THE GONZALEZ LAB
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We have entered the Anthropocene, a new era that defines the great influence of humans on the biosphere. Our research is broadly focused on biodiversity change, the causes of change, and how populations adapt to human impacts. We also study the consequences of biodiversity change for the stability and functioning of ecosystems. As a corollary we are gaining a better understanding of how the effects of anthropogenic environmental change can be mitigated, and the conservation solutions we can adopt to maintain biodiversity. To do this, we combine theory and experiments in the field and lab, and use large databases to synthesize knowledge, and landscape modelling explore solutions for landscape connectivity. To ensure our research is useful far beyond the ivory tower we have established partnerships with government, NGOs, businesses and citizen groups. 

1. Biodiversity change and Connected Protected Area Networks

Our overall goal is to design robust ecological networks for that maintain biologically diverse, resilient and sustainable ecosystems under projected scenarios of climate and land use change over the coming century. We are designing connected protected area networks (Gonzalez et al. 2018) as a way to support biodiversity, and maintain the provision of ecosystem functions and services over time. Our current research focus is the Saint Lawrence Lowlands ecoregion. We are working with the Quebec government to increase functional connectivity of the ecoregion and therefore help realize its commitment to the resolution 40-3 on Ecological Connectivity, Climate Change Adaptation and, Biodiversity Conservation. 
Visit our project webpage to find out more.

Corridors of connectivity in the Saint Lawrence Lowlands

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Regions of connectivity around Montreal and the Saint Lawrence lowlands ecoregion.
​Light green colours are areas of high priority for the protection of functional connectivity (from Rayfield et al. 2019).

2. A mesoscopic theory of biodiversity

Network approaches to biodiversity science have provided a powerful set of tools and models that are providing new insights into how diverse networks of interacting species assemble and disassemble over space, through time. This is the study of ‘networks of networks’ (Gonzalez et al. 2011), or the ecology and evolution of metacommunities. We are combining theory and experiments to extend metacommunity theory to understand how the structure, function and robustness of ecological networks respond to human impacts, such as habitat loss and climate change (Thompson, Rayfield and Gonzalez 2016LEAP, Thompson and Gonzalez 2017). Our model systems include laboratory microcosms of microbes and microarthropods, aquatic mesocosms (see LEAP page) and natural networks of mosses and associated bryofauna (Chisholm, Lindo and Gonzalez 2011) to test the assumptions and predictions of our theory (e.g., Pillai, Gonzalez and Loreau 2010, Gouhier, Guichard and Gonzalez 2010, Thompson and Gonzalez 2017).

3. Evolutionary rescue

Evolutionary Rescue: Global environmental change is causing unprecedented rates of population extirpation, giving rise to concern that the rate of environmental change may exceed the capacity of populations to adapt. In many cases environmental change is so widespread and rapid that that individuals can neither accommodate to them physiologically nor migrate to a more favorable site. Extinction will ensue, unless the population adapts genetically through natural selection. According to theory whether populations can be rescued by evolution depends upon several crucial variables: population size, the supply of genetic variation and the degree of maladaptation to the new environment. Using robot-based techniques in experimental evolution we are testing the conditions for evolutionary rescue. We use yeast (Saccharomyces cerevisiae) and bacteria (e.g. Pseudomonas sp.) as model organisms. In a striking match with theory we found that evolutionary rescue is possible, and that the recovery of the population may occur within twenty-five generations (Bell and Gonzalez 2009). Results so far reveal that rapid evolution is an important component of the response of small populations to environmental change, and confirms the value of considering the interaction between ecology and evolution when their dynamics occur on the same time scales (see also Bell and Gonzalez 2011). We have also studied the eco-evolutionary dynamics of antibiotic resistance (e.g., Perron, Gonzalez and Buckling 2007, 2008).

Most recently we have been doing experiments on community rescue in the lab (Low-Decarie et al. 2011) and in the field at the LEAP mesocosm platform (Bell et al. 2019). Community rescue research extends ER for single populations to entire assemblages of populations interacting and responding to human impacts, suggest contamination by herbicides. 

See the special issue Evolutionary Rescue in Changing Environments

4.  Economic inequality and biodiversity change

Humans, both individually and collectively, are powerful drivers of environmental change. In collaboration with Greg Mikkelson and Garry Peterson we have studied how socioeconomic factors (for example economic inequality) affect biodiversity loss (Mikkelson et al. 2007, Gonzalez 2007). Tim Holland conducted a MSc on the problem and corroborated our earlier results that suggest that economic inequality is a significant predictor of biodiversity loss and that statistical models do better when inequality is included as an independent variable (Holland, Peterson and Gonzalez 2009). 

Current research is evaluating the effects of economic inequality as a predictor of the incidence of invasive species at the national scale. Early results by Emma Weisbord for her honours thesis suggest that economic inequality can explain national variation in the numbers of invasive species. 
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Variation in national income equality around the world as measured by the national Gini coefficient. The Gini coefficient is a number between 0 and 1, where 0 corresponds with perfect equality (where everyone has the same income) and 1 corresponds with perfect inequality (where one person has all the income, and everyone else has zero income).
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  • Home
  • Team
  • Research
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