Lessons from the Past: Modeling past human adaptation to climatic change in the Mediterranean Basin

Amenophys (an OTMed Project)

A. Kirman (Aix Marseille University), A. Bondeau (Institut Méditerranéen de Biodiversité et d’Ecologie Marine et Continentale), D. Contreras, (Aix Marseille University), J. Guiot (CNRS), N. Hanaki (Aix Marseille University), S. Thoron (Universite Paris Est Creteil) , L. Bernard (U. Strasbourg), E. Hiriart (Maison Méditerranéen de la Science de l’Homm

Our Common Future Under Climate Change The Mediterranean Basin in a warmer and drier world: challenges and opportunities

9 July 2015 Paris, France

Introduction

• Our question is not if climate change impacts the Mediterranean's human inhabitants, but how. What we want to know is not whether climate influenced human communities, but in what ways it did so and which variables were particularly influential in mediating those effects.

• There is a huge literature based on the assumption that climate changes must have had an impact on human activity but this work has struggled to go beyond correlations

• We aim to articulate these links between paleoenvironmental and cultural change and to thereby improve our understanding of cultural trajectories, of human ecological footprints, and provide a strategy for untangling human and environmental histories

• Our modeling approach aims to provide explicit, well-formulated mechanisms linking humans and environments, thereby enabling the articulation of testable hypotheses that can be compared to empirical evidence.

• Basic Problems

…among many…

An Integrated Modelling Approach

(potential) vegetation

& yields

Paleoclimate human activity landscape

• LPJmL: agricultural productivity

• Landscape (r.landscape.evol): topography + soils + landcover

• Paleoclimate: temperature and precipitation

• Agent-Based Model (NetLogo): food production + social organization

potential productivity

(foraging)

anthropogenic impacts

po

tential

pro

du

ctivity (agricu

lture/p

astoralism

)

agricultu

ral p

ractices

Agent Based Models

• Agents have behavioural rules of the form:

• If condition a is satisfied take action 1

• Condition a might be, if crop yield diminishes by more than 10%

• Action 1 might be « expand the area under cultivation »

• How many rules and how to choose?

• Reinforcement Learning and Holland’s « classifier system »

Figure 5.4 Transect through Provence, from the Camargue to Mont-Ventoux,

showing bioclimatic zones…

from Blondel et al 2010:Fig.5.4

• Study area captures the majority of local bioclimatic zones • ±50 km2

• 30m pixel • since we are using the study area by way of realistic example and not attempting

to build a model that will replicate its actual history, the selection is somewhat arbitrary

• for any area, the empirical record will be incomplete, and we will have to cast a somewhat wider net for the data needed to parameterize the model

The Study Area for empirical calibration

• NetLogo – Agent-Based Modeling platform – [url]

• Agents (households) make decisions about subsistence practices based on terrain, estimated and realized yields, and social/political interactions

• How elastic was pre-industrial agriculture – flexible in its labor inputs, crop mixtures, intens- and extensification?

• Could it maintain yields in the face of climatic shifts, and upon what did that ability depend (purely technological and agronomic factors, or also social/political ones)?

From questions about the past:

• What land-use and land-tenure alternatives are there to modern Western systems, and what impact(s) might they have on responses to climatic change?

• Are there favorable aspects of small-scale agriculture that large-scale agriculture can mimic in order to become more resilient?

• …in short: how did past climatic change impact humans?

To parameters for modeling sustainable futures:

A lesson from June 2015