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1
Bertin w. Anderson
2 and Robert D. Ohmart
3
Abstract.--Predictive models were developed from data collected monthly on vegetation and wildlife for over seven years (1972-1979) in order to design wildlife enhancement projects. Implementation of projects was conducted between
1979-1981 to gain information on costs, develop methodologies on revegetation, and to test model-generated predictions.
INTRODUCTION
We began ecological studies along the lower
Colorado River in 1972 to explore the possibility of managing riparian vegetation in order to (1) reduce total water loss caused by evapotranspiration, (2) reduce total floodplain vegetation so that flood flows could pass relatively unimpeded,
(3) blend items (1) and (2) with wildlife enhancement, and (4) explore the economic and technical feasibility of revegetating relatively large areas. we summarize our findings and discuss predictive models that have emanated from our work over the past decade. Detailed analyses are not presented, but pertinent references are cited.
THE DATA BASE
Riparian vegetation along the lower Colorado
River encompassed some 40,000 ha when our work began. Initially, the Bureau of Reclamation, the funding agency, decided that the study would require three years; one year for planning and two years for intensive field work. Soon they rendered the wise decision to extend the intensive field work phase by two additional years. The field work was to provide an in-depth data base for showing more clearly the relationships between riparian vegetation and densities of wildlife associated with these habitats (table 1).
1Paper presented at the Riparian Ecosystems and Their Management: Reconciling Conflicting
Uses Conference. [University of Arizona, Tucson,
Apri~ 16-18, 1985].
Bertin W. Anderson is a Research Associate of the Center for Environmental Studies at Arizona
Stat} University, Tempe, Ariz.
Robert D. Ohmart is an Associate Director of the Center for Environmental Studies at Arizona
State University, Tempe, Ariz.
After analysis, terrestrial vegetation was divided into 23 habitats based on the vertical configuration of the vegetation and on the numerically dominant vegetation ij{esent within a stand.
Stand size ranged from 10 to 40 ha with the vast majority of stands occupying 20 ha. Each stand was classified as belonging to one of the 23 habitats (Anderson et ale 1983, Anderson and Ohmart
1984).
Vegetation Classification we developed a classification system that was easy to remember and apply in the field with few needed plant community measurements, or one that someone very familiar with the system could use to make a good field estimate with no needed measurements. We recognized only six different vertical configurations and only six divisions on the basis of numerically dominant vegetation. The ultimate classification of a stand involved two steps: (1) determining the vertical configuration, and (2) determining the dominant plant species. Each of these steps required specific recall of only six relatively easy-to-remember categories (Anderson et ale 1983, Anderson and Ohmart 1984).
Wildlife Densities
Birds
Densities were obtained by censusing birds in each stand three times each month. A density estimate for each species was made on the basis of these three censuses. Density estimates for each of the habitat types was the average density of the stands representing that habitat for a given month and also across months for a given season.
Averaging bird densities resulted in masking some of the variance in the data. However, we felt that most of this variance was due to highly localized edaphic features associated with each stand rather than any fundamental difference in
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Table 1. Summary of data used to construct three predictive models, the kinds of predictions they make, and the tests made to determine their general level of accuracy.
Model baseline data Output Model testing
I A. Vegetation data from
23 habitats within
40,000 ha of riparian vegetation along the lower Colorado River.
Species richness values and densities of rodents and birds by season with confidence limits.
Three times on the lower Colorado
River. Performance: poor to excellent depending on species or guild involved; poor to excellent for most species.
II
B. Bird and rodent species richness and relative density estimates for all species from 23 habitats seasonally for five years
(3,600 censuses each year).
A. Number of times each of
59 wildlife categories ranked among the top three for the 23 habitats. These data are determined from and B above.
A
A quick method for estimating impact of perturbation; estimates of the extent to which revegetation offsets losses incurred.
Twice on the lower
Colorado River.
Performance: satisfactory to good.
B. Correlation between the number of wildlife categories ranked among the top three for each habitat and the number of cottonwood (Populus fremontii), willow (Salix gooddingii), and honey--mesquite (Prosopis glandulosa) trees present.
C. Relationship between mortality and biomass production for the species in B.
III A. Relationship of tree height growth, biomass production, and tree mortality to depth of tillage, soil type, and soil salinity for 608 honey mesquite, 930 cottonwood, 1258 willow, and 300 blue palo verde
(Cercidium floridum) trees.
Estimates of tree growth, biomass production, and survival after three years for cottonwood, willow, honey mesquite, and blue palo verde trees.
Five tests on the lower Colorado
River and a test in progress on Rio
Grande in Texas.
Performance: high accuracy in all categories for all species.
B. Soil classification every 0.3 m, salinity and extent of tillage at each planting site for each species in A.
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bird use between stands within a habitat (Anderson et ale 1983). This point is discussed further below. Data by habitat type for each season is presented in Anderson and Ohmart (1977).
Rodents
Nocturnal rodent populations were sampled annually by snap-trapping in all habitats several times during the cool (October-March) and warm
May-September) months. Relative species densities were expressed as the number caught per 270 trap nights (the summation of trap nights per trapping season). Species richness and relative densities of rodents were obtained for all habitat types for five years.
MODEL I
Relationships Between Wildlife and Vegetation
The first step in determining the relationship of bird and rodent community attributes with the vegetation was to subject the vegetation data to principal components analysis (PCA) to obtain the major independent trends in the data (i.e., principal components [PC's]). These independent trends were treated as plant community variables.
An alternative would have been to use the original variables in subsequent analyses. Had we chosen to do this, we would have undoubtedly arrived at most of the same conclusions. We chose PCA because many of the original variables were significantly intercorrelated. This would have made interpretation of results a slow and tedious process had we chosen to use the original variables rather than variables derived through PCA
(Anderson et ale 1983, Anderson and Ohmart 1984).
In the next step, we developed regression equations to show the relationship between bird community attributes such as species richness, bird species diversity, density of visiting insectivores, etc., as well as densities of each species to the four derived vegetation variables.
This was repeated each season for each of five years. Similar calculations were made for rodents during the cool and warm times of year. Some of these analyses are presented in Anderson et ale
(1983), Meents et ale (1983), and Anderson and
Ohmart (1985).
These analyses were all by habitat, which, as pointed out above, concealed some of the variance in the stand-by-stand data because of averaging.
It was possible that averaging concealed many important bird-vegetation and rodent-vegetation associations. To be certain that this procedure did not lead to incorrect conclusions the data were reanalyzed with entirely different statistical procedures and on a stand-by-stand basis, thus exposing more of the variance in the data. Conclusions from these analyses were nearly identical to those at the habitat level (Anderson et ale
1983, Rice et ale 1983a, b).
It was fortunate that we studied a significant proportion of the habitats available continuously over all seasons for five years. Had our sample,
125 through either time or space, been smaller, we might have arrived at a number of different conclusions, all wrong with respect to the complete five-year data set (Rice et ale 1983a, b, 1984;
Anderson and Ohmart 1985).
Input Data
Vegetation data for 15 variables (foliage density for each of four strata, patchiness at these strata, foliage height diversity, number of shrubs, number of trees per ha for each of four species, number of trees with mistletoe
[Phoradendron californicum] per ha) were used as input data. This information was included in the model and was used for calculating total foliage density, total patchiness, foliage height diversity, and, ultimately, for calculating a factor score for each of four PC's. These PC factor scores were then used to calculate predicted values for various wildlife species.
If measurements are not available for the whole range of variables or if no measurements were taken, then the person using the model (hereafter called the "user") has to enter which of the
23 habitat types is of particular interest. The model will then use factor scores for the average stand within this habitat type for predicting wildli fe use.
It is possible that the user has calculated vegetation factor scores for a particular stand or wishes to manipulate factor scores to better understand the relationship between factor scores and wildlife use of an area. In this case, the user enters the factor scores to obtain wildlife values associated with those factor scores.
Output Data
Output data consist of predictions of the wildlife that will use the vegetation and, at the user's discretion, can consist of data for individual bird or rodent species for any season or all seasons, or it can consist of information such as total species richness, density of permanent resident insectivorous birds, density of visiting insectivorous birds, etc. The user has the option of retrieving all of this information for all or any single season or he can retrieve information for only the species in which he is particularly interested. The output consists of the five-year average from the original data base. Variation around this mean is also given.
Uses of Output
One of the major uses of the output is to aid in assessing impacts on wildlife of disturbances and for making management decisions. For example, if the user wishes to evaluate the impact that elimination of 30% of a stand of cottonwood-willow habitat would have on birds and nocturnal rodents, he enters the vegetation data for the undisturbed site and perturbates the site in the model. Then he can compare wildlife losses before and after perturbation, prior to any site modification. If the severity of loss is too great, the planners may decide to alter or reduce site disturbance.

If the modification is accepted, then the results will give guidelines for possible approaches to mitigation.
This model generates much detailed data that may not be of interest to a user who is concerned with only one or a few species. It could conceivably take hours or days to evaluate the output. At times it may be desirable to obtain information concerning the possible consequences of disturbances on the wildlife and to determine possible mitigation measures more quickly than is possible with this model. To meet this need, a second model was developed.
MODEL II
This model was designed to provide a very quick, but reasonable assessment of the impact that a particular action might have on wildlife.
The model is based on the number of wildlife categories that ranked in the top three in a given habitat for each season (table 1). For example, if bird species richness in cottonwood-willow II ranked second in spring, this habitat would receive a score of 1. If bird species richness in this habitat ranked third in summer, the habitat score would increase by 1. Altogether, we considered 59 wildlife-seasonal categories (Anderson and Ohmart 1984:Table 13-1). Thus, a habitat in which all wildlife categories ranked in the top three across all seasons would receive a score of
59. The total was determined for each of the 23 habitats recognized. This information was then used in a correlation analysis involving the number of cottonwood, willow, and honey mesquite trees per unit area in each of 23 habitats.
The correlation between the number of high ranks for the various faunal components and the number of cottonwood-willow and honey mesquite trees was highly significant (R=0.86, F=28.2, df
2,20, P<O.OOI). Using the regression for this correlation gives the number of wildlife categories ranking in the top three for a given habitat is 9.3110g
CW/ha + 6.31log
nM/ha +(-0.9).
10 10
The number of categories ranked 1n the top three for each habitat is programmed into the model.
The equation is used to calculate the number of categories occurring in the top three at various rates of revegetation. For example, if 40 ha of screwbean mesquite (Prosopis pubescens) IV are destroyed the loss is 7 (the number of categories ranked in the top three across a whole year). If
50% of the area is revegetated with 40 cottonwood and/or willow and 40 honey mesquite per 0.4 ha, there will be 24.1(0.5)=12 categories in the top three in the new habitat at or near maturity.
This represents an enhancement of 12/7 = 171 -100
71% •
However, the above assumes that great care will be taken in the revegetation effort and that mortality of planted trees will not exceed 9%.
Mortality decreases the value of the revegetation effort in two ways. First, any trees that die decrease the value of the mitigation effort.
However, we have found that if soil conditions
(soil type, layering, and salinity) cause the
126 death of some trees, it is unlikely that growth of the survivors will be unaffected. In fact, as mortality increases biomass production increases log linearly, which means that as mortality increases, biomass production of survivors will probably be severely restricted. Thus, a threeyear-old cottonwood 4.5 m tall has roughly 30% of the biomass of a tree of the same age that was planted under ideal conditions and is 9 m tall.
Input
The user is first asked by the model to state which of the 23 habitats is to be considered, then he is asked whether or not the proportion of the cleared area that is to be revegetated is known.
If these questions are answered affirmatively, the user is then asked to enter this information. If the questions are answered negatively, the user is asked what percent enhancement is desired.
Finally, the user is queried as to how much mortality among planted trees would be considered acceptable.
Output
If the habitat is disturbed, the proportion of the area that will be revegetated, and the mortality rate for planted trees is acceptable, the model will then provide the percent enhancement that can be anticipated. If the percent enhancement desired is known, the model will indicate the proportion of the area that must be revegetated with tree density as prescribed by the user.
MODEL III
The third model that we have developed can be used for assessing the suitability of an area for revegetation. The user must enter the depth to which tillage will be provided, the electroconductivity of the soil relative to the depth of the water table, and whether competition from weeds or grass is expected to be a problem. Output is a prediction of mean growth, percent mortality, and percent of maximum biomass production after three years of growth. This information is provided for honey mesquite, blue palo verde, cottonwood, and willow.
This information is vital for determining the potential of an area when revegetating with the tree species mentioned above. Efficacy of the model is highly dependent upon quality of input data. If electroconductivity data comes from only two locations on a 40-ha plot, it is likely that model-generated predictions will be wrong. Similarly, if large samples are taken nonrandomly, model-generated predictions may be wrong. An adequate number of soil samples taken randomly over the site are essential data inputs.
TESTS OF THE MODELS
Wildlife Models
We have been conducting experimental revegetation work on a 35-ha plot since 1977 and on a

20-ha plot since 1980. Tests of predictions generated from models I and II indicate a satisfactory capability of our models to make accurate predictions (table 1). The wildlife models have also been thoroughly tested in a project involving experimental clearing. A total of more than 250 ha were included in this study, which extended over 42 months. Model predictions were found to be relatively accurate. Results from these tests are available in Anderson and Ohmart (1982, 1984, in press).
Vegetation Model
The model for tree growth was accurate to about 0.2 m for 150 cottonwoods with tillage to 3 m, the water table at 3.6 m, a soil salinity of
1.7 EC, and competition from weeds absent. The model was similarly accurate for 30 cottonwoods under the same conditions except with plant competitors present. The model was highly accurate for 20 honey mesquite and 130 cottonwood grown under highly saline conditions. Currently, we are testing predictions for 40 willows and 35 cottonwoods under conditions of low salinity, tillage to
2.6 m, water table at 2.9 m, and with little competition from weeds. All of these tests have been conducted in the floodplain of the lower Colorado
River. A much larger test is under way along the
Rio Grande in Texas. This study includes 100 fig
(Ficus sp.), 100 fruiting mulberry (Morus sp.),
800 cottonwood, 200 willow trees, 300 quail bush
(Atriplex lentiformis), and 300 wolfberry (Lycium andersonii) planted according to experimental design under a wide range of soil and salinity condi tions. Some testing will also be done wi th the wildlife models.
CONCLUSIONS
We have studied several riparian systems in the desert Southwest over the past decade. Our studies on the Colorado River have been the most intensive and have led to the development of the above models. These models should be useful to individuals and agencies responsible for managing the riparian resource on the Colorado River. We believe that the effectiveness of our models is due mainly to the fact that our studies included sizeable samples through time and space. Similarly, the models based on experimental revegetation projects should have value in designing mitigation enhancement plans. Furthermore, if guidelines are followed (but, unfortunately, to date, they typically have not been) money and valuable time can be saved, i.e., trees will be planted only in areas where local biotic and abiotic factors are such that rapid growth, high biomass production, and high survival rate will maximize every dollar spent to insure enhancement or mitigation for wildli fe •
Our Colorado River wildlife data have less value when their results are applied to other areas. Such attempts, if made without caution, could lead to egregious errors (e.g., see Hunter et aI, this symposium). We are currently attempting to discover patterns that riparian habitats in the desert Southwest may have in common. Once we have a good understanding of these patterns, then our revegetation models will apply over a broader range of riparian habitats.
ACKNOWLEDGMENTS
We would like to thank the many field biologists whose careful census efforts provided data for these analyses. Cindy D. Zisner typed the manuscript and she and Jane R. Durham provided editorial assistance. We especially thank the
U.S. Bureau of Reclamation for their continued support of this long-termed study through Contract
No. 7-07-30-V0009.
LITERATURE CITED
Anderson, B. W., and R. D. Ohmart. 1977.
Wildlife use and densities annual report.
U.S. Bureau of Reclamation, Lower Colorado
Region, Boulder City, Nev.
Anderson, B. W., and R. D. Ohmart. 1982.
Revegetation for wildlife enhancement along the lower Colorado River. U.S. Bureau of
Reclamation, Boulder City, Nev.
Anderson, B. W., and R. D. Ohmart. 1984. A vegetation management study for the enhancement of wildlife along the lower
Colorado River. U.S. Bureau of Reclamation,
Lower Colorado Region, Boulder City, Nev.
Anderson, B. W., and R. D. Ohmart. 1985.
Riparian revegetation as a mitigating process in stream and river restoration. p. 41-80. In
J. A. Gore, ed., The restoration of rivers and streams: theories and experience, 280 p.
Butterworth Publishers, Boston, Mass.
Anderson, B. W., and R. D. Ohmart. In press.
Evaluation of the impact of vegetation removal. U.S. Bureau of Reclamation, Lower
Colorado Region, Boulder City, Nev.
Anderson, B. W., R. D. Ohmart, and W. C. Hunter.
1983. Quantifying variables for classifying desert riparian vegetation. p. 32-44. In
Proceedings of the workshop on southwestern habitat types [Albuquerque, N.M., April 6-8,
1983]. USDA Forest Service, Southwestern
Region, Albuquerque, N.M.
Meents, J. K., J. Rice, B. W. Anderson, and R. D.
Ohmart. 1983. Nonlinear relationships between birds and vegetation. Ecology
64:1022-1027.
Rice, J., B. W. Anderson, and R. D. Ohmart. 1984.
Comparison of the importance of different habitat attributes of avian community organization. Journal of Wildlife Management
48:895-911.
Rice, J., R. D. Ohmart, and B. W. Anderson.
1983a. Habitat selection attributes of an avian community: a discriminant analysis investigation. Ecological Monographs
53:263-290.
Rice, J., R. D. Ohmart, and B. W. Anderson.
1983b. Turnovers in species composition of avian communities in contiguous riparian habitats. Ecology 64:1444-1455.
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