POPULATION DYNAMICS OF PARASITIC HELMINTHS AND CLIMATE ANDY DOBSON - - PowerPoint PPT Presentation

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
• Humans – 2.2 x109
• 29% urban
• Ascaris

30%

• 644 million cases
• T.trichura

16%

• 355 million
• Hookworm 21%
• 457 million
• 1997
• 5.6 x 109
• 45% urban
• 24%
• 1273 million cases
• 17%
• 902 million cases
• 24%
• 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

• f Arctic peoples

APD – suggested and drew this figure..!

Arctic Sept sea ice extent (106 km2)

10.0 8.0 4.0 6.0 2.0 Stroeve et al. (2007), Geophys. Res. Lett. 0.0

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

(the laws of thermodynamics will NOT change)

Understand bioenergetic mechanisms driving ecosystems Mathematical models Models can be tested with empirical data under current conditions

Bioenergetic (mechanistic) approach

Predictive models

• I. Metabolic Theory of Ecology (MTE)

Metabolic rate 𝐽 𝑈 = 𝑗0𝑓

𝐹 𝑙 1 𝑈− 1 𝑈

▪ Physiological rates scale with temperature according to Arrhenius relationship and with activation energies E ≈ 0.65 eV

Brown et al. (2004), Ecology

𝜐 𝑈 = 𝜐0𝑓

−𝐹 𝑙 1 𝑈− 1 𝑈

𝜈 𝑈 = 𝜈0𝑓

−𝐹 𝑙 1 𝑈− 1 𝑈

Development rate Mortality rate

Temperature (1/kT)

Ln (Body mass-corrected development rate

Brown et al. 2004 y = -0.57x – 0.92 McCoy & Gillooly 2008

Population growth, carrying capacity, species diversity,…

Indirect Effects: Altered host ranges – new hosts, novel pathogens, host switching? Changing biodiversity – dilution / amplification effects? New stresses on host populations …

Climate Change and Parasites

Direct Effects: Transmission season length Parasite development rates Changing parasite survival

Shorter generation times with warming?

Predictive tools needed for disease management

▪ Trichostrongylid ▪ Most common abomasal nematode in Rangifer ▪ Reduced food intake, pregnancy rates Focus on direct thermal effects first (development time, mortality) Approach: R0 (expected lifetime reproductive output of newborn larva) under various environmental conditions

Ostertagia gruehneri – Caribou

Can we predict impacts of climate change?

(direct: transmission season, development/generation time, mortality,...) (indirect: host ranges, host switching, new stresses on hosts, …)

Host dynamics Free-living infective stages Adult parasites within host

𝐼 = 𝑑𝑝𝑜𝑡𝑢𝑏𝑜𝑢 𝑒𝑀 𝑒𝑢 = 𝜇𝐸𝑀

𝐸 𝑈 𝑄 𝑢 − 𝜐𝑀 𝑈

− 𝜈𝑀 𝑈 𝑀 − 𝜍𝐼𝑀𝐼 𝑒𝑄 𝑒𝑢 = 𝜍𝐼𝐸𝑄𝑀𝐼 𝑢 − 𝜐𝑄 − 𝜈𝑄 + 𝑐𝐼 𝑄 − 𝛽𝐼𝐼 𝑄 𝐼 + 𝑄2 𝐼2 𝑙𝐼 + 1 𝑙𝐼

𝑆0 𝑈 = 𝐸𝑀

𝐸 𝑈 𝐸𝑄𝜇

𝛽𝐼 + 𝑐𝐼 + 𝜈𝑄 ⋅ 𝜍𝐼𝐼 𝜈𝑀 𝑈 + 𝜍𝐼𝐼

P H L

Calculating R0

         

H T H T T C T R

L L L

          exp

What are parameters of R0?

▪ parasite development time ▪ parasite mortality

One could go to the lab…

10°C …and fit a development/ mortality model to data… 10°C

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. Unfeasible to do for all existing and emerging parasites of humans and wildlife…

Temperature [○C]

MTE to the Rescue…

𝜐 𝑈 = 𝜐0𝑓

𝐹𝜐 𝑙 1 𝑈− 1 𝑈

𝜈𝑀 𝑈 = 𝜈0𝑓

−𝐹𝜈 𝑙 1 𝑈− 1 𝑈

Development time Mortality rate with Eτ ≈ Eμ ≈ 0.65 eV

Predictions using Metabolic Theory

▪ Predicts data quite well, but… ▪ … resulting R0 is unrealistic at temperature extremes.

Modification – Sharpe- Schoolfield model for development

𝜐 𝑈 = 𝜐0𝑓

𝐹𝜐 𝑙 1 𝑈− 1 𝑈

0 ∙ 1 + 𝑓

𝐹𝜐

𝑀

𝑙 1 𝑈− 1 𝑈𝑀 + 𝑓 𝐹𝜐

𝐼

𝑙 −1 𝑈+ 1 𝑈𝐼

𝜈𝑀 𝑈 = 𝜈0𝑓

−𝐹𝜈 𝑙 1 𝑈− 1 𝑈

0 ∙ 1 + 𝑓

𝐹𝜈

𝑀

𝑙 1 𝑈− 1 𝑈𝑀 + 𝑓 𝐹𝜈

𝐼

𝑙 −1 𝑈+ 1 𝑈𝐼

Assumes reversible inactivation of enzymes at temperature extremes, slowing or stopping development: A similar modification for mortality:

▪ R0 is unimodal ▪ Optimal temperature is weighted mean of development & survival

• ptima

▪ Captures development & survival thresholds

Predictions of modified model

▪ R0 is unimodal ▪ Optimal temperature is weighted mean of development & survival

• ptima

▪ Captures development & survival thresholds

Predictions of modified model

North South

A geographical perspective

warmer

▪ Impacts will vary geographically ▪ Depending on “baseline” temperature climate change may have positive

• r negative effects

even warmer

▪ Opportunity to predict range shifts

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

Day-of-the-year

Development peaks in summer

A seasonal perspective

Day-of-the-year

Mortality lowest in spring & fall

A seasonal perspective

Probability to survive to infective stage highest in summer

A seasonal perspective

R0 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..!

The framework allows…

Some Conclusions

▪ potential extensions to include indirect effects (realized niche) ▪ a priori estimation of model parameters (even in data-poor systems) ▪ synthesizing (nonlinear) climate impacts on different life history components into single measure of fitness (contrast with degree-day models) How? ▪ predicting temporal and geographical impacts of climate (fundamental niche) Where? ▪ straightforward extension to other host-parasite systems, parasite life cycles, environmental covariates (e.g., moisture), … Who?

Eτ = Eμ

Eτ = 0.65 eV Eμ varied E = 1.2 eV E = 0.65 eV E = 0.2 eV

So how about other species?

The generality of the framework

▪ R0(T) unimodal regardless of parameter values ▪ Location of optimal temperature, skewness, temperature range where R0 > 1 insensitive to almost all model parameters ▪ Key parameters are the activation energies ▪ How much do parameters vary between species?

N = 13 N = 6

Metabolic Theory predicts Eτ = Eμ = 0.65 eV

Metabolic Theory needs to be tested further for parasites!

Quo vadis, parasite?

Persistence / Establishment

Temperature

Persistence / establishment depends

• n whether

R0

total ≥ 1

R0-theory needs to be extended Daily temperature fluctuations, stochastictity

THE MTE APPROACH

• Can explore and test generalities
• Iterative approach with empirical data collection

Advantage threshold Molnar et al. 2013. Gimme Shelter – The relative sensitivity of parasitic nematodes With direct and indirect lifecycles to climate change. Global Change Biology 2013 Direct lifecycle Indirect lifecycle

Temperature oC

die-off die-off die-off Spring warming ???

▪ host condition (immunity) ▪ host survival / reproduction ▪ host density ▪ host ranges ▪ community composition / biodiversity

Each of these can be treated within energetic framework

Climate Change Impacts on Hosts?

Climate may affect… DEBs more appropriate (endotherms / supply- side problems)

Years Numbers

Low impact on fecundity High impact on fecundity Grouse Worms

Years Numbers

• 2. Population level dynamics

Dobson & Hudson Simulations

PARASITIC WORMS - CONCLUSIONS

• Models have a different structure than for viral and bacterial pathogens
• Have to consider statistical distribution of worms per host.
• Free-living infective stages are very sensitive to climate variation and change
• Impact of parasite on fecundity can lead to population cycles
• Impact of worm control is felt long before elimination

PARASITIC HELMINTHS HAVE A HUGE EFFECT ON ABUNDANCE AND HEALTH OF HUMANS AND WILDLIFE

• Many thanks to Susan Kutz, Brett Elkin,

Anne Gunn, Peter Molnar , Peter Hudson

• for many (occasionally sober)

discussions about Arctic wildlife.