Browsing by Author "Hamann, Andreas"

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    Assisted migration to address climate change: recommendations for aspen reforestation in western Canada
    (Ecological applications, 2011) Gray, Laura K.; Gylander, Tim; Mbogga, Michael S.; Chen, Pei-Yu; Hamann, Andreas
    Human-aided movement of species populations in large-scale reforestation programs could be a potent and cost-effective climate change adaptation strategy. Such largescale management interventions, however, tend to entail the risks of unintended consequences, and we propose that three conditions should be met before implementing assisted migration in reforestation programs: (1) evidence of a climate-related adaptational lag, (2) observed biological impacts, and (3) robust model projections to target assisted migration efforts. In a case study of aspen (Populus tremuloides Michaux.) we use reciprocal transplant experiments to study adaptation of tree populations to local environments. Second, we monitor natural aspen populations using the MODIS enhanced vegetation index as a proxy for forest health and productivity. Last, we report results from bioclimate envelope models that predict suitable habitat for locally adapted genotypes under observed and predicted climate change. The combined results support assisted migration prescriptions and indicate that the risk of inaction likely exceeds the risk associated with changing established management practices. However, uncertainty in model projections also implies that we are restricted to a relatively short 20-year planning horizon for prescribing seed movement in reforestation programs. We believe that this study exemplifies a safe and realistic climate change adaptation strategy based on multiple sources of information and some understanding of the uncertainty associated with recommendations for assisted migration. Ad hoc migration prescriptions without a similar level of supporting information should be avoided in reforestation programs.
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    Bioclimate envelope model predictions for natural resource management: dealing with uncertainty
    (Journal of Applied Ecology, 2010) Mbogga, Michael S.; Wang, Xianli; Hamann, Andreas
    Bioclimate envelope models are widely used to predict the potential distribution of species under climate change, but they are conceptually also suitable to match policies and practices to anticipated or observed climate change, for example through species choice in reforestation. Projections of bioclimate envelope models, however, come with large uncertainties due to different climate change scenarios, modelling methods and other factors. 2. In this paper we present a novel approach to evaluate uncertainty in model-based recommendations for natural resource management. Rather than evaluating variability in modelling results as a whole, we extract a particular statistic of interest from multiple model runs, e.g. species suitability for a particular reforestation site. Then, this statistic is subjected to analysis of variance, aiming to narrow the range of projections that practitioners need to consider. 3. In four case studies for western Canada we evaluate five sources of uncertainty with two to five treatment levels, including modelling methods, interpolation type for climate data, inclusion of topo-edaphic variables, choice of general circulation models, and choice of emission scenarios. As dependent variables, we evaluate changes to tree species habitat and ecosystem distributions under 144 treatment combinations. 4. For these case studies, we find that the inclusion of topo-edaphic variables as predictors reduces projected habitat shifts by a quarter, and general circulation models had major main effects. Our contrasting modelling approaches primarily contributed to uncertainty through interaction terms with climate change predictions, i.e. the methods behaved differently for particular climate change scenarios (e.g. warm&moist scenarios) but similar for others. 5. Synthesis and applications. Partitioning of variance components helps with the interpretation of modelling results and reveals how models can most efficiently be improved. Quantifying variance components for main effects and interactions among sources of uncertainty also offers researchers the opportunity to filter out biologically and statistically unreasonable modelling results, providing practitioners with an improved range of predictions for climate-informed natural resource management.
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    A Comprehensive Set of Interpolated Climate Data for Alberta
    (Alberta Sustainable Resource Development, 2010) Mbogga, Michael; Wang, Tongli; Hansen, Christine; Hamann, Andreas
    We present an easily accessible database of interpolated climate data for Alberta that includes monthly, annual, decadal, and 30-year normal climate data for the last 106 years (1901 to 2006), as well as climate change projections for the 21st century from 23 general circulation models. The database builds on the Alberta Climate Model (Alberta Environment 2005) and a set of five future projections that are recommended and widely used by Alberta government agencies (Barrow and Yu 2005). We added 15,000 historical and projected climate surfaces that include variables relevant for biological research and infrastructure planning, such as growing and chilling degree days, heating and cooling degree days, growing season length descriptors, frost free days and extreme minimum temperature. The database can be queried through a provided software package ClimateAB. A representative subset of these climate surfaces has been thoroughly checked against observed weather station data. We report error estimates for historical climate data and discuss the strengths and limitations of this database for use by natural resource managers and researchers.
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    Historical and projected climate data for natural resource management in western Canada
    (Agricultural and forest meteorology, 2009) Mbogga, Michael S.; Hamann, Andreas; Wang, Tongli
    In this paper we present a comprehensive set of interpolated climate data for western Canada, including monthly data for the last century (1901–2006), future projections from general circulation models (68 scenario implementations from 5 GCMs), as well as decadal averages and multiple climate normals for the last century. For each of these time periods, we provide a large set of basic and derived biologically relevant climate variables, such as growing and chilling degree days, growing season length descriptors, frost free days, extreme minimum temperatures, etc. To balance file size versus accuracy for these approximately 15,000 climate surfaces, we provide a stand-alone software solution that adds or subtracts historical data and future projections as medium resolution anomalies (deviations) from the high resolution 1961–1990 baseline normal dataset. For a relative quality comparison between the original normal data generated with the Parameter Regression of Independent Slopes Model (PRISM) and derived historical data, we calculated the amount of variance explained (R2) in original weather station data for each year and month from 1901 to 2006. R2 values remained very high for most of the time period covered for most variables. Reduction in data quality was found for individual months (as opposed to annual, decadal or 30-year climate averages) and for the early decades of the last century. We discuss the limitations of the database and provide an overview of recent climate trends for western Canada.