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Northward expansion trends and future potential distribution of a dragonfly Ischnura senegalensis Rambur under climate change using citizen science data in South Korea

Citizen science is becoming a mainstream approach of baseline data collection to monitor biodiversity and climate change. Dragonflies (Odonata) have been ranked as the highest priority group in biodiversity mo...

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Morphological variables restrict flower choice of Lycaenid butterfly species: implication for pollination and conservation

Butterflies make an important part for plant-pollinator guild. These are nectar feeder or occasionally pollen feeder and thus proboscis of the butterfly species are considered as one of the most important vari...

Honey bees and their brood: a potentially valuable resource of food, worthy of greater appreciation and scientific attention

Despite the consumption of bee brood in several parts of the world, particularly in the tropical areas, the practice has received comparatively little attention. We have reviewed all the available information ...

Attributes and references to honey bees (Insecta; Hymenoptera; Apidae) and their products in some Asian and Australian societies’ folkloristic domains

References to insects in myths, stories, and idioms can be found in almost any culture, but with regard to references involving honey bee species in the Asia-Australian region, little information is available....

RETRACTED ARTICLE: Major environmental factors and traits of invasive alien plants determining their spatial distribution

As trade increases, the influx of various alien species and their spread to new regions are prevalent and no longer a special problem. Anthropogenic activities and climate changes have made the distribution of...

Spatial distribution of halophytes and environment factors in salt marshes along the eastern Yellow Sea

Salt marshes provide a variety of ecosystem services; however, they are vulnerable to human activity, water level fluctuations, and climate change. Analyses of the relationships between plant communities and e...

PollMap: a software for crop pollination mapping in agricultural landscapes

Ecosystem service mapping is an important tool for decision-making in landscape planning and natural resource management. Today, pollination service mapping is based on the Lonsdorf model (InVEST software) tha...

Current status of alert alien species management for the establishment of proactive management systems in Korea

Some of the introduced alien species introduced settle, multiply, and spread to become invasive alien species (IAS) that threaten biodiversity. To prevent this, Korea and other countries legally designate and ...

Dust and sandstorm: ecosystem perspectives on dryland hazards in Northeast Asia: a review

A review of the literature was carried out to study dust and sandstorm (DSS) in terms of its ecosystem processes and relationship to other dryland disasters in Northeast Asia. Drylands are ecosystems that incl...

Changes in nocturnal insect communities in forest-dominated landscape relevant to artificial light intensity

Artificial light at night has recently been identified as a major factor adversely affecting global insect diversity. Here, we compared the insect diversity in Gwangneung Forest Biosphere Reserve, specifically...

Occurrence and diet analysis of sea turtles in Korean shore

Sea turtles, which are globally endangered species, have been stranded and found as bycatch on the Korean shore recently. More studies on sea turtles in Korea are necessary to aid their conservation. To invest...

Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales

Landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but so...

The relationship of mean temperature and 9 collected butterfly species’ wingspan as the response of global warming

Organism body size is a basic characteristic in ecology; it is related to temperature according to temperature-size rule. Butterflies are affected in various aspects by climate change because they are sensitiv...

Non-deep physiological dormancy in seeds of Euphorbia jolkinii Boiss. native to Korea

Euphorbia jolkinii Boiss. is a perennial species native to Jeju Island and the southern coastal area of Korea. Particularly on Jeju Island, the yellow flowers of E. jolkinii Boiss. have a high ornamental value be...

Predation of the Japanese keelback ( Hebius vibakari Boie, 1826) by the Slender racer ( Orientocoluber spinalis Peters, 1866)

The Slender racer ( Orientocoluber spinalis Peters, 1866) has recently been reclassified to the new genus Orientocoluber from Hierophis . Ecological knowledge of this species is limited due to its highly mobile beh...

Major environmental factors and traits of invasive alien plants determine their spatial distribution: a case study in Korea

As trade increases, the influx of various alien species and their spread to new regions are prevalent, making them a general problem globally. Anthropogenic activities and climate change have led to alien spec...

Distribution and habitat use of the endangered Siberian flying squirrel Pteromys volans (Rodentia: Sciuridae)

Understanding the habitat characteristics of the endangered Siberian flying squirrel Pteromys volans is the first step in conserving and managing the forests it requires for nesting, gliding, and feeding. Therefo...

How effective are artificial nests in attracting bees? A review

Recent declines in bee populations, along with increasing demand for pollination services in urban, agricultural, and natural environments, have led to strategies to attract wild bees to these areas. One of th...

Tissue-specific systemic responses of the wild tobacco Nicotiana attenuata against stem-boring herbivore attack

Plants are able to optimize defense responses induced by various herbivores, which have different feeding strategies. Local and systemic responses within a plant after herbivory are essential to modulate herbi...

Estimating potential range shift of some wild bees in response to climate change scenarios in northwestern regions of Iran

Climate change is occurring rapidly around the world, and is predicted to have a large impact on biodiversity. Various studies have shown that climate change can alter the geographical distribution of wild bee...

Trends in the effects of climate change on terrestrial ecosystems in the Republic of Korea

In this review, we aimed to synthesize the current knowledge on the observed and projected effects of climate change on the ecosystems of Korea (i.e., the Republic of Korea (ROK) or South Korea), as well as th...

Principle of restoration ecology reflected in the process creating the National Institute of Ecology

The creation of the National Institute of Ecology began as a national alternative project to preserve mudflats instead of constructing the industrial complexes by reclamation, and achieve regional development....

Small-scale spatial genetic structure of Asarum sieboldii metapopulation in a valley

Asarum sieboldii Miq., a species of forest understory vegetation, is an herbaceous perennial belonging to the family Aristolochiaceae. The metapopulation of A. sieboldii is distributed sparsely and has a short se...

Diel and seasonal activity pattern of alien sika deer with sympatric mammalian species from Muljangori-oreum wetland of Hallasan National Park, South Korea

Sika deer, Cervus nippon , were originally introduced to South Korea from Japan and Taiwan for commercial farming purposes. Unfortunately, they were released into the wild during religious events and have since be...

Effects of different day length and wind conditions to the seedling growth performance of Phragmites australis

To understand shade and wind effects on seedling traits of common reed ( Phragmites australis ), we conducted a mesocosm experiment manipulating day length (10 h daytime a day as open canopy conditions or 6 h dayti...

Categorized wetland preference and life forms of the vascular plants in the Korean Peninsula

In 2020, a categorized list of wetland preferences, major habitats, and life forms of 4145 vascular plant taxa occurring in the Korean Peninsula was published by the National Institute of Biological Resources....

Elevational distribution ranges of vascular plant species in the Baekdudaegan mountain range, South Korea

The climate is changing rapidly, and this may pose a major threat to global biodiversity. One of the most distinctive consequences of climate change is the poleward and/or upward shift of species distribution ...

Study on the photosynthetic characteristics of Eutrema japonica (Siebold) Koidz. under the pulsed LEDs for simulated sunflecks

The sunfleck is an important light environmental factor for plants that live under the shade of trees. Currently, the smartfarm has a system that can artificially create these sunfleks. Therefore, it was inten...

Influence of trees and associated variables on soil organic carbon: a review

The level of soil organic carbon (SOC) fluctuates in different types of forest stands: this variation can be attributed to differences in tree species, and the variables associated with soil, climate, and topo...

Comparison of ecophysiological and leaf anatomical traits of native and invasive plant species

To address the lack of evidence supporting invasion by three invasive plant species ( Imperata cylindrica, Lantana camara, and Chromolaena odorata ) in tropical ecosystems, we compared the ecophysiological and leaf...

Effects of soil water content and light intensity on the growth of Molinia japonica in montane wetlands in South Korea

Montane wetlands are unique wetland ecosystems with distinct physicochemical characteristics, and Molinia japonica often makes dominant communities in montane wetlands in South Korea. In order to figure out the e...

First detection of ranavirus in a wild population of Dybowski’s brown frog ( Rana dybowskii ) in South Korea

Ranavirus is an emerging infectious disease which has been linked to mass mortality events in various amphibian species. In this study, we document the first mass mortality event of an adult population of Dybo...

Cushion plant Silene acaulis is a pioneer species at abandoned coal piles in the High Arctic, Svalbard

Abandoned coal piles after the closure of mines have a potential negative influence on the environment, such as soil acidification and heavy metal contamination. Therefore, revegetation by efficient species is...

Vegetation structure and distribution characteristics of Symplocos prunifolia , a rare evergreen broad-leaved tree in Korea

In Korea, Symplocos prunifolia Siebold. & Zucc. is only found on Jeju Island. Conservation of the species is difficult because little is known about its distribution and natural habitat. The lack of research and ...

Growth performance of planted population of Pinus roxburghii in central Nepal

Climate change has altered the various ecosystem processes including forest ecosystem in Himalayan region. Although the high mountain natural forests including treelines in the Himalayan region are mainly repo...

Correction to: Application of smart mosquito monitoring traps for the mosquito forecast systems by Seoul Metropolitan city

An amendment to this paper has been published and can be accessed via the original article.

The original article was published in Journal of Ecology and Environment 2020 44 :13

Correction to: Effect of precipitation on soil respiration in a temperate broad-leaved forest

The original article was published in Journal of Ecology and Environment 2018 42 :10

Effects of cutting and sowing seeds of native species on giant ragweed invasion and plant diversity in a field experiment

Ambrosia trifida is a highly invasive annual plant, but effective control methods have not been proposed. Among various eradication methods, cutting is a simple measure to control invasive plants, and sowing seed...

Mid-term (2009-2019) demographic dynamics of young beech forest in Albongbunji Basin, Ulleungdo, South Korea

The stem exclusion stage is a stage of forest development that is important for understanding the subsequent understory reinitiation stage and maturation stage during which horizontal heterogeneity is formed. ...

Annual and spatial variabilities in the acorn production of Quercus mongolica

Genus Quercus is a successful group that has occupied the largest area of forest around the world including South Korea. The acorns are an important food source for both wild animals and humans. Although the repr...

Prevalence of Puccinia abrupta var. partheniicola and its impact on Parthenium hysterophorus in Kathmandu Valley, Nepal

Parthenium hysterophorus is a noxious invasive weed in tropical and subtropical regions of the world, including Nepal. Among 11 species of biological control agents released to control P. hysterophorus in Ausrtal...

Ecological impact of fast industrialization inferred from a sediment core in Seocheon, West Coast of Korean Peninsula

Rapid industrialization has caused various impacts on nature, including heavy metal pollution. However, the impacts of industrialization vary depending on the types of industrializing activity and surrounding ...

Influence of roadkill during breeding migration on the sex ratio of land crab ( Sesarma haematoche )

Adult land crabs generally live on land while their larvae live in the sea. In the case of Sesarma haematoche , female crabs migrate from land to sea to release the larvae at the high tide of syzygy night. Artific...

Population structure and regeneration of Himalayan endemic Larix species in three high-altitude valleys in Nepal Himalaya

The Himalayan forests are of great importance to sustain the nature and community resource demands. These forests are facing pressures both from anthropogenic activities and ongoing global climatic changes. Po...

Otolith microchemistry reveals the migration patterns of the flathead grey mullet Mugil cephalus (Pisces: Mugilidae) in Korean waters

The flathead grey mullet Mugil cephalus has the widest distribution among mugilid species. Recent studies based on mitochondrial DNA sequences showed that the species comprises at least 14 different groups, three...

Population size, group and age structure of geladas ( Theropithecus gelada ) in escarpments of Eastern Tigray, Ethiopia: implication for conservation

Geladas ( Theropithecus gelada ), endemic to Ethiopia, are distributed closely related to the escarpments and gorge systems of the country, and large populations are found in the Simien Mountain National Park. This...

Coexistence of plant species under harsh environmental conditions: an evaluation of niche differentiation and stochasticity along salt marsh creeks

Ecologists have achieved much progress in the study of mechanisms that maintain species coexistence and diversity. In this paper, we reviewed a wide range of past research related to these topics, focusing on ...

Re-emergence of the Glossy Ibis ( Plegadis falcinellus ) in inland South Korea

Glossy Ibis ( Plegadis falcinellus ), which has never been recorded in South Korea, appeared on Jeju Island in 2018 and re-emerged in the inland area of Seocheon-gun (South Chungcheong Province) and in Goyang-si (G...

Diet composition of the Korean wild boar Sus scrofa coreanus (Suidae) at Mt. Jeombongsan, Korea

Korean wild boars ( Sus scrofa coreanus Heude), because of their adaptability, are a widespread large mammal; however, they sometimes cause problems by invading farms and eating the crops, creating insufficiencies...

Review on the succession process of Pinus densiflora forests in South Korea: progressive and disturbance-driven succession

Most of the Pinus densiflora forests, occupying the largest area, have been restored in South Korea since the 1970s. As young pioneer forests, the succession process is under way. Since the forests are distribute...

Journal of Ecology and Environment

ISSN: 2288-1220

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Web Ecology  

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Web Ecology (WE) is a platinum open-access journal issued by the European Ecological Federation (EEF), an organization representing the European ecological societies. Web Ecology publishes papers from all fields of ecology without any geographic restriction. It is a forum to communicate results of experimental, theoretical, and descriptive studies of general interest to an international audience. Original contributions, short communications, and reviews on ecological research on all kinds of organisms and ecosystems are welcome as well as papers that express emerging ideas and concepts with a sound scientific background. Papers must be original and not previously published in another journal. We also aim to serve as a publication forum for those European ecological societies that do not maintain their own society journal. Web Ecology also offers the opportunity to publish special issues resulting from conferences or symposiums from ecological societies.

Web Ecology is free to publish and free to read, thanks to the commitment of the European Ecological Federation to science accessibility.

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Research news from the Ecological Society of America’s journals

March  25, 2024 For Immediate Release

Contact : Mayda Nathan, gro.ase null @adyam  

The Ecological Society of America (ESA) presents a roundup of five research articles recently published across its six esteemed journals . Widely recognized for fostering innovation and advancing ecological knowledge, ESA’s journals consistently feature innovative and impactful studies. This compilation of papers explores invasive possum management in New Zealand, afforestation on global rangelands, population regulation in large herbivores and more, showcasing the Society’s commitment to promoting cutting-edge research that furthers our understanding of the natural world.

From Ecological Applications :

A brushtail possum in a tree at night, with city lights in the background

A new Ecological Applications study examines how well urban areas serve as a barrier to invasive brushtail possums in New Zealand. Image credit: Charlotte Patterson

Invasive mammal high-tails it through urban barrier Author contact: Charlotte R. Patterson ( moc.liamg null @nsrttaprc )

Iconic though they may be in their native Australia, brushtail possums are far less venerated in neighboring New Zealand, where the introduced marsupial not only poses a threat to native species but also serves as a reservoir for bovine tuberculosis. While eradication is an increasingly popular approach for removing non-native nuisances like brushtails, such efforts are often undermined by reinvasion of individuals from nearby populations. In this study, researchers in New Zealand investigated the effectiveness of different approaches to eliminating brushtails from the Otago Peninsula, a large landmass abutting the city of Dunedin. Although the urban environment was believed to block the tree-dwelling possums from moving back onto the peninsula following culling, modeling instead indicated that recolonization would occur, albeit at varying rates under different scenarios, unless three specific conditions were met: brushtails were completely removed from the peninsula, a buffer zone was established at the peninsular base, and populations in surrounding areas were subjected to control measures. Based on their analysis, the authors conclude that, although landscape features like cities have the potential to act as barriers to reinvasion, supplementary actions are likely needed to minimize the risk of reinvasion from adjacent populations. Moreover, they add, the results underscore the critical need for further research on the behavior and movement of introduced species—particularly non-native mammals—in urban systems.

Read the article: Eradicating an invasive mammal requires local elimination and reduced reinvasion from an urban source population

From Ecological Monographs :  

For large herbivores, it’s what you eat, not how much Author contact: N. Thompson Hobbs ( ude.etatsoloc null @sbboh.mot )

Variation in food quality and not quantity could explain why large herbivores’ growth rates fall as population density increases, according to a new study. Decreased food availability was long assumed to drive poorer body condition as herbivore abundance increased—the more grazers or browsers, the less there is to graze or browse—but recent research has shown that herbivores consume essentially the same amount of plant material regardless of fluctuations in plant biomass. To account for this discrepancy, this study developed a model exploring interactions between herbivore population dynamics and plant biomass and the roles these relationships play in the processes that control populations of these animals. Model outputs suggested that herbivores in large populations face a scarcity of high-quality food, sapping their nutrition even if they eat as much as ever. The results of the study offer new insights into the density-dependent processes that keep large herbivore populations in check.

Read the article: A general, resource-based explanation for density dependence in populations of large herbivores

From Frontiers in Ecology and the Environment :

Memory a close match for monitoring Author contact: Leandro Castello ( ude.tv null @ordnael )

All societies rely on the exploitation of natural resources to one extent or another, but in many parts of the world, resource monitoring is inconsistent, rare or absent altogether. Faced with a lack of hard data, ecologists are increasingly turning to a more direct approach: simply asking hunters and fishers themselves about the size and composition of their catches in years past. But how trustworthy are individual and collective memories as sources of information about bygone conditions? To address this question, an international team of scientists tested the accuracy of resource-user recall by comparing the recollections of fishers to observed catch data in 24 artisanal and industrial fisheries along the coast of Brazil. Analysis of the 396 survey responses revealed that fisher recall closely matched that of data collected by conventional methods, with reasonable accuracy maintained for as far back as 39 years. The authors point out that traditional knowledge holds several advantages over conventional science, such as increased trust by local communities and more buy-in for hunting and fishing restrictions. These findings led the authors to conclude that harvest recall by hunters and fishers represents a largely untapped reservoir of information about historical resource use, particularly in areas where such data are lacking.

Read the article: Local knowledge reconstructs historical resource use

A forestry guard patrols the Lido Great Green Wall afforestation site in the Dosso region of Niger. Image credit: DAWNING/N Parisse

A new article in Frontiers in Ecology and the Environment questions the value of tree planting on global rangelands. Image credit: DAWNING/N Parisse, from Briske et al., 2024 | CC BY-NC 4.0 DEED

Carbon, carbon on the range   Author contact: David D. Briske ( ude.umat null @eksirbd )

Replacing large segments of the world’s open rangelands with forests to help mitigate climate change—otherwise known as afforestation, a process often described as a “natural climate solution” (NCS)—may do more harm than good, argue the authors of a recent study. While forestlands do soak up and store enormous amounts of carbon, the authors dispute the notion that afforestation of rangelands would result in significant increases in carbon retention, pointing out that gains in aboveground carbon storage through the presence of more trees would be largely offset by concomitant losses in soil carbon, a particularly stable form of carbon storage. This misconception is just one of several that, the authors contend, inflate expectations for afforestation programs. Moreover, they note, in exchange for only small gains in carbon storage, loss of rangelands would severely curtail the numerous local- to global-scale ecosystem services these environments offer, including crop and livestock production and biodiversity conservation. Given the limited value of the benefits of rangeland afforestation, the authors propose that promoting retention of the vast stores of belowground carbon through preservation of global rangelands would be the most effective NCS strategy for climate-change mitigation efforts in these unique ecosystems.   

Read the article: Rangeland afforestation is not a natural climate solution

From Ecosphere :

Dorsal view of a wood turtle, with a red arrow pointing to a chip on the edge of its shell.

A recent study in Ecosphere reveals that wood turtles—or their predators—show signs of handedness. Image credit: Matthew Chatfield, from Honan et al., 2024 | CC BY 4.0 DEED

Wood turtles show their good side Author contact: Caroline Honan ( ude.usl null @nanohc )

Sea turtles use their right flipper to dig nests, snakes tend to coil in one direction, various lizards predominantly lean to the left; even a 289-million-year-old fossil reptile preferred to chew with the right side of its mouth. Surprisingly, however, little research has focused on how this bodily bias is important for reptiles. While examining photographs of the shells of 159 wood turtles, researchers in the US noticed a peculiar pattern that could provide further insight into handedness—technically, “lateralized behavior”—in these reptiles: cracks, dents, chips, and other forms of damage appeared to be more prevalent on the right side of turtle carapaces than on the left. Further analysis revealed that of the 234 damaged shell scutes on 60 individual turtles, significantly more occurred on the right side of the body than on the left. To account for this lopsidedness, the authors propose that turtles may preferentially turn to their left as a defensive posture when facing predators, or perhaps respond more quickly to threats originating from their left side. Turtles may also favor right-hand turns, resulting in their right sides more frequently banging or scraping against rocks and other hard objects in their environment. Alternatively, asymmetrical shell damage may have little to do with turtle behavior at all, instead reflecting handedness in one or more of their predators. Whatever the cause, the study underscores the interplay between biology and behavior and the ubiquity of righties and lefties in nature.

Read the article: Evidence of handedness in turtles

The Ecological Society of America, founded in 1915, is the world’s largest community of professional ecologists and a trusted source of ecological knowledge, committed to advancing the understanding of life on Earth. The 8,000 member Society publishes six journals and a membership bulletin and broadly shares ecological information through policy, media outreach, and education initiatives. The Society’s Annual Meeting attracts 4,000 attendees and features the most recent advances in ecological science. Visit the ESA website at https://www.esa.org .

Follow ESA on social media: Twitter/X – @esa_org Instagram – @ecologicalsociety Facebook – @esa.org

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Nature’s Secrets: Top 200 Ecology Research Topics

Ecology Research Topics

Welcome to the world of Ecology, where the study of nature evolves like an interesting story. Ecology helps us solve the complex relationships between living organisms and their environments. In this fascinating journey, we will see ecology research topics that reveal the secrets of ecosystems, biodiversity, and the delicate balance of nature. 

From understanding how different species react to the impact of human activities on our planet, Ecology offers insights that go beyond the ordinary. 

So, whether you’re fascinated by the web of life in a forest, the dynamics of a coral reef, or the challenges of conservation, these research topics will guide you into the heart of ecological wonders. Let’s start this adventure of knowledge, discovering the hidden secrets that shape the world around us.

What Is Ecology?

Table of Contents

Ecology is the study of how living things interact with each other and their environment. It explores relationships between plants, animals, and their surroundings, helping us understand how nature works and how different elements in ecosystems connect.

What Are The 6 Topics Studied In Ecology?

Ecology studies the relationships between living things and their environment. Here are six topics studied in ecology:

ecology research paper

  • Ecosystems: Examining how living organisms, like plants and animals, interact with each other and their non living surroundings, such as soil, water, and air.
  • Biodiversity: Analyzing the variety of life in different ecosystems, including the number and types of species present.
  • Population Dynamics: Understanding how the numbers of individuals in a species change over time, including factors like birth rates, death rates, and migration.
  • Community Interactions: Exploring how different species in a specific area interact with each other, such as through competition or cooperation.
  • Ecological Succession: Studying the increasing changes in ecosystems over time, including how new communities of plants and animals replace older ones.
  • Conservation Biology: Focusing on protecting and preserving ecosystems and species, especially those facing threats or endangerment.

Top 200 Ecology Research Topics

Now the wait is over and here we will be listing top 200 ecology research topics. And they are as:

Top 10 Ecology Research Topics On Biodiversity Conservation

  • Conservation Genetics and its Role in Biodiversity Preservation
  • Ecological Consequences of Habitat Fragmentation on Biodiversity
  • Monitoring and Assessing Biodiversity in Changing Landscapes
  • Conservation Strategies for Endangered Species
  • The Significance of Protected Areas in Biodiversity Conservation
  • Ecosystem Services and Biodiversity Conservation
  • Citizen Science Initiatives in Biodiversity Monitoring
  • Integrating Indigenous Knowledge in Biodiversity Conservation
  • Climate Change Impacts on Biodiversity and Conservation Measures
  • Human-Wildlife Conflict and its Implications for Biodiversity Conservation

Top 10 Research Topics On Climate Change Impacts

  • Climate Change Effects on Biodiversity and Ecosystems
  • Influence of Climate Change on Global Water Resources
  • Rising Sea Levels and Coastal Ecosystem Vulnerability
  • Climate Change Affects on Agriculture and Food Security
  • Extreme Weather Events and their Ecological Consequences
  • Ocean Acidification: Ecological and Marine Life Impacts
  • Changes in Species Distribution by Climate Change
  • Climate Change and Migration Patterns of Wildlife
  • Effects of Climate Change on Polar and Alpine Ecosystems
  • Climate Change and Human Health: Ecological Perspectives

Top 10 Ecology Research Topics On Habitat Restoration

  • Ecosystem Recovery after Habitat Disturbance
  • Effects of Restoration Techniques on Soil Health
  • Ecological Succession in Restored Habitats
  • Invasive Species Management in Restoration Projects
  • Role of Native Plant Species in Habitat Restoration
  • Impact of Restoration on Wildlife Communities
  • Community Engagement in Urban Habitat Restoration
  • Restoration of Wetland Ecosystems and Biodiversity
  • Historical Ecology and its Role in Habitat Restoration
  • Evaluating Long-Term Success of Habitat Restoration Projects

Top 10 Research Topics On Ecosystem Services

  • Valuation of Ecosystem Services for purpose of Sustainable Resource Management
  • Biodiversity’s Role in Providing Ecosystem Services
  • Climate Change Impacts on Ecosystem Services
  • Urban Ecosystem Services and Green Infrastructure
  • Cultural Ecosystem Services: Linking Nature and Well-being
  • Watershed Services: Sustainable Water Resource Management
  • Forest Ecosystem Services and Sustainable Forestry Practices
  • Marine Ecosystem Services: Conservation and Management
  • Agricultural Practices and Ecosystem Service Delivery
  • Restoration Ecology for Enhancing Ecosystem Services

Top 10 Ecology Research Topics On Wildlife Ecology

  • Behavior and Social Structure of Wild Animal Populations
  • Conservation Genetics in Wildlife Management
  • Human-Wildlife Conflict and Mitigation Strategies
  • Wildlife Habitat Use and Selection
  • Effects of Climate Change on Wildlife Ecology
  • Wildlife Disease Ecology and Emerging Infectious Diseases
  • Predator-Prey Dynamics in Natural Ecosystems
  • Movement Ecology and Migration Patterns
  • Wildlife Monitoring Techniques and Technology
  • Restoration Ecology for Wildlife Habitat Enhancement

Top 10 Ecology Research Topics On Marine Ecology

  • Coral Reef Resilience and Conservation
  • Marine Biodiversity in Deep-Sea Ecosystems
  • Ocean Acidification & its Impact on Marine Life
  • Fisheries Management for Sustainable Marine Ecology
  • Marine Protected Areas and Conservation Strategies
  • Plastic Pollution & its impact on Marine Ecosystems
  • Seabird Ecology and Conservation
  • Mangrove Ecosystems: Function and Conservation
  • Climate Change Impacts on Marine Ecosystems
  • Seagrass Ecology and Restoration efforts in Coastal Areas

Top 10 Research Topics On Urban Ecology

  • Urban Biodiversity and Conservation Strategies
  • Green Spaces & Ecosystem Services in Urban Environments
  • Urban Heat Island Effect and Mitigation Measures
  • Urban Wildlife Ecology and Human-Wildlife Interactions
  • Sustainable Urban Planning and Design for Ecosystem Health
  • Urban Agriculture: Impacts on Biodiversity and Food Security
  • Air Quality and Urban Tree Canopy: A Nexus in Urban Ecology
  • Stormwater Management and Ecological Solutions in Urban Areas
  • Urbanization Effects on Microbial Communities in Soil
  • Citizen Science Contributions to Urban Ecology Research

Top 10 Ecology Research Topics On Forest Ecology

  • Old-Growth Forest Ecology and Conservation
  • Forest Fragmentation and its Impact on Biodiversity
  • Fire Ecology: Natural Processes and Human Intervention
  • Forest Carbon Sequestration and Climate Change Mitigation
  • Dynamics of Tree-Soil Interactions in Forest Ecosystems
  • Invasive Species Management in Forested Landscapes
  • Forest Restoration Ecology and Reforestation Strategies
  • Effects of Logging and Timber Harvesting on Forest Ecology
  • Microbial Communities in Forest Soils: Diversity and Function
  • Ecological Consequences of Climate Change in Forested Regions

Top 10 Research Topics On Invasive Species Management

  • Ecological Impacts of Invasive Species
  • Mechanisms of Invasion Success
  • Early Detection and Rapid Response Strategies
  • Effects of Climate Change on Invasive Species Dynamics
  • Management Strategies for Aquatic Invasive Species
  • Biological Control of Invasive Species
  • Evolutionary Responses in Invasive Species
  • Community-Level Impacts of Invasive Species
  • Economic Costs and Benefits of Invasive Species Management
  • Restoration Ecology After Invasive Species Removal

Top 10 Ecology Research Topics On Conservation Genetics

  • Genetic Diversity and Conservation of Endangered Species
  • Population Genetics of Rare and Threatened Plants
  • Conservation Genomics in Wildlife Management
  • Genetic Adaptation to Changing Environments
  • Genomic Approaches in Assessing Inbreeding Depression
  • Landscape Genetics and Habitat Connectivity
  • Genetic Monitoring for Effective Conservation
  • Genomic Tools in Studying Hybridization and Introgression
  • Conservation Genetics of Migratory Species
  • Genetic Markers for Non-Invasive Monitoring of Wildlife

Top 10 Research Topics On Landscape Ecology

  • Spatial Patterns and Dynamics in Landscape Ecology
  • Connectivity and Fragmentation of Landscape
  • Urbanization and its Impact on Landscape Structure
  • Landscape Heterogeneity and Biodiversity Conservation
  • Ecosystem Services in the Context of Landscape Ecology
  • Remote Sensing and GIS Applications in Landscape Ecology
  • Modeling Landscape Change and Future Scenarios
  • Landscape Ecology and Climate Change Impacts
  • Land-Use Change Effects on Landscape Patterns
  • Resilience and Sustainability in Landscape Ecology

Top 10 Ecology Research Topics On Agroecology

  • Sustainable Farming Practices for Agroecosystem Health
  • Agroecology and Biodiversity Conservation in Agricultural Landscapes
  • Soil Health and Nutrient Cycling in Agroecosystems
  • Organic Farming Systems: Ecological Impacts and Benefits
  • Agroecological Approaches to Pest Management
  • Agroforestry Systems and Ecosystem Services
  • Climate-Resilient Agriculture in Agroecological Frameworks
  • Indigenous and Traditional Agro Ecological Knowledge
  • Integrating Livestock into Agroecosystems for Sustainability
  • Socioeconomic Dimensions of Agroecological Transition

Top 10 Research Topics On Ecological Modeling

  • Spatial and Temporal Dynamics in Ecological Models
  • Integrating Climate Change in Ecological Modeling
  • Agent-Based Modeling in Ecological Studies
  • Ecological Network Models: Structure and Dynamics
  • Predictive Modeling for Conservation Planning
  • Individual-Based Models in Animal Behavior Ecology
  • Dynamic Energy Budget Models in Population Ecology
  • Bayesian Approaches in Ecological Modeling
  • Ecological Niche Modeling for Species Distribution
  • Coupling Ecological and Economic Models for Sustainability

Top 10 Ecology Research Topics On Environmental Pollution

  • Affects of Air Pollution on Ecosystems and Human Health
  • Microplastics in Aquatic Ecosystems: Sources and Effects
  • Soil Pollution and its Consequences for Terrestrial Ecology
  • Noise Pollution and its Effects on Wildlife Behavior
  • Heavy Metal Contamination in Urban Ecosystems
  • Emerging Contaminants: Pharmaceuticals in the Environment
  • Pesticide Pollution and Agricultural Ecosystems
  • Oil Spills and Marine Ecosystems: Recovery and Resilience
  • Plastic Waste in Marine Environments: Ecological Impacts
  • Urbanization and its Role in Environmental Pollution

Top 10 Research Topics On Ecotourism Impact

  • Ecotourism and Biodiversity Conservation
  • Socioeconomic Impacts of Ecotourism on Local Communities
  • Sustainable Practices in Ecotourism Operations
  • Wildlife Disturbance and Ecotourism: Balancing Conservation
  • Ecotourism and Cultural Heritage Preservation
  • Assessing the Environmental Footprint of Ecotourism
  • Ecotourism and Sustainable Resource Management
  • Community Involvement in Ecotourism Development
  • Monitoring and Mitigating Ecotourism Impacts on Fragile Ecosystems
  • Ecotourism Certification and Standards for Responsible Tourism

Top 10 Ecology Research Topics On Plant Ecology

  • Plant-Animal Interactions and Mutualistic Relationships
  • Impacts of Climate Change on Plant Communities
  • Plant Functional Traits and Ecosystem Functioning
  • Plant-Insect Interactions: Pollination and Herbivory
  • Dynamics of Plant Communities in Disturbed Habitats
  • Plant Defense Mechanisms Against Herbivores
  • Allelopathy: Chemical Interactions among Plants
  • Plant Invasions and their Ecological Consequences
  • Influence of Soil Microbes on Plant Health and Diversity
  • Role of Mycorrhizal Fungi in Plant Ecology

Top 10 Research Topics On Evolutionary Ecology

  • Adaptation and Evolutionary Dynamics in Changing Environments
  • Coevolutionary Interactions between Species
  • Evolutionary Consequences of Mutualistic Relationships
  • Evolutionary Ecology of Life History Strategies
  • Evolutionary Responses to Anthropogenic Stressors
  • Evolutionary Ecology of Invasive Species
  • Historical Biogeography and Evolutionary Patterns
  • Evolutionary Ecology of Plant-Animal Interactions
  • Evolutionary Drivers of Biodiversity
  • Evolutionary Consequences of Climate Change

Top 10 Ecology Research Topics On Freshwater Ecology

  • Biodiversity and Conservation of Freshwater Ecosystems
  • Aquatic Macroinvertebrates as Bioindicators of Water Quality
  • Effects of Climate Change on Freshwater Ecology
  • Nutrient Cycling in Freshwater Environments
  • Impact of Invasive Species on Freshwater Ecosystems
  • Dynamics of Aquatic Food Webs in Lakes and Rivers
  • Restoration Ecology of Freshwater Habitats
  • Ecological Consequences of Dams and Water Management
  • Microbial Communities in Freshwater Environments
  • Threats to Freshwater Ecosystems: Pollution and Habitat Loss

Top 10 Research Topics On Microbial Ecology

  • Microbial Diversity in Natural Environments
  • Microbial Interactions in Soil Ecosystems
  • Human Microbiome and Health
  • Microbial Ecology of Extreme Environments
  • Microbes in Aquatic Ecosystems: Dynamics and Roles
  • Microbial Communities in Plant Rhizospheres
  • Microbial Biogeography and Distribution Patterns
  • Impact of Climate Change on Microbial Ecology
  • Microbial Responses to Pollution and Environmental Stress
  • Microbial Roles in Biogeochemical Cycling

Top 10 Ecology Research Topics On Sustainable Agriculture

  • Agroecological Practices for Sustainable Farming
  • Soil Health Management in Sustainable Agriculture
  • Water Conservation Strategies in Agricultural Systems
  • Organic Farming: Impacts on Ecology and Sustainability
  • Integrated Pest Management for Sustainable Agriculture
  • Biodiversity Enhancement through Crop Rotation
  • Agroforestry: Integrating Trees into Agricultural Landscapes
  • Climate-Smart Agriculture Approaches
  • Efficient Nutrient Management in Sustainable Farming
  • Sustainable Livestock Farming Practices

Top 50 Ecology Essay Topics

In addition to the above topics we are giving you a bonus of top 50 ecology essay topics based on different categories and they are as:

Top 10 Essay Research Topics On Environmental Sustainability

  • Climate Change Impacts and Mitigation Strategies
  • Biodiversity Conservation and Ecosystem Restoration
  • Sustainable Agriculture Practices
  • Renewable Energy Solutions
  • Waste Management and Circular Economy
  • Urban Planning for Sustainable Cities
  • Water Conservation and Management
  • Environmental Policies and Governance
  • Corporate Social Responsibility in Sustainability
  • Indigenous Knowledge and Practices in Environmental Sustainability

Top 10 Essay Research Topics On Social Justice and Equity

  • Racial Inequality and Systemic Racism
  • Gender Equality and Women’s Rights
  • LGBTQ+ Rights and Inclusivity
  • Economic Disparities and Poverty
  • Access to Education: Challenges and Solutions
  • Criminal Justice Reform and Fair Policing
  • Disability Rights and Inclusion
  • Indigenous Rights and Land Sovereignty
  • Immigration Policies and Human Rights
  • Healthcare Disparities: Addressing Equity in Access and Treatment

Top 10 Essay Research Topics On Technology and Society

  • Ethical Implications of Artificial Intelligence
  • Digital Privacy and Security Concerns
  • Impact of Social Media on Society
  • The Role of Technology in Education
  • Automation and the Future of Work
  • Cybersecurity Challenges and Solutions
  • Internet of Things (IoT) and Smart Cities
  • Biotechnology and Bioethics
  • Technology and Healthcare: Advancements and Concerns
  • Accessibility and Inclusivity in Technological Innovations

Top 10 Essay Research Topics On Health and Wellness

  • Mental Health Stigma and Awareness
  • Healthcare Disparities in Underserved Communities
  • Impact of Technology on Mental Health
  • Lifestyle Factors and Chronic Disease Prevention
  • Access to Affordable Healthcare
  • Public Health Strategies for Disease Prevention
  • Global Health Challenges and Solutions
  • Integrative Medicine and Holistic Health Approaches
  • Nutrition and its Role in Overall Wellness
  • Aging Population: Health Challenges and Innovations

Top 10 Essay Research Topics On Global Economic Trends

  • The Impact of Globalization on Economic Inequality
  • Sustainable Development Goals and Economic Growth
  • Technological Advancements and Economic Transformation
  • Trade Wars and their Effects on Global Economies
  • The Rise of Gig Economy and Changing Workforce Dynamics
  • Financial Inclusion and Economic Empowerment
  • COVID-19 Pandemic’s Impact on Global Economic Trends
  • Green Finance and Environmental Sustainability in Economics
  • Economic Policies for Post-Pandemic Recovery
  • The Role of Emerging Markets in Shaping Global Economic Trends

As we conclude our exploration of Ecology Research Topics, we’ve uncovered a big collection of subjects into the wonders of our natural world. From studying Biodiversity Conservation to researching Microbial Ecology, these topics offer a deeper understanding of the balance of our ecosystems. 

In addition to these research topics, we’ve provided a bonus of 50 Ecology Essay Topics, adding more layers to your knowledge. Remember, Ecology is like solving nature’s puzzle, and each topic contributes to revealing its secrets. 

We’ve also touched upon the six fundamental topics in Ecology, providing a foundation for your ecological journey. So, let curiosity be your guide, and explore the mysteries that our planet holds.

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50 best ecology topics for your research paper.

ecology topics

Finding the right ecology topics is not the easiest thing in the world. Because topics within the field of ecology vary widely, it may be difficult to make a choice. However, with the list of ecology topics in this article, you’ll find it easier to make a choice. These topics in ecology will help you get ecology project ideas for your ecology research, presentation, etc. So without further ado, let us explore some interesting ecology topics!

Ecology refers to the study of how ecosystems function. It refers to the relationships between living organisms and their environment. Most ecological processes occur very slowly. Sometimes, they could happen rather rapidly. Ecology remains crucial in studying ecosystems and is important for survival.

Ecology Research Paper Topics

We have some interesting ecology research topics spanning many aspects of ecology. With these ecology topics for research paper , you’ll be able to carry out meaningful research. Let’s delve into some of the ecology paper topics we have for you!

  • Novel ways to introduce new predators into an area
  • The discovery of manure and its impact on plant growth
  • The effect of acid rain on trees
  • Effective strategies to reduce the emission of carbon dioxide into the environment
  • Proven strategies to make the environment more sustainable

Evolutionary Ecology Research Topics

The following ecology topics will be sure to get you top grades in your evolutionary ecology research.

  • How maternal structures evolved functional roles to ensure the survival of offspring.
  • How invaders affect the evolution of soil fungal communities.
  • How social complexity in humans evolved.
  • How climate change affects the evolutionary change in natural and managed biodiversity.
  • Transcriptomic changes that allow the successful evolution of plant species from aquatic habitats terrestrial habitats

Human Ecology Topics

Being able to relate humans and our impact on ecology and vice versa is important. What influence do humans have on the environment? The following human ecology topics are sure to get you an A+ in that research!

  • Can people safely live in Megacities?
  • How can Ecologists effectively protect marine species that are at risk?
  • Overconsumption and its effect on the environment
  • Physiological ecology and its importance to us
  • An exhaustive description of the agrarianism philosophy
  • Fast food and possible problems it poses to the environment.
  • Human macroevolution and the future
  • Similarities between Cultural and Genetic Evolution

Ecology Research Project Ideas

Ideas rule the world. However, ideas are not easy to come by. Do you have to come up with an interesting ecology project but have hit the wall? Are you short on ideas? Well, we’ve got some ecology research project ideas and topics that you can explore.

Are you in college and need an ecology project idea? It is no news that college professors require students to have more in-depth information on various subjects. If you want to wow your college professors, then these ideas for ecology project will let you stand out! The listed ideas contain some ecology project ideas for college students. Come, and let’s explore some worthwhile ecology project topics for you!

  • An analysis of the effect of climate change on plant species
  • Green roofs: The working design and why they should be in use
  • Exploring the benefits of natural green effects
  • Mirroring the environment: freeing the environment from toxins
  • How to completely adopt renewable sources of energy.
  • The Principle of competitive exclusion and advantages
  • Novel methods of recycling waste paper more effectively
  • Analyzing the effectiveness of various weed killers

Environment and Ecology Research

Carrying out environment and ecology research is not the easiest research area. However, researching environment-related concepts could be very rewarding. Environmental and ecology research covers areas such as biodiversity, biogeography, ecosystem ecology, wildlife management, and so on.

Here are some interesting topics for ecology papers that will help you in your environmental ecology research. Feel free to discover more environment topics .

  • Climate change and the migration of Polar Bears
  • A look into the major changes in the ecosystem
  • Wind energy: How the environment can help in energy conservation.
  • Analyzing the growth or decline of farming in the last five years
  • Analyzing the impact of fracking on the environment
  • The best methods for measuring worldwide climate change.
  • Are human damages to the environment irreversible?

Ecology Issues

More than ever, the ecosystem is beginning to feel the impact of humans. Most of the activities and actions of humans have negative effects on the environment. These effects are growing every day and becoming increasingly undeniable. We are endangering the lives of future generations of all species!

Many people are still unaware of how their activities bring about negative changes to the ecosystem. Although terms such as “genetic modification” and “climate change” seem commonplace, many cannot connect the dots to see why they actually matter.

Would you like to shed some more light on pressing issues in ecology? Well, we shall provide you with a list of ecological problems you can start with! Here, we shall examine some of the biggest environmental problems we face on our planet today. Explore these ecology issues now!

  • Climate change and the availability of natural resources
  • Presence of reactive nitrogen in the environment
  • Air pollution and its effect on the ecosystem
  • Polluted freshwater ecosystems
  • Conservation of forests

Ecology Experiment Ideas

When you have a solid idea for an experiment, it becomes more fun than ever! Here are some ecology lab ideas that are not only interesting but also practical!

  • Exploring the effects of acid rain on aquatic life
  • How can plants help to measure tap water quality?
  • Hydrogen peroxide and plant roots: the effect
  • Common invasive plants and why they are ubiquitous
  • Effect of fertilizers on the aquatic environment
  • Novel ways to neutralize hazardous waste in the environment

Ecology Topics for Presentation

Are you confused on what to talk about in your next ecology presentation in a group or class? Well, you don’t have to worry anymore! Here are some of the best ecology topics for presentations!

  • How do species survive the harshest of conditions?
  • Why do we have salt marshes?
  • Dead zones in seas: the causes
  • Why human exploration is having negative impacts on the environment
  • Which species is the most successful?
  • Latest technologies to make hazardous waste harmless

We have provided you with 50 well-researched ecology topics and ideas for your ecology research, project, presentation, experiments, and lots more. Use these topics to get that much-needed A+. Our academic writers are always happy to help you. Never forget to do something remarkable always!

environment research topics

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  • Open access
  • Published: 25 March 2024

The evolutionary drivers and correlates of viral host jumps

  • Cedric C. S. Tan   ORCID: orcid.org/0000-0003-3536-8465 1 , 2 ,
  • Lucy van Dorp   ORCID: orcid.org/0000-0002-6211-2310 1   na1 &
  • Francois Balloux   ORCID: orcid.org/0000-0003-1978-7715 1   na1  

Nature Ecology & Evolution ( 2024 ) Cite this article

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  • Molecular evolution
  • Viral evolution

Most emerging and re-emerging infectious diseases stem from viruses that naturally circulate in non-human vertebrates. When these viruses cross over into humans, they can cause disease outbreaks, epidemics and pandemics. While zoonotic host jumps have been extensively studied from an ecological perspective, little attention has gone into characterizing the evolutionary drivers and correlates underlying these events. To address this gap, we harnessed the entirety of publicly available viral genomic data, employing a comprehensive suite of network and phylogenetic analyses to investigate the evolutionary mechanisms underpinning recent viral host jumps. Surprisingly, we find that humans are as much a source as a sink for viral spillover events, insofar as we infer more viral host jumps from humans to other animals than from animals to humans. Moreover, we demonstrate heightened evolution in viral lineages that involve putative host jumps. We further observe that the extent of adaptation associated with a host jump is lower for viruses with broader host ranges. Finally, we show that the genomic targets of natural selection associated with host jumps vary across different viral families, with either structural or auxiliary genes being the prime targets of selection. Collectively, our results illuminate some of the evolutionary drivers underlying viral host jumps that may contribute to mitigating viral threats across species boundaries.

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The majority of emerging and re-emerging infectious diseases in humans are caused by viruses that have jumped from wild and domestic animal populations into humans (that is, zoonoses) 1 . Zoonotic viruses have caused countless disease outbreaks ranging from isolated cases to pandemics and have taken a major toll on human health throughout history. There is a pressing need to develop better approaches to pre-empt the emergence of viral infectious diseases and mitigate their effects. As such, there is an immense interest in understanding the correlates and mechanisms of zoonotic host jumps 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 .

Most studies thus far have primarily investigated the ecological and phenotypic risk factors contributing to viral host range through the use of host–virus association databases constructed mainly on the basis of systematic literature reviews and online compendiums, including VIRION 11 and CLOVER 12 . For example, ‘generalist’ viruses that can infect a broader range of hosts have typically been shown to be associated with greater zoonotic potential 2 , 3 , 5 . In addition, factors such as increasing human population density 1 , alterations in human-related land use 4 , ability to replicate in the cytoplasm or being vector-borne 3 are positively associated with zoonotic risk. However, despite global efforts to understand how viral infectious diseases emerge as a result of host jumps, our current understanding remains insufficient to effectively predict, prevent and manage imminent and future infectious disease threats. This may partly stem from the lack of integration of genomics into these ecological and phenotypic analyses.

One challenge for predicting viral disease emergence is that only a small fraction of the viral diversity circulating in wild and domestic vertebrates has been characterized so far. Due to resource and logistical constraints, surveillance studies of novel pathogens in animals often have sparse geographical and/or temporal coverage 13 , 14 and focus on selected host and pathogen taxa. Further, many of these studies do not perform downstream characterization of the novel viruses recovered and may lack sensitivity due to the use of PCR pre-screening to prioritize samples for sequencing 15 . As such, our knowledge of which viruses can, or are likely to emerge and in which settings, is poor. In addition, while genomic analyses are important for investigating the drivers of viral host jumps 16 , most studies do not incorporate genomic data into their analyses. Those that did have mostly focused on measures of host 2 or viral 3 diversity as predictors of zoonotic risk. As such, despite the limited characterization of global viral diversity thus far, existing genomic databases remain a rich, largely untapped resource to better understand the evolutionary processes surrounding viral host jumps.

Further, humans are just one node in a large and complex network of hosts in which viruses are endlessly exchanged, with viral zoonoses representing probably only rare outcomes of this wider ecological network. While research efforts have rightfully focused on zoonoses, viral host jumps between non-human animals remain relatively understudied. Another important process that has received less attention is human-to-animal (that is, anthroponotic) spillover, which may impede biodiversity conservation efforts and could also negatively impact food security. For example, human-sourced metapneumovirus has caused fatal respiratory outbreaks in captive chimpanzees 17 . Anthroponotic events may also lead to the establishment of wild animal reservoirs that may reseed infections in the human population, potentially following the acquisition of animal-specific adaptations that could increase the transmissibility or pathogenicity of a virus in humans 13 . Uncovering the broader evolutionary processes surrounding host jumps across vertebrate species may therefore enhance our ability to pre-empt and mitigate the effects of infectious diseases on both human and animal health.

A major challenge for understanding macroevolutionary processes through large-scale genomic analyses is the traditional reliance on physical and biological properties of viruses to define viral taxa, which is largely a vestige of the pre-genomic era 18 . As a result, taxon names may not always accurately reflect the evolutionary relatedness of viruses, precluding robust comparative analyses involving diverse viral taxa. Notably, the International Committee on Taxonomy of Viruses (ICTV) has been strongly advocating for taxon names to also reflect the evolutionary history of viruses 18 , 19 . However, the increasing use of metagenomic sequencing technologies has resulted in a large influx of newly discovered viruses that have not yet been incorporated into the ICTV taxonomy. Furthermore, it remains challenging to formally assess genetic relatedness through multiple sequence alignments of thousands of sequences comprising diverse viral taxa, particularly for those that experience a high frequency of recombination or reassortment.

In this study, we leverage the ~12 million viral sequences and associated host metadata hosted on NCBI to assess the current state of global viral genomic surveillance. We additionally analyse ~59,000 viral sequences isolated from various vertebrate hosts using a bespoke approach that is agnostic to viral taxonomy to understand the evolutionary processes surrounding host jumps. We ascertain overall trends in the directionality of viral host jumps between human and non-human vertebrates and quantify the amount of detectable adaptation associated with putative host jumps. Finally, we examine, for a subset of viruses, signatures of adaptive evolution detected in specific categories of viral proteins associated with facilitating or sustaining host jumps. Together, we provide a comprehensive assessment of potential genomic correlates underpinning host jumps in viruses across humans and other non-human vertebrates.

An incomplete picture of global vertebrate viral diversity

Global genomic surveillance of viruses from different hosts is key to preparing for emerging and re-emerging infectious diseases in humans and animals 13 , 16 . To identify the scope of viral genomic data collected thus far, we downloaded the metadata of all viral sequences hosted on NCBI Virus ( n  = 11,645,803; accessed 22 July 2023; Supplementary Data 1 ). Most (68%) of these sequences were associated with SARS-CoV-2, reflecting the intense sequencing efforts during the COVID-19 pandemic. In addition, of these sequences, 93.6%, 3.3%, 1.5%, 1.1% and 0.6% were of viruses with single-stranded (ss)RNA, double-stranded (ds)DNA, dsRNA, ssDNA and unspecified genome compositions, respectively. The dominance of ssRNA viruses is not entirely explained by the high number of SARS-CoV-2 genomes, as ssRNA viruses still represent 80% of all viral genomes if SARS-CoV-2 is discounted.

Vertebrate-associated viral sequences represent 93% of this dataset, of which 93% were human associated. The next four most-sequenced viruses are associated with domestic animals ( Sus , Gallus , Bos and Anas ) and, after excluding SARS-CoV-2, represent 15% of vertebrate viral sequences, while viruses isolated from the remaining vertebrate genera occupy a mere 9% (Fig. 1a and Extended Data Fig. 1a ), highlighting the human-centric nature of viral genomic surveillance. Further, only a limited number of non-human vertebrate families have at least ten associated viral genome sequences deposited (Fig. 1b ), reinforcing the fact that a substantial proportion of viral diversity in vertebrates remains uncharacterized. Viral sequences obtained from non-human vertebrates thus far also display a strong geographic bias, with most samples collected from the United States of America and China, whereas countries in Africa, Central Asia, South America and Eastern Europe are highly underrepresented (Fig. 1c ). This geographical bias varies among the four most-sequenced non-human host genera Sus , Gallus , Anas and Bos (Extended Data Fig. 1b ). Finally, the user-submitted host metadata associated with viral sequences, which is key to understanding global trends in the evolution and spread of viruses in wildlife, remains poor, with 45% and 37% of non-human viral sequences having no associated host information provided at the genus level, or sample collection year, respectively. The proportion of missing metadata also varies extensively between viral families and between countries (Extended Data Fig. 2 ). Overall, these results highlight the massive gaps in the genomic surveillance of viruses in wildlife globally and the need for more conscientious reporting of sample metadata.

figure 1

a , Proportion of non-SARS-CoV-2, vertebrate-associated viral sequences deposited in public sequence databases ( n  = 2,874,732), stratified by host. Viral sequences associated with humans and the next four most-sampled vertebrate hosts are shown. Sequences with no host metadata resolved at the genus level are denoted as ‘missing’. b , Proportion of host families represented by at least 10 associated viral sequences for the five major vertebrate host groups. c , Global heat map of sequencing effort, generated from all viral sequences deposited in public sequence databases that are not associated with human hosts ( n  = 1,599,672). d , Number of vertebrate viral species on NCBI Virus used for the genomic analyses in this study, stratified by viral family. The 32 vertebrate-associated viral families considered in this study are shown and the remaining 21 families that were not considered are denoted as ‘others’.

Humans give more viruses to animals than they do to us

To investigate the relative frequency of anthroponotic and zoonotic host jumps, we retrieved 58,657 quality-controlled viral genomes spanning 32 viral families, associated with 62 vertebrate host orders and representing 24% of all vertebrate viral species on NCBI Virus ( https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/ ) (Fig. 1d ). We found that the user-submitted species identifiers of these viral genomes are poorly ascribed, with only 37% of species names consistent with those in the ICTV viral taxonomy 20 . In addition, the genetic diversity represented by different viral species is highly variable since they are conventionally defined on the basis of the genetic, phenotypic and ecological attributes of viruses 18 . Thus, we implemented a species-agnostic approach based on network theory to define ‘viral cliques’ that represent discrete taxonomic units with similar degrees of genetic diversity, similar to the concept of operational taxonomic units 21 (Fig. 2a and Methods ). A similar approach was previously shown to effectively partition the genomic diversity of plasmids in a biologically relevant manner 22 . Using this approach, we identified 5,128 viral cliques across the 32 viral families that were highly concordant with ICTV-defined species (median adjusted Rand index, ARI = 83%; adjusted mutual information, AMI = 75%) and of which 95% were monophyletic (Fig. 2a ). Some clique assignments aggregated multiple viral species identifiers, while others disaggregated species into multiple cliques (Fig. 2b ; clique assignments for Coronaviridae illustrated in Extended Data Fig. 3 ). Despite the human-centric nature of genomic surveillance, viral cliques involving only animals represent 62% of all cliques, highlighting the extensive diversity of animal viruses in the global viral-sharing network (Extended Data Fig. 4a ).

figure 2

a , Workflow for taxonomy-agnostic clique assignments. Briefly, the alignment-free Mash 53 distances between complete viral genomes in each viral family are computed and dense networks where nodes and edges representing viral genomes and the pairwise Mash distances, respectively, are constructed. From these networks, edges representing Mash distances >0.15 are removed to produce sparse networks, on which the community-detection algorithm, Infomap 54 , is applied to identify viral cliques. Concordance with the ICTV taxonomy was assessed using ARI and AMI. b , Sparse networks of representative viral cliques identified within the Coronaviridae (ssRNA), Picobirnaviridae (dsRNA), Genomoviridae (ssDNA) and Adenoviridae (dsDNA). Some viral clique assignments aggregated multiple viral species, while others disaggregated species into multiple cliques. Nodes, node shapes and edges represent individual genomes, their associated host and their pairwise Mash distances, respectively. The list of viral families considered in our analysis are shown on the bottom-left corner of each panel. Silhouettes were sourced from Flaticon.com and Adobe Stock Images ( https://stock.adobe.com ) with a standard licence.

We then identified putative host jumps within these viral cliques by producing curated whole-genome alignments to which we applied maximum-likelihood phylogenetic reconstruction. For segmented viruses, we instead used single-gene alignments as the high frequency of reassortment 23 precludes robust phylogenetic reconstruction using whole genomes. Phylogenetic trees were rooted with suitable outgroups identified using metrics of alignment-free distances (see Methods ). We subsequently reconstructed the host states of all ancestral nodes in each tree, allowing us to determine the most probable direction of a host jump for each viral sequence (approach illustrated in Fig. 3a ). To minimize the uncertainty in the ancestral reconstructions, we considered only host jumps where the likelihood of the ancestral host state was twofold higher than alternative host states (Fig. 3a and Supplementary Methods ). Varying the stringency of this likelihood threshold yielded highly consistent results (Extended Data Fig. 5a ), indicating that the inferred host jumps are robust to our choice of threshold. In total, we identified 12,676 viral lineages comprising 2,904 putative vertebrate host jumps across 174 of these viral cliques.

figure 3

a , Illustration of ancestral host reconstruction approach used to infer the directionality of putative host jumps. Putative host jumps are identified if the ancestral host state has a twofold higher likelihood than alternative host states. The mutational distance (substitutions per site) represents the sum of the branch lengths between the tip sequence and the ancestral node for which the first host state transition occurred in a tip-to-root traverse. b , Number of distinct putative host jumps involving humans across all viral families considered ( n  = 32). Black dots represent the observed point estimates for each type of host jump. The violin plots show the bootstrap distributions of these estimates, where the host jumps within each viral clique were resampled with replacement for 1,000 iterations. Black lines show the 95% confidence intervals associated with these bootstrap distributions. Silhouettes were sourced from Flaticon.com and Adobe Stock Images ( https://stock.adobe.com ) with a standard licence. A two-tailed paired t -test was performed to test for a difference in the zoonotic and anthroponotic bootstrap distributions.

Among the putative host jumps inferred to involve human hosts (599/2,904; 21%), we found a much higher frequency of anthroponotic compared with zoonotic host jumps (64% vs 36%, respectively; Fig. 3b ). This finding was statistically significant as assessed via a bootstrap paired t -test ( t  = 227, d.f. = 999, P  < 0.0001) and a permutation test ( P  = 0.035; see Methods ). In addition, this result was robust to our choice of likelihood thresholds used during ancestral reconstruction (Extended Data Fig. 5b ), the tree depth at which the host jump was identified (Extended Data Fig. 5c ), and to sampling bias ( Supplementary Notes and Fig. 1 ). The highest number of anthroponotic jumps was contributed by the cliques representing SARS-CoV-2 (132/383; 34%), MERS-CoV (39/383; 10%) and influenza A (37/383; 10%). This is concordant with the repeated independent anthroponotic spillovers into farmed, captive and wild animals described for SARS-CoV-2 (refs. 13 , 24 , 25 , 26 , 27 ) and influenza A 28 , 29 . Meanwhile, there has only been circumstantial evidence for human-to-camel transmission of MERS-CoV 30 , 31 , 32 . Noting the disproportionate number of anthroponotic jumps contributed by these viral cliques, we reperformed the analysis without them and found a significantly higher frequency of anthroponotic than zoonotic jumps (53.5% vs 46.5%; bootstrap paired t -test, t  = 40, d.f. = 999, P  < 0.0001), suggesting that our results are not driven solely by these cliques. Further, 16/21 of the viral families were involved in more anthroponotic than zoonotic jumps (Extended Data Fig. 5d ), indicating that this finding is generalizable across most viruses. Overall, our results highlight the high but largely underappreciated frequency of anthroponotic jumps among vertebrate viruses.

Host jumps of multihost viruses require fewer adaptations

Before jumping to a new host, a virus in its natural reservoir may fortuitously acquire pre-adaptive mutations that facilitate its transition to a new host. This may be followed by the further acquisition of adaptive mutations as the virus adapts to its new host environment 16 .

For each host jump inferred, we estimated the extent of both pre-jump and post-jump adaptations through the sum of branch lengths from the observed tip to the ancestral node where the host transition occurred (Fig. 3a ). However, in practice, the degree of adaptation inferred may vary on the basis of different factors, including sampling intensity and the time interval between when the host jump occurred and when the virus was isolated from its new host. As such, for each viral clique, we considered only the minimum mutational distance associated with a host jump.

We first examined whether the minimum mutational distance associated with a host jump for each viral clique was higher than the minimum for a random selection of viral lineages not involved in host jumps (Fig. 3a and Methods ). Indeed, the minimum mutational distance for a putative host jump within each clique was significantly higher than that for non-host jumps (Fig. 4a ; two-tailed Mann–Whitney U -test, U  = 6,767, P  < 0.0001). Noting that both sampling intensity and the different mutation rates of viral families may confound these results, we corrected for these confounders using a logistic regression model but found a similar effect (odds ratio, OR host jump  = 1.31; two-tailed Z -test for slope = 0, Z  = 6.58, d.f. = 289, P  < 0.0001).

figure 4

a , b , Distributions (Gaussian kernel densities and boxplots) of ( a ) minimum mutational distance and ( b ) minimum dN/dS for inferred host jump events and non-host jump controls on the logarithmic scale. Differences in distributions were assessed using two-sided Mann–Whitney U -tests. c , d , Scatterplots of the ( c ) minimum mutational distance and ( d ) minimum dN/dS for host jump and non-host jumps. Lines represent univariate linear regression smooths fitted on the data. We corrected for the effects of sequencing effort and viral family membership using Poisson regression models. The parameter estimates in these Poisson models and their statistical significance, as assessed using two-tailed Z -tests, after performing these corrections are annotated. For all panels, each data point represents the minimum distance or minimum dN/dS across all host jump or randomly selected non-host jump lineages in a single clique. Boxplot elements are defined as follows: centre line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range.

We then considered the commonly used measure of directional selection acting on genomes, the ratio of non-synonymous mutations per non-synonymous site (dN) to the number of synonymous mutations per synonymous site (dS). Comparing the minimum dN/dS for host jumps within each clique, we observed that minimum dN/dS was also significantly higher for host jumps compared with non-host jumps (Fig. 4b ; OR host jump  = 2.39; Z  = 4.84, d.f. = 263, P  < 0.0001). Finally, after correcting for viral clique membership, there were no significant differences in log-transformed mutational distance ( F (1,528)  = 2.23, P  = 0.136) or dN/dS estimates ( F (1,338)  = 1.66, P  = 0.198) between zoonotic and anthroponotic jumps, or between forward and reverse cross-species jumps (mutational distance: F (1,1588)  = 0.538, P  = 0.463; dN/dS: F (1,1168)  = 0.0311, P  = 0.860), indicating that there are no direction-specific biases in these measures of adaptation. Overall, these results are consistent with the hypothesized heightened selection following a change in host environment and additionally provide confidence in our ancestral-state reconstruction method for assigning host jump status.

However, the extent of adaptive change required for a viral host jump may vary. For instance, some zoonotic viruses may require minimal adaptation to infect new hosts while in other cases, more substantial genetic changes might be necessary for the virus to overcome barriers that prevent efficient infection or transmission in the new host. We therefore tested the hypothesis that the strength of selection associated with a host jump decreases for viruses that tend to have broader host ranges. To do so, we compared the minimum mutational distance between ancestral and observed host states to the number of host genera sampled for each viral clique. We found that the observed host range for each viral clique is positively associated with greater sequencing intensity (that is, the number of viral genomes in each clique; Pearson’s r  = 0.486; two-tailed t -test for r  = 0, t  = 34.9, d.f. = 3,932, P  < 0.0001), in line with the strong positive correlation between per-host viral diversity and surveillance effort reported in previous studies 2 , 3 , 8 . After correcting for both sequencing effort and viral family membership, we found that the mutational distance for host jumps tends to decrease with broader host ranges (Poisson regression, slope = −0.113; two-tailed Z -test for slope = 0, Z  = −9.40, d.f. = 129, P  < 0.0001). In contrast, the relationship between mutational distance and host range for viral lineages that have not experienced host jumps is only weakly positive (slope = 0.0843; Z  = 7.16, d.f. = 127, P  < 0.0001) (Fig. 4c ). Similarly, the minimum dN/dS for a host jump decreases more substantially for viral cliques with broader host ranges (slope = −0.427; Z  = −9.18, d.f. = 116, P  < 0.0001) than for non-host jump controls (slope = 0.143; Z  = 3.08, d.f. = 116, P  < 0.01) (Fig. 4d ). These trends in mutational distance and dN/dS were consistent when the same analysis was performed for ssDNA, dsDNA, +ssRNA and −ssRNA viruses separately (Extended Data Fig. 6 ). These results indicate that, on average, ‘generalist’ multihost viruses experience lower degrees of adaptation when jumping into new vertebrate hosts.

Host jump adaptations are gene and family specific

We next examined whether genes with different established functions displayed distinctive patterns of adaptive evolution linked to host jump events. Since gene function remains poorly characterized in the large and complex genomes of dsDNA viruses, we focused on the shorter ssRNA and ssDNA viral families. We selected for analysis the four non-segmented viral families with the greatest number of host jump lineages in our dataset: Coronaviridae (+ssRNA; n  = 2,537), Rhabdoviridae (−ssRNA; n  = 1,097), Paramyxoviridae (−ssRNA; n  = 787) and Circoviridae (ssDNA; n  = 695). For these viral families, we extracted all annotated protein-coding regions from their genomes and categorized them as either being associated with cell entry (termed ‘entry’), viral replication (‘replication-associated’) or virion formation (‘structural’), and classifying the remaining genes as ‘auxiliary’ genes.

For the Coronaviridae , Paramyxoviridae and Rhabdoviridae , the entry genes encode surface glycoproteins that could also be considered structural but were not categorized as such given their important role in mediating cell entry. The capsid gene of circoviruses, however, encodes the sole structural protein that is also the key mediator of cell entry and was therefore categorized as structural. To estimate putative signatures of adaptation in relation to lineages that have experienced host jumps for the different gene categories, we modelled the change in log 10 (dN/dS) in host jumps versus non-host jumps using a linear model, while correcting for the effects of clique membership (see Methods ). Contrary to our expectation that entry genes would generally be under the strongest adaptive pressures during a host jump, we found that the strength of adaptation signals for each gene category varied by family. Indeed, the strongest signals were observed for structural proteins in coronaviruses (effect = 0.375, two-tailed t -test for difference in parameter estimates, t  = 4.35, d.f. = 10,121, P  < 0.0001) and auxiliary proteins in paramyxoviruses (effect = 0.439, t  = 2.15, d.f. = 4,225, P  = 0.02) (Fig. 5 ). Meanwhile, no significant adaptive signals were observed in the entry genes of all families (minimum P  = 0.3), except for the capsid gene in circoviruses (effect = 0.325, t  = 2.68, d.f. = 1,367, P  = 0.004) (Fig. 5 ). These findings suggest that selective pressures acting on viral genomes in relation to host jumps are likely to differ by gene function and viral family.

figure 5

The strength of adaptation signals in genes associated with host jump and non-host jump lineages were estimated using linear models for Coronaviridae ( n  = 10,129), Paramyxoviridae ( n  = 4,233), Rhabdoviridae ( n  = 3,321), and Circoviridae ( n  = 1,373). We modelled the effects of gene type and host jump status on log(dN/dS) while correcting for viral clique membership and, for each gene type, inferred the strength of adaptive signal (denoted ‘effect’) as the difference in parameter estimates for host jumps versus non-host jumps. Points and lines represent the parameter estimates and their standard errors, respectively. Differences in parameter estimates were tested against zero using a one-tailed t -test. Subpanels for each gene type were ordered from left to right with increasing effect estimates.

Given the lack of adaptive signals in the entry proteins, we further hypothesized that within each gene, adaptative changes are likely to be localized to regions of functional importance and/or that are under relatively stronger selective pressures exerted by host immunity. To test this, we focused on the spike gene (entry) of viral cliques within the Coronaviridae since the key region involved in viral entry is well characterized (that is, the receptor-binding domain (RBD)) 33 . We found that dN/dS estimates consistent with adaptive evolution were indeed localized to the RBDs, but also to the N-terminal domains (NTD), of SARS-CoV-2 (genus Betacoronavirus ), avian infectious bronchitis virus (IBV; Gammacoronavirus ) and MERS (genus Alphacoronavirus ) (Extended Data Fig. 7 ). This is consistent with the strong immune pressures exerted on these regions of the spike protein 34 , 35 and the central role of the RBD in host-cell recognition and entry 36 , 37 , 38 . Overall, our results indicate that the extent of adaptation associated with a host jump likely varies by gene function, gene region and viral family.

The post-genomic era has opened opportunities to advance our understanding of the diversity of viruses in circulation and the macroevolutionary principles of viral host range. Leveraging ~59,000 publicly available viral sequences isolated from vertebrate hosts, we inferred that humans give more viruses to other vertebrates than they give to us across the 32 viral families we considered. We further demonstrated that host jumps are associated with heightened signals of adaptive evolution that tend to decrease in viruses with broader host ranges. This indicates that there may be a minimum mutational threshold necessary for viruses to expand their host range. Finally, we showed that adaptive evolution linked to host jumps may vary by gene function and may be localized to specific gene regions of functional importance.

To bypass the limitations of existing viral taxonomies, we used a taxonomy-agnostic approach to define roughly equivalent units of viral diversity, which formed the basis for most of the analyses presented in this study. The use of operational taxonomic units rather than traditional taxonomic species names further allowed us to perform like-for-like analyses across the entire diversity of viruses. Our approach identified cliques that were largely concordant with traditional viral species nomenclature but also highlighted inconsistencies, where in some cases, single viral species appear to form distinct taxonomic groups while other groups of species seem to form a single group solely based on their genetic relatedness (Fig. 2 and Extended Data Fig. 3 ). However, we do not claim that our approach should supersede existing taxonomic classification systems, especially since a robust and meaningful species definition requires the integration of viral properties with finer-scale evolutionary analyses that was not necessary for our purposes. Nevertheless, we anticipate that the development and use of similar network-based approaches may pave the way for the development of efficient classification frameworks that can rapidly incorporate novel, metagenomically derived viruses into existing taxonomies.

Harnessing cliques as a mechanism of identifying clusters of related viruses for phylogenetic inspection allowed us to quantify the number and sources of recent host jump events. One important caveat to this approach is that the viral cliques involved in putative host jumps represent only a fraction of the viral diversity sequenced thus far (Extended Data Fig. 4b ) and the patterns we observed may change as more viruses are discovered. However, we consistently found higher frequencies of anthroponotic than zoonotic jumps across 16 of the 21 viral families (Extended Data Fig. 5d ). Since each of these families are associated with varying viral discovery effort, the consistency of this pattern makes it highly unlikely that surveillance biases are driving the excess of anthroponotic jumps we inferred. Another caveat is that our clique assignment approach clusters viruses within ~15% sequence divergence, which limits our analyses to relatively recent host jump events. However, the limited divergence of the sequences within each clique also allowed us to produce more robust alignments and hence evolutionary inferences.

Of the 599 recent host jumps identified, 64% were inferred as anthroponotic (Fig. 3b ). While the relative importance of anthroponotic versus zoonotic events has been speculated 13 , 29 , 39 , 40 , we provide a formal evaluation of the zoonotic-to-anthroponotic ratio in vertebrates, showing that anthroponoses are equally, if not more, critical to consider than zoonoses when assessing viral spillover dynamics. It stands to reason that the substantial global human population size and ubiquitous spatial distribution position us as a major source for viral exchange. However, it is also likely that behavioural factors might amplify the risk of anthroponotic transmission, for example, through changes in land use, agricultural methods or heightened interactions between humans and wildlife 4 . Overall, our results highlight the importance of surveying and monitoring human-to-animal transmission of viruses, and its impacts on human and animal health.

We observed heightened evolution and adaptive signals in association with host jumps (Fig. 4 ). This result is largely intuitive, since a virus jumping into a new host is likely to be under different selective pressures exerted directly by the novel host environment and indirectly by changes in host-to-host transmission dynamics. The evolutionary signals we captured may include pre-requisite adaptations that enable a virus to infect the new host. In addition, they probably also represent the burst of adaptive mutations which may be acquired following a host jump, which has been demonstrated for multiple viral systems 24 , 41 , 42 , 43 . Further, these signals could potentially reflect a relaxation of previous selective pressures no longer present in the novel host. We note that these signals of heightened evolution could also, in principle, be inflated by sampling bias, where two viruses circulating in the same host are more often drawn from the same population. However, this was largely controlled for in our analysis through comparisons to representative non-host jump lineages that are expected to be affected by the same sampling bias.

We observed lower mutational and adaptive signals associated with host jumps for viruses that infect a broader range of hosts (Fig. 4c,d ). The most likely explanation for this pattern is that some viruses are intrinsically more capable of infecting a diverse range of hosts, possibly by exploiting host-cell machinery that are conserved across different hosts. For example, sarbecoviruses (the subgenus comprising SARS-CoV-2) target the ACE2 host-cell receptor, which is conserved across vertebrates 44 , 45 , and the high structural conservation of the sarbecovirus spike protein 15 may explain the observation that single mutations can enable sarbecoviruses to expand their host tropism 46 . In other words, multihost viruses may have evolved to target more conserved host machinery that reduces the mutational barrier for them to productively infect new hosts. This may provide a mechanistic explanation for previous observations that viruses with broad host range have a higher risk of emerging as zoonotic diseases 2 , 3 , 5 .

Our approach to identifying putative host jumps hinges on ancestral-state reconstruction (Fig. 3a ), which has been shown to be affected by sampling biases 47 , 48 . However, we accounted for this, at least in part, by including sequencing effort as a measure of sampling bias in our statistical models, allowing us to draw inferences that were robust to disproportionate sampling of viruses in different hosts. Our approach also does not consider the epidemiology or ecology of viral transmission, as this is largely dependent on host features such as population size, social structure and behaviour for which comprehensive datasets at this scale are not currently available. We anticipate that future datasets that integrate ecology, epidemiology and genomics may allow more granular investigations of these patterns in specific host and viral systems. In addition, the patterns we described are broad and do not capture the idiosyncrasies of individual host–pathogen associations. These include a variety of biological features— intrinsic ones, such as the molecular adaptations required for receptor binding, as well as more complex ones including cross-immunity and interference with other viral pathogens circulating in a host population.

Overall, our work highlights the large scope of genomic data in the public domain and its utility in exploring the evolutionary mechanisms of viral host jumps. However, the large gaps in the genomic surveillance of viruses thus far suggest that we have only just scratched the surface of the true viral diversity in nature. In addition, despite the strong anthropocentric bias in viral surveillance, 81% of the putative host jumps identified in this study do not involve humans, emphasizing the large underappreciated scale of the global viral-sharing network (Extended Data Fig. 8 ). Widening our field of view beyond zoonoses and investigating the flow of viruses within this larger network could yield valuable insights that may help us better prepare for and manage infectious disease emergence at the human–animal interface.

Data acquisition, curation and quality control

The metadata of all partial and complete viral genomes were downloaded from NCBI Virus ( https://www.ncbi.nlm.nih.gov/labs/virus/vssi/#/ ) on 22 July 2023, with filters excluding sequences isolated from environmental sources, lab hosts, or associated with vaccine strains or proviruses ( n  = 11,645,803). Where possible, host taxa names in the metadata were resolved in accordance with the NCBI taxonomy 49 using the ‘taxizedb’ v.0.3.1 package in R. User-submitted viral species names were compared to the ICTV master species list version ‘MSL38.V2’ dated 6 July 2023.

To generate a candidate list of viral sequences for further genomic analysis, the metadata were filtered to include 53 viral families known to infect vertebrate hosts on the basis of information provided in the 2022 release of the ICTV taxonomy ( https://ictv.global/taxonomy ) 50 and with reference to that provided by ViralZone ( https://viralzone.expasy.org/ ) 51 . We then retained only sequences from viral families comprising at least 100 sequences of greater than 1,000 nt in length. Since the sequences of segmented viral families are rarely deposited as whole genomes and since the high frequency of reassortment 23 precludes robust phylogenetic reconstruction, we identified sequences for single genes conserved within each of these families for further analysis ( Arenaviridae : L segment; Birnaviridae : ORF1/RdRP/VP1/Segment B; Peribunyaviridae : L segment; Orthomyxoviridae : PB1; Picobirnaviridae : RdRP; Sedoreoviridae : VP1/Segment 1/RdRP; Spinareoviridae : Segment 1/RdRP/Lambda 3). These sequences were retrieved by applying text-based pattern matching (that is, ‘grepl’ in R) to query the GenBank sequence titles. For non-segmented genomes, we retained all non-human-associated sequences and subsampled the human-associated sequences as follows: we selected a random subsample of 1,000 SARS-CoV-2 genomes of greater than 28,000 nt from distinct countries, isolation sources and with distinct collection dates. For influenza B, we retained only human sequences with distinct country of origins, sample types and collection dates, and hosts of isolation. For other human-associated sequences, we retained viruses with distinct species, country, isolation source and collection date information. We then downloaded the final candidate list of viral sequences ( n  = 92,973) using ‘ncbi-acc-download’ v.0.2.8 ( https://github.com/kblin/ncbi-acc-download ). Further quality control of the genomes downloaded was performed using ‘CheckV’ (v.1.0.1) 52 , retaining sequences with more than 95% completeness (for non-segmented viruses) and less than 5% contamination (for all sequences). This resulted in a final genomic dataset comprising 58,657 observations (Supplementary Table 1 ) composed of gene sequences for segmented viruses and complete genomes for non-segmented viruses. For simplicity, we will henceforth refer to the gene sequences and complete genomes as ‘genomes’.

Taxonomy-agnostic identification of viral cliques

To identify viral cliques, we calculated the pairwise alignment-free Mash distances of genomes within each viral family via ‘Mash’ (v.1.1) 53 with a k -mer size of 13. This k -mer size ensures that the probability of observing a k -mer by chance, given the median genome length for each clique, is less than 0.01. Given a genome length, l , alphabet, Σ  = {A, T, G, C}, and the desired probability of observing a k -mer by chance, q  = 0.01, this was computed using the formula described previously 53 :

We then constructed undirected graphs for each viral family with nodes and edges representing genomes and Mash distances, respectively. From these networks, we removed edges with Mash distance values greater than a certain threshold, t , before we applied the community-detection algorithm, Infomap 54 . This community-detection algorithm performs well in both large (>1,000 nodes) and small (≤1,000 nodes) undirected graphs 55 and seeks to identify subgraphs within these undirected graphs that minimize the information required to constrain the movement of a random walker 54 . We refer to the subgraphs identified through this algorithm as ‘viral cliques’. Here we forced the community-detection algorithm to identify taxonomically relevant cliques by removing edges with Mash distance values greater than t , which resulted in sparser graphs with closely related genomes (for example, from the same species) being more densely connected than more distantly related genomes (for example, different species). The value of t was selected by maximizing the proportion of monophyletic cliques identified and the concordance of the viral cliques identified with the viral species names from the NCBI taxonomy, based on the commonly used clustering performance metrics, AMI and ARI (Supplementary Fig. 2 ). These metrics were computed using the ‘AMI’ and ‘ARI’ functions in ‘Aricode’ v.1.0.2. To assess whether the viral cliques identified fulfil the species definition criterion of being monophyletic 18 , we reconstructed the phylogenies of each viral family by applying the neighbour-joining algorithm 56 implemented in the ‘Ape’ v.5.7.1 R package on their pairwise Mash distance matrices. We then computed the proportion of monophyletic viral cliques using the ‘is.monophyletic’ function in Ape v.5.7.1 across the various values of t . Given the discordance between the NCBI and ICTV taxonomies, we applied the above optimization protocol to t using the viral species names in the ICTV taxonomy. Using the NCBI viral species names, t  = 0.15 maximized both the median AMI and ARI across all families (Supplementary Fig. 2a ), with 94.3% of the cliques identified being monophyletic (Supplementary Fig. 2b ). Using the ICTV viral species names, t  = 0.2 and t  = 0.25 maximized the median AMI and median ARI across families (Supplementary Fig. 2c ), with 93.7% and 87.8% of the cliques being monophyletic (Supplementary Fig. 2b ), respectively. Since t  = 0.15 produced the highest proportion of monophyletic clades that were approximately concordant with existing viral taxonomies, we used this threshold to generate the final viral clique assignments for downstream analyses (Supplementary Table 1 ).

Identification of putative host jumps

We retrieved all viral cliques that were associated with at least two distinct host genera and comprised at least 10 genomes ( n  = 215). We then generated clique-level genome alignments using the ‘FFT-NS-2’ algorithm in ‘MAFFT’ (v.7.490) 57 , 58 . We masked regions of the alignments that were poorly aligned or prone to sequencing error by replacing alignment sites that had more than 10% of gaps or ambiguous nucleotides with Ns. Clique-level genome alignments that had more than 20% of the median genome length masked were considered to be poorly aligned and thus removed from further analysis ( n  = 6; Supplementary Fig. 3 ). Following this procedure, we reconstructed maximum-likelihood phylogenies for each viral clique with ‘IQ-Tree’ (v.2.1.4-beta) 59 , using 1,000 ultrafast bootstrap (UFBoot) 60 replicates. The optimal substitution model for each tree was automatically determined using the ‘ModelFinder’ 61 utility native to IQ-Tree. To estimate the root position for each clique tree, we reconstructed neighbour-joining Mash trees for each viral clique, including 10 additional genomes whose minimum pairwise Mash distance to the genomes in each tree was 0.3–0.5, as potential outgroups. The most basal tips in these neighbour-joining Mash trees were identified and used to root the maximum-likelihood clique trees. This approach, as opposed to using maximum-likelihood phylogenetic reconstruction involving the outgroups, was used as it is difficult to reliably align clique sequences with highly divergent outgroups.

To identify putative host jumps, we performed ancestral-state reconstruction on the resultant rooted maximum-likelihood phylogenies with host as a discrete trait using the ‘ace’ function in Ape v.5.7.1. Traversing from a tip to the root node, a putative host jump is identified if the reconstructed host state of an ancestral node is different from the observed tip state, has a twofold greater likelihood compared with alternative states and is different from the host state of the sampled tip. Where the tip and ancestral host states were of different taxonomic ranks, we excluded putative host jumps where the ancestral host state is nested within the tip host state, or vice versa (for example, ‘ Homo ’ and ‘Hominidae’). Missing host metadata were encoded as ‘unknown’ and included in the ancestral-state reconstruction analysis. Host jumps involving unknown or non-vertebrate host states were excluded from further analysis. Separately, we extracted non-host jump lineages to control for any biases in our analysis approach. To do so, we randomly selected an ancestral node where the reconstructed host state is the same as the observed tip state and has a twofold greater likelihood than alternative host states, for each viral genome that is not involved in any putative host jumps. For the mutational distance and dN/dS analyses, we retained only viral cliques where non-host jump lineages could be identified. An analysis exploring the robustness of this host jump inference approach to sampling biases (Supplementary Fig. 1 ) and a more detailed description of the inference algorithm (Supplementary Fig. 4 ) are provided in Supplementary Information .

Implementation of this algorithm yielded a list of all viral lineages involving a host jump (Supplementary Table 2 ). Since multiple lineages may involve a host transition at the same ancestral node, we calculated the number of unique host jump events as the number of distinct nodes for each unique host pair. For example, the three lineages Node1 (host A)→Tip1 (host B), Node1 (host A)→Tip2 (host B) and Node1 (host A)→Tip3 (host C) would be considered as two distinct host jump events, one between hosts A and B and the other between hosts A and C. This counting approach was used for Fig. 3a and Extended Data Fig. 5 . The list of all 2,904 distinct host jumps is provided in Supplementary Table 3 .

Calculating mutational distances and dN/dS

Mutational distance and dN/dS estimates may be lineage specific and may depend on sampling intensity. In addition, there is a nonlinear relationship between dN/dS and branch length, that is, the estimated dN/dS decreases with increasing evolutionary distance 62 . Therefore, we opted to compare the minimum adaptive signal (that is, minimum dN/dS) associated with a host jump for each clique. For host jump lineages, mutational distances were calculated as the sum of the branch lengths between the tip sequence and the ancestral node for which the first host state transition occurred (in substitutions per site) using the ‘get_pairwise_distances’ function in the ‘Castor’ (v.1.7.10) 63 R package; this was then multiplied by the alignment length to obtain the estimated number of substitutions (Fig. 3a ). To calculate the dN/dS estimates, we reconstructed the ancestral sequences of ancestral nodes using the ‘-asr’ flag in IQ-Tree, which is based on an empirical Bayesian algorithm ( http://www.iqtree.org/doc/Command-Reference ). We then extracted coding regions from the clique-level masked alignments based on the user-submitted gene annotations on NCBI GenBank (in ‘gff’ format) of each viral genome. We then computed the dN/dS estimates using the method of ref. 64 implemented in the ‘dnastring2kaks’ function of the ‘MSA2dist’ v.1.4.0 R package ( https://github.com/kullrich/MSA2dist ). We calculated the minimum mutational distance and dN/dS across all host jump events in each clique for our downstream statistical analyses, which, in principle, represents the minimum evolutionary signal associated with a host jump in each viral clique. For non-host jump lineages, we similarly computed the minimum mutational distance and dN/dS across the randomly selected lineages. Estimates where dN = 0 or dS = 0 were removed. The list of all minimum mutational distance and minimum dN/dS estimates is provided in Supplementary Tables 4 and 5 , respectively. The dN/dS estimates for the analysis shown in Fig. 5 are provided in Supplementary Table 6 .

For the coronavirus spike gene analysis (Extended Data Fig. 7 ), spike sequences were extracted from the clique-level multiple sequence alignments, with gaps trimmed to the reference sequences (avian infectious bronchitis virus, EU714028.1; SARS-CoV-2, MN908947.3; MERS, JX869059.2). The genomic coordinates for the functional domains of the spike proteins were derived from previous studies 33 , 37 , 65 . Estimates where dN = 0 or dS = 0 were removed. The dN/dS estimates are provided in Supplementary Table 7 .

Statistical analyses

All statistical analyses were performed using the ‘stats’ package native to R v.4.3.1. To generate the bootstrapped distributions shown in Fig. 3b , we randomly resampled the host jumps within each clique with replacement (1,000 iterations) and performed two-tailed paired t -tests using the ‘t.test’ function. Mann–Whitney U -tests, analysis of variance (ANOVA), linear regressions, and Poisson and logistic regressions were implemented using ‘wilcox.test’, ‘anova’, ‘lm’ and ‘glm’ functions, respectively.

A permutation test was performed to assess whether the higher proportion of anthroponotic versus zoonotic jumps was statistically significant. We randomly permuted the host states in each clique for 500 iterations while preserving the number of host-jump and non-host-jump lineages (illustrated in Supplementary Fig. 5 ). The P value was calculated as the number of iterations where the permutated anthroponotic/zoonotic ratio was greater than or equal to the observed ratio.

To assess the relationship between host range and adaptative signals (Fig. 4 ), we used Poisson regressions to model the expected number of host genera observed in each viral clique, λ host range . We corrected for the number of genomes in each clique, g , as a measure of sampling effort, and viral family membership, v , by including them as fixed effects in these models. These models can be formalized for mutational distance or dN/dS, d , with some p number of viral families and residual error, ε , as:

We tested whether the parameter estimates were non-zero by performing two-tailed Z -tests implemented within the ‘summary’ function in R.

To estimate the strength of adaptive signals for coronaviruses, paramyxoviruses, rhabdoviruses and circoviruses (Fig. 5 ) by gene type, we implemented two linear regression models for each viral family. Since the overall adaptive signal may differ for each viral clique, we corrected for this effect by using an initial linear model where the number of viral cliques, viral clique membership and residual are given by q , c and ε , respectively, as follows:

Subsequently, we used the corrected log(dN/dS) estimates represented by the residuals of model 1, ε model 1 , in a second linear model partitioning the effects of gene type by host jump status, j . Given r number of gene types, this model can be formalized as follows:

The estimated effects shown in Fig. 5 , representative of the difference in adaptive signals associated with jump and non-host jump lineages for each gene type, were then computed as:

To test whether this effect is statistically significant, we used a one-tailed t -test, with the t statistic computed using the standard error of the parameter estimates in model 2:

The residuals of model 2 were confirmed to be approximately normal by visual inspection (Supplementary Fig. 6 ).

Data analysis and visualization

All data analyses were performed using R v.4.3.1. All visualizations were performed using ggplot (v.3.4.2) 66 or ggtree (v.3.8.2) 67 . UpSet plots were created using the R package, UpSetR (v.1.4.0) 68 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The full list of accessions considered in this study is provided in Supplementary Data 1 . The data used for the main analyses are provided in Supplementary Tables 2–7 . All reconstructed maximum-likelihood trees and ancestral sequences used for the analyses are hosted on Zenodo ( https://doi.org/10.5281/zenodo.10214868 ) 69 .

Code availability

All custom code used to perform the analyses reported here are hosted on GitHub ( https://github.com/cednotsed/vertebrate_host_jumps ).

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Acknowledgements

We thank R. J. Gibbs, G. Murray and L. P. Shaw for helpful feedback and discussions. C.C.S.T. was funded by the National Science Scholarship from the Agency for Science, Technology and Research (A*STAR), Singapore. F.B. and L.v.D. were funded by the European Commission (Horizon 2021–2024, END-VOC Project). L.v.D. was also funded by the UCL Excellence Fellowship. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency. For the purpose of open access, the corresponding author has applied a ‘Creative Commons Attribution’ (CC BY) licence to any author-accepted version of the manuscript. The authors acknowledge the use of the UCL Myriad High Performance Computing Facility (Myriad@UCL), the UCL Department of Computer Science High Performance Computing Cluster and associated support services in the completion of this work.

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These authors contributed equally: Lucy van Dorp, Francois Balloux.

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UCL Genetics Institute, University College London, London, UK

Cedric C. S. Tan, Lucy van Dorp & Francois Balloux

The Francis Crick Institute, London, UK

Cedric C. S. Tan

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C.C.S.T. performed all analyses. L.v.D. and F.B. jointly supervised the study. C.C.S.T., L.v.D. and F.B. wrote the manuscript.

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Correspondence to Cedric C. S. Tan .

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Extended data

Extended data fig. 1 host and geographical distribution of viral sequences..

( a ) Number of viral sequences, excluding SARS-CoV-2, associated with the top 50 vertebrate hosts observed in the ‘others’ category as shown in main text Fig. 1a . ( b ) Number of viral sequences stratified by the four most-sequenced non-human animals, excluding SARS-CoV-2. The number of viral sequences for the top 10 countries are shown as bar plots. The percentage of viral sequences for the top three most sequenced viral species for each host are annotated.

Extended Data Fig. 2 Distribution of missing metadata for viral sequences.

(Top) Proportion of all viral sequences associated to non-human vertebrates ( n  = 1,599,672) with missing genus information or (bottom) sample collection year, stratified by viral family or country of origin. Countries with no associated sequences are denoted ‘NA’.

Extended Data Fig. 3 Viral cliques for Coronaviridae .

Sparse networks of viral cliques identified (see Methods) and their corresponding user-submitted species names for the Coronaviridae , similar to main text Fig. 2 . Nodes, node shapes, and edges represent individual genomes, their associated host and their pairwise Mash (alignment-free) distances, respectively.

Extended Data Fig. 4 Summary of viral cliques identified.

( a ) Number of viral cliques identified stratified by viral family. Cliques with only animal-associated sequences, human-associated sequences, or both are annotated. ( b ) Percentage of viral cliques involving at least one of the 2,904 putative host jumps inferred, stratified by viral family.

Extended Data Fig. 5 Robustness of host jump inference.

( a ) UpSet plot providing the intersecting host jumps identified via ancestral reconstruction when using a two-fold, five-fold or ten-fold likelihood threshold. ( b ) Bar plot showing the number of anthroponotic and zoonotic events inferred using various likelihood thresholds, ( c ) at different ancestral node depths, and ( d ) stratified by viral family. For (b), the number of anthroponotic and zoonotic host jumps were stratified by the depth of the ancestral node in the tip-to-node traversal. Since multiple host jump lineages can involve the same ancestral node, the tip-to-node depths may vary depending on which lineage is selected. As such, we randomly selected a viral lineage for each distinct host jump event for this analysis.

Extended Data Fig. 6 Adaptation analysis for viral groups.

Analysis of relationships between host range and estimated adaptive signals, similar to Fig. 3 , but only considering ssDNA, dsDNA, +ssRNA or -ssRNA viruses. Distributions of minimum ( a ) mutational distance and ( b ) dN/dS for host jump and non-host jumps on the logarithmic scale. We corrected for the effects of sequencing effort and viral family membership using Poisson regression models. The estimated effects of patristic distance on host range after these corrections are annotated. We tested whether the estimated effects were non-zero using two-tailed Z-tests. For all panels, each data point represents the minimum distance or dN/dS across all host jump or randomly selected non-host jump lineages in a single clique. Line segments represent linear regression smooths without correction.

Extended Data Fig. 7 Adaptive signals in the Coronaviridae spike gene.

Analysis of the log10(dN/dS) estimates associated to different functional domains encoded by the coronavirus spike gene: N-terminal domain (NTD), receptor-binding domain (RBD), fusion peptide (FP), heptad repeats 1 and 2 (HR1 and HR2), central helix (CH), transmembrane (TM), C-terminal domains (CT). Estimates with dN=0 or dS=0 were removed and the remaining number of sequences for each domain and viral clique are annotated. Differences in distributions were tested for using two-sided Mann-Whitney U tests and the corresponding p-values are annotated. Boxplot elements are defined as follows: centre line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range.

Extended Data Fig. 8 The global viral host jump network.

Directed network of the vertebrate viral-sharing network, where nodes and edges represent host genera and the number of viral cliques shared. Edge widths and colour are indicative of the number of viral cliques shared.

Supplementary information

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Supplementary Note, Methods and Figs. 1–6.

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Tan, C.C.S., van Dorp, L. & Balloux, F. The evolutionary drivers and correlates of viral host jumps. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02353-4

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