Evan Graham Turbott

ABSTRACTAimFerns are globally distributed, yet the number of studies examining the historical evolution of African taxa is relatively low. Investigation of the evolution of African fern diversity is critical in order to understand patterns and processes that have global relevance (e.g., the pantropical diversity disparity [PDD] pattern). This study aims to examine when and from where a globally distributed fern lineage arrived in sub‐Saharan Africa, to obtain a better understanding of potential processes contributing to patterns of diversity across the region.LocationGlobal, sub‐Saharan Africa.TaxonAsplenium (Aspleniaceae).MethodsWe analysed five loci from 537 Asplenium taxa using a maximum likelihood (IQ‐Tree) phylogenetic framework. For age estimation, we performed penalised likelihood as implemented in treePL, and executed a Bayesian analysis using BEAST. Biogeographical analyses were carried out using BioGeoBEARS.ResultsMost dispersals into Africa occurred within the last ~55 myr, with the highest diversity of sub‐Saharan African taxa concentrated in two clades, each of which descended from an Asian ancestor. Additional dispersals to sub‐Saharan Africa can be found throughout the phylogeny. Lastly, potential cryptic species diversity exists within Asplenium as evidenced by several polyphyletic taxa.Main ConclusionsWe recover multiple dispersals of Asplenium to sub‐Saharan Africa, with two major lineages likely diversifying after arrival.

Petaluridae (Odonata: Anisoptera) is a relict dragonfly family, having diverged from its sister family in the Jurassic, of eleven species that are notable among odonates (dragonflies and damselflies) for their exclusive use of fen and bog habitats, their burrowing behavior as nymphs, large body size as adults, and extended lifespans. To date, several nodes within this family remain unresolved, limiting the study of the evolution of this peculiar family. Using an anchored hybrid enrichment dataset of over 900 loci we reconstructed the species tree of Petaluridae. To estimate the temporal origin of the genera within this family, we used a set of well-vetted fossils and a relaxed molecular clock model in a divergence time estimation analysis. We estimate that Petaluridae originated in the early Cretaceous and confirm the existence of monophyletic Gondwanan and Laurasian clades within the family. Our relaxed molecular clock analysis estimated that these clades diverged from their MRCA approximately 160 mya. Extant lineages within this family were identified to have persisted from 6 (Uropetala) to 120 million years (Phenes). Our biogeographical analyses focusing on a set of key regions suggest that divergence within Petaluridae is largely correlated with continental drift, the exposure of land bridges, and the development of mountain ranges. Our results support the hypothesis that species within Petaluridae have persisted for tens of millions of years, with little fossil evidence to suggest widespread extinction in the family, despite optimal conditions for the fossilization of nymphs. Petaluridae appear to be a rare example of habitat specialists that have persisted for tens of millions of years.

Since the confirmation of African swine fever (ASF) in South Korea in 2019, its spread, predominantly in wild boars, has been a significant concern. A key factor in this situation is the lack of identification of risk factors by surveillance bias. The unique orography, characterized by high mountains, complicates search efforts, leading to overlooked or delayed case detection and posing risks to the swine industry. Additionally, shared rivers with neighboring country present a continual threat of virus entry. This study employs geospatial analysis and statistical methods to 1) identify areas at high risk of ASF occurrence but possibly under-surveilled, and 2) indicate strategic surveillance points for monitoring the risk of ASF virus entry through water bodies and basin influences. Pearson’s rho test indicated that elevation (rho = -0.908, p-value < 0.001) and distance from roads (rho = -0.979, p-value < 0.001) may have a significant impact on limiting surveillance activities. A map of potential under-surveilled areas was created considering these results and was validated by a chi-square goodness-of-fit test (X-square = 208.03, df = 1, p-value < 0.001). The strong negative correlation (rho = -0.997, p-value <0.001) between ASF-positive wild boars and distance from water sources emphasizes that areas surrounding rivers are one of the priority areas for monitoring. The subsequent hydrological analyses provided important points for monitoring the risk of virus entry via water from the neighboring country. This research aims to facilitate early detection and prevent further spread of ASF.

Parasitic weeds in the genera Orobanche, Phelipanche (broomrapes) and Striga (witchweeds) have a devastating impact on food security across much of Africa, Asia and the Mediterranean Basin. Yet, how climatic factors might affect the range expansion of these weeds in the context of global environmental change remains unexplored. We examined satellite‐based environmental variables such as surface temperature, root zone soil moisture, and elevation, in relation to parasitic weed distribution and environmental conditions over time, in combination with observational data from the Global Biodiversity Information Facility (GBIF). Our analysis reveals contrasting environmental and altitude preferences in the genera Striga and Orobanche. Asiatic witchweed (Striga asiatica), which infests corn, rice, sorghum, and sugar cane crops, appears to be expanding its range in high elevation habitats. It also shows a significant association with heat‐moisture coupling events, the frequency of which is rising in such environments. These results point to geographical shifts in distribution and abundance in parasitic weeds due to climate change.

Aim Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses.InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed.Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

The introduction of bees for agricultural production in distinct parts of the world and poor management have led to invasion processes that affect biodiversity, significantly impacting native species. Different Bombus species with invasive potential have been recorded spreading in different regions worldwide, generating ecological and economic losses. We applied environmental niche and potential distribution analyses to four species of the genus Bombus to evaluate the similarities and differences between their native and invaded ranges. We found that B. impatiens has an extended environmental niche, going from dry environmental conditions in the native range to warmer and wetter conditions in the invaded range. Bombus ruderatus also exhibited an extended environmental niche with drier and warmer conditions in the invaded range than in its native range. Bombus subterraneus expanded its environmental niche from cooler and wetter conditions in the native range to drier and warmer conditions in the invaded range. Finally, B. terrestris showed the most significant variation in the environmental niche, extending to areas with similar and different environmental conditions from its native range. The distribution models agreed with the known distributions for the four Bombus species, presenting geographic areas known to be occupied by each species in different regions worldwide. The niche analysis indicate shifts in the niches from the native to the invaded distribution area of the bee species. Still, niche similarities were observed in the areas of greatest suitability in the potential distribution for B. ruderatus, B. subterraneus, and B. terrestris, and to a lesser degree in the same areas with B. impatiens. These species require similar environmental conditions as in their native ranges to be established in their introduced ranges. Still, they can adapt to changes in temperature and humidity, allowing them to expand their ranges into new climatic conditions.

Background The recent discovery of emerging relapsing fever group Borrelia (RFGB) species, such as Borrelia miyamotoi, poses a growing threat to public health. However, the global distribution and associated risk burden of these species remain uncertain. We aimed to map the diversity, distribution, and potential infection risk of RFGB.MethodsWe searched PubMed, Web of Science, GenBank, CNKI, and eLibrary from Jan 1, 1874, to Dec 31, 2022, for published articles without language restriction to extract distribution data for RFGB detection in vectors, animals, and humans, and clinical information about human patients. Only articles documenting RFGB infection events were included in this study, and data for RFGB detection in vectors, animals, or humans were composed into a dataset. We used three machine learning algorithms (boosted regression trees, random forest, and least absolute shrinkage and selection operator logistic regression) to assess the environmental, ecoclimatic, biological, and socioeconomic factors associated with the occurrence of four major RFGB species: Borrelia miyamotoi, Borrelia lonestari, Borrelia crocidurae, and Borrelia hermsii; and mapped their worldwide risk level.FindingsWe retrieved 13 959 unique studies, among which 697 met the selection criteria and were used for data extraction. 29 RFGB species have been recorded worldwide, of which 27 have been identified from 63 tick species, 12 from 61 wild animals, and ten from domestic animals. 16 RFGB species caused human infection, with a cumulative count of 26 583 cases reported from Jan 1, 1874, to Dec 31, 2022. Borrelia recurrentis (17 084 cases) and Borrelia persica (2045 cases) accounted for the highest proportion of human infection. B miyamotoi showed the widest distribution among all RFGB, with a predicted environmentally suitable area of 6·92 million km2, followed by B lonestari (1·69 million km2), B crocidurae (1·67 million km2), and B hermsii (1·48 million km2). The habitat suitability index of vector ticks and climatic factors, such as the annual mean temperature, have the most significant effect among all predictive models for the geographical distribution of the four major RFGB species.InterpretationThe predicted high-risk regions are considerably larger than in previous reports. Identification, surveillance, and diagnosis of RFGB infections should be prioritised in high-risk areas, especially within low-income regions.FundingNational Key Research and Development Program of China.

Aim Species severely under‐represented in fossil pollen records leave gaps in interpretations and reconstructions of past vegetation. These ‘silent taxa’ leave little or no trace due to low pollen production, dispersal, preservation and taxonomic resolution. An approach for including them is through associating them with other species with reliable pollen representation. Here, we demonstrate a method for selecting such a proxy species for the Holocene using modern vegetation data.LocationNew Zealand.TaxonBeilschmiedia tawa (A.Cunn.) Benth. & Hook. F. ex Kirk (Lauraceae).MethodsWe used vegetation plot data to perform a pairwise co‐occurrence analysis of the New Zealand indigenous forest metacommunity to identify species with a strong positive association with Beilschmiedia tawa (tawa), a common tree severely under‐recorded in the pollen record. For those species, we then modelled their realised climatic niches to identify species with high niche overlap. We discuss how well those species could be interpreted from the Holocene fossil pollen record based on the representation of their pollen taxa.ResultsKnightia excelsa (rewarewa; Proteaceae) is a potential proxy for B. tawa in Holocene fossil pollen records, and other, range‐limited species may provide community‐specific proxies. We show combining resampling with sub‐sampling is a robust method for reducing the high false positive rate associated with large co‐occurrence analyses (1000+ sites) by limiting the sample size to 100 sites.Main ConclusionsWe show that the palaeoecology of silent taxa can be studied via proxy species, allowing their past distributions to be better understood. We highlight the importance of modelling many aspects of the realised niche to understand the usefulness and limitations of the silent–proxy association. Future research should focus on testing the underlying assumptions of the silent–proxy relationship so that models built on modern data can confidently be applied to palaeoecological data.

The objective of this research was to assess the genetic diversity and phylogeography of Bombus terrestris and examine the historical events that shaped its contemporary genetic structures using the COI mitochondrial marker. Specimens of the species were collected from its distribution range alongside the Alborz Mountain range, and GenBank sequences from the Eurasian distribution range were incorporated into the dataset. The COI sequences were employed in Bayesian and Maximum Likelihood analyses to generate phylogenetic trees for the species populations and to investigate the evolutionary history of the species. Additionally, species occurrence points and climate data were utilized in Species Distribution Modeling (SDM) analyses to reconstruct the species range under past, present, and future climate conditions. The ML and BI trees yielded similar topologies, indicating extremely low genetic diversity and a homogeneous structure in the species population distribution range in Eurasia. Demographic analyses suggested that the species may have experienced a bottleneck during the last glacial maximum in Eurasia, followed by a recent expansion. The SDM analyses revealed significant fluctuations in the species range in the past and expansion under present conditions. Given the high dispersal ability of the species, the population expansion rate has surpassed the rate of developing new genetic diversity, and the estimated polymorphic sites for the species are likely relatively recent. This low level of genetic variation can also be attributed to the absence of geographical barriers and the excellent flying ability of the queen bee, leading to sustained gene flow throughout the entire continent. Despite the general correlation between larger populations and higher genetic diversity, bumblebees can expand their population size without increasing genetic diversity when residing in resourceful habitats.

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.