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Understanding the source of non-native introduced populations is crucial for forecasting geographic invasion potential and understanding the ecological consequences of potential establishment. Here we use genomics to identify the source populations and invasion dynamics of two non-native introduced populations from the iconic avian lineage of ‘great speciators’ known as white-eyes (genus Zosterops). We established confidently for the first time that introduced Zosterops populations in Hawaii and southern California are completely unrelated and derived from independent introductions of the species Z. japonicus and Z. simplex, respectively. We used descriptive population genetic statistics to identify a reduction in genetic diversity and increase in private alleles in the southern California population supporting a recent, potentially ongoing, genetic bottleneck in this population. In contrast, the introduced population in Hawaii showed no such characteristics, likely due to a larger founding population size and repeated introductions in this intentionally introduced population. Ecological niche modeling indicated that there is little environmentally suitable habitat for Z. simplex across the continent of North America, suggesting limited invasion potential, assuming niche conservatism. Yet, portions of the introduced Z. simplex population have already surpassed areas projected as suitable, likely because the urbanized environment of southern California offers biotic resources and microhabitats not captured by our model. Because Z. simplex appears to have overcome both the ‘invasion paradox’ of low founding genetic diversity and relatively unfamiliar environmental conditions in southern California, we suggest that this population may continue expanding beyond our environmental niche model projections in other temperate, urban regions.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

Hansen disease (leprosy) is an ancient pathology caused by 2 slow-growing intracellular bacilli, Mycobacterium leprae and M. lepromatosis (1). Both pathogens have the ability to damage peripheral nerves of hosts, [...]

Ecological niche models and species distribution models (ENM and SDM, respectively) are tools that have seen massive use and considerable improvement during the last twenty years. The choice of calibration areas for such models has strong effects on model outcomes and model interpretation, as well as on model transfer to distinct environmental settings. However, approaches to selecting these areas remain simple and/or unlinked to biological concepts. Such models should be calibrated within areas that the species of interest has explored throughout its recent history, the accessible area (M). In this paper, we provide a simulation approach for estimating a species’ M considering processes of dispersal, colonization, and extinction in constant current climate or glacial-interglacial climate change frameworks, implemented within a new R package we developed called grinnell. Using the avian genus Aphelocoma, we explored different parameterizations of our simulation, and compared them to current approaches for M selection, in terms of model performance and risk of extrapolation using the algorithm Maxent and mobility-oriented parity analyses. Model calibration exercises from all approaches resulted in at least one model meeting optimal performance criteria for each species; however, we noted high variability among taxa and M selection methods. More importantly, M hypotheses derived directly from simulations of key biological processes, rather than being based on simple proxies of those processes, and as such are better suited to erecting biologically appropriate contrasts in model calibration, and to characterizing the potential for model extrapolation more rigorously. Major factors in our simulations were environmental layer resolution, dispersal kernel characteristics, and the inclusion of a changing framework of climatic conditions. This contribution represents the first simulation-based method for selecting calibration areas for ENM and SDM, offering a quantitative approach to estimate the accessible area of a species while considering its dispersal ability, along with patterns of change in environmental suitability across space and time.

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.

Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations.

BackgroundEmerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.MethodsTo explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014-2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners.ResultsParticipant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy.ConclusionWe conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.