Geographical Sampling Bias Research Articles

Geographic bias effects on interpretations of secular trends of Hf isotope times series in zircons

The analysis of εHf(t) time series data from zircon reveals notable discrepancies based on sample type (igneous versus detrital), statistical weighting methodology, and geographic sampling bias. These differences warrant caution when interpreting data in the context of tectonic settings and the history of supercontinents. In terms of tectonic setting, accretionary orogens dominate in both sedimentary basins that host detrital zircons and in igneous zircon sources. Because of differences between the various time series, emphasis in this study is on peaks, valleys and secular trends and not on absolute εHf(t) values. Specifically, the igneous time series for εHf(t) in zircons shows more peaks and valleys than corresponding detrital time series for both weighted and unweighted data (weighting corrects for disproportionate geographic sampling). Also, sample-based (each sample considered separately) and site-based (samples grouped by geographic location and age) results align closely to the igneous time series, whereas the site-based detrital series displays more negative εHf(t) values. Regardless of the type of time series, the failure to compensate for disproportionate geographic sampling increases the prospects of producing an unrepresentative time-series. Nine zircon age peaks (both detrital and igneous) have corresponding εHf(t) peaks (3200, 2700, 2500, 2150, 1500, 1100, 750 Ma) and two have corresponding age valleys (1800–2000, 550 Ma). With exception of a geographically widespread 1500 Ma peak, most of the εHf(t) peaks and valleys are controlled by specific geographic regions and are likely not be global in extent.Two distinct periods (200–0 and 1800–1600 Ma) display εHf(t) signatures that rise steadily for 100–200 Myr, coinciding with the final stages of supercontinent assembly and the transition to the retreat of exterior orogens. An εHf(t) peak at 750 Ma and a high at 1400–1100 Ma partly overlap with supercontinent breakup and valleys at 550 Ma and 900 Ma with supercontinent assembly. A large εHf(t) valley at 2000–1800 Ma corresponds with the onset of craton collisions that led to the final assembly of Columbia at 1800–1600 Ma. The steep rise in εHf(t) in the last 200 Myr in both igneous and detrital zircons is controlled by sites in Circum-Pacific orogens in North and South America and Southwest Asia, and it parallels the breakup of Pangea. The general increase in zircon εHf(t) in the last 500 Myr in both detrital and igneous data reflects an increase in the proportion of isotopically juvenile components in accretionary orogens.

The Carbon Isotopic Composition of Archean Kerogen and Its Resilience Through the Rock Cycle

The Archean rock record is limited and there is minimal organic matter available to understand the origin and evolution of life on early Earth. Low carbon isotope ratios have been measured in organic and reduced carbon phases in Archean rocks and have been invoked as biosignatures. However, it can be challenging to distinguish whether these low values reflect biotic formation, abiotic reactions, or post-depositional processes. To re-address this long-standing question, we compiled a comprehensive dataset of carbon isotope ratio measurements from organic carbon phases from Archean units that were analyzed using a variety of geochemical techniques. Our compilation also includes available descriptions and measurements of the stratigraphy, mineralogy, elemental ratios, and metamorphic grade for each data point. Our statistical analyses re-enforce a result that has been noted by prior compilations, that the carbon isotopic composition of Archean organic matter (OM) is broadly more 13C-deplete than the composition of Phanerozoic OM: The median δ13C values ( ±SD) of Archean total organic carbon and kerogen were −30.5±8‰ (n=2421) and −33.7±11.3‰ (n=556; Phanerozoic OM δ13C ±SD = −26.7±4.6‰ with n=449 from a prior compilation). Our study also identifies a previously unrecognized bimodality within the δ13C values of Archean OM that is observed even with subsampling of the data to account for geographic and stratigraphic sampling bias. We describe and model the isotopic and structural changes associated with the transformation of marine Type II kerogen from formation through diagenesis, catagenesis and metagenesis, and metamorphism, as described by trends on a van Krevelin diagram. Empirically, early maturation of organic matter during diagenesis results in shifts up to a few per-mille, which can occur in either direction depending on selective preservation and degradation of compounds. Thermal cracking that occurs during catagenesis can drive increases in δ13C of 5–12‰. At temperatures above greenschist metamorphism, carbon atoms exchange with other reactive carbon pools, driving increases in δ13C of up to 20‰. Together, our analyses suggest that the most metamorphosed graphitic samples from the earliest Archean are likely signatures of alteration, while low and multimodal ranges of δ13C values may preserve records of Archean ecology.

Impact and mitigation of sampling bias to determine viral spread: Evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations.

Bayesian phylogeographic inference is a powerful tool in molecular epidemiological studies, which enables reconstruction of the origin and subsequent geographic spread of pathogens. Such inference is, however, potentially affected by geographic sampling bias. Here, we investigated the impact of sampling bias on the spatiotemporal reconstruction of viral epidemics using Bayesian discrete phylogeographic models and explored different operational strategies to mitigate this impact. We considered the continuous-time Markov chain (CTMC) model and two structured coalescent approximations (Bayesian structured coalescent approximation [BASTA] and marginal approximation of the structured coalescent [MASCOT]). For each approach, we compared the estimated and simulated spatiotemporal histories in biased and unbiased conditions based on the simulated epidemics of rabies virus (RABV) in dogs in Morocco. While the reconstructed spatiotemporal histories were impacted by sampling bias for the three approaches, BASTA and MASCOT reconstructions were also biased when employing unbiased samples. Increasing the number of analyzed genomes led to more robust estimates at low sampling bias for the CTMC model. Alternative sampling strategies that maximize the spatiotemporal coverage greatly improved the inference at intermediate sampling bias for the CTMC model, and to a lesser extent, for BASTA and MASCOT. In contrast, allowing for time-varying population sizes in MASCOT resulted in robust inference. We further applied these approaches to two empirical datasets: a RABV dataset from the Philippines and a SARS-CoV-2 dataset describing its early spread across the world. In conclusion, sampling biases are ubiquitous in phylogeographic analyses but may be accommodated by increasing the sample size, balancing spatial and temporal composition in the samples, and informing structured coalescent models with reliable case count data.

Environmental and geographical biases in plant specimen data from the Colombian Andes

AbstractSpecimen records are a major source of species information for biodiversity research. However, specimen records currently available may be geographically or environmentally biased. Detailed knowledge of biases is useful for understanding and accounting for errors they introduce into analyses of biodiversity patterns. Here we study geographical and environmental biases in online records representing the flora of the Colombian Andes and explore their effect on sample completeness at different spatial scales. We found a strong geographical and environmental sampling bias. Plant records were concentrated close to cities where herbaria and researchers are located. The highlands > 2000 m are better sampled, whereas mid- and lowlands remain poorly sampled (i.e. montane and lowland forest). Sampling completeness (SC) median across the Colombian Andes is < 75% at the scales studied. We explore possible causes of sampling bias, identify critical gaps and priority areas for plant sampling and make recommendations for strategies to increase SC and reduce biases.

Medium‐ to large‐bodied mammal surveys across the Neotropics are heavily biased against the most faunally intact assemblages

AbstractBiodiversity inventories provide critical information on the ecology of natural ecosystems and inform conservation planning at local to regional scales.Based on a systematic review, we compiled data on over 1000 mammal inventories conducted throughout the Neotropics – from Mexico to Argentina – to document the status of assemblage‐wide field mammalogy in the world’s most biodiverse Tropical realm.We analysed data using descriptive statistics and a confusion matrix to understand the prevalence of species' pseudo‐absences, map overall patterns of survey density, and quantify any geographic sampling bias.Based on the 1028 site‐specific inventories published between 1983 and 2020, mean species richness of medium‐ to large‐bodied mammals was 11.9 (± 8.8 standard deviation). Local inventories were distributed across elevational gradients, and overall sampling effort and survey techniques employed were extremely variable. The best‐sampled regions were the Brazilian Atlantic Forest and Mesoamerica, but we identified nine large assemblage‐wide Wallacean shortfalls and a pseudo‐absence rate of 11% (± 4% standard deviation); geographic sampling bias was largely related to human population density, regardless of the assemblages’ intactness presumably found in the undersampled wilderness regions of the Neotropics.We document the 40‐year legacy of mammal field surveys throughout the Neotropics, where several glaring knowledge shortfalls still persist. This calls for an audacious research agenda for future Neotropical mammal studies, which should target sampling in more remote areas, standardise sampling methodology, and provide high‐resolution data that can be used to fill current knowledge shortfalls. To do so, Latin American countries and the wider international community will need to commit greater personnel and financial resources to understanding biodiversity patterns and processes related to the Tropical vertebrate fauna.

Is geographic sampling bias representative of environmental space?

Species occurrence data from public repositories are widely used in biogeography, and conservation research. However, these data are prone to several sampling biases limiting their usefulness in biodiversity studies, and particularly in species distribution models (SDMs). Specifically, a geographic sampling bias can lead to overfitted SDMs while an environmental sampling bias can affect the estimate of the true environmental space occupied by a species. Several methods can be used to correct for sampling biases, but most of these are focused on the geographic component only. We aim to assess if sampling bias correction based on geographical factors can represent a viable solution also for environmental bias. We focused our analyses on plants and invertebrates in Africa. To model sampling bias in the spatial dimension we estimated sampling rates as a function of distance from a set of potential bias factors (roads, cities, waterbodies, and coastline) in a Bayesian framework. To model sampling bias in the environmental dimension we calculated the environmental multivariate distance between the average climate in the species' occurrences and the rest of the study area by using a set of bioclimatic variables. We quantified the spatial relationship between geographic and environmental bias calculating a Local Indicator of Multivariate Spatial Association (LISA), highlighting local clusters of spatial correlation. For both plants and invertebrates, almost 50% of Africa showed a non-significant relationship between the geographical and the environmental bias components, indicating the absence of a direct correspondence between the two types of biases. Furthermore, from 20.4% to 24.4% of the study area showed an opposite pattern between the two biases, clearly indicating that a correction in the spatial component would be detrimental in the environmental component. Our findings showed that geographical factors cannot be considered as good proxies for environmental bias and, consequently, we suggest considering both geographic and environmental corrections when sampling bias is a problem.

Excluding spatial sampling bias does not eliminate oversplitting in DNA-based species delimitation analyses.

DNA barcoding and DNA‐based species delimitation are major tools in DNA taxonomy. Sampling has been a central debate in this context, because the geographical composition of samples affects the accuracy and performance of DNA barcoding. Performance of complex DNA‐based species delimitation is to be tested under simpler conditions in absence of geographic sampling bias. Here, we present an empirical dataset sampled from a single locality in a Southeast‐Asian biodiversity hotspot (Laos: Phou Pan mountain). We investigate the performance of various species delimitation approaches on a megadiverse assemblage of herbivorous chafer beetles (Coleoptera: Scarabaeidae) to infer whether species delimitation suffers in the same way from exaggerate infraspecific variation despite the lack of geographic genetic variation that led to inconsistencies between entities from DNA‐based and morphology‐based species inference in previous studies. For this purpose, a 658 bp fragment of the mitochondrial cytochrome c oxidase subunit 1 (cox1) was analyzed for a total of 186 individuals of 56 morphospecies. Tree‐based and distance‐based species delimitation methods were used. All approaches showed a rather limited match ratio (max. 77%) with morphospecies. Poisson tree process (PTP) and statistical parsimony network analysis (TCS) prevailingly over‐splitted morphospecies, while 3% clustering and Automatic Barcode Gap Discovery (ABGD) also lumped several species into one entity. ABGD revealed the highest congruence between molecular operational taxonomic units (MOTUs) and morphospecies. Disagreements between morphospecies and MOTUs have to be explained by historically acquired geographic genetic differentiation, incomplete lineage sorting, and hybridization. The study once again highlights how important morphology still is in order to correctly interpret the results of molecular species delimitation.

Robust domain-adaptive discriminant analysis

Consider a domain-adaptive supervised learning setting, where a classifier learns from labeled data in a source domain and unlabeled data in a target domain to predict the corresponding target labels. If the classifier’s assumption on the relationship between domains (e.g. covariate shift, common subspace, etc.) is valid, then it will usually outperform a non-adaptive source classifier. If its assumption is invalid, it can perform substantially worse. Validating assumptions on domain relationships is not possible without target labels. We argue that, in order to make domain-adaptive classifiers more practical, it is necessary to focus on robustness; robust in the sense that an adaptive classifier will still perform at least as well as a non-adaptive classifier without having to rely on the validity of strong assumptions. With this objective in mind, we derive a conservative parameter estimation technique, which is transductive in the sense of Vapnik and Chervonenkis, and show for discriminant analysis that the new estimator is guaranteed to achieve a lower risk on the given target samples compared to the source classifier. Experiments on problems with geographical sampling bias indicate that our parameter estimator performs well.

Geographical sampling bias on the assessment of endemism areas for marine meiobenthic fauna.

Species distribution patterns are constrained by historical and ecological processes in space and time, but very often the species range sizes are geographical sampling biases resulting from unequal sampling effort. One of the most common definitions of endemism is based on the "congruence of distributional areas" criterion, when two or more species have the same distributional limits. By acknowledging that available data of marine meiobenthic species are prone to geographical sampling bias and that can affect the accuracy of the biogeographical signals, the present study combines analyses of inventory incompleteness and recognition of spatial congruence of Gastrotricha, Kinorhyncha, meiobenthic Annelida and Tardigrada in order to better understand the large-scale distribution of these organisms in coastal and shelf areas of the world. We used the marine bioregionalization framework for geographical operative units to quantify the inventory incompleteness effect (by modelling spatial predictions of species richness) and to recognize areas of endemism. Our models showed that the difference between observed and expected species richness in the Southern Hemisphere is much higher than in the Northern Hemisphere. Parsimony Analysis of Endemicity delimited 20 areas of endemism, most found in the Northern Hemisphere. Distribution patterns of meiobenthic species are shown to respond to events of geographical barriers and abiotic features, and their distribution is far from homogeneous throughout the world. Also, our data show that ecoregions with distinct biotas have at least some cohesion over evolutionary time. However, we found that inventory incompleteness may significantly affect the explanatory power of areas of endemism delimitation in both hemispheres. Yet, whereas future increases in sampling efforts are likely to change the spatial congruence ranges in the Southern Hemisphere, patterns for the Northern Hemisphere may prove to be relatively more resilient.

Automated conservation assessment of the orchid family with deep learning.

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.

Sampbias, a method for quantifying geographic sampling biases in species distribution data

Geo‐referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and data sets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence data sets. sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and data sets. We demonstrate the use of sampbias on a data set of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. sampbias is implemented as a well‐documented, open‐access and user‐friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

Early tetrapodomorph biogeography: Controlling for fossil record bias in macroevolutionary analyses

The fossil record provides direct empirical data for understanding macroevolutionary patterns and processes. Inherent biases in the fossil record are well known to confound analyses of this data. Sampling bias proxies have been used as covariates in regression models to test for such biases. Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling bias proxy that incorporates geographic information and test it with a case study on early tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. We find strong evidence that geographic sampling bias explains supposed radiations in dispersal rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test for its effect.

Mycorrhizal types differ in ecophysiology and alter plant nutrition and soil processes.

Mycorrhizal fungi benefit plants by improved mineral nutrition and protection against stress, yet information about fundamental differences among mycorrhizal types in fungi and trees and their relative importance in biogeochemical processes is only beginning to accumulate. We critically review and synthesize the ecophysiological differences in ectomycorrhizal, ericoid mycorrhizal and arbuscular mycorrhizal symbioses and the effect of these mycorrhizal types on soil processes from local to global scales. We demonstrate that guilds of mycorrhizal fungi display substantial differences in genome-encoded capacity for mineral nutrition, particularly acquisition of nitrogen and phosphorus from organic material. Mycorrhizal associations alter the trade-off between allocation to roots or mycelium, ecophysiological traits such as root exudation, weathering, enzyme production, plant protection, and community assembly as well as response to climate change. Mycorrhizal types exhibit differential effects on ecosystem carbon and nutrient cycling that affect global elemental fluxes and may mediate biome shifts in response to global change. We also note that most studies performed to date have not been properly replicated and collectively suffer from strong geographical sampling bias towards temperate biomes. We advocate that combining carefully replicated field experiments and controlled laboratory experiments with isotope labelling and -omics techniques offers great promise towards understanding differences in ecophysiology and ecosystem services among mycorrhizal types.

Modeling relative habitat suitability of southern Florida for invasive Burmese pythons (Python molurus bivittatus)

Invasive Burmese pythons are altering the ecology of southern Florida and their distribution is expanding northward. Understanding their habitat use is an important step in understanding the pathways of the invasion. This study identifies key landscape variables in predicting relative habitat suitability for pythons at the present stage of invasion through presence-only ecological niche modeling using geographical sampling bias correction. We used 2014 presence-only observations from the EDDMapS database and three landscape variables to model habitat suitability: fine-scale land cover, home range-level land cover, and distance to open freshwater or wetland. Ten geographical sampling bias correction scenarios based on road presence and sampling effort were evaluated to improve the efficacy of modeling. The best performing models treated road presence as a binary factor rather than a continuous decrease in sampling effort with distance from roads. Home range-level cover contributed the most to the final prediction, followed by proximity to water and fine-scale land cover. Estuarine habitat and freshwater wetlands were the most important variables to contribute to python habitat suitability at both the home range-level and fine-scale. Suitability was highest within 30 m of open freshwater and wetlands. This study provides quantifiable, predictive relationships between habitat types and python presence at the current stage of invasion. This knowledge can elucidate future targeted studies of python habitat use and behavior and help inform management efforts. Furthermore, it illustrates how estimates of relative habitat suitability derived from MaxEnt can be improved by both multi-scale perspectives on habitat and consideration of a variety of bias correction scenarios for selecting background points.

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias.

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5′ × 5′ grid cell, or “pentad”). The explanatory variables were distance to major road and exceptional birding locations or “sampling hubs,” percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.

Macroecological conclusions based on IUCN expert maps: A call for caution

AbstractAimInternational Union for Conservation of Nature (IUCN) expert maps are increasingly used in macroecological research. However, they have not been produced for this purpose. Macroecological insights based exclusively on this type of data could therefore be misleading. Here we compare, for a large taxonomic group (bats) and an entire biogeographical realm (Africa), the species‐specific discrepancies between IUCN maps and species distribution models (SDMs) that approximate the complete geographical range of species. We then examine the implications for a typical macroecological analysis that explores environmental correlates of species richness.LocationContinental Africa.Time periodAround 2000.TaxaBats (Chiroptera).MethodsWe measure disagreement between IUCN expert maps and SDMs at both the species (geographical ranges) and the aggregated level (range size–frequency distributions and species richness). We further quantify the difference in absolute and relative weight assigned to three variables hypothesized to drive species richness: primary productivity, climatic seasonality and environmental heterogeneity.ResultsLocation, shape and size of individual species’ ranges, derived richness patterns and range size–frequency distributions differ substantially. SDMs predict larger and more complex geographical ranges, and species’ range sizes vary less. The spatial congruence of richness hotspots among both datasets is only 42%. These discrepancies are large enough to alter the absolute explanatory power of environmental correlates, whereas the redundancy in the variation explained increases markedly when richness is inferred using SDM‐based estimates of complete species ranges.Main conclusionsIUCN expert maps differ considerably and systematically from SDMs built to estimate complete species ranges, primarily because of their intentional greater sensitivity to geographical sampling bias. This property is desirable in a conservation context but unwanted in most macroecological analyses. We therefore caution against the use of IUCN expert maps in macroecology and recommend at least gauging the robustness of results using additional range estimates designed to approximate the complete geographical range of species.

Filling in the GAPS: evaluating completeness and coverage of open-access biodiversity databases in the United States.

Primary biodiversity data constitute observations of particular species at given points in time and space. Open‐access electronic databases provide unprecedented access to these data, but their usefulness in characterizing species distributions and patterns in biodiversity depend on how complete species inventories are at a given survey location and how uniformly distributed survey locations are along dimensions of time, space, and environment. Our aim was to compare completeness and coverage among three open‐access databases representing ten taxonomic groups (amphibians, birds, freshwater bivalves, crayfish, freshwater fish, fungi, insects, mammals, plants, and reptiles) in the contiguous United States. We compiled occurrence records from the Global Biodiversity Information Facility (GBIF), the North American Breeding Bird Survey (BBS), and federally administered fish surveys (FFS). We aggregated occurrence records by 0.1° × 0.1° grid cells and computed three completeness metrics to classify each grid cell as well‐surveyed or not. Next, we compared frequency distributions of surveyed grid cells to background environmental conditions in a GIS and performed Kolmogorov–Smirnov tests to quantify coverage through time, along two spatial gradients, and along eight environmental gradients. The three databases contributed >13.6 million reliable occurrence records distributed among >190,000 grid cells. The percent of well‐surveyed grid cells was substantially lower for GBIF (5.2%) than for systematic surveys (BBS and FFS; 82.5%). Still, the large number of GBIF occurrence records produced at least 250 well‐surveyed grid cells for six of nine taxonomic groups. Coverages of systematic surveys were less biased across spatial and environmental dimensions but were more biased in temporal coverage compared to GBIF data. GBIF coverages also varied among taxonomic groups, consistent with commonly recognized geographic, environmental, and institutional sampling biases. This comprehensive assessment of biodiversity data across the contiguous United States provides a prioritization scheme to fill in the gaps by contributing existing occurrence records to the public domain and planning future surveys.

Reserva Natural Laguna Blanca, Departamento San Pedro: Paraguay’s First Important Area for the Conservation of Amphibians and Reptiles?

Geographical sampling bias and restricted search methodologies have resulted in the distribution of Paraguayan reptiles and amphibians being patchily known. Available data is almost entirely based on brief collecting trips and rapid ecological inventories, often several decades apart, which inevitably struggle to detect more inconspicuous species and patterns of abundance. This has led to a deficit in our knowledge of the true distribution and abundance of Paraguayan reptiles and amphibians. The establishment of the NGO Para La Tierra at Reserva Natural Laguna Blanca (RNLB), Depto. San Pedro, Paraguay allowed the first modern sustained, multi-method inventory of Paraguayan reptiles and amphibians to be performed at a single site. Despite the small size of the reserve (804 ha), a total of 57 reptiles (12 of national conservation concern) and 32 amphibians (one of national conservation concern) were collected during five years of random sampling, qualifying RNLB as the most biodiverse reserve for reptiles and amphibians in the country. Six species occurring at RNLB have been found at no other Paraguayan locality. Legal protection for this private reserve expired in January 2015 and the conservation implications of the inventory results are discussed. It is proposed that the long term legal protection of the reserve be considered a national conservation priority and that the diversity of the herpetofauna be recognized with the designation of RNLB as Paraguay’s first Important Area for the Conservation of Amphibians and Reptiles.

Refinement of the supercontinent cycle with Hf, Nd and Sr isotopes

The combined use of Hf, Nd and Sr isotopes is more useful in understanding the supercontinent cycle than the use of only Hf isotopic data from detrital zircons. Sr and Nd seawater isotopes, although not as precise as ɛNd and ɛHf distributions, also record input from ocean ridge systems. Unlike detrital zircons where sources cannot be precisely located because of crustal recycling, both the location and tectonic setting often can be constrained for whole-rock Nd isotopic data. Furthermore, primary zircon sources may not reside on the same continent as derivative detrital zircons due to supercontinent breakup and assembly. Common to all of the isotopic studies are geographic sampling biases reflecting outcrop distributions, river system sampling, or geologists, and these may be responsible for most of the decorrelation observed between isotopic systems. Distributions between 3.5 and 2 Ga based on ɛHf median values of four detrital zircon databases as well as our compiled ɛNd database are noisy but uniformly distributed in time, whereas data between 2 and 1 Ga data are more tightly clustered with smaller variations. Grouped age peaks suggest that both isotopic systems are sampling similar types of orogens. Only after 1 Ga and before 3.5 Ga do we see wide variations and significant disagreement between databases, which may partially reflect variations in both the number of sample locations and the number of samples per location.External and internal orogens show similar patterns in ɛNd and ɛHf with age suggesting that both juvenile and reworked crustal components are produced in both types of orogens with similar proportions. However, both types of orogens clearly produce more juvenile isotopic signatures in retreating mode than in advancing mode. Many secular changes in ɛHf and ɛNd distributions correlate with the supercontinent cycle. Although supercontinent breakup is correlated with short-lived decreasing ɛHf and ɛNd (≤100 Myr) for most supercontinents, there is no isotopic evidence for the breakup of the Paleoproterozoic supercontinent Nuna. Assembly of supercontinents by extroversion is recorded by decreasing ɛNd in granitoids and metasediments and decreasing ɛHf in zircons, attesting to the role of crustal reworking in external orogens in advancing mode. As expected, seawater Sr isotopes increase and seawater Nd isotopes decrease during supercontinent assembly by extroversion. Pangea is the only supercontinent that has a clear isotopic record of introversion assembly, during which median ɛNd and ɛHf rise rapidly for ≤100 Myr. Although expected to increase, radiogenic seawater Sr decreases (and seawater Nd increases) during assembly of Pangea, a feature that may be caused by juvenile input into the oceans from new ocean ridges and external orogens in retreating mode. The fact that a probable onset of plate tectonics around 3 Ga is not recorded in isotopic distributions may be due the existence of widespread felsic crust formed prior to the onset of plate tectonics in a stagnant lid tectonic regime, as supported by Nd and Hf model ages.

Geographical sampling bias in a large distributional database and its effects on species richness–environment models

AbstractAimRecent advances in the availability of species distributional and high‐resolution environmental data have facilitated the investigation of species richness–environment relationships. However, even exhaustive distributional databases are prone to geographical sampling bias. We aim to quantify the inventory incompleteness of vascular plant data across 2377 Chinese counties and to test whether inventory incompleteness affects the analysis of richness–environment relationships and spatial predictions of species richness.LocationChina.MethodsWe used the most comprehensive database of Chinese vascular plants, which includes county‐level occurrences for 29,012 native species derived from 4,236,768 specimen and literature records. For each county, we computed smoothed species accumulation curves and used the mean slope of the last 10% of the curves as a proxy for inventory incompleteness. We created a series of data subsets with different levels of inventory incompleteness by excluding successively more under‐sampled counties from the full data set. We then applied spatial and non‐spatial regression models to each of these subsets to investigate relationships between the species richness of subsets and environmental factors, and to predict spatial patterns of vascular plant species richness in China.ResultsLog10‐transformed numbers of records and documented species were strongly correlated (r = 0.97). In total, 91% of Chinese counties were identified as under‐sampled. After controlling for inventory incompleteness, the overall explanatory power of environmental factors markedly increased, and the strongest predictor of species richness switched from elevational range to annual wet days. Environmental models calibrated with more complete inventories yielded better spatial predictions of species richness.Main conclusionsOur results indicate that inventory incompleteness strongly affects the explanatory power of environmental factors, the main determinants of species richness obtained from regression analyses, and the reliability of environment‐based spatial predictions of species richness. We conclude that even large distributional databases are prone to geographical sampling bias, with far‐reaching implications for the perception of and inferences about macroecological patterns.