Robert Traub

Given the heightened vulnerability of deserts to climate change, this study aims to provide a comprehensive analysis of avian species diversity across ten global deserts to identify distinct diversity gradients and relatedness. Identify the difference from global patterns in avian migratory proportions and the underlying drivers for assessing the vulnerability and resilience of these desert ecosystems. Crowd-sourced avian diversity data of 2374 species from GBIF.org was used as a key analytical tool to study the diversity gradient across the ten major deserts. The variance in correlation patterns between avian ecological and behavioral traits across deserts were analyzed employing data of 1930 common avian species from AVONET. The analysis included comparisons of bird diversity, migratory patterns and trophic niches between Tropic of Cancer (TCan) and Tropic of Capricorn (TCap) deserts. Significant variations in bird diversity among the deserts were found. Deserts near the TCan exhibited higher bird diversity than in TCap deserts. TCan deserts had a higher prevalence of migratory species, facilitated by a broader niche breadth among sedentary species, which reduces niche competition and allows the influx of migratory invertivores. Proportion of migratory birds is higher in TCan deserts due to wider trophic niche but is significantly lower than the global average for the same latitude range. The findings highlight the need for targeted conservation strategies to protect avian diversity in the TCan deserts and mitigate extinction risks in TCap deserts, ensuring the resilience of these critical ecosystems.

Globally, vector‐borne diseases are increasing in distribution and frequency, affecting humans, domestic animals, and wildlife. Science‐based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling. We assessed the distribution of the soft tick subspecies Ornithodoros turicata americanus. This tick species is a potential vector of African swine fever virus (ASFV), a pathogen responsible for an ongoing global epizootic that threatens agroindustry worldwide. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning. Our input for the model consisted of systematically collected detection data from 591 sampled field sites and 12 historical species records, as well as four variables describing climatic and soil characteristics. We found that a combination of climatic variables describing seasonality and temperature extremes, along with the amount of sand in the soil, determined the predicted intensity of occurrence of this tick species. When projected in geographic space, this distribution model predicted 62% of Florida as suitable habitat for this tick species. The ISDM presented a higher TSS and AUC than the Maxent conventional model, while sensitivity was similar between both models. Our case example shows the utility of ISDMs in disease ecology studies and highlights the broad range of geographic suitability for this important disease vector. These results provide important foundational information to inform future risk assessment work for tick‐borne relapsing fever surveillance and potential ASF introduction and maintenance in the United States.

From the largest nesting aggregation ever recorded for the genus Melissodes, we took diverse bionomic measurements of Melissodes bimaculatus (Two-spotted Longhorn Bee). Our results show a protandrous reproductive strategy occurring from July through August in New York. We observed parasitism by the kleptoparasitic bee Triepeolus simplex as well as nest-architecture modifications to ease this burden that support the selfish-herd hypothesis. In this population, we also found a proclivity for grass (Poaceae) pollen, a previously undocumented diet preference for Two-spotted Longhorn Bees. We further showed that this bee species has widespread climatically suitable habitat, with expected range expansion under future climate conditions. Altogether, our results offer novel insights into the ecology of theTwo-spotted Longhorn Bee and its gregarious nesting behavior.

Knowledge of shortfalls could modify the geographic distribution patterns and limit the actions to conserve the biodiversity, even in the taxa best known. In addition, forest fires also could modify those patterns, but the potential effects of both factors have not been tested. Our aim was to analyze the effect of the Linnean and Wallacean shortfalls in the first evaluation of wildfire impacts on 22 amphibian and 13 mammal species distributed in Guanajuato, Mexico. We evaluated those shortfalls using the non-parametric estimator Chao2 and the Qs estimator and through maps of species richness patterns. To evaluate the effects of wildfires, we produced a fire recurrence map and quantified the burned area within species distributions and in 24 Protected Natural Areas (PNA) in the state. The Linnean shortfall showed some species missing to record in Guanajuato for both taxa, while the Wallacean shortfall showed poor quality of knowledge. Fire recurrence was high within 5 PNA. The richness patterns affected by fires covered nearly 17% of the surface of Guanajuato. Improving the knowledge of biogeographical patterns could provide better tools to stakeholders to decrease the negative impact of fires within PNA.

Global warming significantly impacts amphibian populations globally, and modeling helps understand these effects. Here, we used MaxEnt and MigClim models to predict the impact of climate change on habitat suitability for Hoplobatrachus chinensis. Our results indicate that temperature is a key factor affecting H. chinensis distribution. Increasing temperatures positively correlated with habitat suitability, with suitable habitat expanding northward by 2060 while maintaining suitability in the southern parts of the range. We found a 25.18% overlap between the current potential suitable habitat of H. chinensis and agricultural wetlands. Our model indicated that H. chinensis might be able to track shifts in suitable habitats under climate change given a 15 km dispersal ability per generation. Climate change will likely expand suitable habitat for H. chinensis. Our predictions offer important guidance for the conservation of the species, especially for the integrated role of natural and agricultural wetlands such as rice paddies.

We analysed the wild bee community sampled from 1921 to 2018 at a nature preserve in southern Michigan, USA, to study long-term community shifts in a protected area. During an intensive survey in 1972 and 1973, Francis C. Evans detected 135 bee species. In the most recent intensive surveys conducted in 2017 and 2018, we recorded 90 species. Only 58 species were recorded in both sampling periods, indicating a significant shift in the bee community. We found that the bee community diversity, species richness and evenness were all lower in recent samples. Additionally, 64% of the more common species exhibited a more than 30% decline in relative abundance. Neural network analysis of species traits revealed that extirpation from the reserve was most likely for oligolectic ground-nesting bees and kleptoparasitic bees, whereas polylectic cavity-nesting bees were more likely to persist. Having longer phenological ranges also increased the chance of persistence in polylectic species. Further analysis suggests a climate response as bees in the contemporary sampling period had a more southerly overall distribution compared to the historic community. Results exhibit the utility of both long-term data and machine learning in disentangling complex indicators of bee population trajectories.

Species’ range size is a fundamental unit of analysis in biodiversity research, given its association with extinction risk and species richness. One of its most notable patterns is its positive relationship with latitude, which has been considered an ecogeographical rule called Rapoport’s rule. Despite this rule being confirmed for various taxonomic groups, its validity has been widely discussed and several taxa still lack a formal assessment. Different hypotheses have been proposed to explain their potential mechanisms, with those related to temperature and elevational being the most supported thus far. In this study, we employed two level of analyses (cross-species and assemblage) to investigate the validity of Rapoport’s rule in spiny lizards (genus Sceloporus). Additionally, we evaluated four environmental-related hypotheses (minimum temperature, temperature variability, temperature stability since the last glacial maximum, and elevation) posed to explain such pattern, contrasting our results to those patterns expected under a null model of range position. Our results provided support for Rapoport’s rule at both levels of analyses, contrasting with null expectations. Consistently, minimum temperature and elevation were the most relevant variables explaining the spatial variation in range size. At the cross-species level, our null simulations revealed that both variables deviated significantly from random expectations. Conversely, at the assemblage level, none of the variables were statistically different from the expected relationships. We discussed the implication of our findings in relation to the ecology and evolution of spiny lizards.

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.

Global warming has seriously disturbed the Earth’s ecosystems, and in this context, Asian honeybee (Apis cerana) has experienced a dramatic decline in recent decades. Here, we examined both direct and indirect effects of climate change on A. cerana through ecological niche modeling of A. cerana, and its disease pathogens (i.e., Chinese sacbrood virus and Melissococcus plutonius) and enemies (i.e., Galleria mellonella and Vespa mandarinia). Ecological niche modeling predicts that climate change will increase the potential suitability of A. cerana, but it will also cause some of the original habitat areas to become unsuitable. Outbreak risks of Chinese sacbrood disease and European Foulbrood will increase dramatically, while those of G. mellonella and V. mandarinia will decrease only slightly. Thus, climate change will produce an unfavorable situation for even maintaining some A. cerana populations in China in the future. Genetic structure analyses showed that the A. cerana population from Hainan Island had significant genetic differentiation from that of the mainland, and there was almost no gene flow between the 2, suggesting that urgent measures are needed to protect the unique genetic resources there. Through taking an integrated planning technique with the Marxan approach, we optimized conservation planning, and identified potential nature reserves (mainly in western Sichuan and southern Tibet) for conservation of A. cerana populations. Our results can provide insights into the potential impact of climate change on A. cerana, and will help to promote the conservation of the keystone honeybee in China and the long-term sustainability of its ecosystem services.