# Presentation: 200624

## Experiences Developing Monitoring for Forested Vegetation of NCCN

- Andrea Woodward
- USGS-Forest & Rangeland Ecosystem Science Center

## Acknowlegments

- Alice Shelley, Tamre Cardoso – Terrastat consulting
- Paul Geissler – USGS
- Tony Olsen- EPA
- Steve Acker, Regina Rochefort, Mignonne Bivin, Laurie Kurth – NPS
- Karen Hutten - USGS

**Notes:**
Terrastat Consulting helped with the power analysis of pilot data and FIA data. Paul Geissler repeatedly helped us over hurdles. Tony Olsen provided advice and the code for drawing GRTS samples. Park service staff from Olympic, Mount Rainier and North Cascades National Parks were instrumental in designing the protocol and supervising field crews. Karen Hutten researched and wrote the SOPs and managed the protocol development project field seasons.

## OLYM

**OLYM**

**NOCA**

**MORA**

**FOVA**

**LEWI**

**EBLA**

**SAJH**

**Notes:**
The North Coast and Cascades Network of national parks is comprised of 7 parks in western Washington and northwestern Oregon (Olympic, Mount Rainier and North Cascades National Parks, Ebey’s Landing National Historic Reserve, Fort Vancouver National Historic Site, Lewis and Clark National and State Historical Parks, and San Juan Island National Historical Park. The Forest Monitoring Protocol pertains to only the three large parks. NCCN is one of 32 geographically related networks of national parks organized to facilitate development of monitoring programs.

## PICTURE

**Notes:**
Steep slopes, non-wadeable rivers and remote areas contribute to the tough challenges of access in large, mountainous national parks. Concerns are for the safety of crews and the cost of measuring points. The trade-off is between doing a number of points in accessible areas or one in a relatively inaccessible area. Eventually we decided that in the interest of crew well-being we would limit our sampling, and therefore our inference, to areas that are safely accessible. See Kurt Jenkin’s presentation for more detail on the challenges of access in these parks.

## Challenges for Monitoring Vegetation

- High annual variability (weather, phenology, observers)
- Slow to show biologically significant change
- Interpretation based on categories (PAGs)
- Cannot distribute sample based on PAGS because they are expected to change
- We have no accurate map of vegetation at PAG scale
- The same species are not everywhere on landscape
- Systematic sampling over-represents common types
- Plots are very susceptible to wearing out

**Notes:**
Vegetation presents some particular challenges for monitoring. The bullets on this slide are fairly clear, with the possible exception of the points about PAGs. PAGS are Plant Association Groups, or categories of vegetation communities. They are the basis for our understanding of the adaptation of specific species to environmental conditions, and unless we replicate PAGs in our sampling, we will not have enough replicates of individual species to detect trend. However, we expect individual species to re-assort independently to form new PAGs in response to climate change. So we cannot stratify on the basis of present-day PAGS, even if we had a map that would allow us to do that. 

## SOILS

- Chemistry
- Organisms
- Physical Properties

**Terrestrial and Aquatic Wildlife **
**Communities and Habitat**

_**Hydrology**_
- Flow regimes
- Glaciers
- Snowpack

**Land Use--**
_**current/historic**_
- External land
-   cover  type

_**Disturbance**_
- Fire
- Pathogens/
- disease/
- parasitism
- Geologic

_**Meteorology**_
- Temperature
- Precipitation
- Snowpack

_**HumanActivity**_
- Visitor Use
- Park Mgmt.
- Harvest (legal
- & illegal)
- Wildlife Mgmt.

_**Pollution**_
- Deposition

**Vegetation Change/**
_**Landscape Scale**_
- Veg. Types include
- Forested, riparian, alpine/ subalpine, prairies, hardwoods, coniferous

**Vegetation Change/**
_**Community Scale**_
- Communities include:
- Subalpine parklands, forests***, ***riparian***, ***wetlands***, ***low elevation non-forested (i.e.,_** **_prairies, dunes)
- Metrics may include: tree spp. & seedling cover, crown condition, non-vascular, exotics, herbivory, forest processes

**Vegetation Change/**
_**Individual Species**_
- Invasive/Exotic
- Rare
- Harvested (legal &
- illegal)
- Culturally Significant
- Metrics may include: distribution, abundance

**SYSTEM DRIVERS**

**VEGETATION**
**RESPONSE**

- Especially Recreational Impacts

**Notes:**
This conceptual model describes the interactions among system drivers, vegetation and other ecosystem elements. The important point to take away is that spatial scale is important for describing change in vegetation.

## Steps for Designing Sample Frame for Monitoring

- Establish measurable objectives
- Determine trend/change detection method
- Determine temporal sampling frame
- Conduct power analysis to estimate number of samples
- Construct spatial sample frame

**Notes:**
These are the steps we followed to develop the forest vegetation monitoring protocol. In this presentation they are presented linearly, but they were anything but linear in practice. For example, the original objectives may prove to be infeasible based on the results of the power analysis (determining how many plots you need to detect the desired amount of trend) compared with the available funds. In this case, you have to go back to the beginning and revisit the objectives.

## Objectives

- From scoping meetings:
- “What are the status and trends of forest composition and structure.”

**Notes:**
This is an example of the breadth of some monitoring questions developed during our scoping meetings. This question is much too general and non-specific to design a monitoring program around. Instead, your monitoring question must state specifically when, where, what and eventually how (see next slide for example). 

## Objectives Suitable for Designing Sample Frame

- During the growing season of mature and old-growth forests, what are trends in cover % of herbs, shrubs and grasses by species, allometry of trees by species, seedling number by species, and growth & mortality rate of trees by species at the scale of PAGs.
- (Investigate usefulness of FIA data)

**Notes:**
When: growing season (which also needs to be defined); where: old-growth forests (criteria must be developed); what: % cover, allometry, etc. We had a secondary objective to evaluate the usefulness of FIA data for answering our monitoring questions. More about FIA later.

## Steps for Designing Sample Frame for Monitoring

- Establish measurable objectives
- Determine trend/change detection method
- Determine temporal sampling frame
- Conduct power analysis to estimate number of samples
- Construct spatial sample frame

**Notes:**
Change is what happens between 2 measurements, trend is what happens over 3 or more measurements.

## Trend/Change Detection – period means comparison

- Cover

- D1

- Year

**Notes:**
Our original thought was to minimize the effects of interannual variation, especially due to annual variation in weather, by measuring plots for 3 consecutive years (light blue dots) with a longer interval in between. The average of each period (shown in magenta) should be a good reflection of the condition of the vegetation at time1. Likewise, the average of 3 years measured after some time should describe the vegetation at time2. This method avoids the situation of measuring a wet year during time1, a dry year at time2, and concluding that no change has happened. This approach is essentially the same as calculating the slope of a regression line running through the 6 points.

## Trend/Change Detection

- Cover

- D1

- D3

- D2

- Year

**Plot 3, PAG1**

**Plot 2, PAG1**

**Plot 1, PAG1**

**Notes:**
The replicates in this analysis are plots, and the test statistic is the slope of the line or the difference for each plot between time1 and time2. The statistical test is to ask whether this sample of slopes (D1, D2, and D3) indicates that the true slope is different than zero.

## Steps for Designing Sample Frame for Monitoring

- Establish measurable objectives
- Determine trend/change detection method
- Determine temporal sampling frame
- Conduct power analysis to estimate number of samples
- Construct spatial sample frame

**Notes:**
The temporal sample frame reflects the decision about monitoring trend versus describing status. With a fixed budget, there is a trade-off between status and trend. For example, if you can afford to measure 6 plots per year, one extreme way to distribute the effort is to monitor the same 6 plots every year. Then you would have the best possible estimate of trend (given 6 plots), but you would only have examples of 6 places. On the other hand, you could visit 6 different plots every year. After 10 years you would visit 60 plots with this design but only 6 in the first design. In the second design you would have a good description of status because you have data from many places. However, because you have a variance component due to site in this design, you will not have a strong indication of trend. Alternatively, you could visit the same 3 plots every year plus 3 new ones. Then over 10 years you would have visited 33 plots for status while having an estimate of trend from 3 plots. With vegetation plots, you also have to consider that the plots cannot tolerate annual damage from measurements and still represent the population they are meant to represent.

## Temporal Sampling Frame

|  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | X | X |  |  |  |  | X | X |  |  |  |  | X |
| 2 |  | X | X |  |  |  |  | X | X |  |  |  |  |
| 3 |  |  | X | X |  |  |  |  | X | X |  |  |  |
| 4 |  |  |  | X | X |  |  |  |  | X | X |  |  |
| 5 |  |  |  |  | X | X |  |  |  |  | X | X |  |
| 6 |  |  |  |  |  | X | X |  |  |  |  | X | X |
|  |  |  |  |  |  |  |  |  |  |  |  |  |  |

- YEAR

- PANEL

- (X = three plots based on power analysis)

**Notes:**
This is the temporal sample frame that we eventually decided to follow. Years are across the top of the chart and panels are down the side. A panel is a group of plots that are all measured in the same years. In this example, panel 1 is measured in years 1, 2, 7, 8, and 13. Each X stands for 3 plots. We decided to measure the plots for 2 consecutive years (rather than the 3 described previously) because of budget constraints. By overlapping the panels in time, we have a “connected” design, meaning that individual pairs of years can be compared with each other. The connected design will enable us to eventually develop weather covariates to explain some of the interannual variation between measurements. This scheme applies to each vegetation category we decide to monitor (more about that later). Notice that it takes 13 years to powerfully detect change (i.e., repeat each plot twice) and 19 years to detect trend.

## Steps for Designing Sample Frame for Monitoring

- Establish measurable objectives
- Determine trend/change detection method
- Determine temporal sampling frame
- Conduct power analysis to estimate number of samples
- Construct spatial sample frame

**Notes:**
Power expresses the probability that you have not missed a change when one actually occurred (Type II error). It depends on alpha (likelihood of Type I error = probability of saying there was a change when there actually wasn’t) number of plots, and amount of trend you desire to detect. There is a trade-off between power and alpha for the same amount of sampling effort. Because it is usually considered worse to make a Type II error when monitoring, the likelihood of a Type I error is usually relaxed to 0.10 rather than the 0.05 used to indicate statistical significance in experimental situations. A maximum acceptable Type II error rate is often considered to be 0.20, which is a power of 80%.

## Power Analysis

- Conducted with pilot data from plots in 2 extreme environments in OLYM
- 3 plots per environment (per PAG)
- Measured for 3 consecutive years
- Analysis was based on simulations

**Notes:**
Our power analysis was conducted based on 3 years of pilot data as described in the slide. The analysis was conducted by simulating a plus or minus 4% annual exponential change given the variance structure of the data. While there are “canned” programs to conduct power analyses, it is better to use a simulation approach. This is because the canned programs often have assumptions that are not met by a particular data set. It is better to tailor the analysis to the actual data to be collected using simulations. We contracted to get the needed statistical expertise to conduct the simulations and power analyses.

## PAG map of OLYM

- Sitka Spruce

- Douglas fir

**Notes:**
We conducted power analysis in two plant association groups representing environmental extremes in Olympic National Park (i.e., relatively dry, could Douglas-fir forests and relatively warm, wet Sitka spruce forests).

## Picture of D-fir

**Notes:**
The Douglas-fir plots had small trees, even though they were hundreds of years old, and a relatively sparse understory, and they occurred on steep slopes).

## Picture of Sitka spruce

**Notes:**
The Sitka spruce plots occurred in mostly flat valley bottoms, and had huge trees plus a lush understory.

## 10

- 15

- 20

- 25

- 5

- 20

- 40

- 60

- 80

- 100

- Plots

- Power

- 24 subplots,
-    3 panel repeats

- 12 subplots,
-    2 repeats

- 24 subplots,
-    2 repeats

- 12 subplots,
-    3 repeats

**LIBO, +4% trend, alpha = 0.10**

**Notes:**
Results from the power analysis for one species illustrate that more time (panel repeats), and more subplots result in higher power. It is also true that more plots result in higher power. When choosing a sample frame, you need to consider the innate rate of change of your subject, management patience for feedback, and the time that can be spent on each plot (number of plots versus the number of subplots), and the budget.

## Plots

- Power

- 24 subplots,
-    3 panel repeats

- 12 subplots,
-    2 repeats

- 24 subplots,
-    2 repeats

- 12 subplots,
-    3 repeats

**LIBO, +4% trend, alpha = 0.10**

- 18 plots/PAG
- 20 subplots/plot

**Notes:**
Our final decision was to establish 18 plots per PAG (3 new plots per year over 6 years, see slide 14), with 20 subplots for herbaceous vegetation per plot.

## Steps for Designing Sample Frame for Monitoring

- Establish measurable objectives
- Determine trend/change detection method
- Determine temporal sampling frame
- Conduct power analysis to estimate number of samples
- Construct spatial sample frame

**Notes:**
We still have the problem that we cannot distribute our sample based on PAGs because we expect them to change in the future. If we were able to map PAGs and sample some of them, the sample would be meaningless once the map changed in response to new PAGs with new boundaries.

## Spatial Sample Frame

- The power analysis tells us how many plots we need per PAG
- The NCCN implementation budget tells us how many plots we can afford in total

**Notes:**
The cost of each plot reflects the personnel time required and the logistic costs of getting the personnel to the plot.

## Target Populations

**Notes:**
Our target populations are vegetation classes within elevation bands. We chose this list to represent an environmental gradient (low, wet to high, dry). Additionally, Sitka spruce is iconic for Olympic National Park; this western hemlock type can be found in all three parks; subalpine fir is expected to be most sensitive to global climate change.

## Target Populations

- Also: slopes <35o, 100-1500m from trail, no river or glacier crossings, mature forests

**Notes:**
Other restrictions on our target populations accommodate the need for safety, minimal impact on visitor experience, and the desire for mature forests (to avoid confusion from successional processes). It is important to note that our inference is also restricted to these populations. For example, we are not inferring to all Sitka spruce, only that which occurs below 300m and meets all of the other constraints. By using elevation bands, however, we can base our sample on a landscape feature (elevation) that will not change significantly in the foreseeable future (unless Mount Rainier erupts).

## Elevation – Vegetation Relationship

**Notes:**
So we are basing our sample on elevation bands, which have an intuitive relationship with vegetation. Notice the rings of colors around Mount Rainier.

## Vegetation in One Elevation Band

**Notes:**
If we draw a schematic elevation band on the mountain, it is easy to see that there are a number of vegetation and land cover classes in the band.

## Vegetation in One Elevation Band

**X**

**X**

**X**

**X**

**X**

**X**

**X**

**X**

**X**

**RANDOM SAMPLE:**
**X% is Rock**
**Y% is Shrub**
**Z% is Subalpine**
**                ****fir**
**Etc.**

**‘****Domain’ of interest – **
**Establish intensive monitoring**

**Characterization of Vegetation in this Elevation Band**

**Notes:**
If we were to take a random sample in this elevation band, what would we draw? Perhaps we would find X% of the plots falling on rock, Y% on shrub, etc. by visiting the points. From this sample we will have a characterization of the elevation band (proportion of the area in each class). Since we can only afford to monitor one vegetation class (PAG) in this elevation band, we will establish intensive monitoring plots only at those points that fall in our “domain of interest”. Essentially we are post-stratifying our sample for the purpose of taking intensive measurements. In fact, this is a really durable sample. One could add another domain in the future; one could add another elevation band in the future; if plant species re-assort into different combinations, this sample provides a random sample of the new classes. One very important note: it is very important to have a quantitative description of the vegetation classes so that anyone could go to each point and put it in the same class.  We were lucky to have a quantitative key to plant associations in the national parks produced by the Washington Natural Heritage Association. If you do not have a key, then you risk introducing subjectivity into the selection of plots. (“I think this one looks good.”) 

## General Random Tessellation Stratified Sample (GRTS)

- A combination of simple random and systematic
- Guarantees that all possible samples are geographically evenly dispersed
- Allows for greater sampling intensity in rarer areas
- Points found not to be in target population can be deleted

**Notes:**
How do we establish the random sample? We choose the GRTS method, developed by EPA for EMAP. It is a cross between a random sample and a systematic sample.

## Simple Survey Designs

- Simple Random Sample

- Systematic Sample

- Spatially Balanced Design – GRTS

-          10             4
-    1             6
-     5            8
-     3        9           2

**Notes:**
Simple random samples are often disappointingly clumped. Systematic samples are evenly spread, but all points may not be accessible and they may be spaced to coincide with some natural pattern on the landscape (e.g., ridges and valleys – all points on ridges and none in valleys). A GRTS sample is designed to be spatially balanced but without a repeating pattern. If one takes any consecutive group of numbers (in the “firing order”), the sample should not be extremely clumped and it should not be repeating. (This is my feeble attempt at illustrating a GRTS sample). 
This sample has the added (and highly valued) feature that any point that does not fall in the sample frame can be deleted. These samples are drawn with a GIS system, and the system does not have perfect knowledge. For example, you may draw a point that is on a flat area but is surrounded by impassable cliffs or requires a glacier crossing to reach. This point would not be suitable for the sample. 
Another important point: it is important to think about every possible reason for deleting a point and describing the criteria ahead of time so that the decision is not subjective. For example, how much tree cover is required for the plot to be a forest? What percent of the plot has to have slope greater than 35 degrees to be excluded? Which rivers are considered impassable and which can be safely forded during the field season? How old is ‘mature’ forest? Etc.

## Elements of Spatial Sample Frame – Random Sample

**Notes:**
Here are the GRTS samples drawn for Sitka spruce (green) and western hemlock (purple) in Olympic National Park. In order to avoid unnecessary field work, we first examined these points with the GIS system, looking for any reasons the points should be rejected. We visited the remaining points, described the vegetation class using the quantitative key, and then chose the first 18 appropriate points for establishing intensive plots. Meanwhile, we have a description of the relative abundance of all vegetation classes in each elevation band.

## Final Plot Sites

## A% Class 1

- B% Class 2
- Etc.

- M% Class 1
- N% Class 2
- Etc.

- Y% Class 8
- Z% Class 9
- Etc.

- decades

- decades

- 0-300m

- 600-900m

- 1500-1800m

**TIME**

**Notes:**
The outcome is monitoring information nested in time and space. Over decades we can revisit all of the point in an elevation banc to determine whether the relative proportion of vegetation classes, and maybe even the classes themselves, have changed ...

## A% Class 1

- B% Class 2
- Etc.

- M% Class 1
- N% Class 2
- Etc.

- Y% Class 8
- Z% Class 9
- Etc.

- decades

- decades

- 0-300m

- 600-900m

- 1500-1800m

**TIME**

**INTENSIVE**

**Notes:**
At a domain of interest in the sample taken from each elevation band, we can take more frequetn intensive measurements where we will have good power to detect trends in species abundance, tree mortality and growth, etc....

## A% Class 1

- B% Class 2
- Etc.

- M% Class 1
- N% Class 2
- Etc.

- Y% Class 8
- Z% Class 9
- Etc.

- decades

- decades

**FIA:**

- 0-300m

- 600-900m

- 1500-1800m

**TIME**

**INTENSIVE**

**Notes:**
FIA data will give us less detail about species abundance over a longer time frame for maybe two vegetation domains, and for over entire parks and the network without the ability to distinguish individual domains with much power. However, power will increase over many cycles of sampling.

## A% Class 1

- B% Class 2
- Etc.

- M% Class 1
- N% Class 2
- Etc.

- Y% Class 8
- Z% Class 9
- Etc.

- decades

- decades

**FIA:**

- Remote Sensing

- 0-300m

- 600-900m

- 1500-1800m

**TIME**

**INTENSIVE**

**Notes:**
We will have remotely sensed data describing entire parks at a low level of resolution. With this heirarchy of detail, timing and spatial extent, we hope to effectively monitor forested vegetation in this three large parks.

## FIA Power Analysis

- The monitoring question determines the methods, so we want to learn to what extent FIA methods will answer our questions
- Issue 1: FIA questions are different than ours (spatial scale and focus on trees)
- Issue 2: we are using only a subsample of their plots

**Notes:**
Forest Inventory and Assessment is a monitoring program conducted by the USDA Forest Service. Originally it was established on Forest Service and private lands (when access was granted). Since about 2000, the Forest Service has been mandated to collect data in national parks. We would like to take as much advantage of these data as possible, recognizing that they are operating at a much larger spatial scale and with different objectives than we are.

## Slide 38

**Notes:**
This is a map for the approx. 123 plots in Olympic National Park. There are approx. 93 in North Cascades and approx. 45 in Mount Rainier. Notice that the rows of points surrounding the Hoh River (the river flowing almost due west on the west side of the peninsula) straddle the river valley and land on the surrounding ridges. This illustrates the problem identified earlier as a potential with a systematic sample.

## Summary of FIA methods

- Sample frame is systematic at 5 km spacing
- They sample 10% of plots per year
- Original focus of network was on trees
- Understory and shrub cover is estimated in subplots to 1% for species with cover >3%
- Herb cover is estimated over 24 ft radius plots
- Seedlings are counted in subplots as 1, 2, 3, 4, 5, 6 or more (categorical data)
- Plot centers are permanently marked but subplots are not

## Goal for Using FIA Plots

- Our goal is to have enough data for one common PAG from plots taken across NCCN (e.g., western hemlock) to achieve adequate power for the variables of interest to us
- We expect that understory cover may be problematic

**Notes:**
In fact, it looks like we will have two vegetation classes with sufficient power across the three parks.

## Variances

| Species | Pilot (observer + annual) | FIA 
(observer only) |
| --- | --- | --- |
| ACTR | 0.07 | 0.70 |
| BLSP | 0.15 | 0.17 |
| MANE | 0.06 | 0.22 |
| OXOR | 0.28 | 0.50 |
| POMU | 0.21 | 0.12 |
| RUSP | 0.17 | 0.17 |
| VASP | 0.05 | 0.13 |

**Notes:**
FIA variances for the cover estimates of particular species tend to be higher that for the pilot data, meaning they would have lower power if analyzed in the same way.

## FIA

|  | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  |
| 2 |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |
| 3 |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |
| 4 |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |
| 5 |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |
| 6 |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |  |
| 7 |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |  |
| 8 |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |  |
| 9 |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |  |
| 10 |  |  |  |  |  |  |  |  |  | X |  |  |  |  |  |  |  |  |  | X |

**YEAR**

**PANEL**

**Notes:**
However, it takes 20 years to repeat all of the samples once (to detect change) and 30 years to repeat them twice (to detect trend). With this longer time frame, the FIA data do have good power (20 years) or very good power (30 years). But this is a much longer time than our sample requires and it does not indicate park-specifc changes – rather the analysis applies to the sample collected across all three parks. Nevertheless, we are optimistic that we will be able to make good use of these data.

## How well do physically based definitions of vegetation populations work?

- Can we get away from subjective samples?

**Notes:**
When we first began to think about monitoring vegetation (10 years ago!) we were encouraged by statisticians to base our sample stratification on static landscape features (e.g., elevation, aspect) rather than vegetation classes. Some plant ecologists were not going to accept this without proof that we could produce an interpretable sample. This was long before we were sophisticated enough to realize that we could use elevation bands and domains. Nonetheless, the investigation of a physically-based sample was interesting and the results follow.

## Benefits of Sampling Vegetation Categories (PAGs)

- Expect changes to be interpretable in terms of PAGs
- Environment-vegetation relationships are fairly well understood for PAGs
- An environment-based model is available to extrapolate PAGs across landscapes

## Problems with Sampling Vegetation Categories

- We don’t have sufficiently accurate map
- Category definitions are subjective because environmental conditions are not discrete
- Individual components of communities are expected to respond independently to change

## Slide 46

**Notes:**
The blue line is the Elwha River in Olympic National Park, flowing north from the bottom of the picture to the top. We chose this as an experimental drainage.

## Sampling Strata

- Aspect- North and South
- Slope- <20o, 20-35o
- Elevation- 1-500,500-750,750-1000m
- River Position- Lower, Middle, Upper
- Mature Stands

**Notes:**
These strata were mapped and sampled randomly. River position will be explained with the next slide. These strata were based on correlating these variables with vegetation types and then validating them by trying to predict vegetation from them (working with a modeled vegetation map). The two classes of slope were meant to distinguish valley bottom from valley walls.

## Slide 48

**Notes:**
The 500-750 m elevation band is shown in orange. Notice that at the southern part of the map, the band is in the valley bottom, further north it is on the lower valley wall, and towards the mouth of the river, it is high on the valley wall. The northern section is the ‘lower’ river, the middle section is the “middle” river and the southern section where the orange band contacts the river is the “upper” river.

## Slide 49

**Notes:**
Elevation bands and locations of the random points we were able to visit are mapped.

## Slide 50

**Notes:**
Here sampled points are coded by vegetation class. Notice that silver fir is only in the upper river, Douglas-fir is on southwest-facing slopes, the hemlock types are the least segregated.

## Slide 51

| Original
Strata | Lower | Middle | Upper |
| --- | --- | --- | --- |
| 750-1000 m | DF or WH/salal | WH/salal | SilF/Als. Huck or WH/salal |
| 500-
750 m | DF or WH/salal | WH/salal | SilF/Als. Huck or WH/salal |
| 0-500 m | WH/Ore.gp., Swd. fern | ?? |  |

**Notes:**
Using the original strata, this is how things fell out (imagine a cross-section of the river valley with elevation increasing from the bottom to the top of the chart and the river mouth at the left). WH/Ore.gp, Swd. Fern is the moist western hemlock type, and WH/salal is the dry tyoe.

## Slide 52

**Notes:**
Notice that the river is running practically north-south in the last section toward the mouth. Before that, it runs southeast-northwest for a distance. This is where most of the Douglas-fir occurs.

## Slide 53

## Slide 54

| More
River
Cat. | Lower
N-S | Lower
NE-SW | Middle
N-S | Upper
E-W |
| --- | --- | --- | --- | --- |
| 500-
1000m | WH/salal or WH/ore.grp, swd.fern | DF/salal | WH/salal | SilF/
Als.Huck |
| 0-500m | WH/ore.gp.,swd.fern | WH/ore.gp.,swd.fern | ?? |  |

**Notes:**
If the diagonal section of the river is separated out, it distinguishes the DF/salal from WH/salal.

## Conclusions

- Physically based stratification can result in an interpretable sample
- Important physical features are at a larger grain than anticipated