new website for collecting survey data

I've got a new website up which is aimed at collecting survey data.

Right now there are surveys for the following ecological sites

Sandstone/Shale Uplands (Wupatki)

Clayey Fans (Petrified Forest)

as of right now both are Phase 1 surveys, aimed at nailing down the State-and-transition models. You can find the surveys by going to posts ending in "Phase one".



http://thresholdsurveys.blogspot.com

Survey Monkey

This is a test link for an online survey application I'd like to use for expert opinion surveys.

It's hosted by a free service called survey monkey, and you can just put a link to it in your blog. they've got a bunch of different question types (e.g. mulitple choice, text box, rating scale, etc.), unlike the gadgets I've found for blogger.



Click here to take survey

Update & solicitation for new paper ideas

I have 3 reviewers approved for the book chapter. Craig Allen is considering, still waiting for a response from Dave Pyke. Jeff Herrick and Brandon Bestelmeyer have bowed out as potential co-authors, but I did get a couple ideas form Brandon, who said positive things about the MS.

So...I'll keep that moving.

In the mean time, I'd like to figure out what is next. I've mostly had my fill for right now of the survey stuff. I'd like to work on some actual datasets pretty soon.

First here's a list of potential products, or partly done thingies I've been thinking about
1) Detailed MS on 2 Wupatki ecosites, or Wupatki plus some other Southern Network sites. I have found that there is some data from Kathryn Thomas/Monica McTeague which at least would be useful for validating state and phase concepts for the 2 Wupatki ecosites and the Petrified Forest ecosites. My motivation is that alot of detail was cut for the chapter, here we could present a few data-poor cases.

what I have is a good complete model for LImy Uplands, A draft model for sandstone uplands (not informed by any surveys), and nothing whatsoever for Petrified Forest. The data sets are the I&M plots (10 each for each ecosite), and the McTeague-Thomas veg releves which are of limited usefulness.

It's hard to imagine this going to a fancy journal, and to complete it I'd have to do the survey process again. I may want to look into putting questionaires on a website again. So this is back burner, but would be nice to get the most of the detail that came out of the survey process.

2) Multiple STMs integrated across climatic gradients. SD Sandy Loam, D Sandy Loam, and Upland (Sandy?) Loam form the canyonlands dataset are not really
discrete, rather they exist along a gradient which is subject to change. I
don't know what this would look like, but there could be models for each,
and then a meta-model which allows reasonable transitions among them under
climate change scenarios. We can build the paper around the idea that STMs
derived only from past observations aren't good enough if global change is
altering everything. Perhaps the submodels will be constructed based upon
our apriori ideas and verified to the degree possible using Mark's dataset.
And perhaps we could try the expert survey thing to help us fill in
transition probabilities (at least ordinal ones) form sub-model to
sub-model.

This one sounds pretty viable, maybe we could even send it to Global Change
Biology, it would be very different from what they normally publish which
could work to our advantage.

3) A comprehensive treatment of the main forage-producing ecosites in the
CANY and surrounding region. I see the logic of grouping SD Sand, and
multiple soil types within SD Sandy Loam. it seems like a Journal of Arid
Environments paper right out of the gate (nothing wrong with ending there,
but would be nice to take a crack at Ecosystems or something like that). I
wonder what our novel contribution could be, to make it interesting enough
for a more widely-read journal? We could try to develop those path
analysis-logistic regression hybrid models for threshold
estimation...that's one idea (have found some dissing of logistic models
for threshold estimation in the lit, so piecewise regression might actually
be a better basis for such a method). We could develop models for SD Sand,
SD Sandy Loam Begay, and SD Sandy Loam other soils, then develop a model
averaging procedure based upon soil texture for any reasonably similar
ecosite.

I did work up Desert Sand and analogs (on the blog). But it's not a good
enough analysis to be stand alone I think. I did the best possible with
available data...but I see alot of potential criticisms. So this ecosite
could be folded in too.

What do you guys think...where should I go next?

Desert Sand - - rough draft results

We identified: 1) a “reference grassland” group with the indicators (Indicator value >30, corresponding to P ≤ 0.10) Achnatherum hymenoides, Sporobolus spp., and Hesperostipa comata., 2) A “sand shrub community” with the indicators Artemisia filifolia, Bouteloua gracilis, and Poliomintha incana, 3) An “annual-invaded shrubland” with the indicators Coleogyne ramosissima, Psorothamnus fremontii, and Bromus tectorum, and 4) A “coppiced system” with the indicators Ephedra spp., Artemisia filifolia, and Opuntia spp (Fig 1, Fig 2). These four groups provide a reasonable degree of confirmation of our apriori expectation of a three state model (with four phases), and confirms the existence of our conception of the reference state and the coppiced state (Fig 2). A new state was identified however, the invaded shrubland. This state was defined by dominance by Coleogyne ramosissima or Psorothamnus montanum (in drier sites), and the presence of Bromus tectorum. Coleogyne may facilitate Bromus invasion by providing climatic buffering and litter resources (ref.). We did not find evidence in our dataset of an annualized state, dominated by Bromus or Salsola spp., but cannot rule out that it exists. One interpretational caveat is that there was a strong correlation between the data source and cluster membership. The most extreme example was that over 70% of the reference grassland sites were from the Miller et al. Canyonlands data, whereas the other groups were reasonably well spread across the various data sources. The Canyonlands data was primarily gathered within a national park which has been ungrazed for 40 years, a degree of rest not matched in any other datasource. Nevertheless, we used these clusters as a reasonable approximation of state and phase membership.





























[ maybe i'll change this silly color scheme for some error bars instead ]

The set of four community-structure based states and phases corresponded well to several functional attributes of the ecosystem (Fig 3). The variance explained in our ANOVA models corresponded well to the hypothesized causal sequence, with more proximate causes exhibiting a higher R², and ultimate causes lower R² values: BSC cover, Plant cover < style=""> The reference state (RG and SS) retained 2-4 × as much biological crust cover, and consequently much higher soil aggregate stability. Somewhat surprisingly, all of the shrub-dominated states or phases had greater total perennial vegetative cover than the reference grassland, although it should be noted that grassland cover varies from year to year. The sand shrub phase of the reference state retained a relatively low shrub:grass ratio, whereas the coppiced and invaded shrub communities were almost exclusively woody-dominated. Perhaps most instructive were differences in the gap size distribution (R² = 0.49 – 0.66). The reference grassland was characterized by 30 – 50% shorter gap length, and about twice as many gaps per meter. The difference in the shape parameter of the gamma distribution illustrated this contrast most clearly, indicating a much stronger positive skew in the gap size distribution. The invaded shrub state also differed from the coppice state in this functional attribute reinforcing that it is a distinct state; in general the invaded state was characterized by a larger number of smaller gap sizes. Data was not available to examine these properties adequately in the sand shrub phase.

Our logistic regressions provided the values of several of these predictors at which transition form reference state to the coppiced state are 25%, 50% and 100% probable (Table 2). The solutions for mean gap length and k failed to converge, likely because of the large magnitude of the difference between reference and coppiced samples. However they did produce reasonable values which should be interpreted cautiously.

Desert Sand - - rough draft methods

[Stay tuned for results later today]

Methods:

There was no single, well-replicated dataset which purposefully measured numerous relevant indicators regarding ecosystem dynamics in Desert Sand (Sand Sagebrush) ecosites. However we did identify a total of 42 data points, 24 of which are currently mapped as Desert Sand, while the remainder are close analogs, e.g. Semi-desert sand (fourwing saltbush). Semi-desert sand analog sites were carefully pruned to remove sites that poorly matched the characteristic vegetation of Desert Sand. Replication of various indicators varied, as did methodology. Most data sources contained some form of plant community composition data, usually cover, although some range assessments estimated above ground biomass instead. All told seven different data sources were used (Table 1).

To calibrate our apriori state-and transition model, we conducted a cluster analysis based upon plant community structure data. We acknowledge that structure alone is often not the best technique for designation of states and phases, but in this data-sparse case it was the best replicated form of data and represented our best option. A series of data standardization steps were necessary to merge the various datasets because of differing methodologies. First we reduced the number of species considered to those which are known to be important in processes (e.g. Ephedra spp. and coppicing), dominate at least one site, or were frequent occurrences in multiple sites. Native annuals and perennials which annually die back were excluded because their detection rate likely varies based on time of year. Some species which were identified to varying levels of precision were lumped, including Ephedra spp., and Sporobolus spp. To account for different quantification procedures, we converted the perennial species abundances to proportional abundance of the total cover. We included two non-native annuals Bromus tectorum and Salsola spp. as presence and absence data, because detection rate varies from year to year, and within years, and some data measured frequency rather than cover.

Our cluster analysis used a flexible beta group linkage method to form clusters from a Bray-Curtis distance matrix (McCune and Grace 2002). We obtained 2, 3, 4 and 5 group clusters and selected the 4 cluster option because it provided enough detail to distinguish among multiple states and phases, was most consistent with our apriori conception of this ecosite, was easily interpretable, and was least strongly driven by disproportionate weighting of the invasive annuals (an artifact created by our data standardization protocol). To help define the characteristics of the four groups we applied Indicator Species analysis (Dufrene & Legendre 1997). As a visualization of our clusters we used a non-metric multi-dimensional scaling ordination, using a Monte-Carlo test to determine optional dimensionality (McCune and Grace 2002).

Using cluster analysis-defined state and phase memberships we applied one way ANOVA to describe the degree to which the various groups differed from one another in the following key structural and functional indicators: total plant cover, total crust cover, soil aggregate stability, gap size distribution (detailed below), and perennial shrub:grass ratio. Gap size distribution was characterized by four separate but related variables: the mean gap length and mean number of gaps per meter, and the scale (k) and shape (θ) parameters of the gamma distribution. The large majority of the available samples fit a gamma distribution reasonably well because it is useful for modeling positively skewed data. This is a very flexible distribution which resembles an exponential distribution when the shape parameter is low and the scale parameter is high, but grades from log-normal-like to normal-like when the shape parameter is high, and the scale parameter is low. To improve normality, or heterogeneity of variance, we applied logarithmic transformations in some case prior to ANOVA.

We used logistic regression equations to estimate critical values in these functional indicators in the transition sequence form the reference state to the coppiced state. Because of the heterogeneous nature of data collection protocols in the various studies it was impossible to conduct a multiple logistic regression without severely compromising sample size due to missing values. As an alternative, we used separate simple logistic regressions for each of seven functional indicators which we felt were most directly linked to the processes underlying the coppicing phenomenon, to estimate the indicator values at which state transition was 25%, 50%, and 100% probable. The first two values can be thought of as a conservative and liberal preventative threshold, whereas the third value is a restoration threshold. We focused on the transition from the reference state to the coppiced state, because of availability of multiple relevant predictors. Transition to the invaded shrub state may be driven by different processes less related to sand redistribution, and which are less well represented in our data.

Limy Uplands near final

This is the latest incarnation of the Limy uplands model. I am not soliciting additional edits to the model structure. The only thing that will be updated are confidence estimates, and threshold estimates will be added. Think about if you'd like to do any simplifying for the book chapter.

In related news, I may have a real-life rancher (Billy Cordasco, ranch manager for Babbitt Ranches where most Limy Uplands are) doing the phase 2 survey.

Sorry the tables are a bit hard to read, but the image formats I can upload don't work great.

Phase 1 results

so far I have 3 surveys back (my minimum), and imposed a deadline of May 20 to get the remaining ones back. I'm holding out hope for Paul Whitefield (NPS natural resource jack of all trades)and Monica McTeague (who did the veg map). I never really got firm, specific commitments from NRCS folks....but who knows.

I have 2 USGS people: Kathryn Thomas, Kirsten Ironside,
and a retired NPS guy that did his master's in Wupatki in the late 80's, Steve Cinnamon. Everybody had good new insights. Kirsten actually reran future climate sims to answer a question which was extra credit.

So far, the USGS folks are cautious in their confidence (around 50%), and more likely to provide additions to the model. I think this process will have a tendency to make models more complicated rather than more simple...not necessarily a problem...just an observation since I will now add a new state (woodland), and a new phase to state 1 (shrubland).

Phase 2 survey draft 2

REVISED MODEL
Please skim the revised state-and-transition model just enough to refresh your memory if you completed a phase 1 survey. If this is your first survey, and the first time you’ve seen a version of this model, please take the time to review it in depth. This model has been reviewed and revised according to responses to the phase 1 survey. Pay special attention to the section “modifications” (in red), and then please answer question 1.

[revised model will appear here]

Question
1) Please estimate your overall confidence that this model, which takes into account proposed modifications from previous surveys, is the correct model of the most important ecosystem states, processes and dynamics of the ecosite in question.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, this model is no better than any other model”, 50% means “Because this model is reasonable I would tend to believe it until evidence to the contrary is presented”, 100% means “The model is so well-supported by evidence and accumulated knowledge, that I am certain it is correct.” If you answered this question in a phase 1 survey, and your answer is the same, you do not need to answer. ]



BACKGROUND/ EXPLANATION OF PHASE 2 SURVEY
This survey is designed to estimate critical cutoff points in key indicators which may signal a transition in progress. Please refer to Figure 2. In the limey uplands ecosite, from a management standpoint, we are concerned with the sequence of changes that results in loss of fire and savannization of grassland ecosystems, and our model can be simplified to represent just these dynamics (Fig. 2). Within a given state, the system may exhibit considerable dynamics and variability in response to drivers such as grazing, fire management, and climate variability. The system is considered to be resilient as long as it retains the capacity to maintain or recover key structural and functional properties of the current state. If the system is on a trajectory toward a new state (i.e., one characterized by fundamentally different structural and functional properties), management may avert a state transition if passive restoration (e.g., cessation of grazing or fire suppression) are implemented while the system still retains resiliency. Managers who wish to avert an undesirable state transition can define the quantitative value or range of values that trigger implementation of passive restoration. Such management trigger values can be described as preventative thresholds and established on the basis of one or more key monitoring variables that are related to the ecological processes that underlie the undesirable state transition. It is important to recognize that there is no one single value that can be determined for a particular preventative threshold. Key considerations in estimating the preventative threshold are (1) that the preventative threshold must trigger management action while the system still retains resiliency, (2) that the preventative threshold be established to account for potential lag effects both in actual management implementation and in ecological response, and (3) that the preventative threshold account for the potential occurrence of extreme climatic events or other factors which may unexpectedly and rapidly result in loss of system resilience to factors under the control of management.

If resilience is exceeded prior to the implementation of passive restoration, then the system will not have the capacity to recover its original structure and function without the imposition of active restoration (such as seeding or vegetation manipulation, or controlled burning) by management. At this point, a state transition has occurred and the system has crossed a restoration threshold when simple passive restoration practices are no longer effective and active restoration is required to recover properties of the original state. This second type of threshold can also be defined by the quantitative value or range of values of one or more key monitoring variables.

Fig. 2. Simplification of a key transition sequence from the above model (Fig. 1), illustrating two types of thresholds, preventative, and restoration which can be estimated based on values of key indicators.

For Transition 5, we will present you with several relevant indicators and prompt you to answer 6 questions. To keep the model broad, the indicators apply to the whole Limy uplands ecosite, inside and outside of Wupatki, and encompassing different grazing and fire suppression regimes.

Indicators

I1. Stocking rate. An allotment-scale measure of grazing pressure. May trigger T5 if high enough.

I2. Density of cowpies. A site-scale measure of grazing pressure. May trigger T5 if high enough.

I3. Length of rest from grazing. If high enough, may reverse T5, as long as T5 has not progressed beyond restoration threshold.

I4. Time since fire. A site-scale measure of either grazing-triggered reduction of fuels, or fire suppression practices. May trigger T5 if high enough.

I5. Total plant cover. An easy to measure surrogate for biomass, i.e. fuel load. Decreases as T5 progresses.

I6. Basal plant cover + litter. An index related both to amount and connectivity of fuel. Decreases as T5 progresses.

I7. Average length of bare patches. An inverse measure of fuel connectivity. A bare patch is devoid of plant basal cover or litter. Increases as T5 progresses.

I8. Average length of combustible patches. A measure of connectivity. Decreases as T5 progresses.

Fig. 3. An illustration of measuring combustible patch length. Dashed line represents length.

I9. Number of trees per hectare. A measure of colonization rates, and a glimpse of the future if trees mature. Increases as T5 progresses.

I10. Average tree height. A measure of resistance to fire. Increases as T5 progresses, may signal completion of transition.

For each indicator we will ask you to estimate the preventative threshold point, and the restoration threshold point in the specified units. Answer based on past experience, accumulated knowledge, and general principles.We do not expect anyone to know the answer, we are collecting opinions and educated guesses. If you wish, you may consult a data source that you are already aware of, but we do not expect you to undertake an extensive literature review to find new information. As a reference, to help you get within the ballpark, we have provided estimates of current status of monitoring plots, or other useful reference points, when available. You may interpret “current status” as belonging to whichever state or phase you think is correct. Don’t be worried if you don’t have much confidence in your estimate, you will also be asked to rate your confidence in your estimate, and your confidence in anyone’s estimate. Confidence will be taken into account, and will be a part of the final product.

Please answer the following questions for each indicator in the table provided below, where appropriate. *** indicates required questions:

QUESTIONS

For questions 2 and 2, we’d like you to try and complete at least 50% of the response table, even if your confidence in your answers is low. The more complete it is, the better. If you truly have no idea whatsoever about the answer to a particular question (~ 0% confidence), you may leave it blank, but please try if you can.

***2) Please estimate the preventative threshold, the point at which transition is likely to be initiated, and imminent if no passive restoration measures are taken. Please answer in appropriate units in table below.

***3) Please estimate the restoration threshold, the point at which transition is imminent even if passive restoration is undertaken. Please answer in appropriate units in table below.

4) Would you add any indicators to this list that would be crucial in tracking transition 5? If so please type in the indicator (in the “other indicator” cells) and units in appropriate boxes in the table below, and provide your preventative and restoration threshold estimates there.

***5) Overall, please estimate your confidence in your threshold estimates, including any provided in question 4.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, these estimates are no better than any other estimates”, 50% means “Because these estimates are reasonable I would tend to believe them until evidence to the contrary is presented”, 100% means “The estimates are so well-supported by evidence and accumulated knowledge, that I am certain it is correct.”]

***6) Please take a moment to think of any scientist or other person, who is to your knowledge the best qualified to estimate the values you estimated in questions 1 & 2. This person could be yourself, or any other person. “Best” qualified may or may not mean highly qualified; sometimes no one is highly qualified. Now, in the hypothetical scenario that this person had made these estimates, using all of the data, knowledge and experience available to them, estimate how much confidence you would have that they are the correct values.

[use the same scale as question 3, 0-100%]

7) Now please consider, for any individual threshold estimates you provided, if any of the values of “confidence in your own estimate”, or “confidence in anyone’s estimates” differ from the answers to questions 5 and 6. Fill them into the table below.

Response matrix. Required answers are indicated in yellow, try to fill in at least 50% of these. Cells filled in black require no answers.

*AUM = animal unit months, a typical unit of stocking rate. If 3 cows each graze for 6 months, this constitutes (3 * 6) 18 AUMs. Usually expressed on a per area basis, e.g. acre^-1 in the United States.

** The current stocking rate in the national monument has been 0 since 1989. For reference, these estimates were provided by CP Bar ranch directly to the North of Wupatki which is currently grazed.

*** note: this estimate is the mean length between basal plant cover, it does not take into account litter as does our proposed indicator. Mean length between basal plant cover and/or litter patches would be smaller.

Thank you for taking time out of your schedule to complete this questionnaire, we greatly appreciate your input!

Please let us know if you would be willing to do the same type of survey for additional ecosites which might fall under your area of expertise.

Limy Uplands Phase 2 survey: second draft

{I realized alot of the information in the previous draft could easily be rolled into the phase 1 survey. so this one ended up being very short and focused.Could use comments whenever you are able. Phase 1 surveys are currently with 4-5 respondents. I asked for them back in 1 week (probably unrealistic, but worth a try). So it would be nice to send out phase 2 as soon as i get them back.}

REVISED MODEL

Please skim the revised state-and-transition model just enough to refresh your memory. There is no need to study it in depth again. These have been reviewed and revised according to your response to the phase 1 survey. Pay special attention to the section “modifications” (in red).

[model with revisions, and confidence values from phase 1 will appear here]

BACKGROUND/ EXPLANATION OF SURVEY

This survey is designed to estimate critical cutoff points in key indicators which may signal a transition in progress. Please refer to Figure 2. In the limey uplands ecosite, from a management standpoint, we are concerned with the sequence of changes that results in loss of fire and savannization of grassland ecosystems, and our model can be simplified to represent just these dynamics (Fig. 2). Within states, considerable change in response to triggers such as grazing or fire suppression, is acceptable and the system maintains resiliency (the ability to recover key properties of the resistant stage with no management action). At some point these triggers, push a system to a point where a state transition is initiated. This is the preventative threshold. At this point a system cannot recover on its own to something resembling the resistant state under the existing management condition, but may recover resiliency if facilitating practices such as the cessation of grazing or fire suppression activities are enacted. In this way a state transition can be thwarted, and resiliency recovered, with simple actions. If no facilitative practices are undertaken the system can proceed to a restoration threshold. This is the point at which facilitative practices will not be effective, and a state transition is imminent unless active restoration is enacted. Even then, a state change may not be reversible.

Fig. 2. A simplified state and transition model focused on the dynamics surrounding transition 5 (savannization). The model delineates where system resiliency is relevant, and separates a transition sequence using preventative and restoration threshold points.

For Transition 5, we will present you with several relevant indicators and prompt you for 6 responses.

Indicators

I1. Total plant cover. An easy to measure surrogate for biomass, i.e. fuel load. Decreases as T5 progresses.

I2. Basal plant cover + litter. An index related both to amount and connectivity of fuel. Decreases as T5 progresses.

I3. Average length of bare patches. An inverse measure of fuel connectivity. A bare patch is devoid of plant basal cover or litter. Increases as T5 progresses.

I4. Average length of combustible patches (Fig. 3). A measure of connectivity. Decreases as T5 progresses.

Fig. 3. An illustration of measuring combustible patch length. Dashed line represents length.

I5. Number of trees per hectare. A measure of colonization rates, and a glimpse of the future if trees mature. Increases as T5 progresses.

I6. Average tree height. A measure of resistance to fire. Increases as T5 progresses.

For each indicator we will ask you to estimate the preventative threshold point, and the restoration threshold point in the specified units. Answer based on past experience, accumulated knowledge, and general principles.We do not expect anyone to know the answer, we are collecting opinions and educated guesses. If you wish, you may consult a data source that you are already aware of, but we do not expect you to undertake an extensive literature review to find new information. As a reference, to help you get within the ballpark, we have provided estimates of current status of monitoring plots in the table when available. You may interpret “current status” as belonging to whichever state or phase you think is correct. Don’t be worried if you don’t have much confidence in your estimate, you will also be asked to rate your confidence in your estimate, and your confidence in anyone’s estimate. Confidence will be taken into account, and will be a part of the final product.

Please answer the following questions for each indicator in the table provided below, where appropriate. *** indicates required questions:

***1) Please estimate the preventative threshold, the point at which transition is likely to be initiated, and imminent if no facilitative actions are taken. Please answer in appropriate units in table below.

***2) Please estimate the restoration threshold, the point at which transition is imminent even if facilitative actions are taken. Please answer in appropriate units in table below.

***3) Overall, please estimate your confidence in your threshold estimates.

[Please answer on a subjective scale of 0 – 100% certainty. Enter any value in this range. To help you answer: 0% means “It’s anyone’s guess, these estimates are no better than any other estimates”, 50% means “Because these estimates are reasonable I would tend to believe them until evidence to the contrary is presented”, 100% means “The estimates are so well-supported by evidence and accumulated knowledge, that I am certain it is correct.”]

***4) Please take a moment to think of any scientist or other person, who is to your knowledge the best qualified to estimate the values you estimated in questions 1 & 2. This person could be yourself, or any other person. “Best” qualified may or may not mean highly qualified; sometimes no one is highly qualified. Now, in the hypothetical scenario that this person had made these estimates, using all of the data, knowledge and experience available to them, estimate how much confidence you would have that they are the correct values.

[use the same scale as question 3, 0-100%]

5) Now please consider if any of your individual estimates of “confidence in your own estimate”, or “confidence in anyone”s estimates” differ from the answers to questions 3 and 4. Fill them into the table below.

* note: this estimate is the mean length between basal plant cover, it does not take into account litter as does our proposed indicator. Mean length between basal plant cover and/or litter patches would be smaller.

Thank you for taking time out of your schedule to complete this questionnaire, we greatly appreciate your input!

Please let us know if you would be willing to do the same type of survey for additional ecosites which might fall under your area of expertise.