Semi-desert stony loan (ATCO) - modeling transitions

We considered all five transitions proposed in our state-and-transition model for semi-desert stony loam. We found that a complex model was not required to provide a mechanistic model of transition among these states and phases, based on the small amount of data we had available. Rather, univariate logistic regression models based on three different, functionally significant predictors were constructed. Transitions 1 and 2 follow the same model based upon the ratio of grazing decreaser plants to increaser plants (summed cover) as defined by the NRCS ecological site description. Transition 2 is simply a reversal of transition 1. Transitions 3 and 4 follow the same model based upon the ratio of the summed cover of perrennial grasses to the summed cover of shrubs.Transition 4 is simply a reversal of transition 3. Transition 5 was based upon the summed cover of exotic annuals.

Transition 1 (and 2)
Transition 1 can be modeled by the logistic function, with a pseudo R2 of 0.56:

probability of transition =
1/1 +e -(1.6089 - 8.9706(decreasers/increasers))

Transition 2 can be modeled by using the negatives of the slope and intercept parameters like so:

probability of transition =
1/1 +e -(-1.6089 + 8.9706(decreasers/increasers))

Transition 3 (and 4)
Transition 3 can be modeled by the logistic function, with a pseudo R2 of 0.82:

probability of transition =
1/1 +e -(15.86395 - 31.3721(grass/shrub))

Transition 4 can be modeled by using the negatives of the slope and intercept parameters like so:

probability of transition =
1/1 +e -(-15.86395 + 31.3721(grass/shrub))

Transition 5
Transition 5 can be modeled by the following logistic function. (This function, based upon the available data was "perfect" in the sense that the two groups of data (S2 and S3) did not overlap at all, leading to a pseudo R2 of 1.0. Thus, multiple parameters are possible solutions and the solution provided below is unstable. In practice this is not terribly important because the boundary between the two states is in the range between 27 and 28% exotic annual cover):

probability of transition =
1/1 +e -(198.2512 - 720.8806(exotic annual cover))

Simulations
We solved the above equations for all reasonable values of each of these predictors. The results are plotted in Figure 1.

Fig. 1. Logistic equations of Transitions 1,3, and 5, predicitng the probability of transition. Dashed horizontal lines depict critical probabilities (5%, 25%, 50%, 95%) (click to enlarge image)










This modeling exercise provides real values which can trigger management actions. Our critical probabilities are: 5% the threshold beyond which transitions are a reasonable possibility, 95% the threshold beyond which transitions are almost certain, 50% the threshold at which transition or lack thereof are equiprobable, and 25% the threshold beyond which transitions are becoming a common event. The values corresponding to all 5 transitions are tabulated below (Table 1).

Table 1. Critical probabilities of transition given values of monitorable predictors for 5 transitions (click to enlarge).






















Interpretation:
Unlike many semi-arid ecosystems, grazing pressures primarily take the form of shifts in vegetation composition and do not degrade soil surfaces. All of the transitions are assumed to be driven at least partially by grazing pressure. If Decreasers/Increasers is used as an index of the grazing pressure on community composition, a negligible probability of transition from S1P1 to S1P2 exists is decreasers compose 51% or more of the community. If grazing is allowed to decrease this ratio to 30% a transition would not be an uncommon event, and if the ratio decreases to 18% transition is just as probable as no transition. If these probabilities of transition are too high given management priorities, relaxation of grazing might be prescribed. Using this predictor, near certainty (95% transition probability) of transition is not observed even if all decreasers are eliminated. This implies that there is another unmeasured driver of transition that is not incorporated into the model.

If transition does occur to S1P2, the ecosystem is then at-risk for transition to a new state with an increase in prevalence of grazing tolerant grasses (State 2). Whether this constitutes degradation depends upon management goals. Again, grazing pressure is assumed to underly this transition. Grasses may compose up to 41% of the community without a transition being possible. If, however grasses (primarily Pleuraphis jamesii) are allowed to increase to 51% a transition is about as probably as not. Transition becomes nearly certain as grasses compose 60% of the community. Again, if these probabilities of transition are too high given management priorities, relaxation of grazing might be prescribed.

The primary management concern with State 2 is that it may be susceptible to annualization. The best measure of annualization is the intra- and interannual variation in vegetation cover. We did not have this data to work with, thus we used exotic annual cover as our predictor. Regardless of land use preferences, annualization by exotics is never desired. Our data indicate a very sharp boundary between S2 and S3. Below 27 % exotic annual cover, transition is highly improbable. Above 28% exotic annual cover, transition is nearly certain. Thus if 27% exotic annual cover is close to being attained management intervention should occur to increase perrenial species or reduce exotic annuals.

Semi-desert stony loam (ATCO) - Validation of ecosystem states

Data
We analyzed 15 cases from 3 sources in the NCPN database, primarily in and around Capitol Reef National Park. Shane Green of the NRCS kindly provided some vegetative composition data from an additional 14 sites that were on file, the location of these was not documented in the data files.

Caveats and alternative explanations
In general, our observed clusters seem to provide a reasonable validation of the NRCS ecological site description. We see a gradient of communities dominated by putative decreaser species identified in the description, grade into ones dominated by increaser species, which in turn grades into states dominated by exotic invasive grasses. Tp our knowledge, all of our sites are currently in active grazing allotments and have likely been impacted by livestock in recent years; from these results we would infer that the more strongly impacted cluster had experienced more grazing activity.

We cannot rule out observer bias in generating these differences. The annualized cluster is exclusively from data provided by the NRCS. The less impacted cluster are primarily sites sampled by the NPS Inventory and Monitoring Program, whereas the other cluster consists of a mixture of sites from the Capitol Reef vegetation mapping project, and from the Grand Staircase-Escalante National Monument Rangeland Health Assessment. Biases could be present resulting from different personnel collecting data, or the more southerly distribution of the samples in the more impacted cluster.

Cluster analysis
We subjected all available data on plant community composition to hierarchical cluster analysis to determine if clusters could validate the existence of a priori states and phases. All species with fewer than 3 occurrences were excluded from the analysis. We applied to relativization by row maximum transformation to focus the analysis on relative species abundance. A double relativization to further standardize data was not appropriate, because the NRCS data did not provide absolute abundance of species, only relative abundance. Based on a rather simple apriori concept of a state with 2 phases (one dominated by grazing decreasers, and one dominated by grazing increasers), and an additional devegetated state subject to erosion, we examined 2,3 and 4 cluster solutions. All of these were "clean" clusters with little overlap and all could be potentially be explained by a hypothesized unquantified grazing gradient (from lower right to upper left in Fig. 1).

Fig. 1. NMDS ordination diagram depicting results of cluster analysis. Clusters do not overlap, and seem to correspond to a plausible degradation sequence (Click image to enlarge).



















Based upon the four cluster solution, we determined the degree of correlation of various species with the clusters. We found that there were clusters indicated by: 1. Atriplex canescens, 2. Atriplex confertifolia, 3. Pleuraphis jamesii, and Bromus tectorum (Fig 2).

Fig. 2. Image of the same ordination as above, with symbols resized to reflect the relative abundance of key species (click image to enlarge).
















The clusters depicted in panels a - c seem to correspond well to NRCS concepts of vegetation changes under grazing pressure (loss of decreasers, expansion of increasers). All three of these could be interpreted as different phases of the same state; because of the shift in growth form dominance we interpreted the perrenial grass rich cluster (open circles) as its own state. We were surprised to also detect a Bromus tectorum-dominated cluster, which we deemed functionally distinct enough to be its own state, and due to floristic similarities and closeness in ordination space hypothesized that it can arise from the P. jamesii-dominated cluster. The resultant state-and-transition model can be viewed here.

Semi-desert stony loam (ATCO)

Background
Semidesert stony loam (shadscale) ecological sites, are situated on stony, often cobbley alluvial and colluvial soils. They are deep and well drained, and typically on mild to moderate slopes. They are at least slightly calcareous. A large proportion of the soil surface is covered by rock, making the soil well-armored against wind erosion, but these soils may be susceptible to water erosion. The current draft ecological site description does not provide a state-and-transition model, but does mention some ecosystem dynamics in response to grazing and fire. In the description, grazing decreasers included Hesperostipa comata, Achnatherum hymenoides, Artemisia bigelovii and Ephedra torreyana (all palatable except for Ephedra). Grazing increasers include Artemisia tridentata, Gutierrezia sarothrae, Pleuraphis jamesii, and Atriplex confertifolia. Although fire dynamics were mentioned, our a priori state and transition model did not include fire-related transitions because these communities are characterized by low cover (usually Helianthus, Juniperus, Pinus, or Bromus tectorum.

With these assertions in mind we developed the following state-and-transition model:

Fig. 1. State-and-transition diagram for Semi-desert stony loam (shadscale). Solid boxes refer to ecosystem states, and dashed boxes represent phases within those states (red being at-risk of state transition). Click image to enlarge.

















State 1. REFERENCE SHRUBLANDS.
State one consists of sparse shrublands over growing shallow soils with a large amount of surface rock. Atriplex confertifolia is a common species, which may co-occur with Atriplex canescens, Artemisia spp., Ephedra torreyana. There may be a grass component such as Hesperostipa comata, Achnatherum hymenoides, Pleuraphis jamesii or Elymus elymoides. Rock cover is 35% on average, ranging from 9 - 75%. Biological soil crusts may be present but habitat is constrained by rock cover. Plant cover averages 18%, with most sites having less cover, and some extremely productive sites with cover up to 45%. Basal gaps average 1.25 m. Shrubs are usually about twice as abundant as grasses. A large proportion of the soil that is armored by rock, but soil aggregate stability of exposed soil surface may be low, averaging 2-3 using the Herrick soil stability kit. Like many ecological sites surface soil stability is controlled largely by biological crust cover, which explain around 60% of the variation in aggregate stability.

Fig. 2. Photograph of Semidesert stony loam (ATCO) from Witwicki et al. 2009. Click to enlarge image.
















SIPhase1: PALATABLE SHRUBLANDS
Total plant cover is about 15%, and a typical shrub to grass ratio is ~ 5:1. These sites are more likely to contain cool season bunchgrasses such as Achnatherum hymenoides and Hesperostipa comata, the palatable shrubs Artemisia bigelovii and Atriplex canescens, and the unpalatable Ephedra torreyana. Biological crust cover is usually sparse.

S2Phase2: LESS PALATABLE SHRUBLANDS
Total plant cover is about 8 %, and a typical shrub to grass ratio is ~ 3.3:1. These sites are most likely to contain higher relative abundance of Atriplex confertifolia, along with unpalatable shrubs Guttierezia sarothrae. The warm season perrennial grass Pleuraphis jamesii may increase in importance. Biological soil crust cover is usually sparse. Juniperus osteosperma may occasionally be an important species.

State 2. GRAZING IMPACTED GRASSY SHRUBLAND
Total plant cover is about 28%, though smaller statured species are dominant, suggesting that standing biomass may be lower than the previous state. A typical shrub to grass ratio is ~ 1. This state is dominated by the grazing tolerant grass, Pleuraphis jamesii, and the palatable shrubs are greatly reduced. Guttierrezia sarothrae is a common sub-dominant and Bromus tectorum may have invaded.

State 3. ANNUALIZED
Information on total plant cover is lacking, Palatable shrubs have largely been replaced by unpalatable ones such as Coleogyne ramosissima and G. sarothrae. Grasses typically account for more cover than shrubs by a ratio of ~3.5:1. This is driven by annual grasses which account for ~70% of the grass cover; the perrennial grass to shrub ratio is close to 1. Bromus tectorum dominates and as a result, vegetative cover fluctuates within and among years. The exposed soil surface is erodible, especially in highly erosive thunderstorms.


Proceed to this link to read a summary of a data-derived validation of the phases of state 1, and this link to read a summary of a data-derived effort at modeling transitions.

Estimating thresholds for semi-desert shallow sandy loam (JUOS-CORA) and desert shallow loam (CORA)

Semidesert shallow sandy loam
Transition 9: S1P1 Grassy Shrublands to S2 Annualized

There was only a single state transition for which we had sufficient data to model a state transition. Transition 9 from S1P1 Grassy Shrublands to S2 Annualized was the only case where both the initial state/phase and the final state were documented in the dataset. A previous cluster analysis was used to assign samples to clusters corresponding to these designations. We used the NCPN integrated dataset to extract 46 samples representing either S1P1 or S2. We focused our efforts on the following data: 1. currently grazed (binary) as an indication of recent disturbance regime, 2. crust cover as an indicator of recent disturbance regime and resistance of soil surface to exotic annual establishment, 3. total plant cover as an index of recent consumption of forage and competitive barriers to the establishment of exotic annuals, 4. Relative cover of exotic annuals as an index of the degree of invasion, standardized by the site productivity, 5. State membership (S1P1 or S2). We developed the following path model to articulate our hypothesis about how transition 9 may occur. Using linear regressions we established the mathematical relationships expressed in the path model. Total plant cover and crust cover were well predicted by simple linear regressions using grazing as a predictor, after data transformation. Relative cover of exotic annuals was well predicted by a full-factorial regression model of all three of these variables. The second order interaction was particularly important in this predictive equation. State membership was well predicted particularly by relative cover of exotic annuals, with contributions from BSC cover and total plant cover.

The equations were as follows

1. log total plant cover = 1.56 – 0.27 (grazing)

2. √crust = 2.67 – 2.35 (grazing)

3. exotic annual rel. cover =
5.93 + 0.30 (grazing)
- 4.10 (log total plant cover)
+ 0.05 (√crust)
- 8.85 (grazing × log total plant cover)
2.45 (√crust × log total plant cover)
+ 0.21 (√crust × grazing)
-5.93 (√crust × grazing × log total plant cover)

4. probability of transition =
1/1 +e -(-0.0797 – 1.15(log total plant cover) – 1.96(√crust) + 2.69( exotic annual rel. cover))

Fig. 1. A path model illustrating the hypothesized mechanism underlying T9. Boxes represent measured variables, darkly filled ones being binary. The oval represents interaction terms. Directed arrows are causal dependencies. Black arrows represent linear regression relationships, arrow width indicates the strength of the relationship. Gray arrows represent information flow form predictors in a logistic regression equation (Click image to enlarge).
















This system of equations, applied in the sequence illustrated in the path model formed the basis of the simulation of data with the correlative structure of the real data. Each linear regession was modeled with an appropriate amount of random error (based on the RMSE, the standard deviation around the regression line). In order to simulate a wide variety of combinations of the predictors of probability of transition, we solved the system of equations, with error, for continuous grazing values ranging from zero to one, at a resolution of two decimal places. Although in the real data, grazing is a binary variable, it is easy to envision a continuous grazing intensity underlying this coarse indicator. We solved this 100 separate times, and calculated a mean probability and 95% confidence interval for each level of grazing (Fig 2). We determined the lowest grazing level at which probability of transition was 25% and 50% (normally we would do so for 5% and 95%, but a grazing intensity of 0 resulted in probability greater than 5%, and and a grazing intensity of 1 resulted in probability < style="font-weight: bold;" size="2">

Fig. 2. Modeled probabilities of transition from S1P1 to S2, given a range of grazing scenarios. Dashed lines represent critical probabilities (5%, 25%, 50%). The right panels indicate values of monitored indicator variables corresponding to grazed and ungrazed scenarios, and transition probabilities of 25% and 50% (Click to enlarge image).










Interpretation:
This ecological site is relatively resistant to transition to an annualized state, as the greatest probabilities of transition observed were only about 50%. However, even if grazing is ceased there is still a small probability (~5%) that transition will occur. This may be because of past grazing induced transitions that persisted after grazing ceased, or that despite low grazing disturbance, invasion occurred anyway, perhaps aided by climate events.

This modeling exercise provides real values which can trigger management actions. Our critical probablitities are: 5% the threshold beyond which transitions are a reasonable possibility, 95% the threshold beyond which transitions are almost certain, 50% the threshold at which transition or lack therof are equiprobable, and 25% the threshold beyond which transitions are becoming a common event. For sites in the S1P1 state our analyses indicate that when relative exotic annual cover reaches about 0.70, and/or its predecessors, crust cover and total plant cover, are reduced to 3.7 and 31.4, respectively, an assessment of the necessity of management actions (e.g. modifications of grazing regime) may occur. When relative exotic annual cover reaches about 1.0, crusts are reduced to 1.2% and total plant cover is reduced to 24.3%, a management action is needed(e.g. cessation of grazing or active restoration). If a management response is not undertaken, a transition will become reasonably likely, though not certain. Although we lacked the data to test the assumption, we suspect that a similar mechanism underlies the corresponding Transition 3 in the ecological site Desert shallow sandy loam (CORA), and that a similar management recommendation can be made regarding this site.

Semidesert shallow sandy loam Transition 1 & 2: S1P2 Wooded shrublands - crusted to S1P3 Wooded shrublands (and vice-versa) Transition 3 & 4: S1P5 Blackbrush shrublands - crusted to S1P6 Blackbrush shrublands (and vice-versa) Desert shallow sandy loam Transition 1 & 2: S1P1 Blackbrush shrublands - crusted to S1P2 Blackbrush shrublands (and vice-versa)
These 6 transitions were considered together because they are within-state transitions among phases, they all involve a very similar dependency of soil stability upon biological crust cover. They are also presumed to be reversible, if the stressor generating surface disturbance is removed. In both of the ecological sites (Semidesert shallow sandy loam, Desert sandy loam) we hypothesize shrubland or woodland states with the potential to support a high cover of biological crusts. In both we also hypothesize another state, a severely eroded state, which might eventually arise from the crusted woodlands or shrublands. The severely eroded state was not directly observed. We hypothesize the existence of intermediate phases lying along these degradation sequences, woodlands and shrublands lacking crusts, but otherwise similar to their crusted counterparts. This is because in response to livestock grazing, the most common stressor, the condition of the soil surface is likely to be impacted faster than the predominantly woody, unpalatable vegetation, due to the inherent fragility of the soil surface. This phase transition is expected to increase erodibility, which may or may not lead to enhanced erosion. Under further pressure we hypothesize that the stature and spatial patterning of the woody vegetation chnages in such a way that the ability of the vegetation to buffer erosivity of wind and water is reduced. When both erodibility and erosivity are increased, a highly eroded state is possible. We acknowledge that a surface disturbance which both disturbs the soil surface and degrades vegetation (e.g. off road vehicles, seismic exploration) could conceivably lead to a state transition to an eroded states without the intervening phase.

Within the sites falling within blackbrush shrubland or wooded shrubland clusters (Semidesert Desert shallow sandy loam cluster analysis) we used the NCPN database to plot regressions of soil stability by biological soil crust cover. In the database there were 7 cases of blackbrush shrublands with soil stability and biological crust measurements for the desert shallow sandy loam ecological site. There were 21 cases of blackbrush shrublands . There were 18 cases of wooded shrublands in the semidesert shallow sandy loam ecological site with the appropriate data. We used the square of the soil stability value because the data generated by the Herrick soil stability test (Herrick et al. 2001) is curvilinearly related to continuous measures of aggregate stability; the transformation helps linearize the scale. As hypothesized above spatial pattern of vegetation did not appear to be strongly dependent on recent grazing history, and was not considered a useful predictor of these transitions. We prepared 4 different regressions (Fig. 3: 1) Semi-desert shallow sandy loam wooded shrublands 2) Semi-desert shallow sandy loam blackbrush shrublands, 3) Semi-desert and desert shallow sandy loam blackbrush shrublands, 4) All of the above, combined. The desert shallow sandy loam blackbrush shrublands were not replicated well enough to be considered on their own, thus we pooled them with similar sites from semidesert shallow sandy loam to generate 3 above. Since regressions 1-3 were strikingly similar we also pooled all data and generated a regression for all simultaneously.

Fig. 3. Linear regressions of soil stability as a function of biological crust cover in: a. Wooded shrublands, b. Semidesert blackbrush shrublands, c. Desert and semidesert blackbrush shrublnds, and d. Pooled data (click image to enlarge)
















Because these are not transition among states we were not expecting threshold-like behavior, rather we expected and saw very strong linear dependencies. Nevertheless, we use the regression equations to estimate the biological crust cover at which soil stability was 5%, 25%, 50%, and 95% reduced to provide benchmarks for managing these ecosystems (Table 1).

Table 1. Biological crust cover at critical points in the degradation of soil stability (click image to enlarge). Percentages are based upon a maximal value of 36, the square of the maximal value of the Herrick soil stability test (Herrick 2001).












Interpretation:
All of the regression equations, regardless of which input data was used, resulted in very similar values. Therefore the pooled regression may be the most useful because it pertains to all of the data. This regression suggests that to maintain the potential soil stability, biological crust cover ought to be maintained at 38.9%.

If land is being managed for uses which require surface disturbance, degradation of soil stability can be maintained at 25% or less if crust cover is maintained at at least 28.3%. Likewise degradation can be maintained at 50% of less if at least 15.1% crust cover is retained. Beyond this point, a site is increasingly likely to have passed into an at-risk phase. The at-risk phases, if further damaged, are susceptible to transition to severely eroded states

References
Herrick, J.E., W.G. Whitford, A.G. de Soyza, J.W. Van Zee, K.M. Havstad, C.A. Seybold, M. Walton. 2001. Soil aggregate stability kit for field-based soil quality and rangeland health evaluations. Catena 44:27-35.

Desert and semidesert shallow sandy loam- Validating existence of states and phases

Methods
Our approach to validating and revising a priori state-and-transition models it to conduct cluster analysis of vegetation community composition and biological crust cover. Some authors have argued that, to focus on functional properties of plant communities such analyses should be conducted based primarily on functional rather than structural indicators. Functional groups of plants (e.g. perrennial grasses, exotic annual grasses, native palatable shrubs) rather than species-level data, are proposed as one way to describe function but would optimally be used in addition to other pertinent indicators of ecosystem function. There are two problems with ascribing state or phase membership based solely on plant functional groups: 1. Some key transitions might involve a shift in dominance among two plants in the same functional group. Despite membership in the same functional group, this represents a stark structural transformation and could represent an unmeasured functional transformation. 2. When working with existing data, rather than collecting a dataset, plant community structure data are the most commonly available detailed data, other more directly functional data are often lacking or incomplete. Therefore, we pragmatically chose to use the richest data we had, plant community structure, in addition to biological crust cover, a structural and functional indicator.

We based our analysis on the NCPN integrated dataset. To standardize the various datasets collected by different observers using different techniques, we applied two steps. 1. Removal of rare species was conducted, because these species are so infrequent they primarily introduce noise. We removed all species with fewer than 10 occurrences in EITHER desert or semi-desert shallow sandy loam. We further removed all species with fewer than 5 occurrences in semi-desert shallow sandy loam, prior to clustering. Finally we removed all species with fewer than 3 occurrences in the desert shallow sandy loam. 2. We applied a double relativization transformation. First each column (a species) is rescaled form 0 - 1. Second, each row is rescaled from 0-1. This equalizes the influence of each column, then purges the influence of total abudance in the sample.

We chose a hierarchical clustering method rather than a fuzzy clustering method because community structure data contain many zeros. In such situations, methods compatible with the Bray-Curtis distance (hierarchical clustering) are preferred over those requiring Euclidean distance (fuzzy clustering), because they do not interpret shared absences as a source of similarity among samples. We used a flexible beta linkage method with beta = - 0.25.

Based on the number of clusters in our a priori models (4), we examined results for 2 - 8 cluster solutions. Cluster analyses are subjective descriptive tools and should not be viewed as strict hypothesis tests. We used the following guidelines to select the best number of clusters: 1) Based on threshold theory, that intermediates between states are unstable and would be uncommonly observed, we chose a number of clusters which displayed a low degree of overlap in ordination space, 2) Acknowledging that we may not observe all of the clusters in our a priori model (and that their absence does not prove they do not exist), and that additional clusters may exist that we did not anticipate, we selected a solution with a number of clusters reasonably close to our a priori expectations, 3) we accepted clusters which were a good match with our a priori expectations if they existed, 4) we accepted unanticipated clusters when they were consistent with a mechanistic explanation as to how they could arise (e.g. dictated by abiotic factors, or a likeley otucome of a given disturbance). We selected the solution that best satisfied all of the above criteria. To help us define the characteristics of our clusters we applied indicator species analysis (Dufrene & Legendre 1997), and viewed NMDS ordinations.

Results
Semi-desert shallow sandy loam
Due to it's much better replication, we analyzed semi-desert sandy loam first. We selected a 5-cluster solution (Fig.1 & 2). One of the hypothesized states (annualized) was confirmed and retained as a state in the final model. One hypothesized state (severely eroded) was never observed, but its absence does not prove that it cannot exist, only that it was not observed, thus it is retained in the final state-and-transition model. The other two hypothesized states (crusted wooded shrublands and uncrusted wooded shrublands) were revised as follows. Three unanticipated clusters were observed (grassy shrublands, rocky shrublands). These were interpreted as spatial phases of the reference state which appear to be dictated by differences in soil depth and degree of surface rock cover. One hypothesized state (crusted wooded shrublands) proved to be two distinct clusters (wooded shrublands, blackbrush shrublands), apparently dictated by precipitation. These were reinterpreted as distinct spatial phases of the reference state. Another hypothesized state was the uncrusted wooded shrubland, a hypothesized outcome of surface disturbance in crusted wooded shrublands. In both the wooded shrublands and blackbrush shrublands clusters, there is a gradient of crust cover, corresponding to time since grazing, however these do not sort into distinct clusters. Therefore we reinterpreted the crusted and uncrusted counterparts of wooded shrublands and blackbrush shrublands as four distinct phases within the reference state.

Fig. 1. NMDS ordination of a 5 cluster solution in 3 dimensions. a. most clusters separate well when viewing the two strongest axes (the horizontal axis is rotated to maximize correlation with time since grazing), with the exception of a wooded shrubland cluster; The annualized cluster is best correlated with current or recent grazing. b. A view of the third axis demonstrates that the wooded shrublands also separate from the other clusters (click to enlarge image).

















Fig. 2. Six versions of the above NMDS ordination, illustrating indicator species of the various clusters. In each panel, the symbols are resized based on the abundance of a single species or biotic component. It is clear that particular species correlate well with particular clusters. a. Biological crust cover, an indicator of blackbrush and wooded shrublands. b. C. viscidiflorus, an indicator of rocky shrublands. c. C. ramosissima, an indicator and namesake of blackbrush shrublands. d. P. edulis, an indicator of wooded shrublands. e. Opuntia, an indicator of annualized. f. A. hymendoides, an indicator of grassy shrublands (click to enlarge image).

















These revisions result in a 3 state state-and-transition model with 6 phases of the reference state. Three of these phases are at-risk and potentially subject to transition out of the reference state. Transitions are modeled and discussed in a separate exercise.

Desert shallow sandy loam
Using our a priori model and knowledge gained from our analysis of semi-desert sandy loam we conducted a similar exercise for desert shallow sandy loam. We selected a three cluster solution (Fig. 3 & 4). As above, one state (annualized) was observed and confirmed, and another (severely eroded) was not observed but retained as a possibility in the final model. Because this ecological site is drier there was no distinction between wooded shrublands and blackbrush shrublands, only blackbrush shrublands occurred. We also observed a cluster strongly reminiscent of the rocky shrublands identified in semi-desert shallow sandy loam. Finally, we did not observe a phase corresponding to grassy shrublands, but we infer its existence as a precursor to annualized states. The sample size was considerably lower (40), only about half of which were not currently disturbed, thus it is entirely reasonable that such a phase exists but was not detected.

Fig. 3. NMDS ordination of a 3 cluster solution (click to enlarge image).

















Fig. 4. Three versions of the above NMDS ordination, illustrating indicator species of the 3 clusters. In each panel, the symbols are resized based on the abundance of a single species or biotic component. It is clear that particular species correlate well with particular clusters. a. C. ramosissima, an indicator and namesake of blackbrush shrublands. b. C. viscidiflorus, an indicator of rocky shrublands. c. G. sarothrae, an indicator of annualized. (click to enlarge image).














Our final 3 state state-and-transition model closely resembled that developed for semi-desert shallow sandy loam, except that phases with tree overstories were omitted. Transitions are modeled and discussed in a separate exercise.

Desert shallow sandy loam (CORA)

The following state and transition model was developed based upon a priori knowledge of the ecological site (e.g., past experience and published literature) ecological principles and logical hypotheses. To the greatest degree possible, empirical data was used to validate concepts in the model. A confirmation of the existence of states using cluster analyses can be viewed at this link. Analyses aimed at modeling transitions can be viewed at this link.

Fig. 1. State-and-transition diagram for desert shallow sandy loam. Solid boxes represent ecosystem states. Dashed boxes indicate phases within states (red signifies a phase that is at-risk of transition to another state). Arrows indicate transitions. In some cases, phases within the reference state are not connected to any others by arrows; this is our method of representing spatial variants of the reference state that are dictated by abiotic factors (click to enlarge image)

S1. REFERENCE SHRUBLANDS. Multiple distinct vegetative communities can be observed. They appear to largely be dictated by abiotic factors rather than disturbance and successional processes. Soil depth and proportion of the surface covered by rocks seem to dictate dominant vegetation, and biological crust cover (as rock increases, the amount of available habitat for crusts decreases). Most of the reference communities contain Coleogyne ramosissima. Sites with low to moderate surface rock, and shallow depth (indicated by exposures of bedrock) tend to favor C. ramosissima shrublands.

S1P1. ROCKY SHRUBLANDS. This phase is characterized by surfaces dominated by small rocks. The vegetative community is quite distinct, being dominated by Chrysothamnus viscidiflorus. Elymus elymoides and Atriplex canescens are the most common palatable species. Biological crusts are unimportant, as there is little available habitat. Invasion by Bromus tectorum is uncommon, and of minor severity.

S1P2. BLACKBRUSH SHRUBLANDS – CRUSTED. This phase is characterized by low surface rock cover, and shallow soils indicated by bedrock exposures. The vegetation is naturally dominated by C. ramosissima and Ephedra spp. Biological crusts are common but cover is generally low. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions from this phase are modeled at this link.

S1P3. BLACKBRUSH SHRUBLANDS. This phase is identical to S1P5, except that biological crust cover may be compromised by surface disturbances. Total plant cover may be reduced. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions to this phase are modeled at this link.

S1P4. GRASSY SHRUBLANDS. This phase was not directly observed in available data, but is inferred based upon a parallel phase in semidesert shallow sandy loam sites. It is presumed to be the precursor of S2, though this cannot be tested directly since S2 sites occur only (with one exception) in currently grazed sites. Based on the native palatable species in S2, this phase might contain Aristida purpurea and Pleuraphis jamesii. Biological crusts are probably common but not abundant.

Transitions from this phase are discussed at this link.

S2. ANNUALIZED. Based upon physical attributes (relatively low exposed bedrock and surface rock) and some floristic similarities, this state is likely to arise via grazing disturbance to S1P1. It is dominated by native unpalatable shrubs such as Gutierrezia sarothrae. Also, Bromus tectorum may be a major community component, even codominating. Due to the potentially high contribution of B. tectorum to total cover, inter- and intra-annual variation in total cover is possible. Biological crusts are typically eliminated or occur in low abundance.

Transitions to this state are discussed at this link.

S3. SEVERELY ERODED. This state is largely theoretical. When a site is naturally lacking in surface rocks, its soil erodibility can be enhanced by loss of biological crusts (this occurs previously in the transition from SIP3 to SIP4). Erosivity, the ability of erosive forces to move sediment, is largely modified by properties of the plant community. When both erodibility and erosivity are high, erosion is certain to occur. If grazing intensity or drought mortality (or other disturbance such as ORVs, seismic explorer rigs, etc.) is so great that the erosivity-dampening properties of the vegetative community are degraded, a positive feedback may be initiated whereby erosion prevents vegetation recovery.

OTHER TRANSITIONS. This ecological site is closely aligned with Semidesert shallow sandy loam (JUOS-CORA), which can be viewed simply as a wetter version of Desert shallow sandy loam. Recent global-change type droughts in the Colorado Plateau region suggest that drought mortality can occur quickly in pulses. Pinus edulis, an important species of Semidesert shallow sandy loam (JUOS-CORA) is particularly susceptible. We can envision that a prolonged drying trend or an extreme drought could transition the states and phases od Semidesert shallow sandy loam (JUOS-CORA) to corresponding states and phases here in the Desert shallow sandy loam (CORA) ecological site. A state-and-transition model can illustrate possible transitions between these two ecological sites.

Fig. 2. A state-and-transition model illustrating the states and phases of both Semidesert shallow sandy loam and Desert shallow sandy loam. Transitions in blue indicate transitions precipitated by droughts linked to climate change (click to enlarge image)

NCPN integrated dataset

I compiled an integrated dataset form multiple sources with representation of 9 ecological sites found in the NPS I&M Northern Colorado Plateau Network (NCPN): Desert Sandy Loam (ATCA), Semidesert sand (ATCA), Semidesert sandy loam (ATCA), Desert shallow sandy loam (CORA), Semi-desert shallow sandy loam (PIED-JUOS-CORA), Semidesert Alkali sandy loam, Semidesert stony loam, Semidesert very steep stony loam Semidesert sand (CORA). 627 records were compiled from 7 data sources. These include: 1) the Arches National Park vegetation mapping dataset (Coles et al. 2009) which provides plant community composition and some ground cover data by cover class. 2) the Capitol Reef National Park vegetation mapping dataset (Clark et al. 2009) which provides quantitative plant community composition and some ground cover data from two data collection periods and multiple disturbance regimes.
3) the NPS Inventory and Monitoring Network dataset (Witwicki 2009a, Witwicki 2009b) which provides quantitative plant community composition, soil stability, gap size distributions, ground cover and multiple years of sampling, 4) The Grand Staircase-Escalante National Monument rangeland health assessment data set (Miller et al. 2005) which provides quantitative plant community data, soil stability, and ground cover, 5) Miller et al. unpublished which provides plant community composition, soil stability, gap size distributions, ground cover among other data and multiple disturbance histories, 6) The Canyonlands vegetation mapping dataset (unpublished), 7) The NPS monitoring protocol development dataset (Miller et al. 2007) which provides plant community composition, soil stability, gap size distributions, ground cover among other data.

Time since grazing is estimated conservatively by subtracting the last possible date of grazing activity from the date of data collection. In the case of the NCPN dataset, the first year of plot establishment was used. In the entire database this calculation resulted in time since grazing estimates of: 0, 3, 10, 14, 20, 21, 26, 27, 31, 32, 33, 34, 44, and never grazed.

References
Clark D, Dela Cruz M, Clark T, Coles J, Topp S, Evenden A, Wight A, Wakefield G, Von Loh J. 2009. Vegetation classification and mapping project report, Capitol Reef National Park. Natural Resource Technical Report NPS/NCPN/NRTR - 2009/187. National Park Service, Fort Collins, Colorado.

Coles J, Tendick A, Manis G, Wight A, Wakefield G, Von Loh J, Evenden A. 2009. Vegetation Classification and mapping report, Arches National Park. Natural Resources Technical Report NPS/NCPN/NRTR-2009/253. National Park Service, Fort Collins, Colorado.

Miller, Mark E. 2008. Broad-scale assessment of rangeland health, Grand Staircase-Escalante National Monument, USA. Rangeland Ecology and Management 61:249-262.

Miller, Mark E., Witwicki, Dana L., Mann, Rebecca K., and Tancreto, Nicole J., 2007, Field evaluations of sampling methods for long-term monitoring of upland ecosystems on the Colorado Plateau: U.S. Geological Survey Open-File Report 2007-1243, 188 p.

Witwicki D. 2009. Integrated upland monitoring in Canyonlands National Park: Annual Report 2008. Natural Resource Technical Report NPS/NCPN/NRTR - 2009/236. National Park Service, Fort Collins, Colorado.

Witwicki D. 2009. Integrated upland monitoring in Capitol Reef National Park: Annual Report 2008. Natural Resource Technical Report NPS/NCPN/NRTR - 2009/237. National Park Service, Fort Collins, Colorado.

Semi-desert shallow sandy loam (JUOS-CORA)

The following state and transition model was developed based upon a priori knowledge of the ecological site (e.g., past experience and published literature) ecological principles and logical hypotheses. To the greatest degree possible, empirical data was used to validate concepts in the model. A confirmation of the existence of states using cluster analyses can be viewed at this link. Analyses aimed at modeling transitions can be viewed at this link.


Fig. 1. State-and-transition diagram for desert shallow sandy loam. Solid boxes represent ecosystem states. Dashed boxes indicate phases within states (red signifies a phase that is at-risk of transition to another state). Arrows indicate transitions. In some cases, phases within the reference state are not connected to any others by arrows; this is our method of representing spatial variants of the reference state that are dictated by abiotic factors (click to enlarge image)
















S1. REFERENCE SHRUBLANDS & WOODLANDS. Multiple distinct vegetative communities can be observed. They appear to largely be dictated by abiotic factors rather than disturbance and successional processes. Soil depth and proportion of the surface covered by rocks seem to dictate dominant vegetation, and biological crust cover (as rock increases, the amount of available habitat for crusts decreases). Most of the reference communities contain Coleogyne ramosissima. Sites with low to moderate surface rock, and shallow depth (indicated by exposures of bedrock) tend to favor C. ramosissima shrublands or Pinus-Juniperus woodlands. Their relative prevalence is likely influences by regional factors such as precipitation, and local factors such as bedrock fissures for rooting.

S1P1. GRASSY SHRUBLANDS. This phase is characterized by few exposures of bedrock, and low levels of surface rock. Such sites are dominated by the grass Achnatherum hymenoides, and palatable shrubs such as Artemisia bigelovii or Eriogonum corymbosum. It can be inferred that soils are relatively deep compared to other phases. In a low-disturbance state, biological crust cover is frequent but modest, usually 5-10%. May be invaded by Bromus tectorum, but it is not a major component.

Transitions from this phase are modeled at this link.

S1P2. WOODED SHRUBLANDS – CRUSTED. This phase is characterized by low surface rock cover, and shallow soils indicated by bedrock exposures. Juniperus osteosperma and/or Pinus edulis are characteristic of this phase along with various shrubs including Coloeogyne ramosissima, Shepherdia rotundifolia, Mahonia fremontii, Ephedra viridis and Artemisia tridentata. Such sites with high available habitat and possibly perched water, have a high propensity to support biological crusts with cover often reaching 15% or greater. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions from this phase are modeled at this link.

S1P3. WOODED SHRUBLANDS. This phase is identical to S1P2, except that biological crust cover has been compromised by surface disturbances. Total plant cover may be reduced. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions to this phase are modeled at this link.

S1P4. ROCKY SHRUBLANDS. This phase is characterized by surfaces dominated by small rocks. The vegetative community is quite distinct, being dominated by Chrysothamnus viscidiflorus and Hymenoxys richardsonii. Poa fendleriana is the most common palatable species. Biological crusts are unimportant, as there is little available habitat. Invasion by Bromus tectorum is uncommon, and of minor severity.

S1P5. BLACKBRUSH SHRUBLANDS – CRUSTED. This phase is characterized by low surface rock cover, and shallow soils indicated by bedrock exposures. The vegetation is naturally dominated by C. ramosissima and Ephedra spp. Such sites with high available habitat and possibly perched water, have a high propensity to support biological crusts with cover often reaching 20% or greater. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions from this phase are modeled at this link.

S1P6. BLACKBRUSH SHRUBLANDS. This phase is identical to S1P5, except that biological crust cover has been compromised by surface disturbances. Total plant cover may be reduced. Invasion by Bromus tectorum is uncommon, and of minor severity.

Transitions to this phase are modeled at this link.

S2. ANNUALIZED. Based upon physical attributes (relatively low exposed bedrock and surface rock) and some floristic similarities, this state is likely to arise via grazing disturbance to S1P1. It is dominated by native unpalatable shrubs such as Gutierrezia microcephala, and Opuntia spp. Also, Bromus tectorum may be a major community component, even codominating. Due to the potentially high contribution of B. tectorum to total cover, inter- and intra-annual variation in total cover is possible. Biological crusts are typically eliminated or occur in low abundance.

Transitions from this state are modeled at this link.

S3. SEVERELY ERODED. This state is largely theoretical. When a site is naturally lacking in surface rocks, its soil erodibility can be enhanced by loss of biological crusts (this occurs previously in the transition from SIP5 to SIP6, and from S1P2 to S1P3). Erosivity, the ability of erosive forces to move sediment, is largely modified by properties of the plant community. When both erodibility and erosivity are high, erosion is certain to occur. If grazing intensity or drought mortality (or other disturbance such as ORVs, seismic explorer rigs, etc.) is so great that the erosivity-dampening properties of the vegetative community are degraded, a positive feedback may be initiated whereby erosion prevents vegetation recovery.

OTHER TRANSITIONS. This ecological site is closely aligned with Desert shallow sandy loam (CORA), which can be viewed simply as a drier version of Semidesert shallow sandy loam. Recent global-change type droughts in the Colorado Plateau region suggest that drought mortality can occur quickly in pulses. Pinus edulis is particularly susceptible. We can envision that a prolonged drying trend or an extreme drought could transition the states and phases presented here to corresponding states and phases in Desert shallow sandy loam. Another state-and-transition model can illustrate possible transitions between these two ecological sites.

Fig. 2. A state-and-transition model illustrating the states and phases of both Semidesert shallow sandy loam and Desert shallow sandy loam. Transitions in blue indicate transitions precipitated by droughts linked to climate change (click to enlarge image).

Analysis of succession on Clayey Fans

Rebound from grazing, and vegetational successional dynamics were analyzed in two ways: 1) using a dataset describing the first 20 years of vegetation change on two transects (Johnson unpublished, Rowlands unpublished), and 2) using a space-for-time replacement analysis based on a vegetative mapping dataset collected from 2003 - 2006, and exploiting a south to north gradient in completion of the park boundary fence from 1934 - 1963.

1. George Johnson, a ranger at Petrified Forest, initiated a range recovery study in 1972, on two transects representing Clayey Fans. The transects were very close to one another and represented a former corral and watering area for cattle (Johnson 1972). Johnson sampled vegetation in 1972, 1974, 1976, 1978, 1982, and 1984. Peter Rowlands relocated and sampled the transects in 1992. Regrettably no one has resampled the transects after the drought years of 1996 and 2002-2003.

Rowlands (1992) identified several interpretational problems, chief among them: 1) The transects are in atypical low-lying areas which have higher than typical potential for vegetative cover, 2) Johnson sampled in early spring in 1972 & 1974, but shifted to the summer monsoon season afterward, 3) the initial years of the study also coincided with increasing precipitation, making it difficult to separate dynamics driven by succession, and those driven solely by precipitation, and 4) Johnson used a small number of points for a point intercept method. Nevertheless, these data represents the only quantitative document of temporal change in vegetation on Clayey Fans.

Figure 1. Increase in vegetation over time after the cessation of grazing on two transects on Clayey Fans (Click for larger version).
















Total cover increases impressively (almost an order of magnitude on transect B1), and nearly linearly over this time span (Figure 1). Rowlands cautioned that the increase in Johnson's data may be due to annually increasing precipitation during this time. However, the vegetation continued increasing at a similar rate through the late 80's and early 90's, when this trend did not hold, in my opinion suggesting that a simple "time since fencing" model is the most parsimonious predictor of this change.

Figure 2. Vegetation change along Johnson (1972) transects. Rows are points on the transects, columns represent years. Basal cover is shown, except when there was no basal cover, canopy cover is shown. Rowlands (1992) did not document spatial information, therefore the 1992 data is omitted from this figure (click for larger image).
















Vegetation change did not conform to the model expressed in the ecological site description. Under intense grazing pressure, we would have expected a major decline in Sporobolus airoides, and replacement with annuals, cacti, Ericameria and Guttierrezia. The latter three were never observed. The 1972 communities were Sporobolus-dominated, and remained so throughout the monitoring period. There was a clear spatial dependency from one year to the next, where new colonization events strongly tended to be in a previously occupied position on the transect or adjacent (Figure 2). Most of the occupied portions on the transects can be traced backward to a position previously occupied by Sporobolus, or one adjacent. This paints a picture of Sporobolus as grazing-resilient facilitator of community recovery. Transect B1 seemed to add species over time, with multiple shrubs becoming more important, especially Atriplex spp. In contrast, transect B2 had most of its species present from very early on.

2. Petrified Forest National Park is composed of north and south blocks connected by a narrow strip of park lands. Fencing and phasing out of grazing was conducted gradually, starting in the south in the mid 1930's. The north boundary fence was not completed until 1963. The distribution of clay fans within the park is also bimodal with a northern cluster and a southern cluster. Therefore, differences in the vegetation monitoring dataset (Thomas 2009) between the north and south clusters in terms of vegetation composition and cover might reflect different lengths of time since fencing. Of course we cannot rule out that any differences are due to other factors that differ among the north and south clusters.

The total vegetative cover is clearly different between these two clusters: the longer rested south cluster has 50% greater vegetative cover than the north. Species richness is almost identical among the two clusters and the variance is large. An NMDS ordination of species composition indicates a modest degree of separation among the clusters. We can account for which species account for this separation in 2 ways: 1) rotating the ordination so that axis 1 maximizes the difference between north and south clusters, and then obtaining the correlations of each species with axis 1, and 2) conducting and indicator species analysis. The most important species were identified as having an indicator value > 25. Their correlation with the "northerliness" axis is presented (Kendall's Tau) along with the probability value generated in the indicator species analysis. The major correlates with the southern group -- those fenced longer -- were B. gracilis (tau = -0.38, P = 0.001), and P. jamesii (tau = -0.32, P = 0.01), and Salsola tragus (tau = -0.49, P = 0.001). The major correlates with the north group were Isocoma drummondii (tau = 0.40, P<0.0001), style="font-style: italic;">Parryella filifolia (P = 0.03). B. gracilis and P. jamesii, though often thought of as grazing increasers, are 2-3 times more abundant in the longer rested southern cluster. Surprisingly, Salsola is also more strongly associated with longer rest in this data, although it tends to be associated with less vegetated, erosion prone landscapes. It is difficult to to interpret the association of I. drummondii and P. filifolia with the more recently fenced north cluster. Both are known from more sandy habitats, and the germination of I. drummondii is suppressed by salt (Baskin & Baskin 1998) suggesting that this may have more to do with gradients of sand deposition than grazing; a hypothesis that we cannot rule out for any of these patterns.


Figure 3. Vegetational differences among north(fenced later) and south (fenced earlier) clusters of Clayey Fans sampling sites (Thomas et al. 2009). a. Cover is ~ 50% greater in the southern cluster. b. Richness does not differ. c. An NMDS ordination showing a moderate degree of separation between north and south clusters in unrelativized vegetation composition. (click image for larger version)















Conclusions: Despite that both of these analyses have their flaws, we can draw some inferences based upon the agreement between these two datasets. There is much uncertainty in drawing these conclusions, but they are based on the best evidence available:

1. The data do not strongly support that Sporobolus airioides is a grazing decreaser any more than other community members. Like the majority of species its abundance seems to be lower when time since grazing is shorter, however it may act as a pioneer facilitation the establishment of vegetated patches. There is no landscape-wide difference in abundance for this species among north and south clusters of sites.

2. Likewise the data do not strongly support that Bouteloua gracilis or Pleuraphis jamesii are grazing increasers. They were largely absent from Johnson's transects in 1972, and they are distinctly more abundant in the south cluster of the sites from Thomas et al. (2009) which have been fenced longer.

3. The most obvious putative effect of grazing is that cover can increase dramatically when grazing ceases. This demonstrates a large degree of resilience.

4. There is no evidence that Ericameria of Guttierezia increase in the presences of grazing, or decrease in its absence, in this ecosite. Ericameria may be common near streams, but otherwise both of these species are minor players.

References
Baskin, CC, Baskin, JM. (1998) Seeds: Ecology, biogeography, and evolution of dormancy and germination. Academic Press, San Diego

Johnson, GE (1972) (Appendices 1974, 1976, 1978, 1982, 1984). A report on a vegetative recovery study made on two study plots in Petrified Forest National Park. National Park Service, Unpublished Report.

Rowlands, PG (1992) Vegetation monitoring at Petrified Forest National Park: I. Preliminary report and summary of vegetational change observed on long-term monitoring plots. Cooperative Park Studies Unit, Northern Arizona University, Unpublished Report.

Thomas, KA, McTeague, ML, Cully, A, Schulz,K, Hutchinson, JMS (2009) Vegetation classification and distribution mapping report: Petrified Forest National Park. National Resource Technical Report NPS/SCPN/NRTR—2009/273. National Park Service, Fort Collins, Colorado.