Dismantling the myth that we can't do anything to help wild animals | Cameron Meyer Shorb

Scale of Wild Animal Populations:

• Wild animals constitute approximately 99% of all sentient minds on Earth.
• Population estimates relative to humans (8.2 billion):
  • Wild mammals (88 billion) ≈ size of a credit card.
  • Birds (200 billion) ≈ size of a standard envelope.
  • Reptiles and amphibians (2 trillion combined) ≈ size of a few sheets of paper.
  • Fish (10 trillion) ≈ size of a medium-sized desk.
  • Terrestrial invertebrates (100,000 trillion) ≈ size of a FIFA regulation soccer field.

Nature of Wild Animal Suffering:

• Primary causes of suffering include predation, starvation, extreme weather, and disease/parasites.
• Predation accounts for 50–90% of deaths in many vertebrate taxa, often involving slow digestion or crushing.
• Parasites and pathogens cause chronic, debilitating suffering without human-style healthcare; some species die from blood loss due to tick infestations.
• Life histories often involve massive juvenile mortality; most individuals do not reach adulthood.
• Social hierarchies can cause bullying, territorial aggression, and exclusion from mating, creating significant distress for specific individuals.
• Animals may experience fear, pain, and stress from environmental conditions, though they also experience positive states like leisure, social bonding, and satiety.

Uncertainty and Scientific Measurement:

• Current science lacks comprehensive data on the subjective experiences of most wild animals.
• Welfare indicators are categorized into physical (stress hormones, biomarkers of aging, inflammation) and behavioral (vocalizations, play, preference testing, cognitive bias).
• Stress hormones like cortisol correlate more with arousal than negative affect (valence); they indicate high energy states (e.g., play or fear) but not necessarily suffering.
• More reliable ("beaver") indicators include qualitative behavioral assessments, preference testing, DNA methylation, and neural markers.
• Researchers must use multiple overlapping metrics to triangulate welfare states, as no single indicator is perfect.

Intervention Strategies and Tractability:

• Selective Reforestation: Planting specific forest types (e.g., oak vs. pine) to minimize suffering based on known welfare impacts of different ecosystems, integrated into existing land management policies.
• Contraception: Reducing infant mortality in bottom-up regulated populations without increasing total population size, thereby lowering competition and starvation rates.
• Vaccination: Implementing oral bait vaccines (e.g., for rabies) to eliminate painful diseases; requires monitoring for trophic cascades and non-target effects.
• Fire Management: Using controlled prescribed burns to reduce the frequency and intensity of wildfires, preventing animals from being burned alive.
• Gene Drives: Potential for eradicating harmful parasites (e.g., screwworms) or introducing disease resistance; currently viewed cautiously due to regulatory and ecological risks.
• Harm Reduction: Interventions that do not alter total population size are preferred to avoid indirect ecosystem collapse.

Ecological Risks and Feedback Loops:

• Interventions can trigger complex trophic cascades; e.g., wolf removal led to coyote expansion, red fox decline, rodent increase, and higher Lyme disease rates.
• Ecosystems are resilient "webs" rather than fragile organisms, but changes in one species can still significantly alter the welfare of others.
• Unintended consequences are a major risk; interventions require small-scale pilots, monitoring, and reversibility (e.g., contraception vs. species eradication).
• The "Poor Mediator Problem": Solving one problem (e.g., human poverty or animal disease) may temporarily increase suffering elsewhere (e.g., higher animal populations competing for resources), but progress is justified to build knowledge and social capacity.

Philosophical and Ethical Objections:

• "Nature is Good": The argument that naturalness has intrinsic value is rejected in favor of individual welfare; natural suffering is not ethically justified simply because it occurs.
• Anthropomorphism: Using human-like emotions as hypotheses for animal experiences is useful for generating questions but requires empirical validation to avoid bias.
• Extremism: If wild animal suffering is net negative, the logical conclusion might be to reduce wild populations or "unwild" habitats, though this is currently a hypothetical end-state rather than a near-term goal.
• Human Responsibility: Humans should not prioritize solving only self-caused problems (e.g., factory farming) to the exclusion of wild suffering, as solving human-induced issues will alter wild ecosystems anyway.

Organizational Focus and Funding:

• Wild Animal Initiative (WAI) aims to build the field of wild animal welfare science through research, grants, and training.
• Key Contributions: Published a comprehensive review of 30 welfare indicators to standardize measurement; established the first university program dedicated to wild animal welfare at NYU.
• Funding Landscape: Open Philanthropy previously provided major funding but recently narrowed focus; the Navigation Fund has stepped in to sustain existing grants.
• Career Opportunities: The field requires expertise in ecology, animal behavior, and physiology; collaboration across disciplines is essential to overcome knowledge gaps.
• Psychological Resilience: Working in the field requires managing emotional distress; guilt and shame are counterproductive for productivity, whereas understanding determinism helps maintain sustainable motivation.

Future Outlook:

• Long-term vision includes "novel ecosystems" designed for higher welfare and legal systems that grant wild animals representation.
• Technological advancements (e.g., CRISPR, targeted gene drives) could eventually allow for pain modulation or disease eradication at scale.
• Success is defined by institutionalizing wild animal welfare science, similar to the current status of conservation biology.
• Optimism is grounded in the potential for empirical progress to reveal robust interventions, rather than immediate total solutions.