Pathogens and Planetary Change – Comprehensive Study Notes

Introduction

• Anthropocene characterized by a planetary “dysbiosis” – rapid, human-driven shifts in host–microbe relationships with negative outcomes for human, animal, and ecosystem health.

• Historically, pandemics were “once-in-a-century” events; since 1900 there have been 10, including two after 2010.

• Spillover frequency ↑ ≈ $5\%$ yr⁻¹; mortality from high-impact spillovers ↑ ≈ $9\%$ yr⁻¹.

• Concurrent crises (“polycrisis”): biodiversity loss, climate change, land-use change, wildlife trade, and rising infectious diseases reinforce one another.

Key Points (Author Highlights)

• Human activities created a planetary polycrisis: pandemics, climate change, sixth mass extinction.

• Same drivers (climate change, land change, agriculture, wildlife use) threaten biodiversity and drive infectious-disease rise.

• Biodiversity loss generally harms human health.

• Targeting high-risk spillover interfaces (e.g., avian-influenza farms, coronavirus wildlife markets) could block some pandemics.

• Even with interface interventions, robust health systems and preparedness are essential for living in the Anthropocene.

• World needs real-time, cross-species pathogen biosurveillance infrastructure.

Biodiversity & Infectious Disease

Multiple Disciplinary Lenses

• Macroecology/systematics → parasites = major fraction of biodiversity.

• Community ecology → species interactions & diversity gradients shape disease across space/time.

• Conservation biology → emerging diseases threaten species survival; nascent movement to conserve (mostly harmless) parasites.

• One Health → integration of conservation, vet medicine, public health; focuses on animal–human interfaces.

• Planetary Health → links climate crisis, mass extinction, emerging disease trends over century-scale.

Host Diversity Drives Pathogen Diversity

• Parasitism evolved >$200$ times in ≥$15$ animal phyla.

• “Diversity begets diversity” at large scales: positive correlation between host-species richness & parasite richness.

• Tropical vertebrate hotspots presumed pathogen hotspots, but evidence limited by huge data gaps & sampling bias (Box 2).

• Viral discovery skewed toward high-income nations; major undersampling in Amazonia/Latin America.

Biodiversity Drives Disease Emergence

• >$70\%$ of emerging human diseases zoonotic; ≈$2/3$ from wildlife.

• Viruses pose exceptional risk: rapid diversification, species-jump propensity, outbreak potential from single case.

• Estimated tens–hundreds of thousands of mammalian viruses capable of infecting humans.

• Apparent “hyper-reservoirs” (e.g., bats) confounded by sampling bias; still, immune traits (constitutive IFN-α, dampened inflammation) may allow bats to tolerate & thus select for virulent, cross-species-ready viruses.

• Primates: immune similarity to humans ➔ viral “pre-adaptation.”

Biodiversity Loss ➔ ↑ Transmission (Dilution Debate)

• Meta-analyses show: natural richness ≈ mixed effects, but loss of richness within communities typically elevates pathogen transmission.

• Mechanisms:

  • Loss of predators → prey boom ("healthy herds effect").

  • Preferential loss of low-competence hosts → prevalence ↑ in remaining competent hosts ("dilution effect").

  • Fast-lived, disturbance-adapted “weedy” species (e.g., Peromyscus leucopus) often competent reservoirs.

• Not all parasites benefit; many co-decline with hosts; parasite loss can paradoxically ↑ risk from certain pathogens via reduced competition or immune modulation.

Disease ➔ Biodiversity Loss

• Wildlife epizootics/panzootics increasingly drive population crashes (e.g., chytridiomycosis > $90$ amphibian extinctions; white-nose syndrome in bats).

• Spillbacks: $\ge 35$ animal spp. infected with SARS-CoV-2; great apes susceptible to human respiratory viruses.

• High-pathogenicity avian influenza H5N1 2.3.4.4b (since 2020) causing unprecedented wild-bird/mammal mortality (e.g., $27\%$ Humboldt-penguin pop. lost 2023).

Common Anthropogenic Drivers

Land-Use Change

• $\approx 1/3$ global land converted since 1960s.

• Habitat loss/fragmentation → nutritional stress, altered behavior/contact, wildlife displacement into human landscapes.

• Example gradients (Fig. 2): forest fragmentation → Lyme disease; but conversion can suppress some pathogens if hosts/vectors excluded.

Agriculture & Livestock

• Livestock biomass ≈ $6.3\times10^8\,\text{t}$ > humans (≈ $3.9\times10^8\,\text{t}$) > wild mammals (≈ $2\times10^7\,\text{t}$).

• Long co-evolution with humans; act as bridge/amplification hosts (e.g., influenza subtypes, MERS-CoV, Nipah via pigs).

Climate Change

• >$50\%$ of known human diseases aggravated by climatic factors.

• Temperature-transmission unimodal curves well studied for mosquitoes (malaria, dengue, Zika); warming drives range expansion (Anopheles, Aedes).

• Range shifts facilitate novel wildlife contacts; extreme heat poses direct stress yet poorly studied for disease.

Wildlife Use (Trade, Farming, Hunting)

• Involves $\approx 25\%$ of vertebrate species; major extinction driver.

• Creates crowding, stress, inter-species mixing; high-risk human contacts along supply chains.

• Fewer than $10\%$ of emerging viruses traced to wildlife trade, but include highest-impact ones (SARS-CoVs).

Other Drivers

• Invasive species (e.g., Aedes aegypti/albopictus, An. stephensi) facilitate pathogen spread.

• Pollution: fertilizer runoff → schistosomiasis habitat; antibiotic leakage → antimicrobial resistance.

Case Studies (Fig. 3)

• Lyme Disease: Forest fragmentation, predator loss, rising temps → Ixodes & Borrelia expansion; dilution effect well documented.

• Hendra Virus (Australia): Habitat conversion & urban roosting lead to bat residency, waning immunity, food-stress shedding pulses (El Niño); spillover via horses.

• Influenza A: Genetic drift/reassortment in poultry; markets & trade critical; wetland loss may crowd migratory birds; climate link speculative.

• Coronaviruses: Few human CoVs but high impact (SARS-CoV, MERS-CoV, SARS-CoV-2); wildlife trade & livestock both key; evidence for dilution effect in West-African bats; land/climate hypotheses still tentative.

Data Sources & Gaps (Box 2)

• Two main data types: species-level host–pathogen associations & organism-level prevalence/intensity.

• Major issues: geographic (outside N. Hemisphere), taxonomic (rodents, bats under-sampled), publication bias (negative results lost).

• Initiatives: VIRION, Global Mammal Parasite DB, MalAvi, PREDICT, PHAROS, WHISPers.

• Best practices: share raw, metadata-rich, negative & positive data; harmonize taxonomy; adopt minimum data standards.

Surveillance & Monitoring Innovations

• SMART app for ranger mortality reports; PHAROS & USGS WHISPers for real-time sharing.

• Non-invasive tech: drones collecting air/eDNA; AI image/sound + eDNA = “next-generation biomonitoring.”

• Biodiversity repositories (GBIF, museums) provide host/vector distribution data for risk mapping, gap analyses, historical reconstructions.

Management & Intervention Strategies

• Education for safe wildlife co-existence; avoid harmful culls (vampire-bat rabies, badger TB examples).

• Vaccination of wildlife/livestock (e.g., condor H5N1 emergency vaccine).

• Ecosystem-based: forest protection, afforestation/reforestation, prescribed burns (tick control) – but empirical tests scarce.

• Win-win criteria: deep system knowledge, local leadership, low cost/aligned incentives.

• Complementary public-health needs: poverty alleviation, universal health coverage, outbreak-response capacity.

Governance Landscape

• Fragmented: WHO/IHR focus on response; CBD, CITES on conservation; now converging via Quadripartite One Health Joint Plan (2022-26).

• Proposed WHO Pandemic Agreement (in negotiation): may embed One Health surveillance & benefit-sharing obligations; cost ≈ $\$22–31.2$ b yr⁻¹.

• Barriers: financing, corporate interests, unequal vaccine access; initiatives like Cali Fund & LISTEN principles aim for equitable data/benefit sharing.

Future Research Directions

1. Broaden evidence base: fill taxonomic, geographic, ecological gaps; embrace longitudinal designs; share fine-scale infection data.

2. Link ecology to human health outcomes: integrate social-ecological variables; obtain finer outbreak data; collaborate with ministries & communities.

3. Align priorities with pandemic risk & neglected diseases: study high-risk viruses (influenza, primate retroviruses) & high-burden NTDs; evaluate ecosystem-restoration impacts on health.

Glossary (selected)

• Arbovirus – arthropod-borne virus.

• Host competence – ability to amplify & transmit pathogen.

• One Health – integration of human, animal, environmental health.

• Spillover – animal→human transmission; Spillback – human→animal.

• Panzootic – global animal outbreak; Pandemic – global human outbreak.

Numerical & Statistical References

• >90\% of amphibian extinctions linked to chytrid.

• Livestock biomass $\approx 630\text{ Mt}$; human biomass $\approx 390\text{ Mt}$; wild mammals $\approx 20\text{ Mt}$.

• Projected deforestation by 2050: $\approx 3\times10^8\,\text{ha}$.

• Projected warming to $1.5^{\circ}\text{C}$ within 25 years.

• Projected future spillover ↑ $4\times$; mortality ↑ $12\times$ without intervention.

Connections to Prior Principles & Real-World Relevance

• “Diversity begets diversity” echoes classic macroecology (species–area, latitudinal gradients).

• Dilution vs. amplification aligns with ecological theory on community assembly & trophic cascades.

• Case studies illustrate application of eco-immunology, landscape ecology, and socio-ecological systems theory.

• Governance discussion integrates environmental treaties and global-health security architecture, highlighting ethical dimensions (data equity, vaccine access).

Introduction

• Anthropocene characterized by a planetary “dysbiosis” – rapid, human-driven shifts in host–microbe relationships with negative outcomes for human, animal, and ecosystem health.

• Historically, pandemics were “once-in-a-century” events; since 1900 there have been 10, including two after 2010.

• Spillover frequency ↑
  $\approx 5\%$
  yr⁻¹; mortality from high-impact spillovers ↑
  $\approx 9\%$
  yr⁻¹.

• Concurrent crises (“polycrisis”): biodiversity loss, climate change, land-use change, wildlife trade, and rising infectious diseases reinforce one another.

Key Points (Author Highlights)

• Human activities created a planetary polycrisis: pandemics, climate change, sixth mass extinction.

• Same drivers (climate change, land change, agriculture, wildlife use) threaten biodiversity and drive infectious-disease rise.

• Biodiversity loss generally harms human health.

• Targeting high-risk spillover interfaces (e.g., avian-influenza farms, coronavirus wildlife markets) could block some pandemics.

• Even with interface interventions, robust health systems and preparedness are essential for living in the Anthropocene.

• World needs real-time, cross-species pathogen biosurveillance infrastructure.

Biodiversity & Infectious Disease

Multiple Disciplinary Lenses

• Macroecology/systematics → parasites = major fraction of biodiversity.

• Community ecology → species interactions & diversity gradients shape disease across space/time.

• Conservation biology → emerging diseases threaten species survival; nascent movement to conserve (mostly harmless) parasites.

• One Health → integration of conservation, vet medicine, public health; focuses on animal–human interfaces.

• Planetary Health → links climate crisis, mass extinction, emerging disease trends over century-scale.

Host Diversity Drives Pathogen Diversity

• Parasitism evolved
  >200
  times in
  $\ge 15$
  animal phyla.

• “Diversity begets diversity” at large scales: positive correlation between host-species richness & parasite richness.

• Tropical vertebrate hotspots presumed pathogen hotspots, but evidence limited by huge data gaps & sampling bias (Box 2).

• Viral discovery skewed toward high-income nations; major undersampling in Amazonia/Latin America.

Biodiversity Drives Disease Emergence

• 
  >70\%
  of emerging human diseases zoonotic;
  $\approx 2/3$
  from wildlife.

• Viruses pose exceptional risk: rapid diversification, species-jump propensity, outbreak potential from single case.

• Estimated tens–hundreds of thousands of mammalian viruses capable of infecting humans.

• Apparent “hyper-reservoirs” (e.g., bats) confounded by sampling bias; still, immune traits (constitutive IFN-α, dampened inflammation) may allow bats to tolerate & thus select for virulent, cross-species-ready viruses.

• Primates: immune similarity to humans
  $\Rightarrow$
  viral “pre-adaptation.”

Biodiversity Loss

$\Rightarrow$
↑ Transmission (Dilution Debate)

• Meta-analyses show: natural richness
  $\approx$
  mixed effects, but loss of richness within communities typically elevates pathogen transmission.

• Mechanisms:- Loss of predators → prey boom ("healthy herds effect").

  • Preferential loss of low-competence hosts → prevalence ↑ in remaining competent hosts ("dilution effect").

  • Fast-lived, disturbance-adapted “weedy” species (e.g., Peromyscus leucopus) often competent reservoirs.

• Not all parasites benefit; many co-decline with hosts; parasite loss can paradoxically ↑ risk from certain pathogens via reduced competition or immune modulation.

Disease

$\Rightarrow$
Biodiversity Loss

• Wildlife epizootics/panzootics increasingly drive population crashes (e.g., chytridiomycosis >
  $90$
  amphibian extinctions; white-nose syndrome in bats).

• Spillbacks:
  $\ge 35$
  animal spp. infected with SARS-CoV-2; great apes susceptible to human respiratory viruses.

• High-pathogenicity avian influenza H5N1 2.3.4.4b (since 2020) causing unprecedented wild-bird/mammal mortality (e.g.,
  $27\%$
  Humboldt-penguin pop. lost 2023).

Common Anthropogenic Drivers

Land-Use Change

• 
  $\approx 1/3$
  global land converted since 1960s.

• Habitat loss/fragmentation → nutritional stress, altered behavior/contact, wildlife displacement into human landscapes.

• Example gradients (Fig. 2): forest fragmentation → Lyme disease; but conversion can suppress some pathogens if hosts/vectors excluded.

Agriculture & Livestock

• Livestock biomass
  $\approx 6.3\times10^8\,\text{t}$

Climate Change

• 
  >50\%
  of known human diseases aggravated by climatic factors.

• Temperature-transmission unimodal curves well studied for mosquitoes (malaria, dengue, Zika); warming drives range expansion (Anopheles, Aedes).

• Range shifts facilitate novel wildlife contacts; extreme heat poses direct stress yet poorly studied for disease.

Wildlife Use (Trade, Farming, Hunting)

• Involves
  $\approx 25\%$
  of vertebrate species; major extinction driver.

• Creates crowding, stress, inter-species mixing; high-risk human contacts along supply chains.

• Fewer than
  $10\%$
  of emerging viruses traced to wildlife trade, but include highest-impact ones (SARS-CoVs).

Other Drivers

• Invasive species (e.g., Aedes aegypti/albopictus, An. stephensi) facilitate pathogen spread.

• Pollution: fertilizer runoff → schistosomiasis habitat; antibiotic leakage → antimicrobial resistance.

Case Studies (Fig. 3)

• Lyme Disease: Forest fragmentation, predator loss, rising temps → Ixodes & Borrelia expansion; dilution effect well documented.

• Hendra Virus (Australia): Habitat conversion & urban roosting lead to bat residency, waning immunity, food-stress shedding pulses (El Niño); spillover via horses.

• Influenza A: Genetic drift/reassortment in poultry; markets & trade critical; wetland loss may crowd migratory birds; climate link speculative.

• Coronaviruses: Few human CoVs but high impact (SARS-CoV, MERS-CoV, SARS-CoV-2); wildlife trade & livestock both key; evidence for dilution effect in West-African bats; land/climate hypotheses still tentative.

Data Sources & Gaps (Box 2)

• Two main data types: species-level host–pathogen associations & organism-level prevalence/intensity.

• Major issues: geographic (outside N. Hemisphere), taxonomic (rodents, bats under-sampled), publication bias (negative results lost).

• Initiatives: VIRION, Global Mammal Parasite DB, MalAvi, PREDICT, PHAROS, WHISPers.

• Best practices: share raw, metadata-rich, negative & positive data; harmonize taxonomy; adopt minimum data standards.

Surveillance & Monitoring Innovations

• SMART app for ranger mortality reports; PHAROS & USGS WHISPers for real-time sharing.

• Non-invasive tech: drones collecting air/eDNA; AI image/sound + eDNA = “next-generation biomonitoring.”

• Biodiversity repositories (GBIF, museums) provide host/vector distribution data for risk mapping, gap analyses, historical reconstructions.

Management & Intervention Strategies

• Education for safe wildlife co-existence; avoid harmful culls (vampire-bat rabies, badger TB examples).

• Vaccination of wildlife/livestock (e.g., condor H5N1 emergency vaccine).

• Ecosystem-based: forest protection, afforestation/reforestation, prescribed burns (tick control) – but empirical tests scarce.

• Win-win criteria: deep system knowledge, local leadership, low cost/aligned incentives.

• Complementary public-health needs: poverty alleviation, universal health coverage, outbreak-response capacity.

Governance Landscape

• Fragmented: WHO/IHR focus on response; CBD, CITES on conservation; now converging via Quadripartite One Health Joint Plan (2022-26).

• Proposed WHO Pandemic Agreement (in negotiation): may embed One Health surveillance & benefit-sharing obligations; cost
  $\approx \$22–31.2$
  b yr⁻¹.

• Barriers: financing, corporate interests, unequal vaccine access; initiatives like Cali Fund & LISTEN principles aim for equitable data/benefit sharing.

Future Research Directions

1. Broaden evidence base: fill taxonomic, geographic, ecological gaps; embrace longitudinal designs; share fine-scale infection data.

2. Link ecology to human health outcomes: integrate social-ecological variables; obtain finer outbreak data; collaborate with ministries & communities.

3. Align priorities with pandemic risk & neglected diseases: study high-risk viruses (influenza, primate retroviruses) & high-burden NTDs; evaluate ecosystem-restoration impacts on health.

Glossary (selected)

• Arbovirus – arthropod-borne virus.

• Host competence – ability to amplify & transmit pathogen.

• One Health – integration of human, animal, environmental health.

• Spillover – animal→human transmission; Spillback – human→animal.

• Panzootic – global animal outbreak; Pandemic – global human outbreak.

Numerical & Statistical References

• 
  >90\%
  of amphibian extinctions linked to chytrid.

• Livestock biomass
  $\approx 630\text{ Mt}$; human biomass
  $\approx 390\text{ Mt}$; wild mammals
  $\approx 20\text{ Mt}$.

• Projected deforestation by 2050:
  $\approx 3\times10^8\,\text{ha}$.

• Projected warming to
  $1.5^{\circ}\text{C}$
  within 25 years.

• Projected future spillover ↑
  $4\times$
  ; mortality ↑
  $12\times$
  without intervention.

Connections to Prior Principles & Real-World Relevance

• “Diversity begets diversity” echoes classic macroecology (species–area, latitudinal gradients).

• Dilution vs. amplification aligns with ecological theory on community assembly & trophic cascades.

• Case studies illustrate application of eco-immunology, landscape ecology, and socio-ecological systems theory.

• Governance discussion integrates environmental treaties and global-health security architecture, highlighting ethical dimensions (data equity, vaccine access).

Summary

The Anthropocene is marked by a planetary "dysbiosis," where human activities are causing rapid shifts in host-microbe relationships, leading to negative health outcomes for humans, animals, and ecosystems. Pandemics have become more frequent, with spillover events and associated mortality increasing significantly. This is part of a larger "polycrisis" encompassing biodiversity loss, climate change, land-use change, and wildlife trade.

Main Take-away Points:

• Human activities are direct drivers of the polycrisis: The same anthropogenic factors (e.g., land-use change, climate change, agriculture, wildlife use) that cause biodiversity loss also fuel the rise of infectious diseases.

• Biodiversity loss generally increases pathogen transmission: While complex, meta-analyses often show that the loss of host richness within communities elevates pathogen transmission, exemplified by the "dilution effect" where the preferential loss of low-competence hosts increases pathogen prevalence in remaining competent hosts.

• Vigilance and integrated approaches are crucial: Effective strategies require real-time, cross-species pathogen biosurveillance, robust health systems, and interventions at high-risk spillover interfaces, integrating conservation, veterinary medicine, and public health (One Health).

Key Examples Supporting These Points:

• Increased Pandemic Frequency: Historically "once-in-a-century," there have been
  $10$
  pandemics since 1900 (two after 2010), with spillover frequency increasing by
  $\approx 5\%$
  annually.

• Zoonotic Origin and Wildlife Connection: More than
  $70\%$
  of emerging human diseases are zoonotic, with approximately
  $2/3$
  originating from wildlife.

• Impact of Biodiversity Loss on Disease:

  • Disease-driven extinctions: Chytridiomycosis caused
    >90
    amphibian extinctions; white-nose syndrome devastates bat populations.

  • Lyme Disease: Forest fragmentation and predator loss linked to Ixodes and Borrelia expansion illustrate the dilution effect.

• Anthropogenic Drivers and Specific Pathogens:

  • Agriculture & Livestock: Act as bridge/amplification hosts for diseases like influenza and MERS-CoV (e.g., Nipah via pigs).

  • Wildlife Use (Trade, Farming): Creates high-risk human contacts; SARS-CoVs are examples of high-impact viruses traced to wildlife trade.

  • Climate Change: Aggravates
    >50\%
    of known human diseases, driving range expansion for vectors like Anopheles and Aedes (malaria, dengue).

  • Hendra Virus: Habitat conversion leading to bat residency and spillover via horses due to food stress.

• Surveillance and Governance Needs: The necessity for innovations like non-invasive tech (drones, AI for biomonitoring) and harmonized global efforts (Quadripartite One Health Joint Plan, proposed WHO Pandemic Agreement) reflects the fragmented governance landscape and need for equitable benefit-sharing.