index

Organizers

Nicolas Casajus (CESAB)
Vincent Calcagno
Emanuel A. Fronhofer
Isabelle gounand
Claire Jacquet
Sonia Kéfi
François Massol

Organizers/helpers

Nicolas Casajus (CESAB)
Vincent Calcagno
Emanuel A. Fronhofer
Isabelle gounand
Claire Jacquet
Sonia Kéfi
François Massol
Quentin Petitjean (Wed)
Jhelam Deshpande (Tues + Fri)

Your turn!

name, position (PhD/post-doc/engineer), institute (location)

Objective of the course

“train young researchers in theory-based approaches to model ecological data”

Program of the week: courses

Day 1 (8h30-17h30): Introduction to theoretical modeling
- S. Kéfi, I. Gounand, V. Calcagno
Day 2 (9h-17h): Temporal series and dynamics in ecology
- E. Fronhofer and J. Deshpande
Day 3 (9h-17h): Spatial data, macro-ecology and co-occurrences
- V. Calcagno and Q. Petitjean
Day 4 (9h-17h): Interaction networks, trophic networks and complexity in ecology
- F. Massol, C. Jacquet
- Guest: M. Barbier
Day 5 (9h-16h): Group projects (mornings), presentations (afternoon)

Program of the week: evening seminars

Day 1: Introduction to theoretical modeling
- 18h-19h: Kim Cuddington (Waterloo Univ., Canada)
Day 2: Temporal series and dynamics in ecology
Day 3: Spatial data, macro-ecology and co-occurrences
- 17h30-18h30: Davide Martinetti (INRAE, Avignon)
Day 4: Interaction networks, trophic networks and complexity in ecology
- 17h30-18h30: Sonia Kéfi (CNRS, Montpellier)
Day 5: Group projects (mornings), presentations (afternoon)

Program of the week: group projects

Day 1 (8h30-17h30): Introduction to theoretical modeling
- S. Kéfi, I. Gounand, V. Calcagno; Evening talk: K. Cuddington
Day 2 (9h-17h): Temporal series and dynamics in ecology
- E. Fronhofer and J. Deshpande
Day 3 (9h-17h): Spatial data, macro-ecology and co-occurrences
- V. Calcagno and Q. Petitjean; Evening talk: D. Martinetti
Day 4 (9h-17h): Interaction networks, trophic networks and complexity in ecology
- F. Massol, C. Jacquet; Guest: M. Barbier; Evening talk: S. Kéfi
Day 5 (9h-16h): Group projects (mornings), presentations (afternoon)

Program of today

Why theoretical models?

Big data

https://blog.digitalcook.fr/big-data-et-intelligence-artificielle/
Every day, humanity generates 2.5 trillion (2.5 billion billion) bytes of text, image and sound data.

Big data

https://www.theguardian.com/society/2023/apr/30/artificial-intelligence-tool-identify-cancer-ai

Big data

do we still need models and theory?

What do you mean ‘theory’?

“Scientific theories contain universal or general propositions regarding the system in question.”

Maris et al., 2017

What do you mean ‘theory’?

“The transformation of an idea in narrative form into a logical, testable theory often, though not always, involves the use of models.”

Otto and Rosales, 2020

What are models?

If I say the word “model” what comes to your mind (everything, even not related to science)? (write on board) What are the common elements?

PICTURES: Models (Claudia Schiffer): https://www.yahoo.com/lifestyle/17-black-models-look-fashion-151000791.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAFBD4rb8N9Nr2Krbglih49HjGV5q0FypwuPz2nUuiHvAC8sPvzO1np6G8jrU-Bm4eWG2G6nygxw3kiARhqUjswZVRkjiydfIDn0sCNfooxZKhoL8iD9LrSqSpI3pLa24kr5fs8MdNh_fhOr5jyjBkwOrfpcpMXiRF8-LVXP2qyOn Photo: Jamie McCarthy / Staff (Getty Images)

miniatures: https://www.bloomberg.com/news/articles/2015-02-02/play-god-with-this-customizable-miniature-city

cars: https://www.anmodelcars.com/en/blog/top-10-des-voitures-miniatures-les-plus-cheres-au-monde–b13.html

maps: https://www.ionos.fr/digitalguide/web-marketing/vendre-sur-internet/placer-des-reperes-sur-google-maps/

mathematical models: https://robotnor.no/expertise/fields-of-competence/mathematical-modeling-for-control/ https://www.imperial.ac.uk/news/162709/researchers-develop-more-reliable-method-working/

What are models?

Models are simplified, idealized representations of reality.

What are models?

They can be:

verbal
conceptual (diagram of boxes and arrows)
quantitative/mathematical (sets of equations)

What are models?

Note: Theory and maths are not inexorably linked.

Many excellent theories do not involve maths (e.g., Darwin’s theory of evolution), and many uses of maths in ecology are not theory (e.g., practical applications of statistics).

What are models?

“All models are wrong, but some models are useful”

George E.P. Box, 1976

https://en.wikipedia.org/wiki/All_models_are_wrong

What are models?

Ecological systems are complex…

How to capture the complexity of reality?
We can’t!

What are models?

Modeling implies a choice about what to include and what to leave out.

What are models?

A tension between realism, generality, and precision.

Levins, 1966

https://v4.chriskrycho.com/2016/realism-generality-and-precision-in-tension.html We want to work with manageable models which max generality, realism and precision towards the goals of understanding and predicting…. But this cannot be done. These goals typically compete with each other so real models are mathematical descriptions that result from tradeoffs among these goals which depend on our needs (cf Levins 1966). - One can sacrifice generality to realism and precision. E.g. fisheries; good measurement of shot-time behavior, numerical solution, precise testable predictions applicable to a particular situation). - One can sacrifice realism to generality and precision. E.g. physics-like models (general equations such as LV) -> the way in which nature deviates from theory will indicate where further complexity will be useful. - One can sacrifice precision to realism and generality. E.g. MacArthur 1965. People concerned with the qualitative behavior (not quantitative). Graphical models.

Why do we need models/theory?

What for?

understand
- e.g. provide a mechanistic explanation for how plants grow as fast or slowly as they do
predict
- e.g. make predictions about the population size in the future

Science aims to explain and understand. https://undsci.berkeley.edu/understanding-science-101/what-is-science/science-aims-to-explain-and-understand/ Models do not investigate nature. Instead, they investigate the validity of our own thinking. Models are ‘thinking aids’. They exist because we need them to help us explore things: none of us was born with a supercomputer (P. 8 Kokko). The most important function of models is to order our thoughts. With models, we formulate what we (think we) know about the world, and we may use them to perform thought experiments through ‘what if’ scenarios. Modèle = experimentation pour poser des questions. In sum, models allow to explore ideas that might not be doable experimentally or for financial reasons.

Another important use of models is to function as an idealization of the world. Models force us to make a choice concerning which aspects of ‘the real world’ we include into our description, and which ones we choose to ignore (for the moment). This is true for verbal as well as mathematical models. Mathematical formulations reveal implicit assumptions and thus force us to be explicit. The translation of ideas in a theoretician’ s mind into math on a page serves several purposes:

Note that the choice made on what to sacrifice might depend on the stage of a research program: At the conceptual stage of research models are used to further develop theory. Models allow to do thoughts experiments. ‘All else being equal’ what is the effect of a certain factor or process? Conceptual models are thinking aids rather than investigations of real phenomena. At the other extreme lies the practical stage, where models are used to closely examine real-life phenomena. At the practical stage models are used for purposes such as inference from empirical data, making quantitative predictions concerning future system dynamics, or effects of studied factors, and guiding management decisions. These models comprise ‘statistical’ models, ‘computer models’, etc. Their aim is to make accurate statements about the studied system given the circumstances in the real world.

Big data

reveal correlations
but correlations are not causality

Big data

https://www.tylervigen.com/spurious-correlations

Big data

do not distinguish causal from non-causal correlations
can still be predictive!

Big data

BUT:

predictions can fail
predict without understanding…. act without understanding?
big data don’t create theory; they need theory to be exploited
(Note: this last statement could evolve in the future)

Big data

do we still need models and theory?
yes!

The scientific method

problem identification
hypothesis
prediction
testing
refinement

The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century.

Science (through the scientific method) can build on previous knowledge and develop a more sophisticated understanding of its topics of study over time.

‘Problem’ identification: It involves careful observation which leads to the formulation of a question.
Hypothesis: A hypothesis is a conjecture (hypothetical explanations), based on the observation/the knowledge obtained while formulating the question, that may explain any given behavior. A scientific hypothesis must be falsifiable, implying that it is possible to identify a possible outcome of an experiment or observation that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested. Falsifiability is a deductive standard of evaluation of scientific theories and hypotheses, introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934). A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test.
Prediction: The prediction step deduces the logical consequences of the hypothesis before the outcome is known.
Testing: Hypotheses are tested by conducting experiments or gathering observations. The purpose of the test is to determine whether observations agree with or conflict with the expectations deduced from a hypothesis.
Refinement (or elimination) of the hypotheses based on the experimental findings.

Although procedures vary from one field of inquiry to another, the underlying process is frequently the same. In sum, the process is as follows: making conjectures (hypotheses), deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original conjecture was correct.

The scientific method

The scientific method is an iterative, cyclical process through which information is continually revised.
A feedback loop involving data and models.

A scientific understanding of the biological world arises when ideas about how nature works are formalized, tested, refined, and then tested again.

Scientific inquiry should operate as a feedback loop in which theory that describes the natural world is developed, tested empirically through carefully articulated hypotheses, modified to better represent reality, and then tested again.

When this feedback loop works, theory provides a framework to guide inquiry, experimental design, and the interpretation of observed patterns, supplies mathematical tools to harness information from collected data, and connects individual experiments to general ideas about how nature operates.

In turn, empirical research can be used to support, refute, or revise theoretical predictions, indicate which theoretical assumptions are consistent with the natural world, and point theoreticians to overlooked processes that can be integrated into models.

BUT

A current disconnection between theoretical and empirical research.

45% of articles on empirical ecology make no mention of any theory (Scheiner 2013)
fewer than 10% of ecologists and evolutionary biologists agree with the statement that ‘theoretical findings drive empirical work’ in their fields (Haller 2014)

Why this disconnection?

A lack of theoretical training in ecology (Rossberg et al. 2019).
A lack of motivation from some theoreticians to engage with the language of empiricists (Grimm 1994) or with the elements of nature that empiricists focus on (Krebs 1988).
A lack of mutual appreciation between empiricists and theoreticians (Haller 2014).
Persistent communication barriers (Servedio 2020).

This barrier presents a major challenge to the full integration of theoretical and empirical work in ecology.
A better integration of theory into empirical work is needed!
This is especially important in the context of global change.
- Hence this course!

Organizers

Organizers/helpers

Your turn!

Objective of the course

Program of the week: courses

Program of the week: evening seminars

Program of the week: group projects

Program of today

Why theoretical models?

Big data

Big data

Big data

Big data

What do you mean ‘theory’?

What do you mean ‘theory’?

What are models?

What are models?

What are models?

What are models?

What are models?

What are models?

What are models?

What are models?

Why do we need models/theory?

Big data

Big data

Big data

Big data

Big data

The scientific method

The scientific method

BUT

Why this disconnection?

Thanks