Influence of different data cleaning solutions of point‐occurrence records on downstream macroecological diversity models

Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson''s r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.     

Opportunistic data reveal widespread species turnover in Enallagma damselflies at biogeographical scales

An information tradeoff exists between systematic presence/absence surveys and purely opportunistic (presence‐only) records for investigating the geography of community structure. Opportunistic species occurrence data may be of relatively limited quality, but typically involves numerous observations and species. Given the quality–quantity tradeoff, what can opportunistic data reveal about spatial patterns in community structure? Here we explore opportunistic data in describing geographic patterns of species composition, using over 4600 occurrence records of Enallagma damselflies in the United States. We tested phylogenetic scale (genus level, Enallagma major clades, Enallagma subclades) and spatial extent (U.S. vs watershed regions), hypothesizing that nonrandom structure is more likely at larger spatial extents. We also used three sets of systematic presence/absence surveys as a benchmark for validating opportunistic presence‐only records. Null model analysis of matrix coherence and species replacements showed many cases of nonrandom structure and widespread species turnover. This outcome was repeated across spatial and environmental gradients and community composition scenarios. Turnover dominated across the U.S. and two watersheds spanning biogeographic boundaries, but random assemblages were prevalent in a third watershed with limited longitudinal extent. Turnover also pervaded each level of phylogeny. Opportunistic presence‐only datasets showed identical patterns as systematic presence/absence datasets. These results indicate that extensive opportunistic data can be used to detect species turnover, especially at geographic scales where range margins are crossed.     

Phylogenetic relatedness and plant invader success across two spatial scales

Aim  Successful invaders often possess similar ecological traits that contribute to success in new regions, and thus under niche conservatism, invader success should be phylogenetically clustered. We asked if the degree to which non-native plant species are phylogenetically related is a predictor of invasion success at two spatial scales.
Location  Australia – the whole continent and Royal National Park (south-eastern Australia).
Methods  We used non-native plant species occupancy in Royal National Park, as well as estimated continental occupancy of these species from herbarium records. We then estimated phylogenetic relationships using molecular data from three gene sequences available on GenBank ( matK , rbcL and ITS1 ). We tested for phylogenetic signals in occupancy using Blomberg's K .
Results  Whereas most non-native plants were relatively scarce, there was a strong phylogenetic signal for continental occupancy, driven by the clustering of successful species in Asteraceae, Caryophyllaceae, Poaceae and Solanaceae. However, we failed to detect a phylogenetic signal at the park scale.
Main Conclusions  Our results reveal that at a large spatial scale, invader success is phylogenetically clustered where ecological traits promoting success appear to be shared among close relatives, indicating that phylogenetic relationships can be useful predictors of invasion success at large spatial scales. At a smaller, landscape scale, there was no evidence of phylogenetic clustering of invasion success, and thus, relatedness plays a much reduced role in determining the relative success of invaders.     

Global patterns of fern species diversity: An evaluation of fern data in GBIF

Despite that several studies have shown that data derived from species lists generated from distribution occurrence records in the Global Biodiversity Information Facility (GBIF) are not appropriate for those ecological and biogeographic studies that require high sampling completeness, because species lists derived from GBIF are generally very incomplete, Suissa et al. (2021) generated fern species lists based on data with GBIF for 100 km × 100 km grid cells across the world, and used the data to determine fern diversity hotspots and species richness–climate relationships. We conduct an evaluation on the completeness of fern species lists derived from GBIF at the grid–cell scale and at a larger spatial scale, and determine whether fern data derived from GBIF are appropriate for studies on the relations of species composition and richness with climatic variables. We show that species sampling completeness of GBIF is low (<40%) for most of the grid cells examined, and such low sampling completeness can substantially bias the investigation of geographic and ecological patterns of species diversity and the identification of diversity hotspots. We conclude that fern species lists derived from GBIF are generally very incomplete across a wide range of spatial scales, and are not appropriate for studies that require data derived from species lists in high completeness. We present a map showing global patterns of fern species diversity based on complete or nearly complete regional fern species lists.     

Warming water and leaf litter quality but not plant origin drive decomposition and fungal diversity in an experiment

The fungi associated with leaf litter play a key role in decomposition and can be affected both by the warming water and the invasion of non-native species in riparian vegetation. Warming water and invasion of non-native riparian species on stream fungal communities have been studied mainly in temperate ecosystems. We tested the effects of warming water and non-native plant Psidium guajava on leaf litter decomposition, conidia density, species richness and beta diversity of tropical stream fungi. Thus, we carried out an experiment using the current mean temperature of streams from northwestern Paraná in South Brazil (22 °C) and two temperatures above the current mean temperature (26 °C and 29 °C). We also used the leaves of a non-native plant (P. guajava), and two native plants (one of similar nutritional quality, and the other of higher nutritional quality than the non-native species) occurring in Neotropical streams riparian vegetation. Warming water accelerated leaf litter decomposition and reduced conidia density and fungal richness in native and non-native plants. However, species composition and beta diversity were not affected by water temperature. Our study showed that warming affects the fungi of streams, the main microorganisms responsible for decomposition and that the nutritional quality of the leaves may be more important than the origin of riparian plant species. Despite this, further investigations should be conducted on the interaction of P. guajava with the flow of nutrients in these environments and how it can affect other ecosystem processes and the food chain. Efforts to study the effects of water warming and biological invasion on the attributes and distribution of fungi in streams are vital, making them a tool for the conservation of riparian ecosystems.     

Tidying up international nucleotide sequence databases: ecological, geographical and sequence quality annotation of its sequences of mycorrhizal fungi

Sequence analysis of the ribosomal RNA operon, particularly the internal transcribed spacer (ITS) region, provides a powerful tool for identification of mycorrhizal fungi. The sequence data deposited in the International Nucleotide Sequence Databases (INSD) are, however, unfiltered for quality and are often poorly annotated with metadata. To detect chimeric and low-quality sequences and assign the ectomycorrhizal fungi to phylogenetic lineages, fungal ITS sequences were downloaded from INSD, aligned within family-level groups, and examined through phylogenetic analyses and BLAST searches. By combining the fungal sequence database UNITE and the annotation and search tool PlutoF, we also added metadata from the literature to these accessions. Altogether 35,632 sequences belonged to mycorrhizal fungi or originated from ericoid and orchid mycorrhizal roots. Of these sequences, 677 were considered chimeric and 2,174 of low read quality. Information detailing country of collection, geographical coordinates, interacting taxon and isolation source were supplemented to cover 78.0%, 33.0%, 41.7% and 96.4% of the sequences, respectively. These annotated sequences are publicly available via UNITE (http://unite.ut.ee/) for downstream biogeographic, ecological and taxonomic analyses. In European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena/), the annotated sequences have a special link-out to UNITE. We intend to expand the data annotation to additional genes and all taxonomic groups and functional guilds of fungi.     

Using Species Distribution Models For Fungi

Species distribution models (SDMs) are an emerging tool in the study of fungi, and their use is expanding across species and research topics. To summarise progress to date and to highlight important considerations for future users, we review 283 studies that apply SDMs to fungi. We found that macrofungi, lichens, and pathogenic microfungi are most often studied. While many studies only aim to model species response to environmental covariates, the use of SDMs for explicitly predicting fungal occurrence in space and time is growing. Many studies collect fungal occurrence data, but the use of pre-collected records from reference collections and citizen science programs is increasing. Challenges of applying SDMs to fungi include detection and sampling biases, and uncertainties in identification and taxonomy. Further, finding environmental covariates at appropriate spatial and temporal scales is important, as fungi can respond to fine-scale environmental patterns. Fine-scale covariate data can be difficult to gather across space, but we show remote-sensing measurements are viable for fungi SDMs. For those fungi interacting with host species, host information is also important, and can be used as covariates in SDMs. We also highlight that competition among fungi, and dispersal, can affect observed distributions, with the latter particularly prominent for invasive fungi. We show how one can account for these processes in models, when suitable data are available. Finally, we note that environmental DNA records create new opportunities and challenges for future modelling efforts, and discuss the difficulties in predicting invasions and climate change impacts. The application of SDMs to fungi has already provided interesting lessons on how to adapt modelling tools for specific questions, and fungi will continue to be relevant test subjects for further technical development of SDMs.     

How Global Is the Global Biodiversity Information Facility?

There is a concerted global effort to digitize biodiversity occurrence data from herbarium and museum collections that together offer an unparalleled archive of life on Earth over the past few centuries. The Global Biodiversity Information Facility provides the largest single gateway to these data. Since 2004 it has provided a single point of access to specimen data from databases of biological surveys and collections. Biologists now have rapid access to more than 120 million observations, for use in many biological analyses. We investigate the quality and coverage of data digitally available, from the perspective of a biologist seeking distribution data for spatial analysis on a global scale. We present an example of automatic verification of geographic data using distributions from the International Legume Database and Information Service to test empirically, issues of geographic coverage and accuracy. There are over 1/2 million records covering 31% of all Legume species, and 84% of these records pass geographic validation. These data are not yet a global biodiversity resource for all species, or all countries. A user will encounter many biases and gaps in these data which should be understood before data are used or analyzed. The data are notably deficient in many of the world's biodiversity hotspots. The deficiencies in data coverage can be resolved by an increased application of resources to digitize and publish data throughout these most diverse regions. But in the push to provide ever more data online, we should not forget that consistent data quality is of paramount importance if the data are to be useful in capturing a meaningful picture of life on Earth.     

Broad-scale determinants of non-native fish species richness are context-dependent

Identifying the factors determining the non-native species richness (NNSR) in a given area is essential for preventing species invasions. The relative importance of human-related and natural factors considered for explaining NNSR might depend upon both the spatial scale (i.e. the extent of the gradients sampled) and the historical context of the area surveyed. Here, using a worldwide database of freshwater fish occurrences, we tested whether the relative influence of human and ecological determinants of non-native fish species establishment at the scale of the biogeographic realm was consistent (i) with that observed worldwide, and (ii) among the different biogeographical realms. The prominent role of human activity in shaping the global (i.e. worldwide) pattern of NNSR cannot be directly extrapolated to the biogeographic realms. Furthermore, the relationships between human and ecological determinants and NNSR vary strikingly across biogeographic realms, revealing a strong context dependency of the determinants of NNSR. In particular, the human-related factors play a predominant role in explaining the establishment of non-native species in economically developed realms, while in the other realms environmental characteristics of the river basins best explained geographical patterns of NNSR. In the face of future biological invasions, considering both the spatial scale and the historical context of the surveyed area is crucial to adopt effective conservation strategies.     

Balancing data sharing requirements for analyses with data sensitivity

Data sensitivity can pose a formidable barrier to data sharing. Knowledge of species current distributions from data sharing is critical for the creation of watch lists and an early warning/rapid response system and for model generation for the spread of invasive species. We have created an on-line system to synthesize disparate datasets of non-native species locations that includes a mechanism to account for data sensitivity. Data contributors are able to mark their data as sensitive. This data is then ‘fuzzed’ in mapping applications and downloaded files to quarter-quadrangle grid cells, but the actual locations are available for analyses. We propose that this system overcomes the hurdles to data sharing posed by sensitive data.     

Colonization and coexistence of non-native ants on a model Atlantic island

Aims

Colonization by non-native ants represents one of the gravest potential threats to island ecosystems. It is necessary to identify general mechanisms by which non-native species are able to colonize and persist in order to inform future prevention and management. We studied a model-island assemblage of 17 non-native ant species with aim of identifying the spatial source of introductions and assessing how such a diversity of species are able to coexist.

Location

Data were collected on Ascension Island: an ideal study system for its intermediate area, compact shape, spatial heterogeneity, lack of native ant species, and availability of non-native ant records dating back to the 1800s.

Methods

We collected over 47,000 individual ants from 73 sites using a range of baited traps and survey techniques. We combined this novel data with past occurrence records in order to determine whether human settlements have historically been the source of ant introductions and to quantify the mean rate at which species have dispersed across the island. Analysis of standardized field data revealed the extent to which ants were partitioning ecological niche space via (1) habitat separation, (2) fine-scale resource partitioning and (3) climatic heterogeneity.

Results

Ants were radiating at a linear rate of approximately 0.5 km² per year from human settlements on this island, with the most widespread species having been introduced earliest. After accounting for incomplete colonization, we found no evidence to suggest habitat separation between species. Instead, we found significant niche separation through resource partitioning and weather-dependent activity patterns.

Main Conclusions

Our results indicate that non-native ants can coexist in very close proximity and are therefore capable of existing at great diversity on even small islands. It is inevitable that ant colonization will continue without increased biosecurity measures, habitat restoration around settlements and conservation of native species populations.     

Automation of random conical tilt and orthogonal tilt data collection using feature-based correlation

Visualization by electron microscopy has provided many insights into the composition, quaternary structure, and mechanism of macromolecular assemblies. By preserving samples in stain or vitreous ice it is possible to image them as discrete particles, and from these images generate three-dimensional structures. This ‘single-particle’ approach suffers from two major shortcomings; it requires an initial model to reconstitute 2D data into a 3D volume, and it often fails when faced with conformational variability. Random conical tilt (RCT) and orthogonal tilt (OTR) are methods developed to overcome these problems, but the data collection required, particularly for vitreous ice specimens, is difficult and tedious. In this paper, we present an automated approach to RCT/OTR data collection that removes the burden of manual collection and offers higher quality and throughput than is otherwise possible. We show example datasets collected under stain and cryo conditions and provide statistics related to the efficiency and robustness of the process. Furthermore, we describe the new algorithms that make this method possible, which include new calibrations, improved targeting and feature-based tracking.     

A Framework for Evaluating Heterogeneity and Landscape-Level Impacts of Non-native Aquatic Species

Non-native species are a major component of global environmental change, and aquatic systems are especially vulnerable to non-native species impacts. Much of the research on aquatic non-native species impact has occurred at the local or site level. In reality, non-native species impacts play out across multiple spatial scales on heterogeneous landscapes. How can we ‘scale up’ our understanding of site-level impacts to the broader landscape scale? To address this disconnect, we synthesize our current understanding of key components of landscape-scale non-native species impacts: geographic range, abundance, and local impacts. Most aquatic non-native species have small ranges, while a few have large ranges. However, aquatic non-native species are often far from saturated on landscapes, and occurrence records are often woefully incomplete. Aquatic non-native species are often at low abundances where they are present, reaching high abundance in a small number of locations. Finally, local-scale impact can be estimated from abundance, but this requires knowledge of the abundance–impact relationship. Considering these multiple components enables understanding of non-native species impacts at broader spatial scales. Although the landscape-level impacts of aquatic non-native species may be high, the spatial distribution of site-level impacts is uneven, and highly impacted sites may be relatively uncommon. This heterogeneity in impacts provides an opportunity to optimize and prioritize non-native species management and prevention efforts.     

Persistence and extirpation in invaded landscapes: patch characteristics and connectivity determine effects of non-native predatory fish on native salamanders

Studies have demonstrated negative effects of non-native, predatory fishes on native amphibians, yet it is still unclear why some amphibian populations persist, while others are extirpated, following fish invasion. We examined this question by developing habitat-based occupancy models for the long-toed salamander (Ambystoma macrodactylum) and non-native fish using survey data from 1,749 water bodies across 470 catchments in the Northern Rocky Mountains, USA. We first modeled the habitat associations of salamanders at 468 fishless water bodies in 154 catchments where non-native fish were historically, and are currently, absent from the entire catchment. We then applied this habitat model to the complete data set to predict the probability of salamander occupancy in each water body, removing any effect of fish presence. Finally, we compared field-observed occurrences of salamanders and fish to modeled probability of salamander occupancy. Suitability models indicated that fish and salamanders had similar habitat preferences, possibly resulting in extirpations of salamander populations from entire catchments where suitable habitats were limiting. Salamanders coexisted with non-native fish in some catchments by using marginal quality, isolated (no inlet or outlet) habitats that remained fishless. They rarely coexisted with fish within individual water bodies and only where habitat quality was highest. Connectivity of water bodies via streams resulted in increased probability of fish invasion and consequently reduced probability of salamander occupancy. These results could be used to identify and prioritize catchments and water bodies where control measures would be most effective at restoring amphibian populations. Our approach could be useful as a framework for improved investigations into questions of persistence and extirpation of native species when non-native species have already become established.     

spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models

Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. We here provide a worked example for the Caribbean spiny pocket mouse, where the results obtained match those of manual thinning.     

Citizen science data predict high potential for macrofungal refugia outside protected riparian areas

Protected areas with restricted management can provide refugia for fungi, but are usually selected based on conservation strategies for flora and fauna. Despite the important role of fungi in floodplains, they are rarely considered in conservation projects. The SwissFungi database covering all biogeographic regions in Switzerland, and consisting of 84% citizen science data, provided a valuable basis to define fungal riparian species: 99.29% of 990 species were reported at least once from the riparian zone, while 15% of species showed a measurable riparian affinity. Species distribution modelling for 129 riparian macrofungi revealed that the predicted distribution is driven by temperature for most species. There were significantly more records per species inside compared to outside protected areas, when correcting for size differences (21% of the area in Switzerland is protected). In contrast, the model predicted significantly more suitable habitat outside currently protected areas. Unprotected areas harbor a high potential for the creation of newly protected areas for the conservation of riparian fungi. The database information and the modelling approach provided crucial information for future monitoring and conservation projects along rivers.     

广东省鸟类新记录及其与自然和社会经济因素的关联性

物种多样性的保护必须以充分了解地区物种资源的分布状况为前提。科研监测是传统而有效的数据获取手段, 但研究地点大多集中在自然保护区。公众观鸟记录活动扩大了物种数据收集的时空范围, 是科研监测的有益补充, 但观鸟活动开展的范围和强度受经济水平的影响明显, 呈现空间上的不均匀性。为明确自然保护地分布格局和地区经济水平对鸟类物种多样性记录的影响, 本研究以广东省为例, 对比了公众观鸟和科研监测所记录的鸟类物种分布地区差异。结果表明: 从2016年至2020年, 公众观鸟为广东省贡献了17种鸟类新分布记录和10种再确认记录, 但有100种历史科研监测记录的鸟类种类未记录到。线性回归分析结果显示: 广东各市地区生产总值(GDP)对于公众观鸟记录的鸟类物种丰富度具有最高的解释率(校正R² = 0.582), 而保护地面积则是科研监测记录的鸟类物种丰富度的主要解释因子。对于市级总体鸟类物种丰富度, GDP的解释率为保护地面积的4.2倍。研究结果反映了经济发展对公众观鸟活动的有力支撑, 促进了广东鸟类新记录的发现, 但也显示广东省目前的物种多样性地区分布信息存在一定偏差。建议根据各市经济发展水平和自然环境差异, 制定相应的物种多样性监测与保护措施, 并结合科研与公众力量各自的优势, 加强对物种多样性被低估地区的本底资源调查与监测。