Tongli Wang

My research interests include the following areas:

1. Ecological niche modelling – Climate change is resulting in a mismatch between the climate that trees are historically adapted to and the climate that trees will experience in the future. Such a mismatch may lead to maladaptation, which can compromise productivity and increase the vulnerability of forest ecosystems. Climate niche modelling is essential to tackle these problems. Dr. Wang is one of the pioneers in this field, and developed climate niche models for the Biogeoclimatic Ecosystem Classification (BEC) system and projected their shifts under future climates using a machine-learning algorithm (Hamann and Wang 2006, Wang et al. 2012a). These projections have been used to develop stocking standards and regional forest adaptation plans, and the climate-based seed-transfer system by the BC Ministry of Forests Land and Natural Resources Operations (MFLNRO) (O’Neill et al. 2017). His modeling expertise has also been applied to model some major forest species in Asia Pacific (Wang et al. 2016b). He is in the process of developing climate niche models for all native species in BC. He is also in the process of developing an extended BEC system to cover neighboure provinces in east and States in the south.

2. Ecological genetics – The importance of ecological genetics has never been so whelming due to the increasing concern of climate change.  Local adaptation and among-population variation in relationship with environment, climate variables in particular, are the key areas of ecological genetics. Dr. Tongli Wang has extensive experience and expertise in this field.  He has developed a method to generate anchor points (Wang et al. 2006b) and to consider other factors (O’Neill et al. 2007, O’Neill et al. 2008) to improve the traditional climate response functions. More importantly, he has developed a novel approach to integrate environmental (test site) and genetic (seed source) effects of climate into a single model, called the “universal response function” (URF) to predict the performance of a population from any seed source planted at any site (Wang et al. 2010). It allows the genetic and environmental effects to be quantified. This new approach makes the best use of the provenance trials and improves the model accuracy, and has advanced the modelling methodology in this field. This approach has recently been used by researchers in lodgepole pine (Wang et al. 2010), Douglas-fir (Chakraborty et al. 2015, Chakraborty et al. 2016, Chakraborty et al. 2018), white pine and black spruce (Yang et al. 2015).

3. Landscape genomics – With a rapid accumulation of genomic data for several important forest tree species, it has become possible to study the spatial genomic variation among populations and its association with climate variables. Dr. Wang’s expertise in ecological modelling, ecological genetics and genomics modeling (Holliday et al. 2012) make him well positioned in this new field. Results of landscape genomics will play a critical role to define populations (or seed management units) in combination with results from ecological genetics as mentioned above.

4. Development of scale-free climate models – High-quality climate data are essential to conducting the studies mentioned above. Although a large volume of climate data have become available in recent years, these datasets are in grid format at various spatial resolutions. The climate models that Dr. Wang (taking the lead) developed are scale-free and location specific with higher accuracy. The models cover BC (Wang et al. 2006a), western North America (Wang et al. 2012b), North America (Wang et al. 2016a) and Asia Pacific (WANG et al. 2017).  These models integrate paleo, historical and future climate data into a single package, and generates a large number of biologically relevant climate variables. These models have over 1000 subscribers and cited for over 1000 times. They have become essential tools for climate and climate change related studies and applications. Wang et al. (2016a) is awared as Highly Cited Paper (top 1%) in the field of geosciences. More information is available at: http://cfcg.forestry.ubc.ca/projects/climate-data/climatebcwna/