The main objective of the BIOS² research program is to enhance the capacity of Canadian organizations to evaluate the status of biodiversity and to incorporate biodiversity scenarios into decision making.
A global strategy, built on collaborative efforts between scientists, governments, private industries and NGOs, is required to meet this objective. Novel technologies such as next generation sequencing (barcoding) and open access databases are changing the way we perform biodiversity assessments and specific skills are needed to build on these tools. We expect that a national program for biodiversity monitoring and forecasting will be beneficial for Canadians because it will improve biodiversity assessments and ecosystem management, accelerate risk evaluation and make the entire process more transparent and rigorous. It will benefit both the evaluation of single projects (e.g., our partner Hydro-Québec is a pioneer in Québec for conducting environmental impact assessments of hydropower plants) and entire industrial sectors (e.g. our partner ABMI is working with the oil industry from Alberta to monitor its global impact on biodiversity).
Research funded by the BIOS² program will more specifically target the following objectives:
Research Objective 1: Real time evaluation of Canadian biodiversity with open science tools
The development of computing facilities and biodiversity database such as GBIF (and its Canadian implementation Canadensys by co- applicant A. Bruneau), OBIS, GenBank, BOLD, TreeBase, Try, and others have led to a major step forward in the quality and the type of science that is currently done. We have seen recently improved techniques and more reliable estimates of biodiversity for various groups such as trees [1], birds [2], arthropods [3] and fishes [4]. Open access databases also allow a more accurate estimate of the rate of extinctions [5], including for the rarest and least documented groups [6]. Information on traits and evolutionary history can be integrated for better assessments of biodiversity changes (e.g. [7]). We will use and adapt standards for essential biodiversity variables [8] and monitor them, in real time, and provide means to visually represent their spatial distribution and temporal changes in Canada.
Research Objective 2: Evaluate past changes in Canadian biodiversity in the last century
We currently have a limited understanding of the historical changes in the Canadian biodiversity that happened as a consequence of human development. We know that changes have occurred, in the form of species extinctions, introductions or alteration of dominance, but we currently are unable to document them across the wide range of organisms that are found in our environment. Despite the wide recognition that humans impact biodiversity, scientists recently grasped the size of the challenge they face to document and forecast these changes [9-11]. Fortunately, we can build on a good system of environmental impact monitoring that was set-up by federal and provincial laws. The current challenge is to integrate available information and perform a global assessment of past biodiversity changes across the country. Fellows will team up with partners to synthesize available information and perform this assessment.
Research Objective 3: Develop and implement tools to predict future biodiversity changes and impacts
Sustainable development requires that we anticipate possible changes in biodiversity in response to climate change, fragmentation and other drivers of global changes. We have seen increasing interest in the use of biodiversity scenarios in the last decade (e.g. [12]). Our team has experience in developing new integrative models designed for forecasting and conservation at regional scales (e.g. [13-14]). This objective will support projects aimed at developing advanced statistical methods and simulation modeling tools designed to support scenario analysis of the future Canadian biodiversity.
Research Objective 4: Set-up tools and methods to enhance transfer of computational biodiversity science into conservation and decision making
Advanced data driven analyses, statistical techniques and modeling tools will be useful for Canadian employers if they can be transferred and adapted for decision-making. This objective will support projects that aim at co-designing user-friendly tools with partners and train professionals in their usage (see e.g.[15] for an example). This includes software development, implementing visualization and communication tools, as well as documentation. Fellows will conduct internships at collaborating organizations to develop state-of-the-art customized tools for biodiversity monitoring and forecasting.
References
[1] Slik et al. 2015. An estimate of the number of tropical trees. PNAS 112: 7472-7477. [2] Jetz and Fine. 2012. Global Gradients in Vertebrate Diversity Predicted by Historical Area-Productivity Dynamics and Contemporary Environment. PLoS Biology 10: e1001292. [3] Basset et al. 2012. Arthropod diversity in a tropical forest. Science 338: 1481-1484. [4] Stuart-Smith et al. 2015. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature doi:10.1038/nature16144. [5] Ceballos et al. 2015. Accelerated modern human-induced species losses: entering the sixth mass extinction. Science Advances 1:e1400253. [6] Regnier et al. 2015. Mass extinction in poorly known taxa. PNAS 112: 7761-7766. [7] Thuiller et al. 2011. Consequences of climate change on the tree of life in Europe. Nature. doi:10.1038/nature09705. [8] Pereira et a. 2013. Essential Biodiversity Variables. Science 339: 277-278. [9] Vellend et al. 2013. A global meta-analysis reveals no net change in local scale plant biodiversity over time. PNAS 110: 19456-19459. [10] Dornelas et al. 2014. Assemblage time series reveal biodiversity change but not systematic loss. Science 344: 296-299. [11] Gonzalez, A. et al. 2016. Estimating local biodiversity change: a critique of papers claiming no net loss of local biodiversity. Ecology 97: 1949-1960. [12] Pereira et al. 2010. Scenarios for global biodiversity in the 21st century. Science 330: 1496-1501. [13] Thuiller et al. 2013. A road map for integrating eco-evolutionary processes into biodiversity models. Ecology Letters. doi: 10.1111/ele.12104. [14] Rayfield et al. 2016. Multipurpose habitat networks for short-rangeand long-range connectivity: a new method combining graph and circuit connectivit. Methods in Ecology and Evolution DOI: 10.1111/2041-210X.12470. [15] Mitchell et al. 2015. The Montérégie Connection: linking landscapes, biodiversity, and ecosystem services to improve decision making. Ecology and Society 20:15.
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