Landscape Conservation Cooperatives (LCC): Landscape by Design Session
Thursday May 21, 2015, 2-330 pm ; Room 325 Lory Student Center
Moderator: Sean Finn
LCD = landscape by design
LCC = landscape conservation cooperatives
GRB = green river basin
CC = climate change
SDM = species distribution model
PCA = principle core area
Speaker 1: Kevin Johnson (Southern Rockies LCC)
-Science Coordinator SRLCC
Green River Basin (WY, UT, CO)
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Why selected, hope to do with LCD, integrating climate into LCD
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Geographic focus within landscape, find geographies with multiple focal resources, are there people already working there interested in furthering collaboration, where has SRLCC already invested funds and where have partners shown willingness to put money forward for conservation on the ground
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3 geographies within SRLCC: Green River, Four Corners, Rio Grande basin
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What specifically are we thinking in GRB? Recipe of conservation actions, where on landscape consv’n actions can occur to support what partners are trying to accomplish on landscape o Mirrored after several LCD projects in Columbia Basin, Arid Lands
Initiative
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LCD: partnership-driven method to assess current and anticipated future conditions o Spatially-explicit depiction of desired future condition o Mgmt. prescriptions for achieving conditions o Both process and product (spatial depiction)
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Marxan as a tool to guide Landscape conservation o Model for optimizing that combines cost and conservation objectives to produce potential consvn scenarios
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MaxEnt as a tool to guide probabilistic suitable habitat conditions o Probability surface for species presence
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Green River Basin o 64% shrub/scrub o 6% deciduous forest o 16% evergreen forest
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Lots of BLM-owned land but also USFS, state land holdings, Northern Ute
Nation in Utah, USFWS refuges, Bureau of Reclamation reservoirs, 2 NPS units
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Nearly 20 projects funded between SRLCC and GNLCC: o Warm and cold water fish o Stream temp modeling o Animal movement data o Downscaled climate data/projections

o Vulnerability assessments
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Also State wildlife plans and other disparate projects
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How do we bring climate into existing syntheses?
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Dave Theobald, Conservation Science Partners – Principle Investigator o Oversight team: CPW, UDW, WY Game & Fish, BLM – WY, FWS, WY
Landscape Conserv, USGS…
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Preliminary starting points, targets o Burrowing owl, o Cultural rscs, o Mule deer and elk, o Native fish, o Antelope…
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Mgmt. challenges o Oil/gas and energy development o Invasive species (tamarisk, leafy spurge, hounds tongue) o Flow mgmt. for GR and its tributaries
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Reservoirs change hydrograph of river
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What actions can be taken on upland to help address flow mgmt.?
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Two driving questions
1.
How do we incorporate climate projections into future condition scenarios?
2.
How do we use climate projections in valuing today’s conservation delivery against tomorrow’s future condition?
Speaker 2: James Broska (Great Plains LCC)
-Science Coordinator GPLCC
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Pilot Landscape Conservation Design
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Trying to realize what LCCs are developed for o How do we plan and design as partnership at landscape level
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LCD (process and product): o Collaborative conservation: defines conservation goals, establishes conservation targets, describes desired landscape conditions to support conservation objectives o Collaborative products: science-based, spatially-explicit, identify priority areas
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Pilot region selection criteria: o Build on GPLCC funded science o Engage several GPLCC states o Capitalize on ongoing partner work o Region strongly affected by identified threats
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Pilot LCD team dvpt: o Technical team o Advisory team: bring partners to the table
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Flow diagram: o Pink: steering committee, blue: technical team and science advisory team

o Need to establish partners, goals objectives
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assess current state of ecosystem
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develop conservation opportunity assessment
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predicting response of habitats to future land change (scenario planning and threats)
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collaboratively select sites for conservation delivery
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implement conservation actions
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monitor and assess effectiveness
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Goal: functioning and sustaining grassland landscapes o “Increase the size and connectivity of grassland fragments that provide habitat for priority species” o threats: CC, energy dvpt, tillage of prairie for ag o planning: SDM
 look at specific geography, LCC includes four level III ecoregions (denotes generally similar ecosystems) o pilot area encompasses area at southern end of LCC, southern high plains; northern extent stops at Arkansas River
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What are we designing for? o Species focused approach
 Set pop’n objectives for priority species to guide LCD process and follow elements of Strategic Habitat Conservation
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Biological planning, conservation design, conservation delivery, inventory/monitoring, and research o Habitat-based approach
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Assessment of current state of major ecosystems and habitat types simultaneously identifying threats to delineate spatial extent of biodiversity, ecological integrity, and ecosystem function o Hybrid approach
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Steps o Consider suite of priority species
 Different niches in system
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Useful as indicators of other species o Assemble data layers o Identify key habitats within pilot geography
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Grassland fragments have different values for different species o SDM
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Species/habitat associations o Associations with other species o Sensitivity to stressors, threaths, change agents
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Scenario planning, different responses for different species.
E.G., wind energy probably does not affect rattlesnakes
 “To increase the size and connectivity of intact grassland fragments…”
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Threat categories o Climate change o Fragmentation o Invasive non-native and native species o Agricultural issues
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CC related science needs o Delineation of spatial/temporal shifts in temp and ppt patterns

o Changes in veg community structure and composition o Changes in species assemblages o Phenology changes for fish, wildlife, plants
Speaker 3: Sean Finn (Great Northern LCC)
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Columbia Basin was early pilots: initial analysis done and outputs maps created
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Work built on back of Arid Lands Initiative
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LCD: strategic, spatially explicit, partnership-driven, outcome oriented o How do we align priorities so they have landscape impacts
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Elements of LCD Columbia Plateau o Model refinement using climate indices o Collective conservation action
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Lots of land use conversion to agriculture
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Shared Priority Areas – where actions should be focused first o Core areas + connectivity zones o Output of optimization modeling provides core areas that provide large contribution to core conservation targets
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Vulnerability Assessment in LCD o Exposure – external threats o Sensitivity – intrinsic qualities that make target susceptible o Adaptive Capacity – resilience and mean conditions
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Proxy data to characterize climate vulnerability examples o Exposure: temperature change velocity, multivariate climate change velocity o Sensitivity: sharp-tailed grouse future climate suitability model, MC2 vegetation instability model o Adaptive capacity: TNC resilience model, Vista landscape condition model
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Model structure and fuzzy logic model o Data set
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metric
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support curve
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aspect of vulnerability
 vulnerability o Data sets represent aspects of vulnerability o Fuzzy logic: all variables remapped into “truth” values using true-false propositions
 Use input data and proposition (e.g., “big sagebrush in the PCA is relatively likely to persist to end of century”)
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Always true, always false, sometimes true
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Determine thresholds to various categories o Spatial data layer for each element of vulnerability (e.g. exposure, sensitivity)
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Greater topographic variation may have less exposure, less likely to change because climate velocity lower
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Measures of sensitivity do not show patterns but places with high sensitivity correlate with vulnerable stuff like sage grouse and wetlands

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Create relative vulnerability map based on spatially explicit maps
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Relative vulnerability vs selection frequency o Y-axis = mean selection frequency o X-axis = relative vulnerability o Upper right: vulnerable and common on landscape
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Priority Core Area scorecard o Tells managers what they should be doing where o Identifies threats (high, medium, low), climate change vulnerability o Used by USFWS (restoration), BLM Spokane District, and Audobon
Society to inform Important Bird Areas, NRCS (designating Grasslands of
Special Significance), WDFW and WDNR
Panel:
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Jeff Morisette (North Central Climate Science Center) – JM
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Jeff Lukas (Western Water Assessment) - JL
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Imtiaz Rangwala (Western Water Assessment) - IR
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Kevin Johnson (Southern Rockies LCC) - KJ
Panel discussion: How do we better integrate climate information into Landscape
Conservation Design?
JM: Think about impact team and what we have as capacity in foundational science area to delve into tool (e.g. efficient SDM). Can we include Marxan into VisTrails? Can it run on core machines and automate the process? NCCSC can help streamline modeling efforts. Can we think more of uncertainty in these vulnerability maps? These maps have nice polygons but how do we better represent uncertainty in LCD outputs?
IR: Quick thoughts: how do incorporate uncertainty into spatially explicit maps? Can do both, for example, SW Colorado project and changes in spruce-fir ecosystem in Gunnison
Basin. 9 GCMs and drew one spatially-explicit map of change; move to drawing 3 maps based on GCM scenarios (e.g. warm and wet) to compare different possibilities under different scenarios. Precision is not accuracy.
JL: Even before we incorporate CC, are we incorporating uncertainty in ecological responses to stressors? People/managers want to see maps and it helps tie resources to landscape, but maps lack uncertainty. Need multiple maps by varying something in the modeling scheme, parameterize different aspects of modeling or use different modeling approaches. Need more transparency when reducing multidimensional data into two dimensions on a map. Managers prefer to have a single number, but if we’re going to be honest about data we can’t give them one number; we shouldn’t be giving them one number even if that’s what they want. Need to be attuned to uncertainty in both climate and ecological response. Important to capture range of uncertainty using differing models or parameters.

KJ: Uncertainty unschmertainty! I don’t care, give me a number! I can implement conservation delivery actions, but when someone says, “I’m not sure what this says”, you’re talking me out of doing something. So just give me something. We understand you can’t give me just one number, but just give me something and let’s move forward with no regrets. [pulls up relative vulnerability map] somewhere in here if I do something it’s bound to do something good, so let’s target our efforts there.
James Broska: I think the geospatial modeling community needs to do a better job of better representing uncertainty so we don’t just have one map. Many times maps go to committee or advisory panel and they need a range so they can make an informed decision. What were model sensitivities?
IR: I feel that with managers that some will just need one map and others won’t.
Whether the managers see a single map or all, at some level there needs to be an assessment of the variation in outputs.
JM: What can we contribute? We can show how different parameters change outputs and different approaches change outputs. If changes completely change the map, then maybe there’s a problem. Maybe we don’t tell managers uncertainty, but we can show high priority areas, areas of model overlap where all models and scenarios overlap; a gradient of 1-9 or shades of red.
Audience: optimization models together with probabilistic models.
Audience: think about maps as being able to represent something more dynamic, spatially and temporally. For climate and action on the ground, there may be areas that you can more strategically target.
Audience: I’m a little worried about the language, “relative vulnerability”. Are models pointing us to areas that are vulnerable and can they show areas that cannot be saved?
Maybe cooperatives should be thinking about what can be protected and what cannot be protected in the face of climate and other stressors.
KJ: We need to consider what we can accomplish if we work in a specific area, we can’t just follow the map.
JL: What is the implicit message of relative vulnerability? Should we focus on less vulnerable areas because high vulnerability areas are too far past being saved?
Sean Finn: polygons are areas that were already identified by partners and we then built on that by including the climate component. Secondary analysis compares one core area against the others. In the core areas, which are more vulnerable and which are less vulnerable, that’s what we tried to do. One of my questions, can we start to generate climate data that specifically address things like exposure and sensitivity? How do we tailor climate projections so that they have a more specific ecological context?

Alison Shipp: If red area is really in trouble and there’s no way it’s sustainable in any scenario, then what is the future climate condition for that area and what is the opportunity to be there that will be viable and quality to that landscape? How do we begin making decisions based on that? If quail can’t survive here, what can, and how can
I start managing for that?
IR: Real world example. Gunnison project wanted to know where should we put our resources to develop grouse habitat. We started with coarse climate recommendations, but moved to working with extreme conditions. For example, what did NDVI in 2002 and a bad drought look like. Which areas hit hard did not come back very well and which did bounce back? How does that inform management decisions? My suggestion is to work with extreme indices or extreme cases and look at sensitive variables like soil moisture.
Audience: how do you figure out if you’re even going in the right direction? We need to get feedback on management actions based on what is happening with climate and changes on the landscape.
JM: There is a difference of scale for different management actions. Let’s keep in mind the scales that we’re working at, how is climate different from microrefugia or management scale?
JL: We always need to ground ourselves in historical record and what has ben done on these landscapes and what has worked before? 1930-1960 was 10% drier in Colorado than 1970-2000, so we’ve already experienced many conditions anticipated in the future, so how have ecosystems responded in the past? Maybe we can substitute space for time and look at gradients and longer term climatic variability.