Research interests

investigating mechanisms increasing population resilience to catastrophic disturbances;

investigating adaptations to extreme events, such as floods and fires;

investigating eco-evolutionary dynamics in animals and plants;

investigating the relationship between early development and fitness;

developing and applying species distribution models, with emphasis on the integration between mechanistic and correlative models;

Resilience to catastrophic disturbances

Catastrophic events are usually generated by complex dynamics that involve the coupling of multiple dimensions at many scales. Although these large unexpected changes happen infrequently, they tend to dictate the long-term dynamical behavior of real-world systems. Catastrophes such as storms, flood, droughts, diseases or invasion by a new competitor or predator occur widely. Several investigations have explored the consequences of episodes of massive mortality on population dynamics and persistence across several taxa. However, the vast majority of studies to date explore neither the adaptive mechanisms helping population recovery after collapses nor the role of catastrophes in shaping the life-histories of the affected species. Given the predicted intensification of weather extremes and their increasing temporal autocorrelation, the complex dynamics associated with adaptation and responses to catastrophic events call for wide and intense scientific investigations.

Marble trout, which I use as a model system in this line of research, live in harsh habitats which are frequently affected by flash floods and debris‑flow, often causing massive fish mortalities. Despite very low population sizes and genetic variability and the absence of immigration, these populations have persisted for centuries. This begs the question: (i) what are the mechanisms allowing the persistence of populations affected by repeated catastrophes, and (ii) what are the consequences of repeated catastrophes in terms of the evolution of life-histories, genetic variability and phenotypic plasticity?

Genetics of adaptation to extreme events

Adaptive genetic divergence among populations experiencing different stressors occurs rapidly, over small geographical scales even with gene flow, and occurs for traits that directly influence the viability of populations. Rapid genetic and life-history evolution in response to changing trends in climate variables and anthropogenic factors (such as overharvesting, habitat degradation, and habitat fragmentation) is well-documented for a variety of species and traits. Adaptive genetic shifts in life-history traits can also be associated with catastrophic disturbance events, but variations in disturbance timing, predictability, frequency, and severity make it difficult to predict the direction and strength of selection.

The advent of next-generation sequencing (NGS) technologies makes it possible to obtain a large amount of DNA sequence data quickly and at low cost, opening a new era of investigation into genome diversity. Variation in genetic markers located in or near functional gene regions can result in changes in the amino acid sequence of a protein or in its level of expression, thus affecting life-history traits including mortality, growth rate, body size or age at maturity. Investigation of such variation can potentially demonstrate associations between genetic variability, fitness, and environmental factors, as has been successfully shown for fish species, including Atlantic salmon Salmo salar and Alaskan sockeye salmon. In the case of species with expected low levels of genetic variation (e.g., small population sizes), the use of SNPs would be the recommended approach rather than the use of microsatellites, due to the higher abundance of SNPs in the genome. Because of easy standardization and low genotyping error, SNPs have great potential for long‐term, multigenerational parentage studies reconstructing pedigrees in wild populations for evolutionary studies, and they have become the molecular marker of choice in the majority of recent studies on selection at the genome level.


Early development and fitness

Carry-over effects (COEs) occur when processes in one season influence the success (generally defined as a component of fitness) of an individual in the following season. A particular subset of COEs is represented by the development of phenotypes in response to early environment (i.e., in fish between the egg stage and the first stages of independent life). A number of studies have demonstrated trade-offs between early life conditions and fitness in adulthood, but the development of testable mechanistic models has been slowed by the complexity of these phenomena, as multiple drivers and factors may interact to modify their severity. Early availability of resources seems to induce a plastic response in body growth trajectories of organisms, which become relatively fixed later in life.
Understanding how COEs influence individual fitness and how they interact with density‑dependent processes will strongly contribute to our understanding of population dynamics in animal systems, with important implications for conservation and management across taxa.

Development and integration of species distribution models

Rapid anthropogenic changes in climatic conditions and land use necessitate accurate predictions of how species will respond to environmental changes. Correlative models implicitly incorporate biological processes by statistically estimating environment-range associations from occurrences, while mechanistic models explicitly include biological and ecological processes from the phenotypes of organisms. The integration between correlative and mechanistic models provides a promising “middle ground” approach that could provide practical predictions of range dynamics in changing environments.
My research is focused on (a) the development of a dynamic framework integrating correlative and mechanistic species distribution model to assess how the relationships and predictions developed by correlative models can be used as partial input for mechanistic models, (b) develop ensemble machine learning approaches