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Quantifying the Impact of Environmental Parameters on Biodiversity - - PowerPoint PPT Presentation

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Quantifying the Impact of Environmental Parameters on Biodiversity Clovis Galiez Grenoble Statistiques pour les sciences du Vivant et de lHomme May 25 th 2020 C. Galiez (LJK-SVH) Quantifying the Impact of Environmental Parameters on

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Quantifying the Impact of Environmental Parameters

  • n Biodiversity

Clovis Galiez

Grenoble Statistiques pour les sciences du Vivant et de l’Homme

May 25th 2020

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 1 / 14

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Goal

Context: global warming

What impact?

− − − − − − − − → Scientific question How to quantify the impact on ecosystems of a change in environmental parameter (such as temperature)?

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 2 / 14

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Goal

Goal: enable identification of critical ranges

The goal of this PhD project is to provide a measure of impact of an environmental variable on ecosystems....

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 3 / 14

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Goal

Goal: enable identification of critical ranges

The goal of this PhD project is to provide a measure of impact of an environmental variable on ecosystems.... ...and ultimately detect tipping points.

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 3 / 14

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Approach

Approach

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 4 / 14

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Approach

Sampling DNA directly in the environment

Technology now enables to measure abundance of species by DNA sequencing directly from the environment. The (very) big picture: Biological sample Metagenome

DNA sequencing

− − − − − − − − − →    bioinformatics magic Abundance of species1

1NB: The data readily available for the project!

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 5 / 14

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Approach

Data-driven approach

We consider existing ecosystems as possible optimal equilibrium given the environmental parameters (e.g. temperature). Our approach We will devise a distance between sample distributions at various temperature to quantify the biodiversity shift.

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 6 / 14

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Steps of the project

Project steps

see also supplementary slides for Gantt and milestones

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 7 / 14

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Steps of the project

WP1: Simulation of assemblages

We will simulate unseen assemblages by interpolating data with conditional variational autoencoders (cVAE). cVAEs will be learned on real data samples and benchmarking will be done using synthetic data generated with user-defined biotic and abiotic rules.

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 8 / 14

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Steps of the project

WP2: ecosystem sensitivity to environmental changes

In the example of a temperature increase, the hypothesis is that an

  • rganism assemblage at T1 will shift to the closest (in terms of a given

dissimilarity D) assemblage at T2. Optimal Transport (OT) theory provides a good framework2 to evaluate the cost of an environmental parameter change on the ecosystem.

2see also supplementary slides for more details

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on Biodiversity QIEP-B 9 / 14

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Steps of the project

WP3: Application to real data

Data is readily available, with expertize among the consortium: Alpine ecosystem (LECA): Orchamp eDNA data

Goal: measure 1. the impact of temperature change using eDNA samples on altitude gradients, and 2. ecosystem adaptation to brutal shifts

Gut microbiome (TIMC): amplicon DNA (16S barcodes)

Goal: assess the impact of environmental conditions of humans on their gut microbiome in term of shift in biodiversity and biological functions

Marine (LS2N): Tara Oceans shotgun metagemomics

Goal: measure the impact of gloabl temperature change in the ocean in terms of ecological services and functions

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 10 / 14

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Consortium

Consortium

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 11 / 14

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Consortium

Consortium composition

*: In PersyvalLab

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 12 / 14

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Summary

Summary of the QIEP-B project

Highlights of the QIEP-B project: We devise a data-driven and model-free method for tackling global change monitoring and forecasting of biodiversity This project will contribute to strangthen the links between Grenoble labs (LJK, TIMC, LECA) and open up to a new collaboration in Nantes (LS2N). This project widens the scope of the PersyvalLab to data-driven research applied to ecology.

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 13 / 14

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Questions

Questions?

clovis.galiez@univ-grenoble-alpes.fr

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 14 / 14

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  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 15 / 14

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Timeline

Timeline

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 16 / 14

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Timeline

Gantt chart

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 17 / 14

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Timeline

Milestones

WP1 Develop simulation of assemblages WP1 Conditional Variational Autoencoders (cVAE) for learning

  • rganisms assemblages

WP2 Use Optimal Transport throery to compute a distance between environmental conditions WP3 Apply on available data in the consortium (Alpine, ocean and gut ecosystems)

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 18 / 14

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OT for quantification

How OT will be used for assessing impact of environmental parameters on ecosystems?

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 19 / 14

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OT for quantification

Define a similarity between biomes

We can fix a disimilarity Di,j (bioinformatics methods, e.g. Bray-Curtis) matrix between biomes:

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 20 / 14

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OT for quantification

Wasserstein metric

Having a ground disimilarity Di,j between N samples, we lift the metric to the distribution of samples. W(B|T1, B|T2) = min

P∈A(B|T1,B|T2)

  • i,j

Di,jPi,j where A(B|T1, B|T2) = {P ∈ RN×N|P✶N = B|T1 and P ⊤✶N = B|T2}

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 21 / 14

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OT for quantification

Quantification of impact

Given a disimilarity between biomes, we will define for instance: Impact of a change of temperature from T0 to T1 ι(T0, T1) = W(B|T0; B|T1) Hopefully this can help to address questions such as: Detect the ranges of temperature that are the most sensitive to change: s(T) = ι(T,T+δT)

δT

Quantify the impact of a trajectory of evolution of temperature:

b

  • a

ι(T(x), T(x + dx))2.f′(x)dx

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 22 / 14

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WP1 replacement

If WP1 fails to provide good simulation?

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 23 / 14

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WP1 replacement

Sample niche

If WP1 fails, instead of using enriched data by simulated assemblages, we will use only available data. We need to obtain a distribution of samples at a given temperature: To this end...

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 24 / 14

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WP1 replacement

Bayes: reverting sample niche

...we use a simple Bayes rule. p(Bk|T) = p(T|B)k)p(Bk)

  • i

p(T|Bi)p(Bi)

p(T|Bi) p(Bi|T) →

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 25 / 14

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WP1 replacement

Bayes: reverting sample niche

...we use a simple Bayes rule. p(Bk|T) = p(T|B)k)p(Bk)

  • i

p(T|Bi)p(Bi)

p(T|Bi) p(Bi|T) →

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 25 / 14

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WP1 replacement

Bayes: reverting sample niche

...we use a simple Bayes rule. p(Bk|T) = p(T|B)k)p(Bk)

  • i

p(T|Bi)p(Bi)

p(T|Bi) p(Bi|T) →

  • C. Galiez (LJK-SVH)

Quantifying the Impact of Environmental Parameters on BiodiversityQIEP-B 25 / 14