“Resilience” is a buzzword that we hear everywhere these days. But it encapsulates a crucial aspect of natural forests, which are used to facing periodic disturbances (fires, epidemics, windthrow, etc.) even in the absence of human intervention. Resilience is simply the capacity of forests to recover their functions (carbone storage, etc.) after these disturbances, and sometimes also their composition and their structure.
Resilience is thus a very important notion for the future of Canadian forests, as many studies predict that they will have to face more disturbances (like fires or insect epidemics) along with the constant stress of a changing climate. But resilience has its limits: when the disturbances and stresses become too frequent or intense, the resilience of ecosystems diminishes.
That is why the DIVERSE project aims to help increase the resilience of forests in Canada. The first step? Assessing the past and current resilience and vulnerability of the forests of Canada. In this way, we’ll be able to see where forests and trees are currently the most vulnerable to future disturbances and stressors. We’ll also identify what past disturbances have led to the current status of vulnerability for the forests that we observe today.
The aim of Theme 1 is to assess the resilience and vulnerability of Canadian forests to biotic and climatic disturbances. This will be done through different studies that will start from field measurements at the scale of sampling plots, towards Canada-wide estimates of resilience through machine learning models.
In Theme 1, the resilience of forest stands will be approximated by two measures : their functional diversity and functional redundancy. Both are at the centre of what the DIVERSE project is about, as several studies suggest their link to forest resilience.
A high functional diversity of response traits (also called functional response diversity) in a forest is indeed thought to increase forest resilience. That is, if a forest is composed of trees showing different functions and responses to disturbances, then it should be able to rebound more effectively when a disturbance finally hits. In that instance, some of the forest’s trees should possess the right functional traits to react promptly to the disturbance, and regrow properly. We can illustrate that with forest fires : a forest with a high functional response diversity will surely contain some trees adapted to regrow quickly after a fire, such as the jack pine and its serotineous cones.
In contrast, the functional redundancy is expected to improve forest resilience by keeping a forest stocked with the right functional response traits, even as it suffers disturbances. For example, a forest stand might have a high redundancy of fire-resistant traits by having lodgepole pines, jack pines and black spruces (all three trees being adapted to fires). If an insect epidemic kills all lodgepole pines and black spruces in the forest, some jack pines will still remain and confer to the forest a good resilience to fires. As such, functional redundancy does not always correlate positively to functional diversity; but it can also be crucial to forest resilience.
A comprehensive field campaign will measure functional traits of trees across Canada to model how these traits vary across diverse ecological gradients, accounting for intraspecific variability in different contexts.
Measurements from the first phase and existing databases will be used to estimate the functional diversity and redundancy of trees in permanent sample plots, revealing changes in forest resilience over the past 40 years in response to disturbances.
Machine learning algorithms will use data from the second phase to estimate and map the functional diversity and redundancy of all Canadian forests, while also assessing tree species’ vulnerability to future climate conditions, especially droughts.
Theme 1 will involve 3 different phases, starting with a field campaign, and going towards larger scale generalizations based on the data and models generated at the previous step.
In the first phase, participants to the theme (including personnel from the partners of DIVERSE, students, and supervisors) will carry out a field campaign to measure functional traits from trees all over Canada. These measures will allow us to statistically model how functional traits of different tree species change across different ecological gradients, taking into account what we call “intraspecific variability”.
Indeed, the average height of a balsam fir might not be the same in the south of Canada than in the north, or on different soils, or on different slopes. As such, having measures for the same species but in different contexts ensures that we will be able to properly model the functional diversity and redundancy of Canadian forests, and thus, their resilience. In addition, many functional trait measures have simply never been recorded for rarer species, or for more remote ecological contexts.
In the second phase, we will use the measurement from the first phase, along with existing functional traits databases, to produce estimates of the functional diversity and functional redundancy of the permanent sample plots located in the 22 DIVERSE’s research sites. These sample plots are part of the National Forest Inventory of Canada, and are visited regularly so that their composition is carefully recorded through time.
Using the ecological context of each plot (i.e. soil, climate, etc.), we will estimate the functional traits of the trees in the plot using statistical models created with the functional trait data we have. For example, we will be able to estimate the seed mass of a white spruce contained in a sample plot by looking at the context of the plot, and comparing it to all of the seed mass measurements for white spruce trees in similar contexts that we have in our database.
Once we have estimated the functional traits of every tree in the permanent plots, we will compute their functional diversity and functional redundancy. Thanks to the decades of recording of forest composition in the permanent plots, we will be able to compute these values for the previous 40 years, showing us how the resilience of the forests in these plots have changed through time. Coupled with past measures of the disturbances that hit these plots, we will be able to see how forest resilience has changed following these disturbances. This will give us insights as to how the resilience of Canadian forests can be reduced through time by disturbances.
In the third phase, we will use our previous results to estimate the functional diversity and redundancy of all forests in Canada. We will do so by using machine learning algorithms and assignation models based on data from the second phase. These models will be calibrated with the relationships between the values of functional diversity and redundancy we measured on the permanent plots and Canada-wide data, like remote sensing data (i.e. satellite imagery, LiDAR, etc.). Using these models, we will be able to produce maps of forest resilience across all Canada, our ultimate objective for this research theme.
Along the way, we will also explore the vulnerability of Canadian tree species to future climate conditions, especially to droughts. For example, we will use estimates of mortality caused by previous droughts in the permanent sample plots of Canada, and combine these with predictions of future climate conditions to assess which species might be the most threatened by future droughts.
Contact us today to find out how your organization can contribute to the DIVERSE Project.
