Habitat suitability and distribution patterns of Rouget's rail (Rougetius rougetii Guérin-méneville, 1843) in Ethiopia.

Geographical distribution and diversity patterns of bird species are influenced by climate change. The Rouget's rail (Rougetius rougetii) is a ground-dwelling endemic bird species distributed in Ethiopia and Eritrea. It is a near-threatened species menaced by habitat loss, one of the main causes of population declines for bird species. The increasing effects of climate change may further threaten the species' survival. So far, the spatial distribution of this species is not fully documented. With this study, we develop current potential suitable habitat and predict the future habitat shift of R. rougetii based on environmental data such as bioclimatic variables, population density, vegetation cover, and elevation using 10 algorithms. We evaluated the importance of environmental factors in shaping the bird's distribution and how it shifts under climate change scenarios. We used 182 records of R. rougetii from Ethiopia and nine bioclimatic, population density, vegetation cover, and elevation variables to run the 10 model algorithms. Among 10 algorithms, eight were selected for ensembling models according to their predictive abilities. The current suitable habitats for R. rougetii were predicted to cover an area of about 82,000 km2 despite being highly fragmented. The model suggested that temperature seasonality (bio4), elevation, and mean daily air temperatures of the driest quarter (bio9) contributed the most to delimiting suitable areas for this species. R. rougetii is sensitive to climate change associated with elevation, which leads shrinking distribution of suitable areas. The projected spatial and temporal pattern of habitat loss of R. rougetii suggests the importance of climate change mitigation and implementing long-term conservation and management strategies for this threatened endemic bird species.

Climate-driven context-dependent structure of population cycles.

Multiannual population cycles of small mammals are of interest within population biology. We propose an approach for multidimensional autoregressive (AR) time series and analyse monitoring data on grey-sided voles (Myodes rufocanus) in Japan to investigate one or possibly multiple multiannual cycles that drive population dynamics. Temperature, through modifying rodent communities, is found to be a key factor shaping population dynamics. Warmer areas are the main habitat for other rodent species resulting in low vole abundance/dominance, as opposed to higher vole dominance in colder areas-a pattern associated with the AR structure and population cycle. Vole populations in simple rodent communities exhibit an AR(2) cycle of 2-3 years. In areas with complex rodent communities, vole dynamics follows an AR(4) process and a combination of two cycles with different lengths. The AR structure varies in relatively small spatial scales, thus widening the scope of AR analyses needed. Historically, vole abundance increased in the late 1970s and decreased from the 1980s, with warm winters shown to be associated with the decline of vole abundance in the AR(4) populations. This significant association between the AR order, population dynamics, temperature and rodent community provides insights into the declining trends observed in rodent populations of the Northern Hemisphere.

Modeling habitat suitability for the lesser-known populations of endangered mountain nyala (Tragelaphus buxtoni) in the Arsi and Ahmar Mountains, Ethiopia.

Habitat suitability models have become a valuable tool for wildlife conservation and management, and are frequently used to better understand the range and habitat requirements of rare and endangered species. In this study, we employed two habitat suitability modeling techniques, namely Boosted Regression Tree (BRT) and Maximum Entropy (Maxent) models, to identify potential suitable habitats for the endangered mountain nyala (Tragelaphus buxtoni) and environmental factors affecting its distribution in the Arsi and Ahmar Mountains of Ethiopia. Presence points, used to develop our habitat suitability models, were recorded from fecal pellet counts (n = 130) encountered along 196 randomly established transects in 2015 and 2016. Predictor variables used in our models included major landcover types, Normalized Difference Vegetation Index (NDVI), greenness and wetness tasseled cap vegetation indices, elevation, and slope. Area Under the Curve model evaluations for BRT and Maxent were 0.96 and 0.95, respectively, demonstrating high performance. Both models were then ensembled into a single binary output highlighting an area of agreement. Our results suggest that 1864 km2 (9.1%) of the 20,567 km2 study area is suitable habitat for the mountain nyala with land cover types, elevation, NDVI, and slope of the terrain being the most important variables for both models. Our results highlight the extent to which habitat loss and fragmentation have disconnected mountain nyala subpopulations. Our models demonstrate the importance of further protecting suitable habitats for mountain nyala to ensure the species' conservation.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation.

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.

Yersinia pestis and Plague: some knowns and unknowns.

Since its first identification in 1894 during the third pandemic in Hong Kong, there has been significant progress of understanding the lifestyle of Yersinia pestis, the pathogen that is responsible for plague. Although we now have some understanding of the pathogen's physiology, genetics, genomics, evolution, gene regulation, pathogenesis and immunity, there are many unknown aspects of the pathogen and its disease development. Here, we focus on some of the knowns and unknowns relating to Y. pestis and plague. We notably focus on some key Y. pestis physiological and virulence traits that are important for its mammal-flea-mammal life cycle but also its emergence from the enteropathogen Yersinia pseudotuberculosis. Some aspects of the genetic diversity of Y. pestis, the distribution and ecology of plague as well as the medical countermeasures to protect our population are also provided. Lastly, we present some biosafety and biosecurity information related to Y. pestis and plague.

Association of vaccination, international travel, public health and social measures with lineage dynamics of SARS-CoV-2.

Continually emerging SARS-CoV-2 variants of concern that can evade immune defenses are driving recurrent epidemic waves of COVID-19 globally. However, the impact of measures to contain the virus and their effect on lineage diversity dynamics are poorly understood. Here, we jointly analyzed international travel, public health and social measures (PHSM), COVID-19 vaccine rollout, SARS-CoV-2 lineage diversity, and the case growth rate (GR) from March 2020 to September 2022 across 63 countries. We showed that despite worldwide vaccine rollout, PHSM are effective in mitigating epidemic waves and lineage diversity. An increase of 10,000 monthly travelers in a single country-to-country route between endemic countries corresponds to a 5.5% (95% CI: 2.9 to 8.2%) rise in local lineage diversity. After accounting for PHSM, natural immunity from previous infections, and waning immunity, we discovered a negative association between the GR of cases and adjusted vaccine coverage (AVC). We also observed a complex relationship between lineage diversity and vaccine rollout. Specifically, we found a significant negative association between lineage diversity and AVC at both low and high levels but not significant at the medium level. Our study deepens the understanding of population immunity and lineage dynamics for future pandemic preparedness and responsiveness.

Climate-driven marmot-plague dynamics in Mongolia and China.

The incidence of plague has rebounded in the Americas, Asia, and Africa alongside rapid globalization and climate change. Previous studies have shown local climate to have significant nonlinear effects on plague dynamics among rodent communities. We analyzed an 18-year database of plague, spanning 1998 to 2015, in the foci of Mongolia and China to trace the associations between marmot plague and climate factors. Our results suggested a density-dependent effect of precipitation and a geographic location-dependent effect of temperature on marmot plague. That is, a significantly positive relationship was evident between risk of plague and precipitation only when the marmot density exceeded a certain threshold. The geographical heterogeneity of the temperature effect and the contrasting slopes of influence for the Qinghai-Tibet Plateau (QTP) and other regions in the study (nQTP) were primarily related to diversity of climate and landscape types.

Greenhouse gas emissions from extractive industries in a globalized era.

The extractive industry consumes vast amounts of energy and is a major contributor to greenhouse gas (GHG) emissions. However, its climatic impacts have not yet been fully accounted for. In this study, we estimated the GHG emissions from extractive activities globally with a focus on China, and assessed the main emission drivers. In addition, we predicted the Chinese extractive industry emissions in the context of global mineral demand and cycling. As of 2020, GHG emissions from the global extractive industry had reached 7.7 billion tons of CO2 equivalents (CO2e), accounting for approximately 15.0% of the global anthropogenic GHG emissions (excluding GHG emissions from land use, land-use change, and forestry activities (LULUCF), with China being the largest emitter, accounting for 3.5% of global emissions. Extractive industry GHG emissions are projected to peak by 2030 or even earlier to achieve low-carbon peak targets. The most critical pathway for reducing GHG emissions in the extractive industry is to control emissions from coal mining. Therefore, reducing methane emissions from mining and washing coal (MWC) should be prioritized.

Coupling of crop and livestock production can reduce the agricultural GHG emission from smallholder farms.

Ensuring global food security and environmental sustainability is dependent upon the contribution of the world's hundred million smallholder farms, but the contributions of smallholder farms to global agricultural greenhouse gas (GHG) emissions have been understudied. We developed a localized agricultural life cycle assessment (LCA) database to calculate GHG emissions and made the first extensive assessment of the smallholder farms' GHG emission reduction potentials by coupling crop and livestock production (CCLP), a redesign of current practices toward sustainable agriculture in China. CCLP can reduce the GHG emission intensity by 17.67%, with its own feed and manure returning to the field as an essential path. Scenario analysis verified that greater GHG emission reduction (28.09%-41.32%) will be achieved by restructuring CCLP. Therefore, this mixed farming is a mode with broader benefits to provide sustainable agricultural practices for reducing GHG emissions fairly.

How to avoid a local epidemic becoming a global pandemic.

Here, we combine international air travel passenger data with a standard epidemiological model of the initial 3 mo of the COVID-19 pandemic (January through March 2020; toward the end of which the entire world locked down). Using the information available during this initial phase of the pandemic, our model accurately describes the main features of the actual global development of the pandemic demonstrated by the high degree of coherence between the model and global data. The validated model allows for an exploration of alternative policy efficacies (reducing air travel and/or introducing different degrees of compulsory immigration quarantine upon arrival to a country) in delaying the global spread of SARS-CoV-2 and thus is suggestive of similar efficacy in anticipating the spread of future global disease outbreaks. We show that a lesson from the recent pandemic is that reducing air travel globally is more effective in reducing the global spread than adopting immigration quarantine. Reducing air travel out of a source country has the most important effect regarding the spreading of the disease to the rest of the world. Based upon our results, we propose a digital twin as a further developed tool to inform future pandemic decision-making to inform measures intended to control the spread of disease agents of potential future pandemics. We discuss the design criteria for such a digital twin model as well as the feasibility of obtaining access to the necessary online data on international air travel.

Carbon flow through continental-scale ground logistics transportation.

The flourishing logistics in both developed and emerging economies leads to huge greenhouse gas (GHG) emissions; however, the emission fluxes are poorly constrained. Here, we constructed a spatial network of logistic GHG emissions based on multisource big data at continental scale. GHG emissions related to logistics transportation reached 112.14 Mt CO2-equivalents (CO2e), with seven major urban agglomerations contributing 63% of the total emissions. Regions with short transport distances and well-developed road infrastructure had relatively high emission efficiency. Underlying value flow of the commodities is accompanied by logistics carbon flow along the supply chain. The main driving factors affecting GHG emissions are driving speed and gross domestic product. It may mitigate GHG emissions by 27.50-1162.75 Mt CO2e in 15 years if a variety of energy combinations or the appropriate driving speed (65-70 km/h) is adopted. The estimations are of great significance to make integrated management policies for the global logistics sector.

Leaf economics fundamentals explained by optimality principles.

The life span of leaves increases with their mass per unit area (LMA). It is unclear why. Here, we show that this empirical generalization (the foundation of the worldwide leaf economics spectrum) is a consequence of natural selection, maximizing average net carbon gain over the leaf life cycle. Analyzing two large leaf trait datasets, we show that evergreen and deciduous species with diverse construction costs (assumed proportional to LMA) are selected by light, temperature, and growing-season length in different, but predictable, ways. We quantitatively explain the observed divergent latitudinal trends in evergreen and deciduous LMA and show how local distributions of LMA arise by selection under different environmental conditions acting on the species pool. These results illustrate how optimality principles can underpin a new theory for plant geography and terrestrial carbon dynamics.

Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees.

Over the past decades, global warming has led to a lengthening of the time window during which temperatures remain favorable for carbon assimilation and tree growth, resulting in a lengthening of the green season. The extent to which forest green seasons have tracked the lengthening of this favorable period under climate warming, however, has not been quantified to date. Here, we used remote sensing data and long-term ground observations of leaf-out and coloration for six dominant species of European trees at 1773 sites, for a total of 6060 species-site combinations, during 1980-2016 and found that actual green season extensions (GS: 3.1 ± 0.1 day decade-1 ) lag four times behind extensions of the potential thermal season (TS: 12.6 ± 0.1 day decade-1 ). Similar but less pronounced differences were obtained using satellite-derived vegetation phenology observations, that is, a lengthening of 4.4 ± 0.13 and 7.5 ± 0.13 day decade-1 for GS and TS, respectively. This difference was mainly driven by the larger advance in the onset of the thermal season compared to the actual advance of leaf-out dates (spring mismatch: 7.2 ± 0.1 day decade-1 ), but to a less extent caused by a phenological mismatch between GS and TS in autumn (2.4 ± 0.1 day decade-1 ). Our results showed that forest trees do not linearly track the new thermal window extension, indicating more complex interactions between winter and spring temperatures and photoperiod and a justification of demonstrating that using more sophisticated models that include the influence of chilling and photoperiod is needed to accurately predict spring phenological changes under warmer climate. They urge caution if such mechanisms are omitted to predict, for example, how vegetative health and growth, species distribution and crop yields will change in the future.

No evidence for persistent natural plague reservoirs in historical and modern Europe.

Caused by Yersinia pestis, plague ravaged the world through three known pandemics: the First or the Justinianic (6th-8th century); the Second (beginning with the Black Death during c.1338-1353 and lasting until the 19th century); and the Third (which became global in 1894). It is debatable whether Y. pestis persisted in European wildlife reservoirs or was repeatedly introduced from outside Europe (as covered by European Union and the British Isles). Here, we analyze environmental data (soil characteristics and climate) from active Chinese plague reservoirs to assess whether such environmental conditions in Europe had ever supported "natural plague reservoirs". We have used new statistical methods which are validated through predicting the presence of modern plague reservoirs in the western United States. We find no support for persistent natural plague reservoirs in either historical or modern Europe. Two factors make Europe unfavorable for long-term plague reservoirs: 1) Soil texture and biochemistry and 2) low rodent diversity. By comparing rodent communities in Europe with those in China and the United States, we conclude that a lack of suitable host species might be the main reason for the absence of plague reservoirs in Europe today. These findings support the hypothesis that long-term plague reservoirs did not exist in Europe and therefore question the importance of wildlife rodent species as the primary plague hosts in Europe.

Ecology, evolution, and conservation of Ethiopia's biodiversity.

Ethiopia is home to one of the richest and most unique assemblages of fauna and flora on the African continent. Contained within its borders are two major centers of endemism, the mesic Roof of Africa (also known as the Ethiopian Highlands) and the arid Horn of Africa, resulting from the country's varied topography and consequent geographic isolation. These centers of endemism are crucial to global conservation as evidenced by their classification within the Eastern Afromontane and Horn of Africa biodiversity hotspots, respectively. Ethiopia's diverse ecosystems and the biodiversity they contain are increasingly threatened by climate change and the growing impacts of Africa's second largest human and largest livestock populations. In this paper, we focus on several key areas of recent and ongoing research on Ethiopian biodiversity that have broadened our understanding of nature and its conservation in Africa. Topics explored include the behavioral ecology of Ethiopia's large social mammals, the ecology and conservation of its unique coffee forests, and Ethiopian approaches to community conservation, fortress conservation, and nature-based solutions. We also highlight the increasing prominence of Ethiopian scientists in studies of the country's biodiversity in recent decades. We suggest promising avenues for future research in evolutionary biology, ecology, systematics, and conservation in Ethiopia and discuss how recent and ongoing work in Ethiopia is helping us better understand and conserve nature in the human-dominated landscapes of Africa and other tropical regions today.

Urbanization affects spatial variation and species similarity of bird diversity distribution.

Although cities are human-dominated systems, they provide habitat for many other species. Because of the lack of long-term observation data, it is challenging to assess the impacts of rapid urbanization on biodiversity in Global South countries. Using multisource data, we provided the first analysis of the impacts of urbanization on bird distribution at the continental scale and found that the distributional hot spots of threatened birds overlapped greatly with urbanized areas, with only 3.90% of the threatened birds' preferred land cover type in urban built-up areas. Bird ranges are being reshaped differently because of their different adaptations to urbanization. While green infrastructure can improve local bird diversity, the homogeneous urban environment also leads to species compositions being more similar across regions. More attention should be paid to narrow-range species for the formulation of biodiversity conservation strategies, and conservation actions should be further coordinated among cities from a global perspective.