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NESI develops River Characterization Tool to support efforts in the Far North To date, very little baseline information has been collected in the Far North. The Far North landscape is too vast and inaccessible for it to be financially viable to acquire enough field data to adequately describe the condition of the entire region. Therefore, it is imperative to leverage existing tools and methodologies that use remotely sensed data for characterizing the landscape. In order to develop resources in an economically sustainable manner, current and accurate science and information must be available to guide management decisions. Northeast Science and Information (NESI) has developed a River Characterization tool as part of the Far North Information and Knowledge Management initiative. The tool provides a user friendly ArcGIS toolbar for scientists and resource managers to perform automated characterization of the riverine landscape across various scales: watershed, river, and reach. This tool integrates and aggregates various types of information into one characterization tool and draws from concepts and methodologies of several existing MNR tools, helping improve efficiency in how to use existing science and information. As the proposed development activities, such as hydro power and mining, have the potential to adversely affect the aquatic resources of the Far North, the river characterization tool will provide a cost effective method to provide initial science and information and determine where more intensive field data collection is required. This ultimately saves money by optimizing the use of available digital data and reduces the time required to collect data in the field. For more information on the river characterization tool, please contact Jennifer Potvin, 705-235- 1260, jennifer.potvin@ontario.ca. NESI’s Terra Team collaborates with UQAT and CFS on the Regional Fire Regime Knowledge of the regional natural disturbance regimes (stand replacing fire) is required to develop a natural benchmark landscape pattern for Ontario’s Boreal forest management planning process. Detailed fire history mapping is expensive and, for Ontario, is only available for the Lake Abitibi Model Forest. Dave Etheridge, NESI’s Forest Landscape Specialist, and Gordon Kayahara, NESI’s Forest Science Specialist, approached Yves Bergeron (Université du Québec en Abitibi-Témiscamingue) and Sylvie Gauthier (Canadian Forest Service) with an idea to extrapolate their work on the fire mapping of the Lake Abitibi Model Forest to the rest of Ontario’s Claybelt using regional forest resource inventories. Dominic Cyr, who worked on his PhD under Professor Bergeron, came up with the novel method of using Bayesian statistics. Classic statistical approaches like regression are normally used and deal with average outcomes. For those “older” Trekkies among us, you will be familiar with the more Bayesian approach of the original Spock who gives the probability of an individual outcome given various levels of prior knowledge. In this paper, two classes for fire history were used: time since fire (TSF) < 150 years and TSF ≥ 150 years. A Bayesian Belief Network was then constructed to place each FRI polygon into one these two fire history classes based upon latitude and the FRI map attributes. The relative abundance of early (jack pine and poplar) and late (balsam fir) successional tree species were the best indicators of the two fire history classes. These variables were followed by photo-interpreted age, stocking, height, and site class. The results suggests that more than 72.4% of the forests located within the Claybelt originated ≥ 150 years from stand replacing fire. We now have a method that gives us an efficient estimate of time since fire classes and the resulting amount of old forests for the entire claybelt, and a process that can be extrapolated to the rest of Ecoregion 3E. We learned that average time since fire is much longer than currently believed, further confirming obtained results through modelling exercises used to develop the provincial Landscape Guide. In addition, the exposure to the Bayesian approach holds promise as a technique for various other MNR tasks such as silviculture effectiveness monitoring or developing succession rules. For additional information on this collaboration, please contact Gordon Kayahara, 705-235-1237, gordon.kayahara@ontario.ca or Dave Etheridge, 705-235-1230, dave.etheridge@ontario.ca
BSFM: What is it What is happening As Ontario has moved to modernize fisheries management programs and improve public participation through Fisheries Management Zone councils, new monitoring programs were required to monitor fish populations and the health of Ontario’s lakes. The Broad-scale Fisheries Monitoring (BSFM) program was designed to help quantitatively determine the status of fisheries resources and evaluate the success of various management activities. In the Northeast Region (NER), we have completed two successful BSFM field seasons in partnership with district staff, and are embarking on our third. Key elements of the monitoring program include water quality sampling, index netting, and angling effort surveys. This year, the NER is further adopting a decentralized delivery model where delivery of the index netting component will be distributed among multiple districts. We plan to conduct netting surveys on approximately 60 lakes across FMZ 8 and FMZ 10 in 2010. Netting surveys help determine sport fish abundance, life history characteristics of key species, and the diversity of the fish community including the presence of invasive species. Many fish are also sampled to assess contaminant levels. Water samples are collected and sent to the Ministry of the Environment for water quality analysis. Aerial angler count surveys are also conducted and these surveys provide the program with estimates of summer and winter fishing pressure leading to estimates of total angling effort (angler hrs/ha/year) on these lakes. Analysis of data collected from hundreds of lakes over multiple years will be required to properly inform management decisions, and this requires an information system designed to house and ultimately serve up all the data collected for the program. Northeast Science and Information (NESI) has taken the lead on developing a BSFM database to complement existing data stored in Fishnet and to link that data to other information that will support its analysis in the future. For additional information on BSFM, please contact: Jeff Amos, 705- 235-1214 or jeff.amos@ontario.ca
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