Crown of the Continent Landscape Analysis/Ecological Indicators Project

Transcription

1 Crown of the Continent Landscape Analysis/Ecological Indicators Project Project Coordinator: Erin Sexton, Crown Managers Partnership (CMP), Flathead Lake Biological Station, University of Montana; (406) ; Principal Investigators: Greg McDermid, Department of Geography, University of Calgary; (403) ; Mike Britten, Rocky Mountain Inventory and Monitoring Network, National Park Service; (970) ; Project Partners: Crown Managers Partnership [Alberta Ministry of Environment, Alberta Ministry of Sustainable Resource Development, Alberta Parks Tourism and Recreation, Parks Canada, British Columbia Ministry of Natural Resource Operations, British Columbia Ministry of Environment, US Forest Service, National Park Service, Rocky Mountain Inventory and Monitoring Network, US Geological Survey, MT Department of Natural Resources and Conservation, Flathead Basin Commission, University of Calgary, University of Montana, Glacier National Park, Waterton Lakes National Park]; Dr. Guillermo Castilla, Alberta Biodiversity Monitoring Institute; Gordon Stenhouse, Foothills Research Institute Grizzly Bear Research Program; Scott Nielsen, University of Alberta; Garth Mowat, BC Ministry of Forests, Lands and Natural Resource Operations; Andrea Moorehouse, Southwest Alberta Grizzly Bear Project; Dr. Clint Muhlfeld, US Geological Survey Project Summary: The Crown of the Continent Landscape Analysis/Ecological Indicators Project is focused on issues of (i) landscape-scale transboundary GIS data acquisition, integration, and synthesis across the Crown of the Continent Ecosystem; (ii) the production of large-area habitat and connectivity models for select wildlife species that are key indicators of terrestrial and aquatic integrity; and (iii) the dissemination of knowledge, data products, and geospatial tools to managers and collaborators through data portals and outreach programs designed to support effective conservation delivery. Specific activities this year will involve (i) the development of CCE-wide models of grizzly bear (Ursus arctos), wolverine (Gulo gulo), cutthroat trout (Salmo clarki), and bull trout (Salvelinus confluentus)

2 occupancy and habitat connectivity; and (ii) the application of Monte Carlo simulations designed to reveal the impact of base-map errors on high-level knowledge products used by resource managers. Need: The Crown of the Continent Ecosystem (CCE) is a transboundary focal landscape, encompassing the area of highest jurisdictional complexity within the Rocky Mountain ecotype area of Great Northern LCC (Figure 1). Ecosystem functioning across the CCE portion of the Great Northern LCC is under pressure from climate change, energy development, and land-use conversions related to increasing levels of human activity. However, our ability to manage the impacts of these stressors is limited by our capacity to understand natural and anthropogenic processes across large, complex landscapes. The CCE Landscape Analysis/Ecological Indicators Project is designed to contribute to this issue through three phases of work. Phase 1 (complete) focused largely on the assembly, assessment, and dissemination of existing geospatial data sets across the transboundary CCE (Sexton et al., 2010; Figure 2). Phase 2 (in progress; the current proposal) emphasizes the refinement of phase 1 data sets into high-level knowledge products regarding specific terrestrial (grizzly bear, wolverine) and aquatic (cutthroat trout, bull trout) habitat and connectivity factors. Phase 3 (forthcoming) will focus on the development of formal work flows and algorithms designed to support landscape-level monitoring activities, Figure 1: The Crown of the Continent Ecosystem. through the semi-automated detection of changes in CCE base maps, and software tools designed to permit the turn-key generation of updated habitat/connectivity models. Expected outcomes of the overall program are centered on developing our capacity to provide information and planning tools to managers and collaborators regarding transboundary indicators of ecological integrity. In particular, we wish to assess the vulnerability of terrestrial and aquatic

3 resources within the CCE to landscape-level level stressors associated with climate change and human development. Our project is distinguished by (i) the regional, transboundary oundary scope of tthe study area, (ii) our emphasis on delivering usable knowledge and planning tools to managers within a monitoring framework, and (iii) our capacity to serve as integrators and disseminators of project outcomes through a strong network of international managers. We are the only group working actively to assemble, refine, and distribute critical geospatial information across the entire trans-boundary boundary CCE, and have established a series of collaborative partnerships within government and academia on both Figure 2: The Crown of the he Continent within the GNLCC. GNLCC sides des of the international border which are designed to address the specific objectives identified in this proposal proposal. As such, the CCE Landscape Analysis/Ecological Indicators Project is an ideal case example for the Great Northern Nort LCC on achieving landscape-scale scale conservation delivery, particular particularly ly for areas of high jurisdictional complexity. With respect to the specific relevance criteria identified by the GNLCC, our project addresses the following needs: 1. Relevance to landscape drivers The CMP and the NPS Rocky Mountain Inventory and Monitoring Network have identified the Landscape Analysis/Ecological /Ecological Indicators Project as a strategic priority for evaluating terrestrial and ecological integrity in the face of landscape landscape-level level conversions associated with human development evelopment and climate change in the focal landscape of the CCE CCE. Derived knowledge and information products will inform management priorities and actions and transboundary collaborative solutions to landscape stressors stressors.

4 2. Relevance to focal resources, ecosystem processes, species, and habitat The CMP has developed a suite of indicators to assist managers in identifying and evaluating threats to the terrestrial and aquatic integrity of the CCE. The CMP ecological indicators are: (i) landscape dynamics (ii) biodiversity (iii) water quality (iv) exotics (v) climate, and (vi) air quality. This project addresses the landscape dynamics indicator directly, and works specifically to transform GISbased landscape information products into usable management knowledge related landscape-scale connectivity and habitat. 3. Relevance to on-theground conservation delivery This project allows for more effective conservation delivery by synthesizing seamless geospatial data for a suite of species from multiple Figure 3: Land cover for the Crown of the Continent Ecosystem: an example of our Phase 1 Data products. jurisdictions across the CCE, and providing managers with science products that allow them to align management outcomes across the two provinces, one state, and two countries. Additional outcomes include trend analysis of landscape-scale change and spatially explicit geospatial layers that are accurate and consistent at the scale of the CCE.

5 4. Geographic scope The CCE is entirely within the geographic boundary of the GNLCC (Figure 3), and encompasses one of North America s most biologically diverse and jurisdictionally complex landscapes. The Crown Managers Partnership was initiated in 2001, and indentified the entire CCE as a focal landscape for conservation in Canada and the U.S. The CMP mission is to promote collaborative action in addressing environmental management issues across the CCE and to better align management for wide-ranging and transboundary species, and their habitats, across the international and inter-provincial boundaries. 5. Partner engagement The CMP is well-positioned to link this project with multiple projects within and beyond the CCE. In addition to collaborating with the National Parks Service Rocky Mountain Monitoring Network (NPS-RMN) and all CMP agencies, this project will add value to multiple other initiatives through data-sharing, information exchange and sharing of methodologies for landscape analysis, including the Alberta Biodiversity Monitoring Initiative (ABMI), the Alberta Foothills Research Institute Grizzly Bear Research Program, and the USGS Northern Rocky Mountain Science Center. We are also making particular efforts to interface and collaborate with other GNLCC-funded projects, such as Muhlfeld and Hauer s work on aquatic ecosystems in the transboundary Flathead. Our project is designed to benefit all partners and programs by addressing the challenge of multi-jurisdictional data integration at a landscape-scale. Support from the GNLCC is leveraged with agency contributions and additional funding sources as available. 6. Ability to show success in FY This project is in the third year of a multi-year program, and as such has the partners, technical expertise and geospatial framework already in place to deliver the baseline and methodology for landscape level trend analysis and the occupancy and connectivity models identified in the proposal. The CMP offers ten years of relationships and experience in over-coming jurisdictional and administrative constraints across the CCE. Objectives: We will address three specific objectives in FY , each associated with our larger Phase 2 project goals: Objective 1. Develop CCE-wide occupancy and connectivity models for grizzly bears, wolverines, cutthroat trout, and bull trout. Species abundance is difficult to determine across large areas and through time, but the seamless geospatial data sets assembled in Phase 1 of our project permit the development of spatially explicit models of occupancy the patches within the CCE that are occupied by a given

6 species and connectivity: the degree to which landscapes facilitate animal movement and gene flow. In February of 2012, we participated in a workshop with grizzly bear biologists from British Columbia, Alberta, and Montana, through which we secured the collaboration and data sets necessary to develop occupancy models of grizzly bears across the study area. This coordinated, international effort brings valuable new resources into the project, and will facilitate first-ever models of grizzly bear occupancy across the entire transboundary CCE. While we anticipate that this broad-scale, regional model will not explain certain local patterns of abundance, our work will (i) test the suitability of assembled data layers and model projections for various sub-populations of bears across the CCE, and (ii) permit the evaluation of alternate hypotheses of top-down (fragmentation, human development) and bottom-up (vegetation, phenology) controls on grizzly bear distribution. Similar efforts are underway to secure data sets and collaborators for parallel work on wolverines, cutthroat trout, and bull trout. Objective 1.a. In support of Objective 1, we propose to leverage funding for the Landscape Analysis work in order to test the interface of our landscape-level data metrics with fine-scale species and habitat data at the sub-regional scale, in order to provide an integrated (both fine- and coarse-scale) assessment of resource vulnerability to landscape stressors. To test this, we will partner with the Southwest Alberta Grizzly Bear Monitoring Project in achieving objectives for assessing grizzly bear population densities and distribution. Fine-scale data from this work will directly inform the landscape-scale occupancy, connectivity and evaluation of drivers of grizzly bear distribution described in Objective 1. Through this partnership, the CMP also achieves the additional strategic priority of promoting consistent methodologies across jurisdictional boundaries. Objective 2. Perform sensitivity analyses designed to reveal the impacts of base-map errors on the derived knowledge products produced in Objective 1. A troubling, but often-overlooked factor in science-based resource management is the impact of data errors and model uncertainties on the derived, high-level knowledge products employed by decision-makers. As a unique collaboration involving technical representatives from the knowledge-production side (the researchers) and highlevel managers from the decision-makers side (the CCE managers), our project is particularly concerned with this issue. In our case, we have assembled available geospatial data layers across the CCE: an area (like the larger GNLCC) that spans multiple jurisdictions, and therefore necessitates the merging of disparate data sets. In order to address this issue, we will conduct a series of sensitivity analyses designed to reveal the impact of base-map errors the high-level occupancy and connectivity models described in Objective 1. In FY , we developed the

7 methods necessary to perform these sensitivity analyses through Monte Carlo simulations (Timmins et al. 2012). The procedures were tested in a case-study area in western Alberta, and will provide the foundation for large-scale analyses within the current project. Objective 3. Communicate project outcomes within the GNLCC. The Landscape Analysis/Ecological Indicators Project will be featured at the 2013 Annual Forum of the Crown Mangers Partnership, providing an opportunity for education and discussion with managers and public stakeholders. Follow-up workshops will be organized with managers, citizens and stakeholders around the CCE for dialogue regarding jurisdictional implications of setting regional targets and thresholds. Our personnel held a GNLCC webinar in September of 2011 ( and will seek additional creative opportunities to communicate project results. In addition to the core project objectives described above, we will also continue our engagement with data-sharing and dissemination activities designed to permit the unrestrained distribution of project data and outcomes. These include both technical efforts made by our data manager to distribute our products to CMP managers and collaborators through our project web site ( and secure data server ( as well as advocacy activities designed to clarify and relax data-ownership issues associated with certain Canadian (particularly Albertan) data sets. Both of these efforts are assisted tremendously through our partnerships with managers within the CMP. Methods: An overview of the tasks required to complete the major objectives of this project are outlined below: Task 1: Develop CCE-wide occupancy models Datasets compiled from Phase 1 will be used to develop occupancy models for grizzly bears, wolverines, cutthroat trout, and bull trout. We will use available data sets describing the presence and absence of target species across the transboundary CCE (e.g. grizzly bear telemetry and DNA data from project collaborators in BC, Alberta, and Montana) to calibrate and validate spatially explicit probability of occurrence models across the full extent of the CCE. We will use zero-inflated Poisson regression to model observed patterns of presence and absence as a function of the various habitat attributes assembled in Phase 1. Our intention is to work at a moderate spatial scale (i.e. grain size in the 250m to 1km range) to represent landscape-level occupancy patterns across the entire study area.

8 Task 2: Create habitat connectivity maps and indices Connectivity maps will be generated using a combination of least-cost path analysis (Schadt et al., 2002, Singleton et al., 2004) and graph-based connectivity analysis (Bunn et al., 2000; Saura and Torne, 2009). Least-cost path analysis determines the least-cost distance between patches by assessing movement resistance and mortality risk of landscapes between patches (Kramer- Shadt et al., 2004). Graph-based analysis creates a network of connected patches based on the least-cost distances between patches, while associated connectivity metrics allow the identification of important connections in the network (Saura and Torne, 2009). Task 3: Conduct a sensitivity analysis to reveal the impact of base-map errors on CCE-wide occupancy and connectivity models Monte-Carlo simulation (Timmins et al., 2012) will be used to evaluate the sensitivity of habitat and connectivity analysis to base map errors and uncertainty. Random errors will be introduced into important base maps before repeating the connectivity analysis. The effect of introduced errors will be assessed by comparing habitat and connectivity indices and maps. If habitat and connectivity results are found to be highly sensitive to base map errors, it would suggest that improvements should be made to the base maps before using these for landscape analysis. Task 4: Data-sharing and dissemination of geospatial data sets, methods, and science products The CMP and GNLCC networks will be leveraged to share data, methodologies, tools and results developed from this project. An interactive website linked to the existing CMP website has been developed, and will receive continued enhancements to showcase natural habitat and habitat connectivity issues within the CCE. In addition, this project will be featuring at the 2013 Annual Forum of the Crown Mangers Partnership, providing an opportunity for education and discussion with managers and public stakeholders. Followup workshops will be organized with managers, citizens and stakeholders around the CCE for dialogue regarding jurisdictional implications of setting targets and thresholds Deliverables: 1. Sexton, E., A. Sobol, J. Burke, G.J. McDermid, and L. O Gan, 2011: Crown Managers Partnership Landscapes Data Review Report A Review of Baseline Geospatial Datasets for the Crown of the Continent Ecosystem Landscape Project. Crown Managers Partnership, 248p. 2. O Gan, Laura, J. Burke, A. Sobol, G.J McDermid and E. Sexton, 2011: NPscape Products Presenting an Example of Maps and Statistical Tables Derived through NPscape, as Applicable to the Crown Managers Partnership Landscapes Analysis. Crown Managers Partnership, 48p. 3. Sexton, E., G.J. McDermid, T. Timmins, G. Castilla, and L. O Gan, 2011: Crown of the Continent Landscapes Analysis/Ecological Indicators Project, GNLCC Webinar;

9 4. Timmins, T., G.J. McDermid, and E. Sexton, 2012: A Monte Carlo simulation approach for examining the effect of land-cover map uncertainty on derived grizzly bear habitat models. American Society for Photogrammetry and Remote Sensing Annual Conference, Sacramento, California, March Landscape Analysis CMP website with complete suite of CCE landscape baseline maps; 6. Integrated database cataloging and synthesizing freely available and derived data cross the Crown of the Continent hosted on a secure web server at the University of Calgary; 7. Crown Managers Partnership Strategic Plan, ; Deliverables: 1. Occupancy and connectivity models and spatially explicit data layers for grizzly bears and other key species at the scale of the CCE 2. Report on grizzly bear occupancy and connectivity analysis at the scale of the CCE, submitted to an appropriate peer-reviewed journal 3. Report on the results of sensitivity analysis and describing the effects of base data errors and uncertainty on landscape analysis, submitted to an appropriate peerreviewed journal 4. Presentations to and workshops with CMP managers, citizens and public stakeholders 5. CMP Annual Forum in 2013 Statement of Compliance: The CMP Landscapes Analysis Team (Project Coordinator, Principal Investigator, Data Manager and GIS Specialist) have read the Great Northern Landscape Conservation Cooperative Information Management, Delivery and Sharing Standards and agree to comply with those standards if the proposal is selected. Schedule: Occupancy models for grizzly bears (2012) Occupancy models for wolverine, cutthroat trout, and bull trout (2013) Connectivity maps and indices (2013) Sensitivity analysis for grizzly bear (2013) Sensitivity analysis for wolverine, cutthroat trout, and bull trout (2014) Data sharing within the GNLCC (on-going) CMP Workshops (2012, 2013) CMP Annual Forum (2013) Multiple peer-reviewed publications arising from project outcomes ( )