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We found Mycobacterium leprae, the most common etiologic agent of Hansen disease or leprosy, in tissues from 9 (18.75%) of 48 nine-banded armadillos (Dasypus novemcinctus) collected across continental Ecuador. Finding evidence of a wildlife reservoir is the first step to recognizing leprosy zoonotic transmission pathway in Ecuador or elsewhere.

Summary: A tradition exists for delineating “hardiness zones” for important plants in horticulture and agriculture. However, these zones are typically based on surviving cold winter conditions, disregarding other stressors. Factors such as the effects of summer heat, aridity, or excessive humidity have been overlooked, limiting our understanding of challenges faced by plants associated with human activities, particularly in a time of rapid global-scale climate change. Annual plants not exposed to winter cold and heat-sensitive plants encountering early summer heat waves may experience significant difficulties. Here, we establish hardiness zone criteria for four climatic dimensions: heat, cold, dryness, and moisture. We explore how this expanded concept of hardiness zones could be implemented in the context of 872 tree species in the United States, as a step toward understanding stressors that plants experience in different climates. The aim is to provide insights that may be informative for horticultural and agricultural practices.

Environmental dimensions, such as temperature, precipitation, humidity, and vegetation type, influence the activity, survival, and geographic distribution of tick species. Ticks are vectors of various pathogens that cause disease in humans, and Ixodes scapularis and Amblyomma americanum are among the tick species that transmit pathogens to humans across the central and eastern United States. Although their potential geographic distributions have been assessed broadly via ecological niche modeling, no comprehensive study has compared ecological niche signals between ticks and tick-borne pathogens. We took advantage of National Ecological Observatory Network (NEON) data for these two tick species and associated bacteria pathogens across North America. We used two novel statistical tests that consider sampling and absence data explicitly to perform these explorations: a univariate analysis based on randomization and resampling, and a permutational multivariate analysis of variance. Based on univariate analyses, in Amblyomma americanum, three pathogens (Borrelia lonestari, Ehrlichia chaffeensis, and E. ewingii) were tested; pathogens showed nonrandom distribution in at least one environmental dimension. Based on the PERMANOVA test, the null hypothesis that the environmental position and variation of pathogen-positive samples are equivalent to those of A. americanum could not be rejected for any of the pathogens, except for the pathogen E. ewingii in maximum and minimum vapor pressure and minimum temperature. For Ixodes scapularis, six pathogens (A. phagocytophilum, Babesia microti, Borrelia burgdorferi sensu lato, B. mayonii, B. miyamotoi, and Ehrlichia muris-like) were tested; only B. miyamotoi was not distinct from null expectations in all environmental dimensions, based on univariate tests. In the PERMANOVA analyses, the pathogens departed from null expectations for B. microti and B. burgdorferi sensu lato, with smaller niches in B. microti, and larger niches in B. burgdorferi sensu lato, than the vector. More generally, this study shows the value of large-scale data resources with consistent sampling methods, and known absences of key pathogens in particular samples, for answering public health questions, such as the relationship of presence and absence of pathogens in their hosts respect to environmental conditions.

The submontane forests in the Congo Basin have not been studied sufficiently in terms of floristic diversity, biomass, and geographic distribution. The region's lack of resources and rugged terrain are among the barriers to sampling biodiversity. This study focuses on two understudied submontane forest areas, Bakossi National Park (BNP) and Mt. Nlonako (MN), which form part of the continental Cameroon Mountains and are believed to have high species diversity and endemism. However, significant gaps exist in biodiversity data across the continental Cameroon Mountains. This study is the first detailed quantitative survey of forests in the BNP and MN through permanent plot sampling. The results of this study can be used to guide policies for managing montane ecosystems in Cameroon and enhancing conservation efforts. The study also provides an opportunity for long-term monitoring of forest health and REDD+ status.

Background Optimizing access to high-quality scientific journals has become an important priority for academic departments, including the ability to read the scientific literature and the ability to afford to publish papers in those journals. In this contribution, we assess the question of whether institutional investment in scientific journals aligns with the journals where researchers send their papers for publication, and where they serve as unpaid reviewers and editors. Methods We assembled a unique suite of information about the publishing habits of our Department of Ecology and Evolutionary Biology, including summaries of 3,540 journal publications by 35 faculty members. These data include economic costs of journals to institutions and to authors, benefits to authors in terms of journal prestige and citation rates, and considerations of ease of reading access for individuals both inside and outside the university. This dataset included data on institutional costs, including subscription pricing (rarely visible to scholars), and “investment” by scholars in supporting journals, such as time spent as editors and reviewers. Results Our results highlighted the complex set of relationships between these factors, and showed that institutional costs often do not match well with payoffs in terms of benefits to researchers (e.g., citation rate, prestige of journal, ease of access). Overall, we advocate for greater cost-benefit transparency to help compare different journals and different journal business models; such transparency would help both researchers and their institutions in investing wisely the limited resources available to academics.

Araucaria angustifolia – ‘araucaria’– is a critically endangered conifer tree of great ecological, economic, and cultural importance occurring in subtropical forests and grasslands in southern Brazil. Using ecological niche modeling we estimated its distribution in the present and future scenarios (2050), assessed sources of uncertainties, evaluated the effectiveness of protected areas, and indicated suitable sites for restoration efforts to maintain viable populations. For this, we generated and compared 11,484 models with different combinations of variables and parameters. The final model included variables related to climate, soil, and topography. The potential distribution of araucaria was estimated to be 489.8 × 10 3km2, with a predicted reduction in 2050 from 45% to 56% (SSP245 and SSP585, respectively). The reduction within remaining habitats is estimated to range from 32% to 44%. Combined, current habitat availability and climate change accounted for a reduction from 70% to 75% of the potential distribution. Only 5% of the current potential distribution is within protected areas, which may be reduced to 1%. In addition, 40% of suitable sites in remaining habitats are not protected, which may reduce to 24–28% in 2050. Restoration efforts may contribute to an increase of 42–45% of the remaining population. We show that estimates of araucaria distribution in the future are variable and dependent on climate projections and scenarios, however, the potential distribution in natural grasslands may remain stable. Coordinated actions involving the protection and restoration of habitats associated with population growth are urgent to improve the persistence of araucaria in the future.

We examined armadillos from museum collections in the United States using molecular assays to detect leprosy-causing bacilli. We found Mycobacterium leprae bacilli in samples from the United States, Bolivia, and Paraguay; prevalence was 14.8% in nine-banded armadillos. US isolates belonged to subtype 3I-2, suggesting long-term circulation of this genotype.

Most ecological studies are by necessity cast on rather short timescales, such that documenting change in phenomena that occur slowly (e.g., over decades to centuries) is quite difficult. Here, we explore variation in and covariation of two sources of information to address rates of treeline change on one mountain face in the central Rocky Mountains: repeat photography and remote sensing. The repeat photography work involved assembling and quantitatively comparing four photographs that span 148 years. The remote sensing analyses covered a shorter period (35 years) but provided quantitative measurements and fuller landscape coverage. The repeat photography results demonstrated pronounced elevational advance of erect forest, at a rate 1.8 m/year during 2004–2020. The remote sensing analyses reflected similar processes but also reflected considerable infilling of tree cover near treeline; consequent increases in Normalized Difference Vegetation Index (NDVI) values in the upper forested areas may thus indicate treeline advance or changes in forest characteristics below treeline. Overall, these results document (1) acceleration in rise of treeline at this site in recent decades and (2) elevational compression of vegetation zones above treeline, with important implications for conservation of nonforested alpine ecosystems. Integrating multiple streams of evidence offers complementary views and insights in analyzing this phenomenon.

Insect pest invasions cause significant damage to crop yields, and the resultant economic losses are truly alarming. Climate change and trade liberalization have opened new ways of pest invasions. Given the consumer preference towards organic agricultural products and environment-friendly nature of natural pest control strategies, biological control is considered to be one of the potential options for managing invasive insect pests. Drosophila suzukii (Drosophilidae) is an extremely damaging fruit pest, demanding development of effective and sustainable biological control strategies. In this study, we assessed the potential of the parasitoid Leptopilina japonica (Figitidae) as a biocontrol agent for D. suzukii using ecological niche modeling approaches. We developed global-scale models for both pest and parasitoid to identify four components necessary to derive a niche based, target oriented prioritization approach to plan biological control programs for D. suzukii: (i) potential distribution of pest D. suzukii, (ii) potential distribution of parasitoid L. japonica, (iii) the degree of overlap in potential distributions of pest and parasitoid, and (iv) biocontrol potential of this system for each country. Overlapping suitable areas of pest and parasitoid were identified at two different thresholds and at the most desirable threshold (E = 5%), potential for L. japonica mediated biocontrol management existed in 125 countries covering 1.87 × 107 km2, and at the maximum permitted threshold (E = 10%), land coverage was reduced to 1.44 × 107 km2 in 121 countries. Fly pest distributional information as a predictor variable was not found to be improving parasitoid model performance, and globally, only in half of the countries, >50% biocontrol coverage was estimated. We therefore suggest that niche specificities of both pest and parasitoid must be included in site-specific release planning of L. japonica for effective biocontrol management aimed at D. suzukii. This study can be extended to design cost-effective pre-assessment strategies for implementing any biological control management program.

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.

Abstract Background The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. In early days of the pandemic, neither vaccines nor therapeutic drugs were available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. Methods We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). Results This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. Conclusions To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.

Abstract Many endangered species are distributed in narrow geographic ranges, often at severe risk of extinction. Ecological niche modeling can be used to identify suitable areas, which can be surveyed to detect additional populations. We combined remotely sensed data (Normalized Difference Vegetation Index) and field sampling to increase distributional knowledge of the species and improve understanding of its conservation status. We identified numerous new sites for a rare plant (Dianthus pseudocrinitus) and tested model‐based predictions of its full geographic distribution. Using only eight occurrences, we developed initial models, which guided detailed field sampling: 11 new sites were identified, but models were unable to predict better than random expectations. A second round of models did show significant predictive ability and pointed to a broader geographic and environmental distribution of the species. As a consequence, the conservation status of the species should be upgraded from Critically Endangered to Endangered. We identified six protected areas (Golestan, Ghorkhoud, Miandasht, Salouk, Sarani, Sarigol) that likely hold populations of this species; one area adjacent to Ghorkhoud Protected Area, if protected, would protect the best‐known populations of the species. Our results highlight the importance of combining modeling with field sampling in characterizing distributions of rare and endangered species.

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.

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.

Amblyomma maculatum (Gulf Coast tick), and Dermacentor andersoni (Rocky Mountain wood tick) are two North American ticks that transmit spotted fevers associated Rickettsia. Amblyomma maculatum transmits Rickettsia parkeri and Francisella tularensis, while D. andersoni transmits R. rickettsii, Anaplasma marginale, Coltivirus (Colorado tick fever virus), and F. tularensis. Increases in temperature causes mild winters and more extreme dry periods during summers, which will affect tick populations in unknown ways. Here, we used ecological niche modeling (ENM) to assess the potential geographic distributions of these two medically important vector species in North America under current condition and then transfer those models to the future under different future climate scenarios with special interest in highlighting new potential expansion areas. Current model predictions for A. maculatum showed suitable areas across the southern and Midwest United States, and east coast, western and southern Mexico. For D. andersoni, our models showed broad suitable areas across northwestern United States. New potential for range expansions was anticipated for both tick species northward in response to climate change, extending across the Midwest and New England for A. maculatum, and still farther north into Canada for D. andersoni.

Abstract Reconstructing ecological niche evolution can provide insight into the biogeography and diversification of evolving lineages. However, comparative phylogenetic methods may infer the history of ecological niche evolution inaccurately because (a) species' niches are often poorly characterized; and (b) phylogenetic comparative methods rely on niche summary statistics rather than full estimates of species' environmental tolerances. Here, we propose a new framework for coding ecological niches and reconstructing their evolution that explicitly acknowledges and incorporates the uncertainty introduced by incomplete niche characterization. Then, we modify existing ancestral state inference methods to leverage full estimates of environmental tolerances. We provide a worked empirical example of our method, investigating ecological niche evolution in the New World orioles (Aves: Passeriformes: Icterus spp.). Temperature and precipitation tolerances were generally broad and conserved among orioles, with niche reduction and specialization limited to a few terminal branches. Tools for performing these reconstructions are available in a new R package called nichevol.

Abstract Climate change ranks among the most important issues globally, affecting geographic distributions of vectors and pathogens, and inducing losses in livestock production among many other damaging effects. We characterized the potential geographic distribution of the ticks Rhipicephalus (Boophilus) microplus, an important vector of babesiosis and anaplasmosis globally. We evaluated potential geographic shifts in suitability patterns for this species in two periods (2050 and 2070) and under two emissions scenarios (RCPs 4.5 and 8.5). Our results anticipate increases in suitability worldwide, particularly in the highest production areas for cattle. The Indo-Malayan region resulted in the highest cattle exposure under both climate change projections (2050), with increases in suitability of > 30%. This study illustrates how ecological niche modeling can be used to explore probable effects of climate change on disease vectors, and the possible consequences on economic dimensions.

Epidemiology is one of many fields that use ecological niche modeling to assess potential distributions or potential range expansions of species. When such models are transferred in space and time, it is important to understand sources and location of uncertainty in their predictions. Here, we used the tick species Rhipicephalus sanguineus sensu lato (distributed in different areas around the world) as an example; for the first time, we characterized its global geographic distribution using ecological niche modeling, and explore the uncertainty involved in transferring models in space and time. We assessed uncertainties based on risks of strict extrapolation and amounts and patterns of variation in our predictions. We integrated occurrence records and climate data to calibrate models for 5 world regions, and to project them to 11 general circulation models (GCMs) and two representative concentration pathway emissions scenarios (RCPs) for 2050. Models created in different calibration areas showed high agreement of suitable areas among model predictions from the eastern United States, southern Mexico, South America, Europe, North Africa, sub-Saharan countries, Asia, and Australia. The global potential distributions of R. sanguineus sensulato were very similar between the two RCPs, but GCMs, model replicates, and model parametrizations contributed importantly to the overall variation detected. Patterns of uncertainty (strict extrapolation areas and variation) in our model predictions depended on the calibration area, and underlined the important implications of not considering variability and extrapolation risk in interpretations of ecological niche model projections.

The COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020–2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear and weighted linear regression analyses. We also assembled 72 variables assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several variables seemed useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. Overall, GHT-based surveillance showed little applicability in the studied countries. GHT for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.

Studies of biodiversity dynamics have been cast on either long (systematics) or short (ecology) time scales, leaving a gap in coverage for moderate time scales of decades to centuries. Large-scale biodiversity information resources now available offer opportunities to fill this gap for many parts of the world via detailed, quantitative comparisons of assemblage composition, particularly for regions without rich time series datasets. We explore the possibility that such changes in avifaunas across the United States and Canada before and after the first three decades of marked global change (i.e., prior to 1980 versus after 2010) can be reconstructed and characterized from existing primary biodiversity data. As an illustration of the potential of this methodology for sites even in regions not as well sampled as the United States and Canada, we also explored changes at a single site in Mexico (Chichén-Itzá). We analyzed two large-scale datasets: one summarizing bird records in the United States and Canada before 1980, and one for the same region after 2010. We used probabilistic inventory completeness analyses to identify sites that have avifaunas that have likely been inventoried more or less completely. We prepared detailed comparisons between the two time periods to analyze species showing distributional changes over the time period analyzed. We identified 139 sites on a 0.05° grid that were demonstrably well-inventoried before 1980 in the United States and Canada, of which 108 were also well-inventoried after 2010. Comparing presence/absence patterns between the two time periods for 601 bird species, we found significant spatial autocorrelation in overall avifaunal turnover (species gained and lost), but not in numbers of species lost. We noted potential northward retractions of ranges of several species with high-latitude (boreal) distributions, while other species showed dominant patterns of population loss, either rangewide (e.g., Tympanuchus cupido) or regionally (e.g., Thryomanes bewickii). We developed linear models to explore a suite of potential drivers of species loss at relatively fine-grained resolutions (

Phytophthora cinnamomi is among the most destructive plant pathogens that affect many host plants in different ecosystems. Economically important hosts of this pathogen include avocado (Persea americana), and this oomycete may cause large-scale destructive epidemics. This study analyzed the potential geographic distribution of P. cinnamomi (pathogen) and avocado (host), and distribution of the pathogen in relation to multiple hosts in the Americas. Niche overlap between hosts and pathogen were also evaluated, using a multistep process of ecological niche modelling and the MaxEnt algorithm. Niche similarity among pathogen populations in different hosts and related niche similarity were also examined. As a complement, a tool was designed to visualize the risk of this pathogen in avocado. Results showed that the pathogen was randomly distributed in the avocado niche environmental space, but the niche of the pathogenwas narrower than that of its principal host. The niche of the pathogen was largely a function of the host niches. Areas with potential to grow avocado could present low risk of P. cinnamomi, but given the invasiveness of this pathogen, they may be affected in the future.

Abstract Simple, single‐species approaches in distributional ecology leave open questions of possible roles of biotic interactions. We sought to understand impacts of climate change on current and future potential distributions of Olea europaea sensu lato (host) and Bactrocera oleae (parasite) in Europe, the Middle East, and Africa (EMEA), taking into account potential interspecific interactions. We used ecological niche modeling approaches: For biotic interactions, an initial host model was incorporated into the calibration of the parasite model and an initial parasite model was incorporated into calibration of the host model, as host and parasite populations may respond to the presence of the other species. Absence of olive fruits could reduce the flight ability of the fly parasite, whereas presence of the parasite may make a site less suitable for the olives. The host model was improved by inclusion of the parasite information; the converse was not true for the parasite model. Our results anticipated a significant reduction in the potential distribution of Olea europeae in Africa and Europe under future climate scenarios; in contrast, future climates appear more broadly favorable for the parasite species, which poses new and increasing risks for olive cultivation in the Mediterranean region.

The field of biodiversity informatics is in a massive, “grow-out” phase of creating and enabling large-scale biodiversity data resources. Because perhaps 90% of existing biodiversity data nonetheless remains unavailable for science and policy applications, the question arises as to how these existing and available data records can be mobilized most efficiently and effectively. This situation led to our analysis of several large-scale biodiversity datasets regarding birds and plants, detecting information gaps and documenting data “leakage” or attrition, in terms of data on taxon, time, and place, in each data record. We documented significant data leakage in each data dimension in each dataset. That is, significant numbers of data records are lacking crucial information in terms of taxon, time, and/or place; information on place was consistently the least complete, such that geographic referencing presently represents the most significant factor in degradation of usability of information from biodiversity information resources. Although the full process of digital capture, quality control, and enrichment is important to developing a complete digital record of existing biodiversity information, payoffs in terms of immediate data usability will be greatest with attention paid to the georeferencing challenge.

A study published recently argued against a relationship between population density and position in geographic and environmental spaces. We found a number of methodological problems underlying the analysis. We discuss the main issues and conclude that these problems hinder a robust conclusion about the original question.

Abstract Background Hantavirus pulmonary syndrome (HPS) is an infection endemic in Chile and Argentina, caused by Andes hantavirus (ANDV). The rodent Oligoryzomys longicaudatus is suggested as the main reservoir, although several other species of Sigmodontinae are known hosts of ANDV. Here, we explore potential ANDV transmission risk to humans in southern South America, based on eco-epidemiological associations among: six rodent host species, seropositive rodents, and human HPS cases. Methods We used ecological niche modeling and macroecological approaches to determine potential geographic distributions and assess environmental similarity among rodents and human HPS cases. Results Highest numbers of rodent species (five) were in Chile between 35° and 41°S latitude. Background similarity tests showed niche similarity in 14 of the 56 possible comparisons: similarity between human HPS cases and the background of all species and seropositive rodents was supported (except for Abrothrix sanborni). Of interest among the results is the likely role of O. longicaudatus, Loxodontomys micropus, Abrothrix olivaceus, and Abrothrix longipilis in HPS transmission to humans. Conclusions Our results support a role of rodent species’ distributions as a risk factor for human HPS at coarse scales, and suggest that the role of the main reservoir (O. longicaudatus) may be supported by the broader rodent host community in some areas.

Abstract Understanding biological invasions is crucial for their control and prevention. Specially, establishing whether invasive species operate within the constraint of conservative ecological niches, or if niche shifts occur at all commonly as part of the invasion process, is indispensable to identifying and anticipating potential areas of invasion. Ecological niche modeling (ENM) has been used to address such questions, but improvements and debate in study design, model evaluation, and methods are still needed to mature this field. We reanalyze data for Gray Squirrels (Sciurus carolinensis), native to North America, but invasive in Europe. Our main finding was that, when the analysis extent is established carefully based on analogous sets of environmental conditions, all evidence of niche shifts disappears, suggesting that previous reports of niche shifts for this species are artifacts of methods and interpretation, rather than biological reality. Niche conservatism should be tested only within appropriate, similar, environmental spaces that are accessible to both species or populations being compared, thus avoiding model extrapolation related to model transfers. Testing for environmental similarity between native and invaded areas is critical to identifying niche shifts during species invasion robustly, but also in applications of ENM to understanding temporal dimensions of niche dynamics.

Premise The field of biodiversity informatics has developed rapidly in recent years, with broad availability of large‐scale information resources. However, online biodiversity information is biased spatially as a result of slow and uneven capture and digitization of existing data resources. The West African Plants Initiative approach to data capture is a prototype of a novel solution to the problems of the traditional model, in which the institutional “owner” of the specimens is responsible for digital capture of associated data. Methods We developed customized workflows for data capture in formats directly and permanently useful to the “owner” herbarium, and digitized significant numbers of new biodiversity records, adding to the information available for the plants of the region. Results In all, 190,953 records of species in 1965 genera and 331 families were captured by mid‐2018. These data records covered 16 West African countries, with most of the records (10,000–99,999) from Côte d'Ivoire, Ghana, Togo, Nigeria, and Cameroon, and the fewest data records from Mauritania (

Research highlights: This study is one of few detailed analyses of plant diversity and vegetation patterns in African dry forests. We established permanent plots to characterize plant diversity, above-ground biomass, and vegetation patterns in a tropical dry forest in Kimbi-Fungom National Park, Cameroon. Our results contribute to long-term monitoring, predictions, and management of dry forest ecosystems, which are often vulnerable to anthropogenic pressures. Background and objectives: Considerable consensus exists regarding the importance of dry forests in species diversity and carbon storage; however, the relationship between dry forest tree species composition, species richness, and carbon stock is not well established. Also, simple baseline data on plant diversity are scarce for many dry forest ecosystems. This study seeks to characterize floristic diversity, vegetation patterns, and tree diversity in permanent plots in a tropical dry forest in Northwestern Cameroon (Kimbi-Fungom National Park) for the first time. Materials and methods: We studied associations between above-ground biomass and species composition, and how different vegetation types vary in terms of species composition, diversity, and carbon storage, in a dry forest in Kimbi-Fungom National Park, Cameroon. Vegetation was inventoried in 17 permanent 1-ha plots. Allometric equations were used to calculate above-ground biomass and carbon. Results: We found an average of 269.8 tree stems ha−1 and 43.1 species ha−1. Five vegetation types: semi-deciduous, gallery, mixed vegetation, secondary and the grassland/woody savanna forest were classified using TWINSPAN analysis. The five vegetation types had an average above-ground biomass of 149.2 t ha−1 and 74.6 tC ha−1 of carbon in the 17 ha analyzed. Canonical correspondence analysis (CCA) showed the importance of semi-deciduous forest over grassland/woody savanna forest. Conclusions: This study demonstrated that the forest of the Kimbi-Fungom National Park is poor in plant diversity, biomass, and carbon, highlighting the need to implement efficient management practices. Fine-scale inventory data of species obtained in this study could be useful in developing predictive models for efficient management of tropical dry forests.

Background Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way. Results This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric. Discussion Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.