Population dynamic, biological invasion and conservation
- Portfolio conservation of atlantic salmon under climate change
I explore the influence of life history diversity within and among populations on persistence. The aim is to predict the response of populations to climate change and human pressures using simulation methods (e.g., IBASAM) taking into account the diversity of life history strategies, notably the dispersal strategies.
- Causes and consequences of invasions of aquasystems by non-native salmonids
I’m currently working with Julien Cucherousset (Link) as part of the european project Biodiversa SalmoInvade (ANR-Biodiversa; Link). Our objective is to investigate the ecological and evolutionary impacts of biological invasions by salmonids at different levels of biological organization, from genes to ecosystems. Indeed, in addition to environmental changes and human pressures they already suffer, native salmonids are exposed to continuous introductions of fellow species or non-native species, deliberately (stocking) or not (accidental escapement).
We also initiated a unique study of the management of aquatic ecosystems in France to quantify the management practices (stocking) and to evaluate the social and economic causes (Link).
- Dynamics of threatened steelhead juveniles (Oncorhynchus mykiss) in a seasonally fragmented stream habitat
This project was initiated during my postdoc with Stephanie M. Carlson. We use a state-spaced model to investigate the selective survival of a steelhead juveniles during drought period using Capture-recapture data. We hypothesize that the summer drought season may represent a population bottleneck, and that years of poor juvenile survival will translate to poor adult returns for that cohort.
Evolution of life histories & Life history strategies
- Life history syndromes: spatial and temporal dispersal as alternative strategies for persisting in stochastic environments
In the context of environmental change and associated uncertainty of environmental conditions, understanding how organisms can environment match is of crucial importance. When environmental conditions shift beyond the range of suitable conditions, organisms may acclimate, adapt or disperse to more suitable environments to achieve maximal survival and reproduction in response to the new conditions. Dispersal is a risk-spreading strategy used by organisms to cope with heterogeneous, stochastic environments. In this project, we argue that organisms vary in their dispersal strategies along a continuum from strongly spatial (movement) to strongly temporal (e.g., overlapping generations) but theoretical and empirical examples exploring the relative importance of different strategies for persisting, are rare. We believe that the relative importance of dispersal in space or time as alternative strategies for spreading risk needs further empirical evaluation, especially research focusing on the consequences of dispersal on persistence probabilities and studies of a broader range of organisms.
- Adaptive phenotypic plasticity and conditional strategies
Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The Environmental Threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We developped a new statistical model that accommodates for this common situation. The Latent Environmental Threshold Model (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. Coupling the model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies.
The code of the LETM as well as an example of data are available in a github repository (see section “Code”).