POPULATION DYNAMICS OF PARASITIC HELMINTHS AND CLIMATE ANDY DOBSON – DOBSON@PRINCETON.EDU ICTP WORKSHOP MAY 2017
GLOBAL BURDEN OF INTESTINAL NEMATODE INFECTIONS • How many people are infected globally? • What impact does this have on them? • How much has the situation changed in last 50 years? M.-S. Chan (1997) Parasitology Today, 13, 438-443
THIS WORMY WORLD…. STOLL, 1947 & CHAN 1997 • 1947 • 1997 • Humans – 2.2 x10 9 • 5.6 x 10 9 • 29% urban • 45% urban • Ascaris • 30% 24% • 644 million cases • 1273 million cases • T.trichura • 16% 17% • 355 million • 902 million cases • Hookworm 21% • 24% • 457 million • 1277 million cases Chan 1997, Global Burden of Intestinal Nematodes, Parasitology Today, 13, 438-443
ASCARIS LUMBRICOIDES Female Male ‘En face’
ASCARIS LUMBRICOIDES • This round worm must be about the most ubiquitous parasite of humans with an estimated 1 billion people infected world wide. In some communities infections rates reach 100%. These large worms, often up to 30 cm in length ,inhabit the intestinal lumen. Each female worm produces approximately 200,000 eggs per day with an estimated total of 27 million during its life span. The eggs are highly resistant to adverse environmental conditions which contributes to its widespread distribution.
IMMUNITY IS MORE SUBTLE AND TRANSIENT…. Complex body structures that produce by-products. Inhabit a variety of tissues and organs, internal and external ! Charismatic at all scales !
PARASITIC HELMINTHS • Nematodes – simple and complex life cycles • Cestodes – always complex, sequential vertebrate & invertebrate hosts • Trematodes – always complex, always a snail for asexual, then vertebrate, sometimes a second invertebrate • Acanthocephalans – always complex, arthropod and vertebrate
You are here! Beringia
Caribou and reindeer are central to the welfare and economies of Arctic peoples
APD – suggested and drew this figure..!
Arctic Sept sea ice extent (10 6 km 2 ) 10.0 8.0 6.0 4.0 2.0 0.0 Stroeve et al. (2007), Geophys. Res. Lett. How can the response of ecosystems be predicted with confidence if they have never been observed under future conditions?
Ecological Impacts of Climate Change Predictive framework needed
Bioenergetic (mechanistic) approach (the laws of thermodynamics will NOT change) Understand bioenergetic mechanisms driving ecosystems Predictive models Models can be tested with Mathematical empirical data models under current conditions
I. Metabolic Theory of Ecology (MTE) ▪ Physiological rates scale with temperature according to Arrhenius relationship and with activation energies E ≈ 0.65 eV Metabolic rate 𝐹 1 𝑈− 1 𝑙 𝑈 𝐽 𝑈 = 𝑗 0 𝑓 0 Brown et al. (2004), Ecology
Development rate Mortality rate −𝐹 𝑈− 1 1 −𝐹 𝑈− 1 1 𝑙 𝑈 𝑙 𝑈 𝜈 𝑈 = 𝜈 0 𝑓 𝜐 𝑈 = 𝜐 0 𝑓 0 0 y = -0.57 x – 0.92 Ln (Body mass-corrected development rate McCoy & Gillooly 2008 Brown et al. 2004 Temperature (1/ kT ) Population growth, carrying capacity, species diversity,…
Climate Change and Parasites Direct Effects: Transmission season length Shorter generation Parasite development rates times with warming? Changing parasite survival Indirect Effects: Altered host ranges – new hosts, novel pathogens, host switching? Changing biodiversity – dilution / amplification effects? New stresses on host populations … Predictive tools needed for disease management
Ostertagia gruehneri – Caribou Can we predict impacts of climate change? ▪ Trichostrongylid ▪ Most common abomasal nematode in Rangifer (direct: transmission season, development/generation time, mortality,...) (indirect: host ranges, host switching, new stresses on hosts, …) ▪ Reduced food intake, pregnancy rates Focus on direct thermal effects first (development time, mortality) Approach: R 0 (expected lifetime reproductive output of newborn larva) under various environmental conditions
Calculating R 0 H P Host dynamics 𝐼 = 𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 L Free-living 𝑒𝑀 𝐸 𝑈 𝑄 𝑢 − 𝜐 𝑀 𝑈 𝑒𝑢 = 𝜇𝐸 𝑀 − 𝜈 𝑀 𝑈 𝑀 − 𝜍 𝐼 𝑀𝐼 infective stages 𝐼 + 𝑄 2 𝑒𝑄 𝑄 𝑙 𝐼 + 1 Adult parasites 𝑒𝑢 = 𝜍 𝐼 𝐸 𝑄 𝑀𝐼 𝑢 − 𝜐 𝑄 − 𝜈 𝑄 + 𝑐 𝐼 𝑄 − 𝛽 𝐼 𝐼 within host 𝐼 2 𝑙 𝐼 𝐸 𝑈 𝐸 𝑄 𝜇 𝐸 𝑀 𝜍 𝐼 𝐼 𝑆 0 𝑈 = ⋅ 𝛽 𝐼 + 𝑐 𝐼 + 𝜈 𝑄 𝜈 𝑀 𝑈 + 𝜍 𝐼 𝐼
What are parameters of R 0 ? exp T T H L L R T C 0 T H L ▪ parasite development time ▪ parasite mortality
One could go to the lab… …and fit a development/ mortality 10°C 10°C model to data… Cohort data: Pre-infective stages Infective stage
… to estimate development and survival as a function of temperature… 5°C 10°C 15°C 20°C 25°C
… and we have done that. Temperature [ ○ C] Unfeasible to do for all existing and emerging parasites of humans and wildlife…
MTE to the Rescue… Development time Mortality rate − 𝐹 𝜈 𝑈− 1 1 𝐹 𝜐 1 𝑈− 1 𝑙 𝑈 𝜈 𝑀 𝑈 = 𝜈 0 𝑓 𝑙 𝑈 𝜐 𝑈 = 𝜐 0 𝑓 0 0 with E τ ≈ E μ ≈ 0.65 eV
Predictions using Metabolic Theory ▪ Predicts data quite well, but… ▪ … resulting R 0 is unrealistic at temperature extremes.
Modification – Sharpe- Schoolfield model for development Assumes reversible inactivation of enzymes at temperature extremes, slowing or stopping development: 𝑀 𝐼 𝐹 𝜐 𝑈− 1 1 𝐹 𝜐 𝑈− 1 1 𝐹 𝜐 −1 𝑈+ 1 0 ∙ 1 + 𝑓 𝑈 𝑀 + 𝑓 𝑙 𝑈 𝑙 𝑙 𝑈 𝐼 𝜐 𝑈 = 𝜐 0 𝑓 A similar modification for mortality: 𝜈 𝑀 𝑈 𝑀 𝐼 −𝐹 𝜈 𝐹 𝜈 𝐹 𝜈 𝑈− 1 1 𝑈− 1 1 −1 𝑈+ 1 0 ∙ 1 + 𝑓 𝑈 𝑀 + 𝑓 𝑙 𝑈 𝑙 𝑙 𝑈 𝐼 = 𝜈 0 𝑓
Predictions of modified model ▪ Captures development & survival thresholds ▪ R 0 is unimodal ▪ Optimal temperature is weighted mean of development & survival optima
Predictions of modified model ▪ Captures development & survival thresholds ▪ R 0 is unimodal ▪ Optimal temperature is weighted mean of development & survival optima
A geographical perspective North South ▪ Impacts will vary geographically ▪ Depending on “baseline” temperature climate change may have positive or negative effects ▪ Opportunity to predict range shifts warmer even warmer
Application to Specific Systems To what degree can parasite range expansion be explained/predicted by climate change? Temperature-dependent host-parasite reaction- diffusion models Kutz et al. (in review)
A seasonal perspective Day-of-the-year
A seasonal perspective Development peaks in summer Day-of-the-year
A seasonal perspective Mortality lowest in spring & fall Day-of-the-year
A seasonal perspective Probability to survive to infective stage highest in summer
A seasonal perspective R 0 as a function of parasite ‘birth’ date
A seasonal perspective Phenological shifts
A seasonal perspective Summer fitness trough becomes more pronounced
A seasonal perspective ▪ Transmission season splits into 2 separate seasons ▪ ‘Wraps - around’, allowing some winter transmission Molnár et al. ( in press ) – Ecol. Lett.
APD – suggested and drew this figure..!
Some Conclusions The framework allows… ▪ synthesizing (nonlinear) climate impacts on different How? life history components into single measure of fitness (contrast with degree-day models ) ▪ straightforward extension to other host-parasite Who? systems, parasite life cycles, environmental covariates (e.g., moisture), … ▪ predicting temporal and geographical impacts of Where? climate ( fundamental niche) ▪ potential extensions to include indirect effects ( realized niche) ▪ a priori estimation of model parameters (even in data-poor systems)
So how about other species? The generality of the framework ▪ R 0 ( T ) unimodal regardless of parameter values ▪ Location of optimal temperature, skewness, temperature range where R 0 > 1 insensitive to almost all model parameters ▪ Key parameters are the activation energies E τ = 0.65 eV E μ E τ = E μ varied E = 1.2 eV E = 0.65 eV E = 0.2 eV ▪ How much do parameters vary between species?
Metabolic Theory predicts E τ = E μ = 0.65 eV N = 6 N = 13 Metabolic Theory needs to be tested further for parasites!
Quo vadis, parasite?
Persistence / Establishment Temperature Persistence / establishment depends on whether total ≥ 1 R 0 R 0 -theory needs to be extended Daily temperature fluctuations, stochastictity
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