 242 385.00 494.80 62.86 560 MOOREA 2759 3279.32 11526.00 84.13 54761 MORANE 168 451.15 595.76 37.24 190 MORUROA 370 890.29 945.25 41.56 598 MOTU ONE (Mar) 135 572.28 713.19 23.59 140 MOTUKUA 19 32.05 33.94 59.28 18 NENGONENGO 176 243.46 269.42 72.29 537 NIAU 155 312.98 460.46 49.52 180 NUKU HIVA 1103 1780.24 2657.89 61.96 2741 PUKAPUKA 70 270.77 253.29 25.85 64 RAIATEA-TAHAA 2598 7575.24 11475.92 34.30 3829 RAIVAVAE 634 1013.92 2085.85 62.53 2483 RANGIROA 898 1608.88 2861.20 55.82 2166 RAPA 1027 1514.15 2297.56 67.83 3731 RAROIA 417 592.38 703.65 70.39 1406 REAO 17 37.44 60.20 45.41 14 RECIF ARAGO 103 238.31 379.73 43.22 157 REITORU 45 103.56 307.88 43.45 17 RIMATARA 1158 3642.02 4230.51 31.80 1664 ROCHER THOMASSET 32 61.25 299.27 52.24 21 RURUTU 543 943.70 1341.85 57.54 905 TAHANEA 218 304.68 327.19 71.55 530 TAHITI 1337 1926.10 3205.79 69.40 6722 TAHUATA 410 971.02 1363.12 42.22 554 TAIARO 83 151.51 254.60 54.78 162 TAKAPOTO 176 393.65 474.64 44.71 252 TAKAROA 246 312.78 310.40 78.65 938 TATAKOTO 37 90.91 217.39 40.70 34 TEKOKOTA 45 175.27 362.37 25.67 22 TEMATANGI 40 93.96 164.33 42.57 42 TEMOE 37 90.72 148.68 40.79 27 TENARARO 41 47.76 85.83 85.85 17 TEPOTO NORD 36 50.37 58.03 71.47 21 TETIAROA 445 607.43 1223.13 73.26 1253 TIKEHAU 637 898.12 1529.56 70.93 3053 TIKEI 42 131.02 454.24 32.06 16 TOAU 222 321.87 347.30 68.97 623 TUBUAI 500 792.99 1222.56 63.05 1230 TUPAI 83 271.05 828.04 30.62 73 TUREIA 14 20.29 43.38 69.00 10 UA HUKA 503 1052.07 1509.22 47.81 851 UA POU 613 1213.43 1925.82 50.52 1078 VAHANGA 19 42.51 124.54 44.70 15 Table 3 . Island-scale Michaelis-Menten model output parameters based on a records approach (N records as sampling units) for the terrestrial ecosystem. Parameters provided are the number of observed species ( Sobs ), the maximum number of species estimated ( Vm ), the number of records required to capture 50% of the maximum number of species estimated ( K ), and the inventory completeness in percentage ( C ). ANAA 29 29.78 26.48 97.39 694 UA HUKA 15 16.74 18.12 89.59 114 TENARARO 5 5.61 10.13 89.09 42 HATUTAA 4 4.55 5.79 87.84 26 TAHUATA 13 15.02 12.98 86.53 79 RAIVAVAE 32 37.81 44.44 84.64 201 RIMATARA 50 62.47 59.03 80.04 225 MOHOTANI 6 7.69 7.01 78.06 23 TIKEHAU 7 9.15 9.87 76.50 26 RURUTU 113 151.07 174.10 74.80 470 TAKAPOTO 5 6.69 5.77 74.70 16 BORA BORA 54 74.67 275.37 72.32 628 MOOREA 527 746.89 1907.54 70.56 4341 MAKATEA 39 57.69 75.58 67.60 151 UA POU 30 46.17 62.60 64.97 102 TAHITI 393 621.56 2899.46 63.23 4699 TETIAROA 72 115.44 170.00 62.37 266 RANGIROA 62 99.61 131.25 62.24 211 TUBUAI 37 59.94 65.72 61.73 100 RAIATEA-TAHAA 268 441.26 728.54 60.74 1070 MANGAREVA 47 80.13 79.56 58.65 113 HUAHINE 239 409.14 579.88 58.42 786 NIAU 17 31.32 37.64 54.28 42 TAHANEA 9 17.19 23.96 52.35 24 RAPA 182 363.61 406.82 50.05 402 HARAIKI 8 18.29 16.43 43.74 13 NUKU HIVA 133 322.48 779.53 41.24 527 FATU HIVA 90 255.38 326.85 35.24 175 MAUPITI 45 133.07 138.71 33.82 70 HIVA OA 118 362.32 469.02 32.57 222 FAKARAVA 17 67.82 69.42 25.07 23 TIKEI 12 140.60 139.25 8.54 13 Supplementary Material Table S1. Bias posterior weights (mean ± SD) showing the relative importance of anthropic accessibility factors (roads, ports/airports, cities, and waterbodies) on sampling efforts in both marine and terrestrial ecosystems. Cities 0.00176 ± 0.00002 0.00361 ± 0.00008 Roads 0.04725 ± 0.00023 0.06189 ± 0.00088 Waterbodies 0.00094 ± 0.00002 0.00121 ± 0.00011 Airports 0.01982 ± 0.00017 0.03098 ± 0.00068 Figure S1. Number of GBIF (Global Biodiversity Information Facility) records for fauna in French Polynesia between 1950 and 2023. Figure S2. Record bias. Correlation between the number of species per island and records across French Polynesia for marine (left panel) and terrestrial (right panel) habitats. Figure S3 . Species Accumulation Curves produced from resampling. Modelled relationships between the number of species and the umber records for marine (left panels) and terrestrial (right panels) habitats. The lines represent Michaelis-Menten model fits, each archipelago is represented by a specific colour (purple: Austral, orange: Gambier, blue: Marquesas, pink: Society, yellow: Tuamotu) and the shaded zone illustrate 95% confidence intervals. The half-saturation constant (K), representing the area required to capture 50% of the total number of expected species, is shown with vertical dashed lines. Figure S4. Poorly-documented French Polynesian islands for both marine (left) and terrestrial (right) ecosystems. The number of records for these islands is represented by variable point sizes (from 1 to 10 records). Islands are colour-coded according to their respective archipelagos. Information & Authors Information Version history V1 Version 1 09 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords fauna french polynesia gbif global biodiversity information facility marine terrestrial Authors Affiliations Kilian Barreiro IFREMER View all articles by this author Laura Benestan IFREMER View all articles by this author Charlotte Moritz CMOANA Consulting View all articles by this author Simon Ducatez IRD View all articles by this author Jérémy Le Luyer 0000-0001-9409-3196 IFREMER View all articles by this author Cristián Monaco 0000-0003-4057-5290 [email protected] IFREMER View all articles by this author Metrics & Citations Metrics Article Usage 633 views 293 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kilian Barreiro, Laura Benestan, Charlotte Moritz, et al. Species richness variation in marine and terrestrial fauna across wide-spread, fragmented territories: assessing inherent challenges of data scarcity at local and regional scales. Authorea . 09 January 2025. 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