Showing papers by "A. Townsend Peterson published in 2021"

Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network.

Abstract: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic reveals a major gap in global biosecurity infrastructure: a lack of publicly available biological samples representative across space, time, and taxonomic diversity. The shortfall, in this case for vertebrates, prevents accurate and rapid identification and monitoring of emerging pathogens and their reservoir host(s) and precludes extended investigation of ecological, evolutionary, and environmental associations that lead to human infection or spillover. Natural history museum biorepositories form the backbone of a critically needed, decentralized, global network for zoonotic pathogen surveillance, yet this infrastructure remains marginally developed, underutilized, underfunded, and disconnected from public health initiatives. Proactive detection and mitigation for emerging infectious diseases (EIDs) requires expanded biodiversity infrastructure and training (particularly in biodiverse and lower income countries) and new communication pipelines that connect biorepositories and biomedical communities. To this end, we highlight a novel adaptation of Project ECHO's virtual community of practice model: Museums and Emerging Pathogens in the Americas (MEPA). MEPA is a virtual network aimed at fostering communication, coordination, and collaborative problem-solving among pathogen researchers, public health officials, and biorepositories in the Americas. MEPA now acts as a model of effective international, interdisciplinary collaboration that can and should be replicated in other biodiversity hotspots. We encourage deposition of wildlife specimens and associated data with public biorepositories, regardless of original collection purpose, and urge biorepositories to embrace new specimen sources, types, and uses to maximize strategic growth and utility for EID research. Taxonomically, geographically, and temporally deep biorepository archives serve as the foundation of a proactive and increasingly predictive approach to zoonotic spillover, risk assessment, and threat mitigation.

Geographic potential of the world’s largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America

Abstract: The Asian giant hornet (AGH, Vespa mandarinia) is the world's largest hornet, occurring naturally in the Indomalayan region, where it is a voracious predator of pollinating insects including honey bees. In September 2019, a nest of Asian giant hornets was detected outside of Vancouver, British Columbia; multiple individuals were detected in British Columbia and Washington state in 2020; and another nest was found and eradicated in Washington state in November 2020, indicating that the AGH may have successfully wintered in North America. Because hornets tend to spread rapidly and become pests, reliable estimates of the potential invasive range of V. mandarinia in North America are needed to assess likely human and economic impacts, and to guide future eradication attempts. Here, we assess climatic suitability for AGH in North America, and suggest that, without control, this species could establish populations across the Pacific Northwest and much of eastern North America. Predicted suitable areas for AGH in North America overlap broadly with areas where honey production is highest, as well as with species-rich areas for native bumble bees and stingless bees of the genus Melipona in Mexico, highlighting the economic and environmental necessity of controlling this nascent invasion.

Rivers, not refugia, drove diversification in arboreal, sub-Saharan African snakes

Abstract: The relative roles of rivers versus refugia in shaping the high levels of species diversity in tropical rainforests have been widely debated for decades. Only recently has it become possible to take an integrative approach to test predictions derived from these hypotheses using genomic sequencing and paleo-species distribution modeling. Herein, we tested the predictions of the classic river, refuge, and river-refuge hypotheses on diversification in the arboreal sub-Saharan African snake genus Toxicodryas. We used dated phylogeographic inferences, population clustering analyses, demographic model selection, and paleo-distribution modeling to conduct a phylogenomic and historical demographic analysis of this genus. Our results revealed significant population genetic structure within both Toxicodryas species, corresponding geographically to river barriers and divergence times from the mid-Miocene to Pliocene. Our demographic analyses supported the interpretation that rivers are indications of strong barriers to gene flow among populations since their divergence. Additionally, we found no support for a major contraction of suitable habitat during the last glacial maximum, allowing us to reject both the refuge and river-refuge hypotheses in favor of the river-barrier hypothesis. Based on conservative interpretations of our species delimitation analyses with the Sanger and ddRAD data sets, two new cryptic species are identified from east-central Africa. This study highlights the complexity of diversification dynamics in the African tropics and the advantages of integrative approaches to studying speciation in tropical regions.

Genome-environment association methods comparison supports omnigenic adaptation to ecological niche in malaria vector mosquitoes.

Abstract: The concept of a fundamental ecological niche is central to questions of geographic distribution, population demography, species conservation, and evolutionary potential. However, robust inference of genomic regions associated with evolutionary adaptation to particular environmental conditions remains difficult due to the myriad of potential confounding processes that can generate heterogeneous patterns of variation across the genome. Here, we interrogate the potential role of genome environment association (GEA) testing as an initial step in building an understanding of the genetic basis of ecological niche. We leverage publicly available genomic data from the Anopheles gambiae 1000 Genomes (Ag1000g) Consortium to test the ability of multiple analytically unique GEA methods to handle confounding patterns of genetic variation, control false positive rates, and discern associations with broadly relevant climate variables from random allele frequency patterns throughout the genome. We found evidence supporting the ability of commonly implemented GEA methods to account for confounding patterns of spatial and genetic variation, and control false positive rates. However, we fail to find evidence supporting the ability of GEA tests to reject signals of adaptation to randomly simulated environmental variables, indicating that discerning between true signals of genome environment adaptation and genome environment correlations resulting from alternative evolutionary processes, remains challenging. Because signals of environmental adaptation are so diffuse and confounded throughout the genome, we argue that genomic adaptation to ecological niche is likely best understood under an omnigenic model wherein highly interconnected, genome-wide gene regulatory networks shape genomic adaptation to key environmental conditions.

Deep learning improves acoustic biodiversity monitoring and new candidate forest frog species identification (genus Platymantis) in the Philippines

Abstract: One significant challenge to biodiversity assessment and conservation is persistent gaps in species diversity knowledge in Earth’s most biodiverse areas. Monitoring devices that utilize species-specific advertisement calls show promise in overcoming challenges associated with lagging frog species discovery rates. However, these devices generate data at paces faster than it can be analyzed. As such, automated platforms capable of efficient data processing and accurate species-level identification are at a premium. In addressing this gap, we used TensorFlow Inception v3 to design a robust, automated species identification system for 41 Philippine frog species (genus Platymantis), utilizing single-note audio spectrograms. With this model, we explored two concepts: (1) performance of our deep-learning model in discriminating closely-related frog species based on images representing advertisement call notes, and (2) the potential of this platform to accelerate new species discovery. TensorFlow identified species with a ~ 94% overall correct identification rate. Incorporating distributional data increased the overall identification rate to ~ 99%. In applying TensorFlow to a dataset that included undescribed species in addition to known species, our model was able to differentiate undescribed species through variation in “certainty” rate; the overall certainty rate for undescribed species was 65.5% versus 83.6% for described species. This indicates that, in addition to discriminating recognized frog species, our model has the potential to flag possible new species. As such, this work represents a proof-of-concept for automated, accelerated detection of novel species using acoustic mate-recognition signals, that can be applied to other groups characterized by vibrational cues, seismic signals, and vibrational mate-recognition.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species.

Abstract: Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

Climatic suitability of the eastern paralysis tick, Ixodes holocyclus, and its likely geographic distribution in the year 2050.

Abstract: The eastern paralysis tick, Ixodes holocyclus is one of two ticks that cause potentially fatal tick paralysis in Australia, and yet information on the full extent of its present or potential future spatial distribution is not known. Occurrence data for this tick species collected over the past two decades, and gridded environmental variables at 1 km2 resolution representing climate conditions, were used to derive correlative ecological niche models to predict the current and future potential distribution. Several hundreds of candidate models were constructed with varying combinations of model parameters, and the best-fitting model was chosen based on statistical significance, omission rate, and Akaike Information Criterion (AICc). The best-fitting model matches the currently known distribution but also extends through most of the coastal areas in the south, and up to the Kimbolton peninsula in Western Australia in the north. Highly suitable areas are present around south of Perth, extending towards Albany, Western Australia. Most areas in Tasmania, where the species is not currently present, are also highly suitable. Future spatial distribution of this tick in the year 2050 indicates moderate increase in climatic suitability from the present-day prediction but noticeably also moderate to low loss of climatically suitable areas elsewhere.

Modelling potential Pleistocene habitat corridors between Afromontane forest regions

Abstract: The unusually high floral and faunal similarity between the different regions of the Afromontane archipelago has been noted by biogeographers since the late 1800s. A possible explanation for this similarity is the spread of montane habitat into the intervening lowlands during the glacial periods of the Pleistocene, allowing biotic exchange between mountain ranges. In this study, we sought to infer the existence and most likely positions of these potential habitat corridors. We focused on sixteen Afromontane endemic tree, shrub, and bird species in the Cameroon Volcanic Line, East African Rift and Great Escarpment. Species were chosen based on distribution above 1200–1500 m in at least two of the major Afromontane regions. Ecological niche models were developed for each species in the present and projected to the mid-Holocene and the last glacial maximum (LGM). Models were thresholded to create binary maps of presence/absence and then summed across taxa to estimate potential LGM and mid-Holocene distributions. We found widespread climatic suitability for our montane taxa throughout the lowlands of Central Africa during the LGM, connecting all regions of the Afromontane archipelago except the Ethiopian Highlands and the Dahomey Gap. During the mid-Holocene, we noted more limited climatic suitability for fewer species in lowland areas. Although we set out to test predictions derived from alternatively hypothesized corridors, we instead found widespread climatic suitability connecting Afromontane regions across the entire Congo Basin for all species.

Potential geographic distribution of Ixodes cookei, the vector of Powassan virus

Abstract: Ixodes cookei Packard, the groundhog tick or woodchuck tick, is the main known vector of Powassan virus (POWV) disease in North America and an ectoparasite that infests diverse small- and mid-size mammals for blood meals to complete its life stages. Since I. cookei spends much of its life cycle off the host and needs hosts for a blood meal in order to pass to the next life stage, it is susceptible to changes in environmental conditions. We used a maximum-entropy approach to ecological niche modeling that incorporates detailed model-selection routes to link occurrence data to climatic variables to assess the potential geographic distribution of I. cookei under current and likely future climate conditions. Our models identified suitable areas in the eastern United States, from Tennessee and North Carolina north to southern Canada, including Nova Scotia, New Brunswick, eastern Newfoundland and Labrador, southern Quebec, and Ontario; suitable areas were also in western states, including Washington and Oregon and restricted areas of northern Idaho, northwestern Montana, and adjacent British Columbia, in Canada. This study produces the first maps of the potential geographic distribution of I. cookei. Documented POWV cases overlapped with suitable areas in the northeastern states; however, the presence of this disease in areas classified by our models as not suitable by our models but with POWV cases (Minnesota and North Dakota) requires more study.

Burrowing habit in Smilisca frogs as an adaptive response to ecological niche constraints in seasonally dry environments

Abstract: As environmental conditions change over time, some species can follow the spatial footprint of their ecological niches or can adapt physiologically to the new conditions; modifying behavior can offer an alternative means of adapting to novel environments. The burrowing habit allows organisms to avoid adverse climatic conditions during part of the year by remaining inside burrows. Smilisca fodiens and S. dentata are two burrowing hylid frogs that inhabit areas beyond the northernmost distributional limits of the other six arboreal species of their genus, and indeed beyond of most American hylids. In this study, we tested whether burrowing habit allows these species to adapt to drier conditions while conserving the climatic niche of the arboreal species. We compared the annual niches of the arboreal species to those of the burrowing species under two assumptions: true seasonal niches and full annual niches. Through ecological niche similarity tests, we performed 24 comparisons in both geographic and environmental spaces. In geographic space, when considering burrowing annual niches, only five of 24 tests indicated similarity, yet as regards seasonal niche, 18 of 24 tests indicated similarity. In environmental space, all tests failed to reject null hypotheses. The analyses showed clearly that burrowing and arboreal species were closer in environmental space when seasonal niches of the burrowing species were used, rather than annual niches. That is, climatic conditions in seasonal niches of burrowing species resemble the annual niches of arboreal species, supporting the proposition that reduction of activity to certain periods of the year is a strategy in burrowing species to conserve their tropical niches while living in dry regions.