# Presentation: 053810

## The St. Joseph River Watershed

## What did we learn?

- Smaller groups are more functional than larger groups
- Starting from different places delays performance of group
- Don’t sweat the details
- Visuals bring clarity, enhance learning
- Establish common language/definitions early and often
- Bite off first chunk in your comfort zone and then move on to the rest of the pie
- Try a prototype and then react

## Stochastic Demographic Population Model

## Population Model

- Problem Statement: What population size is needed to obtain a probability of extinction of less than 10 percent within 100 years?
- Purpose: test our population objectives, i.e., will 1000 individuals provide a probability of extinction<10% for 100 years

**Notes:**
Recommendations highlight add’t purposes

## Population Model

- CWS Life cycle

##  Population Model

- Model Variables
- P  Proportion of adults breeding - sample from an equal distribution
- R  Proportion of reproductively mature females giving birth - sample from an equal distribution
- F  Number of young per female - sample from an equal distribution
- A  Adult survival rate - sample from an equal distribution
- Sn Neonate survival rate age 0 - sample from an equal distribution
- Sji Juvenile survival rate age 1 to 3 - sample from an equal distribution
- Initial N
- First age of breeding age = 3 yrs
- Maximum age of breeding = 7 yrs.

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**Notes:**
P	based on all plain bellied watersnakes
R	assuming all females that participate in breeding give birth
F	copperbelly  value
A	Uncertainty large
Sn	Based on all snakes

## Population Model

- Analytical Approach
- Expert opinion to populate model
- Used stable age-structure to set initial pop
- Evaluated model behavior
- Added catastrophic events

## Population Model

- CWS

## Where are we going next?

- Refine and improve stochastic demographic model
- Examine population objective and assure ourselves that extinction risk is acceptable and revise as needed
- Sensitivity analysis to identify management priority and research

## Problem

- PFW program lacks a standard method for differentiating between CWS habitat restoration projects

## Considerations

- Combined the biological, economical, and practical (real-world) parameters.
- Utilized the SMART (Simple Multi Attribute Ranking Technique) tool for establishment of a decision matrix.
- Decision matrix included 6 attributes
- one directly tied to the biological model output,
- five represented the most important economical and practical parameters to PFW biologists.
- Additional attributes may be added or substituted to our decision matrix.
- Attributes were weighted based on factors that are most important in differentiating between potential CWS habitat restoration projects.

## Private Lands Decision Tool

| Attribute | units | goal | Multi Wet | Forest | Lg Wet | For Wet | Forest | Wetland | For. Wet | Wet Com |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CWS score | 1-10 | max | 10 | 2 | 6 | 4 | 6 | 3 | 8 | 5 |
| LAG TIME | 0-1 | min | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| total cost/acre | $/ac | min | 1500 | 450 | 350 | 1000 | 600 | 500 | 750 | 1000 |
| difficulty | 1-5 | min | 5 | 1 | 3 | 3 | 2 | 1 | 3 | 3 |
| priority species | # | max | 1 | 1 | 1 | 2 | 2 | 0 | 2 | 2 |
| cost share | % | max | 0 | 50 | 50 | 0 | 20 | 0 | 0 | 0 |
| agreement duration | # | max | 10 | 15 | 15 | 15 | 15 | 10 | 30 | 15 |

## Private Lands Decision Tool

| Attribute | units | goal | Weight | Multi Wet | Forest | Lg Wet | For Wet | Forest | Wetland | For. Wet | Wet Com |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CWS score | 1-10 | max | 65 | 65 | 0 | 32.5 | 16.25 | 32.5 | 8.125 | 48.75 | 24.375 |
| LAG TIME | 0-1 | min | 10 | 10 | 0 | 10 | 10 | 0 | 10 | 10 | 10 |
| total cost | $ | min | 5 | 0 | 4.565217 | 5 | 2.173913 | 3.913043 | 4.347826 | 3.26087 | 2.173913 |
| difficulty | 1-5 | min | 5 | 0 | 5 | 2.5 | 2.5 | 3.75 | 5 | 2.5 | 2.5 |
| priority species | # | max | 10 | 5 | 5 | 5 | 10 | 10 | 0 | 10 | 10 |
| cost share | % | max | 5 | 0 | 5 | 5 | 0 | 2 | 0 | 0 | 0 |
| agreement duration | # | max | 5 | 0 | 1.25 | 1.25 | 1.25 | 1.25 | 0 | 5 | 1.25 |
|  |  |  sum | 105 | 0.761905 | 0.19824 | 0.583333 | 0.401656 | 0.508696 | 0.261646 | 0.757246 | 0.479037 |

## Decision Tool Output

## Conservation Delivery

- Paradigm Shift
- Targeting Individual Parcels
- Landowner Protections
- Funding and Time Allocations

## Where are we going next?

- Private lands decision tool is ready to use once CWS score is developed
- Explore funding distribution alternatives to support restoration work in CWS area
- Design evaluation of decision tool
- Share with and bring in other agencies

## Conservation

**Conservation**
**Delivery**

**Notes:**
This is the basic SHC framework that the MBP, JVs and others promoting and striving to implement .  It consists of 5 elements – activities will be going on each of the elements all the time so this is not a linear sequence of events.

Biological planning spins off priority species, population objectives and a synthesis of applied science summarized as models – these are valuable communication tools but they also inform the other SHC elements.

Conservation design is the process of applying models to spatial data – this is important because habiat conservation always has a geographic component – where is an important questions.  Habitat objectives and programmatic priority areas are important products of this element.

*Conservation delivery is what this is all about – efficiently attaining a population response – although this is usually the role of the NWRS, PFW, and States. 

Every management action is based on some degree of uncertainty about the outcome.  Monitoring to assess outcomes compared to expectations and using research to test key assumptions are essential to making future decisions more reliable.

SHC is a way to bridge the chronic gap between research and monitoring and actual management outcomes.  Each of these elements will be discussed in more detail throughout the workshop.

## Recommendations / SHC

- Regions need to increase capacity for modeling
- Share capacity across FWS program boundaries
- Need assistance from USGS
- Accountability measures—outcome based rather than output
- Bite off manageable sized pieces

## Recommendations

- Follow up to workshop to bring team back together
- Determine how to support outside expertise
- Break up workshop into smaller functional groups
- Functional directory of expertise of people to pull in to solve problems