1

REVIEW OF TERRESTRIAL MONITORING PROTOCOLS 

FOR THE GRAND CANYON 

Report of the Protocol Evaluation Panel (PEP) 

Submitted February 4, 2008 

INTRODUCTION 

Panelists

Loretta Battaglia, Assistant Professor, Department of Plant Biology, Southern Illinois 

University 

Darold Batzer, Professor, Department of Entomology, University of Georgia 

Geneva Chong, Research Ecologist, US Geological Survey, Northern Rocky Mountain 

Science Center 

David Cooper, Senior Research Scientist, Department of Forest, Rangeland and 

Watershed Stewardship, Colorado State University – Panel Chairman

Andrew Hudak, Research Forester and Landscape Ecologist, US Forest Service, Rocky 

Mountain Research Station, Moscow, Idaho 

Michael Young, Research Fisheries Biologist, US Forest Service, Rocky Mountain 

Research Station, Missoula, Montana 

Charge to Panelists 

“Review and critique the existing effort/findings and recommend long term core 

monitoring protocols/methods that will meet the established Core Monitoring 

Information Needs associated with terrestrial biological resources (Goal 6).” 

For this report, the PEP provides an assessment of past monitoring efforts and 

recommends long term core monitoring protocols to meet the Management Objectives 

and Core Monitoring Information Needs associated with terrestrial biological resources 

including vegetation and animal communities. Because the PEP identified some 

management objectives and monitoring needs that were incompatible, the PEP also 

included critiques of these sections where relevant; these are addressed in the 

introductory portions of the vegetation and animal monitoring sections. The section on 

vegetation monitoring focuses on remote sensing, in situ sampling, and analyses of both, 

whereas the section on animal monitoring reviews monitoring by taxonomic group and 

notes an assortment of monitoring design and analysis concerns. We urge that 

information obtained within Grand Canyon be compared to reference river reaches 

elsewhere in the southwest to improve the strength of inferences about the effects of dam 
2

operations. We conclude by encouraging the program to strengthen the research culture 

and develop a more open science process in which basic, peer-reviewed research guides 

monitoring efforts. The fluvial geomorphology research program is an excellent model 

to follow as it produces competition, collaboration and excellence in research vital to 

management. 

VEGETATION MONITORING 

Goal 6 and associated Management Objectives (MO) and Core Monitoring Information 

Needs (CMINs) Related to Vegetation Monitoring 

Below we reiterate the stated goal, management objectives, and information needs 

provided to the PEP before the July 2007 meeting that we used as the basis of our 

evaluation. The PEP, however, extended its critique to the management objectives and 

information needs; these issues are listed parenthetically. We recommend the 

development of a research program that supports and guides the monitoring program, 

similar to what was recommended by Urquhart et al. (2000). Activities ranging from 

sample site selection to creation of vegetation maps and models and analyses of remotely 

sensed data all benefit from execution within a research-driven framework. For example, 

one of the questions to the PEP was how frequently should vegetation monitoring occur. 

We feel that question needs to be answered using existing data in an adaptive 

management framework (i.e., as data are acquired and analyzed the recommended 

frequency could change based on improved understanding of the system. Alternatively, 

the frequency could be altered based on hypothesized responses to event such as 

experimental high flows, wildfire, drought, non-native species removal, etc.). 

 

GOAL 6: Protect or improve the biotic riparian and spring communities within 

the Colorado River ecosystem, including threatened and endangered species and 

their critical habitat. (PEP comment: The spring communities are not directly 

influenced by dam operations and management, and we recommend the removal 

of this community from discussion. The threatened and endangered species 

appear to be monitored by other entities, so they should also be removed from this 

discussion, but the GCMRC should collaborate with these other entities for 

needed data or analyses.) 

MO 6.1: Maintain marsh community abundance, composition, and area in the 

Colorado River ecosystem in such a manner that native species are not lost. 

CMIN 6.1.1: Determine and track the abundance, composition, distribution, and 

area of the marsh community as measured at 5-year or other appropriate intervals 

based on life cycles of the species and rates of change for the community. 

(PEP comment: Many marsh communities in the Grand Canyon are largely an 

artifact of river regulation. The reduced peak flows and higher base flows have 

created relatively stable hydrologic conditions favoring marsh development, and 

their overall abundance has likely increased relative to the pre-dam era. The PEP 

recommends that managers reconsider maintenance of these anthropogenically 

created habitats as a priority.) 
3

MO 6.2: Maintain New High Water Zone (NHWZ) community patch number, 

distribution, composition, and area to be no lower than values estimated for 1984. 

CMIN 6.2.1: Determine and track the patch number, patch distribution, 

composition, and area of the NHWZ community as measured at 5-year or other 

appropriate intervals based on life cycles of the species and rates of change for the 

community. 

(PEP comment: the maintenance of a number of patches and distribution does 

not appear realistic given the current nature of the riparian system. For example, 

variation in magnitude of management flows may not be sufficient to maintain a 

dynamic riparian system; hydrologic stabilization and flow reduction have 

allowed dominance of non-native species such as tamarisk and annual grasses. 

These non-native species may pose significant risks to the structure and function 

of the native riparian community (e.g., increased risk of fire, competition for 

space, and stabilization of soil features that were historically more dynamic). 

Thus, the PEP suggests that tamarisk is not a desirable component to be 

maintained in the NHWZ. Detection, eradication, and monitoring for non-native 

species should be incorporated into the vegetation sampling and monitoring 

scheme. The PEP recommends vegetation restoration, including tamarisk (and 

other invasive non-native species) removal. If actions are taken to restore native 

species composition and structure, monitoring should be directed to evaluate the 

effectiveness of such actions for use in an adaptive management framework.) 

MO 6.3: Maintain Old High Water Zone (OHWZ) community abundance, 

composition, and distribution in the Colorado River ecosystem. 

CMIN 6.3.1: Determine and track the abundance, composition, and distribution of 

the OHWZ community as measured at 5-year or other appropriate intervals based 

on life cycles of the species and rates of change for the community. 

(PEP comment: Under the present flow regime, this goal is not attainable without 

planting, irrigation, and land management. The older mesquite and tamarisk in 

this zone will die within the coming decades and there is no recruitment of these 

species. This zone could only be maintained “naturally” by managed floods 

exceeding 90,000-100,000 cfs. If actions are taken to restore native species 

composition and structure (again, emphasis on native species), monitoring should 

be directed to evaluate the effectiveness of such actions for use in an adaptive 

management framework.) 

MO 6.4: Maintain sand beach community abundance, composition, and 

distribution in the Colorado River ecosystem at the target level. 

CMIN 6.4.1: Determine and track the abundance, composition, and distribution of 

the sand beach community as measured at 5-year or other appropriate intervals 

based on life cycles of the species and rates of change for the community. 

(PEP comment: Historically the sand beaches were essentially bare, maintained 

by high spring flows, and lacked developed vegetation. The PEP recognizes that 

determining an appropriate “target level” is challenging in a system that has 
4

moved to an alternative, stable state. Maintaining some of the features of the predam system is desirable; where feasible, some vegetation reduction may be 

warranted on beaches. If actions are taken to reduce/remove vegetation, 

monitoring should be directed to evaluate the effectiveness of such actions for use 

in an adaptive management framework.) 

MO 6.5: Reduce invasive non-native species abundance and distribution. 

CMIN 6.5.1: Determine and track the abundance and distribution of non-native 

species in the Colorado River ecosystem as measured at 5-year or other 

appropriate intervals based on life cycles of the species and rates of change for the 

community. 

(PEP comment: Because of the large effort necessary, natural resource managers 

should determine which nonnative species can be controlled or eradicated, and 

prioritize where such efforts will be directed. Tamarisk is highly invasive in the 

Grand Canyon and can change ecosystem functioning; the PEP recommends that 

reduction of this species should be a high priority. Tamarisk eradication efforts 

are ongoing in the Grand Canyon but have not targeted the Colorado River 

corridor. If actions are taken to reduce and control non-native species, 

monitoring should be directed to evaluate the effectiveness of such actions for use 

in an adaptive management framework.) 

MO 6.6: Maintain seep and spring habitat in the Colorado River ecosystem 

CMIN 6.6.1: Determine and track the abundance, composition, and distribution of 

spring and seep communities as measured at 5-year or other appropriate intervals 

based on life cycles of the species and rates of change for the community. 

(PEP comment: These habitats are largely independent from dam operations, and 

it appears that other agencies are now targeting seeps and springs in the Grand 

Canyon. We recommend that sampling for this CMIN be discontinued by 

GCMRC, and instead rely on information from other agencies.) 

MO 6.7: Maintain riparian habitat in the Colorado River ecosystem capable of 

supporting southwestern willow flycatcher. 

CMIN 6.7.1: Determine and track the abundance, distribution, and reproductive 

success of southwestern willow flycatcher in the Colorado River ecosystem. (PEP 

comment: It appears that other agencies are now targeting the southwestern 

willow flycatcher in the Grand Canyon. We recommend that sampling for this 

CMIN be discontinued by GCMRC, and instead rely on information from other 

agencies. Please also see the comment under Animal Monitoring.). 
5

Terrestrial Monitoring Overview 

We evaluated efforts by Kearsley et al. (2006) to address CMINS associated with 

vegetation monitoring related to Goal 6. Work conducted by Kearsley et al. from 2001 

through 2003 was positively influenced by an earlier review panel (Urquhart et al. 2000). 

Urquhart et al. (2000) made the major recommendation to develop a comprehensive 

monitoring strategy with four components: (1) endangered species assessments; (2) 

monitoring for model development; (3) inventory of plants and animals; and (4) longterm monitoring of the main-stem corridor. Seven additional, specific recommendations 

were: (1) provide scientific direction to the monitoring program; (2) require 

integration/cooperation across disciplines through the RFP process; (3) ensure long-term 

comparability of data through protocol development and training; (4) incorporate a longterm perspective into monitoring plans and methods; (5) develop a terrestrial ecosystem 

model with the same detail as the current aquatic model; (6) determine appropriate level 

of activity related to the supposed Kanab Ambersnail; and (7) prioritize completion of a 

GIS and a land cover map, a random sampling approach, cost effective response 

indicators and protocols, and protocols to ensure inter-disciplinary cooperation. 

From one perspective, Urquhart et al.’s (2000) recommendations were made to the 

Biological Resources Program overall, and we did not receive sufficient information or 

requests to evaluate the Program as a whole. However, some recommendations appear to 

require attention, particularly in relation to developing a research approach to monitoring. 

For example, major recommendation (2), monitoring for model development, still needs 

to be addressed most fundamentally through a research-driven monitoring program, 

which was specific recommendation (1), above. Other specific recommendations (e.g., 5: 

develop a terrestrial ecosystem model) would be greatly facilitated by focusing on 

specific recommendation (7), completion of a GIS. Therefore, even though we were 

mainly tasked with reviewing Kearsley et al. (2006), we feel it is critical that the 

recommendations from the 2000 PEP not be forgotten. 

Kearsley et al. (2006) have responded to the majority of the recommendations in the 2000 

PEP. For example, Kearsley et al. (2006) were unable to select their sampling locations 

based on a vegetation base map, as recommended by Urquhart et al. (2000), because that 

work did not commence until 2002, and it has not been finalized at the writing of this 

review (Ralston et al. in preparation). In the following discussion related to terrestrial 

vegetation monitoring, we discuss recommendations to address three related needs for 

CMINs 6.1.1 – 6.5.1: 

1) Vegetation mapping: develop a repeatable, accurate approach to create vegetation 

maps that can be compared over time to monitor changes in area and location of 

vegetation types and to provide a base layer for designing terrestrial monitoring and 

analyzing terrestrial monitoring data. 

2) Terrestrial vegetation monitoring: develop a repeatable, accurate approach to quantify 

changes in native and non-native plant species composition and structure (cover and 
6

height) and their interactions with other taxa, and provide ground-truth data for 

vegetation mapping. 

3) Habitat analyses: develop a repeatable, accurate approach to quantify interactions 

between abiotic and biotic habitat characteristics and associated vertebrate and 

invertebrate animals or taxonomic groups of interest. 

Urquhart et al’s (2000) and Kearsley et al.’s (2006) recommendations for improved 

terrestrial inventory and monitoring methods included the development of a complete 

vegetation base map that would facilitate their second recommendation of random 

sampling. Ralston et al. (in preparation) describe the objectives for use of one such base 

map: “The primary objective of our current mapping project was to develop a digital 

inventory map of vegetation to enable patch-scale and landscape scale change detection, 

and to establish randomized sampling points for ground surveys of terrestrial fauna 

(principally but not exclusively, birds).” Once this initial map is finalized, we agree that 

future vegetation data should be collected to allow comparisons with previously collected 

data, but we do not believe that the same methods should be used if better and more 

widely used ones exist. 

Vegetation Mapping 

A system for creating vegetation base maps is needed to quantify changes in location and 

area of vegetation types over time. We believe the process and database (map) described 

in Ralston et al. (in preparation) provide an adequate baseline to guide near-term 

sampling efforts and quantify the extent of several vegetation types at the time the data 

were collected. However, changes in available technology since that work was initiated in 

2002, and changes expected in the future, remind us that the vegetation mapping process 

is not static, and improvements should be made continuously. For example, it may be 

more appropriate to sample in the same way as the National Park Service and map 

canyon vegetation as part of the Grand Canyon National Park vegetation mapping project 

(e.g., http://www1.nature.nps.gov/im/units/scpn and 

http://biology.usgs.gov/npsveg/index.html). In addition, other methods may be more 

suitable for correlations with and corrections of remotely sensed data (see remote sensing 

discussion, below) or for analyses of vegetation communities (see statistics discussion, 

below). Improvements can even be made in how existing data are analyzed and 

interpreted. The following review provides guidance regarding the remote sensing 

components of a vegetation mapping program. 

Remote Sensing Component

The Grand Canyon presents a challenging environment for remote sensing applications, 

given the extreme topography. As such, the utility of remote sensing for monitoring must 

first be tested within the research realm. The logistical difficulty of monitoring terrestrial 

ecosystem processes on the ground makes remote sensing an appealing tool for 

decreasing reliance on field work, although no remote sensing tool should ever be 
7

considered a complete substitute for field-based monitoring. The goal should be to 

leverage the remote sensing data to add value to the field data, to the extent possible. 

The two types of remotely sensed data that have been tested for monitoring capability in 

the Grand Canyon have been airborne high resolution digital imagery and airborne 

discrete-return light detection and ranging (lidar) data. Airplane pilots can plan their 

flight lines to coincide with times that the canyon will be sunlit. A minor complication 

with sensors on fixed-wing aircraft, as with passive optical imagery, is in having to 

mosaic multiple straight flight lines arrayed along the twisting path of the river (e.g., 

Ralston et al., in preparation). The idea of using a helicopter-based platform was 

suggested during the panel discussion, and should be explored. A helicopter could 

efficiently follow the winding route of the canyon, or hover above monitoring sites of 

interest for higher density sampling. This would efficiently reduce the flight time and 

data required to produce complete coverage, and probably cost as well. We do not see 

much potential for current space-borne platforms in the Grand Canyon because the spatial 

resolution of most satellite sensors is too coarse, and, even the more fine resolution 

sensors such as IKONOS and Quickbird, are constrained by the narrow time window of 

satellite overpass, which would result in large portions of the canyon being in shadow. 

We recommend evaluating the use of helicopter-mounted lidar with simultaneous 

acquisition of digital imagery during optimum lighting conditions. 

The use of hyperspectral imagery was also mentioned during discussions, but 

hyperspectral data tend to be expensive to acquire and analyze. As with any passive 

optical imagery, data collected along flight lines obtained at different times of the day 

would have varying orientations with respect to the sun angle and thus different albedos. 

This would be a formidable problem in the Grand Canyon that would require advanced 

techniques to correct (Kennedy et al. 1997). If this source of variation between flight 

lines can be effectively removed in the calibration-to-reflectance process, then 

hyperspectral imagery, with upwards of 200 contiguous bands spanning the visible, near 

infrared, and short-wave infrared (SWIR) regions of the electromagnetic spectrum, would 

allow more subtle discrimination of vegetation types than is possible with only the blue, 

green, red, and near infrared bands that typify most digital imagery. This could be useful 

for producing a more detailed vegetation map because varying amounts of lignin in plant 

tissue causes variation in the SWIR that can allow species-level differentiation of some 

species in some systems. Distinguishing tamarisk from mesquite, marshes, and the other 

vegetation types should be easy, while species-level discrimination would be hard. 

Spectral mixture analysis is probably the most robust method available for estimating the 

fractional cover of the response variables. It is attractive because it is applied at the 

subpixel level, providing a direct estimate of fractional cover that is a biophysical 

variable, which is more intuitive, interpretable, and useful to the user than, say, a 

vegetation index (e.g., NDVI) that must be related to the cover attribute of interest, with 

an added error component. 
8

Airborne Imagery 

The Ralston et al. (in preparation) vegetation communities selected as classes for 

mapping seem reasonable based on the data collected from the field, and we liked the 

cascading series of criteria, including fuzzy logic, for assessing classification accuracy. 

However, we think it is important to decide and define what level of classification 

accuracy is required to meet monitoring objectives. While the fuzzy logic may increase 

accuracies for the purpose of a report, it may not be helpful for satisfying actual 

monitoring requirements, and we address preferred alternatives for plot size and location 

and data analyses in the vegetation sampling section. 

We had difficulty interpreting Figure 1 in Appendix A: Image Processing (Ralston et al., 

in preparation). We are skeptical that the “synthetic NIR” band can realistically replace 

the unfortunately saturated NIR band. Leaf cell water content is the primary factor 

causing high reflectance in the NIR, while chlorophyll absorption drives reflectance in 

the red and green channels. We would expect this relationship is nonlinear, not linear as 

is the NIRpan transformation equation at the bottom of p. 52. Is there a reference that 

could be cited? Is there other imagery available that could be employed besides these data 

with the miscalibrated NIR channel? We have the impression (from our visit, not from 

Ralston et al.) that quite a bit of remote sensing data have been accumulated by GCMRC 

(or related entities) within the past decade, and it would be beneficial to evaluate if any 

historical data are useful given currently available analysis techniques. 

We liked the use of texture images as additional bands. We think there is good potential 

to improve classification accuracies further by incorporating topographic, geomorphic, 

and vegetation zone (OHWZ, NHWZ) variables as predictors in a classification scheme. 

RandomForest is a powerful classification tool that bootstraps the data to produce a more 

robust model. The topographic predictors could be extracted from lidar data if a better 

lidar data processing stream was followed (which we’ll discuss next). So we think there 

is a great opportunity here for integrating image and lidar data into producing a baseline 

vegetation classification map and formulating a remote monitoring strategy. 

Airborne Lidar 

The extreme topography in Grand Canyon makes any remote sensing problematic, but 

increases the appeal of lidar, especially to preclude the effects of shadowing from the sun. 

The positional accuracy of the 2005 lidar data is less than desired, or required for 

geomorphological monitoring. We believe some of these issues are related to processing. 

First, the ground height estimated by lidar appears lower (Fig. 2; Ralston et al in 

preparation) than estimated by the total stations used for independent validation of 

ground height. We believe this discrepancy may be the result of a vertical datum 

mismatch, since it says in the Methods that the NAD83 GPS observations were not 

converted to NAVD88. This is not something that can be accomplished in ArcInfo. Two 

programs that can be used for converting vertical datums are CORPSCON from the 

Army Corps of Engineers (http://crunch.tec.army.mil/software/corpscon/corpscon.html) 

and VERTCON from the National Geodetic Survey 
9

(http://www.ngs.noaa.gov/TOOLS/Vertcon/vertcon.html). Another issue is the 

classification of points as ground or vegetation returns. The deconvolution of ground and 

vegetation in topographically complex terrain is difficult. For instance, where dense 

vegetation cover occurs, there may be over-classification of vegetation returns as ground 

returns. This would lead to erroneously high estimates of the ground surface, and thus 

underestimation of vegetation height. Thus, poor estimation of ground height is 

inextricably linked to poor estimation of vegetation structure. However, we doubt that the 

vegetation is too thick for there to be adequate ground returns, because sufficient canopy 

penetration has been demonstrated in high-biomass forests with much higher leaf area 

index than could be approached in the arid Grand Canyon ecosystem, even in the thickest 

tamarisk stands. 

Different lidar vendors use different proprietary algorithms to accomplish the necessary 

task of filtering vegetation returns, which is prerequisite to producing a bare-earth digital 

elevation model. The GCMRC apparently did not receive a bare-earth DEM from the 

vendor, which is very surprising, and strongly suggests poor classification of the ground 

returns (the recently published Evans and Hudak (2007) algorithm (which runs in 

ArcInfo) may be worth using). We suspect that the existing lidar data are adequate for 

creating a more useful bare earth surface model, for example, with improved analysis 

approaches, and we recommend exploring these options further to benefit more fully 

from the initial cost of the data acquisition. 

Summary and Recommendations Regarding Remote Sensing 

It is unrealistic to expect that, if only the spectral resolution were finer, individual species 

could be identified, or if only the spatial resolution were finer, individual plants could be 

quantified. The major emphasis for monitoring should remain on the field data 

component, which we discuss in the next section. Topographic (e.g., elevation, slope, 

aspect), geomorphic, and other predictor variables (e.g., solar insolation) explain much of 

the variability in observed species distributions, as quantified in the field data. There are a 

variety of predictive modeling and mapping tools (e.g., RandomForest, Breiman 2001, 

Breiman et al. 2006), general additive models (GAMS; Moisen and Trescino 2002), and 

imputation (Moeur and Stage 1995, Ohmann and Gregory 2002) that are well-suited for 

exploiting these relationships between dependent response variables gathered at discrete 

points in the field and independent predictor variables derived from spatially continuous 

remote sensing band layers, topographic layers, etc. It is critical that the field data be 

accurately geolocated, to minimize errors due to misregistration between the field and 

remotely sensed data. Time of acquisition of field and remotely sensed data should also 

match as closely as possible. However, since phenological variation is minimal in the 

Grand Canyon, temporal misregistration should not be as problematic as spatial 

misregistration. 

Legacy data. We recommend that the GCMRC make a concerted effort to build upon 

available legacy information for establishing monitoring sites in the field (for both 

remotely sensed and ground samples), and for developing vegetation and topographic 
10

maps (reanalysis of the lidar data). Historical aerial photo data that predate the Glen 

Canyon dam do exist. The earliest photos were acquired in the mid-1930s. Many of these 

are too shadowed to be of any use for even visually resolving vegetation at the bottom of 

the canyon. However, a few vegetated sites are visible, and may even indicate tamarisk 

invasion at this time (we know that tamarisk established prior to Glen Canyon Dam 

construction). There are also 1952 photos from Glen Canyon that should be leveraged 

when establishing monitoring sites within this reach. Dr. Jack Schmidt and his students 

and staff already have orthorectified the historical aerial photos at select sites within each 

reach. These sites were selected to meet geomorphic research or monitoring objectives, 

meaning they should not be expected to bias any vegetation sampling (or arthropods, 

terrestrial vertebrates, etc.). Biological monitoring sites should be linked to 

geomorphological monitoring sites to the largest extent possible to facilitate ecosystemlevel study. Otherwise, it is much more difficult to relate the biological and ecological 

processes to the physical processes that have historically driven the functional dynamics 

of the Grand Canyon ecosystem. Choosing sites based on existence of legacy data need 

not compromise the landscape-level inference afforded by the generalized randomtessellation stratified (GRTS) sampling strategy (recommended by Urquhart et al. 2000), 

as geomorphic reach is an important stratification variable that should be employed, and 

priority should be given to sites within each reach where historical data exist. 

While we approve of scanning legacy photos at the native resolution of the scanner (10 

microns) for archiving purposes, it is probably overkill to map or monitor at such a high 

resolution. Extremely high resolution can create as many or more problems than it solves. 

From an analytical standpoint; the finest resolution is often not the appropriate resolution. 

Semivariogram analysis can objectively inform the decision of what is the practical and 

useful spatial resolution for mapping and monitoring. Field plots will be sufficiently 

spread out along the canyon that spatial autocorrelation will not be a concern. However, 

spatial autocorrelation is a property in the imagery that can be exploited to improve 

vegetation mapping. It would be useful to extract the pixel values from within the 

original TWINSPAN polygons representing the vegetation classes of interest for 

monitoring (because the TWINSPAN vegetation types have been verified in the field), 

and generate semivariograms from them. The range (lag distance of the sill) informs the 

user of the scale of spatial structure captured by the imagery. If the image resolution is 

sufficiently fine, then the spatial scale of the structural attribute of interest can be 

characterized from the imagery, and related to that attribute as measured in the field, for 

mapping. For example, if you can see individual tamarisk trees in the imagery, then the 

range in a semivariogram generated from these pixels will indicate at what scale tamarisk 

trees are spaced or clumped. This knowledge helps guide the choice of texture filter 

window sizes to use to highlight structure attributes of interest (Hudak and Wessman 

1998). 

There are known problems associated with TWINSPAN (see statistical section below), 

and the PEP cautions that the continued use of polygons derived from these previous 

analyses could present problems. We recommend that an appropriate alternative analysis 

be identified and the data used in previous analyses be reanalyzed and compared to the 

TWINSPAN output. We strongly recommend that this step be taken before proceeding 
11

with products from analyses that may be flawed. If there are no major discrepancies 

between the sets of output, then switching over to the new technique(s) should be 

relatively simple. If the different methods do suggest different results, the program may 

need to re-evaluate the original delineations and conclusions they may have drawn from 

these methods. 

These issues with data analysis highlight the need for a more active research arm of the 

monitoring program. If the research program is run in parallel with monitoring, there can 

be a positive feedback loop whereby monitoring is both guided by and contributes to 

research. Analysis of data (especially those already collected), peer-review, and timely 

publication of results are necessary steps in this process. This synergism would benefit 

the overall program and would help to ensure that methods employed, analysis, and 

conclusions of the monitoring efforts result from periodic feedback from the larger 

scientific community. 

Tom Gushue, who handles the GIS and historical aerial photo library, showed us the 

polygon coverages from the TWINSPAN plots of Kearsley. Another coverage should be 

built showing the polygons where 1930s or 1952 historical aerial photos of vegetated 

sandbars exist along the canyon. The locations of Webb’s comparative photo points 

should also be assembled into a GIS coverage, and incorporated into the GRTS sampling 

design to the extent possible. Jack Schmidt’s geomorphic sites overlap 5 of the 9 

TWINSPAN sites, upstream of river mile 90, with just one site per geomorphic reach. 

Exact study site or plot locations would need to be randomly placed within the legacy 

polygons to preclude sampling bias. The cumulative number of all sites with some form 

of image legacy data will be small and thus easy to weave into the larger pools of sites 

selected following the GRTS sampling design. There are substantially more legacy sites 

with ground plot data, and new sites (where there is no legacy information) could be 

viewed as supplementary plots to fill out the GRTS design once all existing plots are 

located in a GIS. 

Plot size (mapping). While a vegetation plot may be representative of the polygon 

sampled, it is another matter to assume that vegetation structure and composition do not 

vary spatially within the polygon. Image pixel variation within a polygon can approach, 

but should not exceed, the pixel variation between polygons. When it comes to 

empirically relating field plot data to remotely sensed data, the plot scale of measured 

data inputs for the attributes of interest should be commensurate with the scale of 

outputting those attributes into a map. Otherwise, you are faced with a scale mismatch 

that will compromise the accuracy of the vegetation classification. Let the size of the plot 

be determined by considerations on the ground. It is advantageous if the plots are large 

enough to include a number of image pixels. Now, you have a number of pixels (and their 

associated variability) to relate to your field data. The mean of these pixels aggregated 

within the plot footprint will correlate better with the field attribute of interest than just 

the pixel value at plot center would. 
12

GRTS Sample Design 

Many monitoring programs are based upon investigator chosen sites that are felt to 

represent the resources being analyzed. However, Urquhart and Kearsley (unpublished 

manuscript) argue that there are a number of problems with this approach. A probability 

based sample may yield a better picture of the status and trends of the resource. The 

distribution of sample plots should be spatially balanced to incorporate a broad range of 

measurement conditions. One problem that arises with this approach is that the sample 

frame will include sites that cannot be sampled due to safety, inaccessibility or other 

reasons. In addition, sample locations must be tied to the major stressors or drivers of 

resource abundance or distribution. 

For the GCMRC Urquhart and Kearsley used a one dimensional generalized randomtesselation stratified (GRTS) design (Stevens and Olsen 2004) to choose transect 

locations, with the goal of producing a spatially balanced probabilistic sample to collect 

vegetation cover and species richness data. The GC was divided into 702 segments, as 

opposed to a continuous length. Sample sites were chosen from the 702 segments, and 

then a random point was selected within each segment for sampling. A total of 150 points 

were selected for use, and transects were established in these locations. In Urquhart and 

Kearsley (unpublished manuscript) a total of 100 sample transects was identified, and 

transects were classified as being within “wide” or “narrow” canyon reaches based upon 

gross geologic characteristics of the Grand Canyon. Narrow reaches have granite or 

limestone bedrock which is resistant to widening, while the wide reaches have shale and 

other softer bedrock that is more erosive. Urquhart and Kearsley also acknowledge that 

on a reach scale the geomorphic characteristics of the river and fluvial deposits are 

controlled by debris fan and eddy complexes. However, the sample sites were not chosen 

with stratification by geomorphic feature or landform. It is unclear whether the sample 

transects are identified as being within debris fans, pools, eddys or bars, and what the 

distribution of transects are within these four major fluvial landforms. 

The panel encourages the close integration of riparian ecological monitoring and 

geomorphic research and knowledge about the Grand Canyon. The importance of faneddy complexes for controlling plant establishment and overall vegetation patterns is 

paramount in river canyons with fan-eddy complexes (Stevens et al. 1995, Cooper et al. 

2003, Birken and Cooper 2006). It is unclear whether the “narrow” and “wide” 

dichotomy captures the larger landscape scale variation in geomorphic, climate and other 

processes that would affect vegetation composition. This should be tested with the data. 

Likely the canyon bottom or canyon rim to rim width is a continuum through the Grand 

Canyon. In addition, the local stream gradient that affects the riparian vegetation is 

controlled by the transect position within the debris fan-eddy complex, and the 

geomorphic environment of each transect should be fully understood. This could be 

quantified by measures of canyon width in the area of each study transect, and also an 

analysis of river gradient, or geomorphic landform. We recommend that the geomorphic 

setting within fan-eddy complexes be verified for each transect and an effort made to 

have an adequate sampling of the vegetation in debris flows, eddy’s, pools and riffle 

environments. In addition, future analyses of vegetation composition and change should 
13

be stratified by geomorphic setting as well as elevation above the river. The vegetation 

of pools will be more similar to each other between wide and narrow canyon reaches, or 

at different points in the Grand Canyon, than the vegetation of pools and debris flows 

within the same river reach. 

Terrestrial Vegetation Monitoring 

Vegetation monitoring provides an assessment of the impacts of Glen Canyon Dam on 

downstream terrestrial systems (flora and fauna) of the Grand Canyon. Vegetation 

sampling plots will be located at study sites, selected in accordance with the GRTS 

scheme, but further distributed within additional strata (e.g., water zones described 

below) and in conjunction with sampling other taxa (e.g., arthropods, small mammals, 

and birds). We commend Kearsley et al.’s (2006) approach, and we recommend that their 

study sites and legacy data continue to contribute to future sample site location selection. 

In the review below we present options for improving future sample site and plot 

location, and provide a specific evaluation of Kearsley et al.’s (2006) statistical analyses 

of their vegetation data and some recommended alternatives to TWINSPAN for Ralston 

et al. (in preparation). Where feasible, the PEP encourages use of vegetation monitoring 

data as additional ground-truthing information to support vegetation mapping efforts. 

Sampling Frequency 

The decision of what is the appropriate temporal sampling frequency should be defined 

by objective analyses of the available historical data, as well as what is practical and 

useful. At the minimum, there needs to be some consideration of the rate of vegetation 

change since dam operations commenced, relative to the ability to detect that change at a 

given sampling interval. If, for example, woody cover can be estimated in the field to an 

accuracy of +/- 5%, while the rate of detectable woody cover change is 1%/year, then it 

makes little sense to monitor woody cover change annually. More could be gained by 

resampling 100 reference plots every 5 years than by resampling 20 reference plots every 

year, for the same effort. Thus, the PEP recommends a sampling cycle guided by 

research. For example, research could demonstrate a fixed appropriate frequency (e.g., 5 

years), however, if events occur that accelerate changes (e.g. prolonged drought, flood 

release, etc.), then sampling should be conducted opportunistically. Further, the PEP 

strongly recommends that planned high flow events be bracketed before and after by 

sampling. Additional off-cycle sampling in the first and second post-event years will also 

be required to capture lags in response, for example. Determination of sampling 

frequency is a critical component for research. 

Plot Location 

New study sites should be located based on the GRTS discussion, above. If the GCMRC 

should permanently mark GRTS study sites, they should be marked with two pins; one at 

the highest elevation and a separate benchmark. Relocations should be made using 

surveying. At each study site, sample sites should be located based on existence of 
14

legacy data (spatial and plot) if any exist. For example, Kearsley et al. (2006) established 

sampling sites based on three zones: the old high water zone (OHWZ, ≥ 90,000 cfs stage 

elevation), the new high water zone (NHWZ, approximately 25,000 - 90,000 cfs), and 

shoreline area (water’s edge to the 25,000 cfs stage elevation). These zones are meant to 

represent the hydrologic gradient imposed by stream flow. These sites and strata are still 

useful for future sampling because of their legacy data. 

However, it is unclear whether designation of these zones (and the 15k, 25k, 35k, 45k, 

and 60k cfs stage elevation points for the Kearsley et al. (2006) vegetation dynamics 

study) takes into account stage elevation changes that may occur from scouring and 

sediment deposition during high flow events. If this is indeed an omission, it may be a 

problem for interpreting patterns of change. Particularly for stage elevation classes (e.g., 

shoreline and NHWZ) where the elevations may change dramatically between 

consecutive sampling periods, the contributions of sediment inputs/outputs should be 

captured and related to any shifts (or non-shifts) in the vegetation. Study sites should be 

randomly selected x-y points, but the actual ground location of plots in a study site (e.g., 

the distance from the water’s edge - the z dimension) should be allowed to vary as 

sediment is eroded or deposited and the shoreline moves; however the same x and y 

points should be sampled each year. The Kearsley et al. (2006) approach sampled points 

for vegetation based upon an elevation, which with sediment deposition could shift the 

points up or down the slope, resulting in a different location for plots in subsequent years. 

For long-term monitoring we suggest monumenting study site centers with rebar or PVC 

so that they need not be relocated at each visit. Where it is feasible for plot data to be 

used in conjunction with remotely sensed data, plots need to be accurately geolocated 

(e.g., accurate to within half the diameter of the plot). Plots do not need to be 

monumented for purposes of ground-truthing, but plots selected for repeated sampling as 

part of the GRTS design do. 

The hydrologic gradient is often steep in the canyon and therefore the zones may be 

spatially compressed at some sites but more spread out at others. There is some concern 

that the vegetation plots may encompass more than one zone at some sites. For example, 

in the vegetation structure and composition chapter of the Kearsley et al. report (2006), 

the plots were of 3 m radius around the pitfall traps. Can we be assured that the plots did 

not straddle more than one zone? This sampling issue is less of a concern in the 

vegetation dynamics study where a 1m x 1m sampling frame is positioned at a given 

stage elevation class using surveying techniques. Vegetation within the frame is then 

sampled at each stage class, and the frame is moved along at the same elevation parallel 

to the river such that two samples are taken on the upstream side and two are taken on the 

downstream side of the river to OHWZ transect line. The resulting 4m strip of 1m2

 plots 

is used to characterize that particular stage elevation class. Although these strips seem 

appropriate for sampling vegetation in narrow stage elevation classes (see additional 

Recommendations Regarding Vegetation below) characteristic of steep riparian gradients 

in the corridor, a summary of the elevation changes along each of the river to OHWZ 

transects would be useful for interpretation. Some evidence should be given that the same 

elevation classes are being sampled across sites and that the plots are not encompassing 

different elevation ranges. 
15

Statistical Analysis 

Statistical analysis by Kearsley et al. (2006) using ANOSIM is an appropriate technique 

for comparing species composition across years or among zones (or among major fluvial 

landforms if desired). The main limitation of this technique and others similar to it (e.g., 

Multi-response Permutation Procedures- MRPP) is that interactions between the factors 

cannot be tested. In this case, interactions between zones and years may very well be 

important to assess for repeat panels. An alternative is to use the permutational 

dissimilarity-based method called NPMANOVA (Anderson 2001). The approach is 

similar to ANOSIM in that a variety of dissimilarity measures can be used as the 

foundation, but it enables testing of more complex factorial and nested models when 

using multivariate (compositional data). Programs for implementing this method are free 

and available for downloading on Dr. Marti Anderson's website 

(http://www.stat.auckland.ac.nz/~mja/). An add-on will soon be available as part of the 

user-friendly software package PRIMER. 

Comparisons between zone assemblages over time are needed to fully evaluate 

compositional trends. There may be differences among zone assemblages, even if longterm changes in the environment are occurring. For example, if there is a drying trend, 

we might expect all zones to have a consistently more xeric species composition, 

although the NHWZ may become more similar to the historic OHWZ with time. 

Directionality in composition should be investigated as part of the long-term monitoring 

effort, in addition to simply testing for differences among the zones/years. For example, 

is the OHWZ becoming more xeric and increasingly dissimilar to the other zones and its 

historic condition? Is the NHWZ becoming less dissimilar to the historic OHWZ (i.e., 

convergence)? We recommend coupling the tests for differences among zones/years with 

an evaluation of the trajectories of these zones through time using non-metric 

multidimensional scaling. 

The diversity analysis appears to include both native and non-native plant species. As one 

of the management objectives is to reduce non-native species, a separate diversity 

analysis with just natives is warranted. Non-native species richness should not be 

included in measures that might be considered positive indicators of ecosystem integrity. 

While it is recognized that tamarisk is considered to be an important component of this 

somewhat novel community, especially in terms of habitat structure for birds (e.g., 

willow flycatcher), including non-natives in measures of community health conflicts with 

established management objectives. 

Two-way Indicator Species Analysis (TWINSPAN), a divisive hierarchical clustering 

technique, is being used for the initial classification of plant communities for the 

vegetation mapping. The foundation of TWINSPAN is Correspondence Analysis (CA), 

and the TWINSPAN classification is derived by dividing up the CA ordination over and 

over. The CA ordination technique is fraught with problems, particularly with complex 

datasets that have more than one underlying gradient (McCune and Grace 2002). The 

"arch effect" is an artifact of the method whereby positions of sampling units (SUs) are 
16

distorted along axis 2 in CA ordination space, and distances between SUs are compressed 

at the ends of the arch but stretched in the middle (McCune and Grace 2002). The 

underlying dissimilarity measure in CA is chi-squared distance, which has been shown to 

exaggerate differences between SUs containing rare species (Minchin 1987). For these 

reasons, CA and TWINSPAN are not recommended (McCune and Grace 2002). 

Therefore, we recommend that all use of this technique for the vegetation mapping and 

ground-based plot sampling be terminated. 

If a hierarchical clustering technique is desired for the vegetation mapping work, we 

recommend using agglomerative cluster analysis with Sorenson’s distance measure and 

Ward’s method for linkage (McCune and Grace 2002). An ordination technique such as 

non-metric multidimensional scaling (Minchin 1987, McCune and Grace 2002) could be 

used as an alternative tool to explore compositional patterns in the vegetation and to 

determine assemblage groups without the assumption of hierarchical structure. 

Recommendations Regarding Vegetation 

Vegetation maps should be developed based on remotely sensed data and other spatial 

layers (e.g., topography and geomorphology). Resolution will need to be fine enough to 

capture patches of a minimum size of all vegetation types of interest, and this resolution 

will need to be decided on by the users/stakeholders (e.g., Stohlgren et al. 1997). The use 

of TWINSPAN for determining vegetation classes should be discontinued in favor of one 

of the alternative multivariate methods suggested above. We understand that this work is 

ongoing, and the use of a new statistical approach may pose challenges at this point, but it 

is highly recommended. 

For ground-truthing the current vegetation map, B. Ralston (personal communication) 

recommends the use of 5 x 20 m transects for assessing broad vegetation classes 

(locations based on appearance, not surveying). For vegetation types that occur in larger 

patches (e.g., OHWZ, NHWZ, marshes and sandbars) consider using the multi-scale, 

circular plot developed in conjunction with the USDA Forest Health Monitoring Program 

(e.g., Barnett et al. 2007). Plots should be placed randomly within the vegetation zone at 

the study site, and plot center should be GPS located for correlation with remotely sensed 

data. If it is difficult to obtain GPS locations, survey the location from any known 

benchmark. 

Once the first vegetation map is completed (e.g., Ralston et al. in preparation), the timing 

of subsequent mapping efforts should be planned to coincide with events expected to 

result in changes (e.g., sufficiently post-flood to expect changes to have occurred). If 

there are no suitable events, then mapping could be repeated on a cycle based on 

expected changes in vegetation (e.g., 5 years mentioned in the CMINs and confirmed by 

B. Ralston, personal communication). 

For vegetation monitoring, we accept the methods in Kearsley et al. (2006) and advise 

continued use of the GRTS scheme and study sites already selected. These sites should be 

used to sample vegetation (plots), fauna (methods are taxa specific), and abiotic variables 
17

(e.g., bare soil, rocks, litter, wood in the vegetation plots; see Chong et al. 2001). When 

it is feasible to utilize these data for ground-truthing remotely sensed imagery, data from 

these 3 plots could be averaged to describe the larger area and correlated with pixels. 

Any future changes in plot size and/or layout should be determined by research with 

maintenance of the minimum sampling unit (1-m2

) allowing comparability across sample 

times if overall plot types are changed. 

We strongly suggest that in the future terrestrial vegetation plots be distributed based 

upon a stratification of the Grand Canyon using fan-eddy complexes. The locations and 

geomorphic processes of these complexes are relatively well understood, likely are 

mapped, and as described previously have a significant influence on riparian vegetation 

composition and dynamics. Use of the fan eddy complex as the basic unit of 

stratification and study would provide a direct link and integration of terrestrial biota with 

geomorphic processes and studies. Another important stratification should be based upon 

bedrock type at river level, which in part determines canyon width and influences 

geomorphic processes of hillslopes and tributary streams that form fan eddy complexes. 

Simply stratifying based upon distance and canyon width (two classes) does not provide a 

direct link to geomorphic processes. 

Sufficiently fine-scaled vegetation maps can be used in resource selection analyses 

related to animal monitoring (e.g., Manly et al. 2002). If even finer resolution is needed 

(e.g., for ground-dwelling arthropods), the 1-m2

 (sub) plot data can be used in correlation 

analyses, for example (also see Kearsley et al. 2006). 
18

ANIMAL MONITORING 

The PEP first evaluated whether past animal monitoring (arthropods, herpetofauna, birds, 

and mammals) from 2001 through 2003 met existing management objectives and core 

monitoring information needs for Goal 6. Our comments are based largely on the 

presentations by Kearsley and Brantley and the Kearsley et al. (2006) report. References 

to page numbers are from that document. 

CMIN 6.1.1. The abundance, composition, distribution, and area of animals in the marsh 

community were not addressed directly. It does not appear that marshes were identified 

as a distinct habitat type for sampling, although there is some notion that these are 

subsumed by a “shoreline” category. We recommend explicitly addressing this 

ambiguity. We do note, however, that the origin of many marsh communities is recent 

and attributable to dam construction and operations because flows have been stabilized 

relative to historical annual fluctuations. The addition of periodic BHBFs is likely to alter 

the distribution of these newly formed marshes through sediment scour and deposition. 

The present sampling regime is unlikely to account for this degree of temporal variation. 

If marsh communities are considered distinct habitats, more intensive sampling will be 

necessary to monitor animal community composition and dynamics. 

CMIN 6.2.1 and 6.3.1. For the NHWZ, this involved the composition and area of the 

animal community and for the OHWZ, the abundance, composition, and distribution of 

the animal community. Birds, arthropods, amphibians, reptiles, and small mammals were 

the target of focused sampling in both zones. Further comments on sampling of these taxa 

are given below. 

CMIN 6.4.1. The composition, abundance, and distribution of animals in sand beach 

habitats were not completely addressed because the differences among sand beaches, 

marshes, and NHWZ habitats are unclear. Monitoring was directed at a shoreline zone, 

the boundaries of which were defined by diel fluctuations of flow during the time of 

sampling (p. 35). It appears that this zone often overlapped with sand beaches (p. 44), but 

also included long stretches of shoreline that would not be considered beach habitat. In 

addition, difficulties in assigning birds to this zone or the NHWZ led to pooling of 

observations from both habitats. As was noted above for marshes, sampling directed 

specifically at beaches will be necessary to address this CMIN. 

CMIN 6.5.1. Some aspects of the distribution and abundance of non-native animals were 

assessed as part of the overall effort. In particular, six species of non-native arthropods 

were detected (p. 75) and a House Sparrow was observed (p. 250). Whether the sampling 

as currently conducted can detect changes in the distribution and abundance of these 

species is unclear (see below). 

CMIN 6.6.1. The composition, abundance, and distribution of seep and spring animal 

communities were not sampled. However, the PEP questions why these communities 

would be sampled by the GCMRC because these habitats are largely independent from 

dam operations, and it appears that other agencies are now targeting seeps and springs in 
19

the Grand Canyon. We recommend that sampling for this CMIN be discontinued by 

GCMRC, and instead rely on information from other agencies. 

CMIN 6.7.1. Aspects of the abundance, distribution, and reproductive success of the 

Southwestern Willow Flycatcher were monitored. Monitoring of this federally listed 

species requires a different protocol and amount of effort than other species being tracked 

by the GCMRC. Because counts are extremely low (from none to at most a few pairs in 

recent decades), it is virtually impossible to link dam operations to variation in the bird’s 

presence or abundance. Given that Grand Canyon National Park has decided to monitor 

this species, we recommend that sampling for this CMIN be discontinued by GCMRC. 

Any observations of Southwestern Willow Flycatchers in routine GCMRC sampling can 

still be relayed to personnel at Grand Canyon National Park to supplement their work. 

In summary, we recommend that for animals CMINs 6.1.1 to 6.5.1 be pursued in any 

future monitoring program, and sampling for CMINs 6.6.1 and 6.7.1 be discontinued as 

these needs are being met by other agencies. The overlap between monitoring needs of 

Grand Canyon National Park and the GCMRC still appears to be substantial for many 

taxa, particularly birds and mammals. Partitioning these efforts may increase overall 

efficiency. 

Monitoring of Particular Animal Taxa 

We concur with many of the conclusions reached by Kearsley et al. (2006) with respect 

to recommendations for monitoring of various taxa (p. 200-209). Below, we offer 

additional comments. 

Arthropods 

Whereas insects and other arthropods may not be organisms of special concern, the 

decision to include them in the overall monitoring effort was probably sound. Their 

abundance and diversity permits rigorous statistical tests to be employed. They also 

provide the primary link between plants and higher animals, and impacts on arthropods 

may be an early signal to broad ecological change. 

The past arthropod sampling program used a variety of sampling methods (plant sweep 

netting, pit-fall traps, light traps, malaise traps, targeted sampling) to attempt to describe 

all arthropods along the riparian corridor. This was a laudable goal, but cannot be 

sustained over the long-term (material collected several years ago is apparently still being 

worked up) and the site-intensive nature of the sampling meant only a relatively small 

number of locations could be sampled (1 per day). However, the data set collected thus 

far should provide the means to develop an efficient long-term monitoring program. 

The Lightfoot-Brantley group indicates that voucher collections have been developed to 

assist future efforts. Those collections should be completed (possibly requiring some 

wrap-up funding), and the collections need to be curated in a form that makes them 

readily accessible to future researchers (not stored piece-meal in museums). While those 
20

collections should be continually up-graded, we concur with the recommendation in the 

Kearsley report (p. 204) that the taxonomic level available in those collections serve as 

the benchmark for future efforts, and we recommend that the past focus on inventory 

aspects be de-emphasized. Workers in aquatic systems (where invertebrate bioassessment 

has been best developed) find that in many cases the genus or even family level is 

sufficient to detect biotic change (Bailey et al. 2001, but see Lenat and Resh 2001). Since 

many taxa have already been classified to species, future workers should use that 

information. However, we recommend that the taxonomic level now available serve as 

the basis for future work. (Collaborating with proposed ATBI efforts may be an efficient 

way to further develop collection taxonomy without additional cost, and as mentioned all 

material collected in GCMRC efforts should be made available to ATBI researchers.) 

Analyses by the Lightfoot-Brantley group identified indicator arthropods for specific 

habitat types (Shore, NHWZ, and OHWZ) and detected several non-native arthropods; 

these taxa will be valuable for future work. The existing data set should be analyzed 

further using a wider variety of multivariate techniques (e.g., nonmetric multidimensional 

scaling) to enable community metrics to be used for monitoring. It appears that most 

indicator taxa were identified from plant sweep netting and pit-fall trapping, so those 

techniques should be the focus of future monitoring efforts. The plant sweep netting, in 

particular, seems appropriate because patterns between plants and arthropods can be 

directly assessed. Unless the other sampling techniques (light traps, malaise traps, 

targeted sampling) can be used without burdening the overall effort (in terms of both field 

logistics and laboratory time), we suggest that they be discontinued. The use of light traps 

and malaise traps in particular habitat zones seems of limited value because organisms 

collected could have originated across a broad range of habitats, not just near the trap. 

By truncating the sampling intensity at any one site, it might be possible to include more 

sites, and develop better statistical rigor. Ideally, arthropod data would be available for 

every plant sample, as links between future plant change and arthropods will provide 

mechanistic clues. However, an arthropod sample at every plant plot would only be 

possible with plant sweep netting, as all other techniques require equipment to be left in 

place for many hours and permit collection of only a single sample per day. Thus, plant 

sweep netting should probably be the primary basis for any future monitoring. Dr. 

Brantley and the Kearsley report (p. 203) indicated that their group was concerned that 

the standardized sweeps did not reach plant surfaces > 2 m high, and some organisms 

might be missed. Sampling tall vegetation only up to 2 m resulted in mostly woody stems 

being sampled, instead of the foliage where many arthropods occurred. However, because 

baseline samples from 2001-2003 were collected only in areas < 2 m high, we 

recommend that that effort be duplicated in future years to enable direct statistical 

comparisons over time rather than developing a completely new methodology. However, 

it seems logical that when vegetation exceeds 2 m then supplemental (but separate) 

samples should be added (sweep nets with extension poles are commercially available). 

Biases of the original design could then be assessed empirically, and appropriate 

modifications designed, and methods to back calibrate 2001-2003 data explored. 
21

Because pit fall trapping produced some unique and valuable results (p. 203), we 

recommend that it be retained. However, because logistically only a single site per day 

can be sampled, only a subset of sites in the overall study could be included. In this 

subset, other time intensive sampling might also be conducted. 

Birds 

The panel was provided little information on the bird sampling program, so our ability to 

make recommendations is limited. We were also informed that Grand Canyon National 

Park has initiated an avian monitoring program. If an avian program is retained by 

GCMRC, we recommend that, as before, bird sampling be directly integrated with the 

other efforts. 

The number of sites used for bird monitoring was based in part on the power analysis by 

Spence (p. 33), who concluded that surveying approximately 64 sites three times during 

the breeding season would be necessary in order to detect trends (but it was not specified 

whether this was in abundance, distribution, or presence; see further comments below). 

This sampling, however, relied on single visits to each of the sites during the breeding 

season, and how this influenced estimation of bird abundance or presence was not 

addressed. At each site, individuals used walking surveys, rather than point-centered 

methods, to gauge relative abundance of species. We agree that walking surveys are more 

appropriate because long, narrow bands of vegetation are characteristic of each zone. 

Avian sampling must also incorporate diurnal and seasonal variation in bird presence and 

behavior, so bird sampling might have to be restricted to a subset of the plant plots (much 

like pit fall trapping for arthropods). The indicator analysis used for arthropods would 

also be useful for birds 

Despite the challenges, including birds seems useful to a monitoring program because 

declines in charismatic birds would better bolster public support for modifying 

management than decreases in either plants or arthropods. Any avian program should 

focus on overall communities rather than specifically target endangered species. As 

mentioned, it appears that the National Park Service is taking the lead on monitoring of 

endangered birds in the Grand Canyon. 

Response of waterfowl and waterbirds was mentioned as a possible priority. If these birds 

could be monitored in a rigorous fashion from boats as trips progressed, and not detract 

significantly from other priorities, their inclusion in the overall monitoring program 

seems appropriate. Perhaps waterfowl surveys could be focused solely in the upper 

reaches of the corridor where they apparently concentrate due to the water clarity. 

Amphibians and Reptiles 

From the information presented to the panel (p. 103-119), it appeared that standardized 

baseline data for amphibians and reptiles was not obtained in the 2001-2003 effort. 

Sampling herpetofauna at the optimal time was difficult. Some species are most readily 

detected while basking in sunlight, and delaying sampling until this time while staying 
22

with the rest of the surveying teams was logistically impossible. Moreover, some of the 

sites selected for vegetation and avian sampling were almost never in sunshine and thus 

unsuitable for amphibian and reptile monitoring. Nonetheless, the species list generated 

by this effort does serve as an initial estimate of the number of species present. 

In contrast, the survey protocol made it difficult to estimate relative abundance, and thus 

these data are of limited use for future comparisons. Because amphibians are in global 

decline and their association with aquatic habitats suggests that they may be vulnerable to 

dam operations, they (and to a lesser extent, reptiles) appear to be excellent candidates for 

long-term monitoring. It may be difficult, however, to integrate surveys of these taxa with 

other monitoring because of the particular demands of sampling this faunal group. 

Reconciling this issue may require either abandoning herpetofaunal sampling or tailoring 

monitoring to more specifically address the abundance and distribution of these species. 

We favor the latter course, and recommend consulting the U.S. Geological Survey’s 

Amphibian Research and Monitoring Initiative website (http://armi.usgs.gov/index.asp). 

Of particular interest is that the proportion of area occupied 

(http://armi.usgs.gov/PAOEstimator.asp) has been proposed as a national standard for 

amphibian monitoring, and it may also be applicable to other species. 

Small Mammals 

Small mammals (9 of the 29 species detected) were the subject of intensive sampling 

associated with the integrated monitoring effort. These species were amenable to 

quantitative sampling, and showed responses to the different vegetation zones that may 

make them useful subjects for detecting the effects of dam operations (perhaps primarily 

change in the OHWZ vegetation). 

This monitoring helped establish up- and downstream boundaries on the distribution of 

beaver, but the detection methods used for this and other large mammals are of uncertain 

value for detecting trends. Other researchers have assessed associations between beavers 

and riparian plants (e.g., Salix and Tamarix) in the Grand Canyon, and have submitted a 

manuscript for journal review. If that paper is accepted by peer reviewers and proceeds to 

publication, GCMRC might consider funding follow up work on beavers because the 

ecosystem engineering capabilities of beavers in the Grand Canyon (in this case 

vegetation rather hydrologic impacts) might be inter-related with dam management. 

However, it would be difficult to integrate beaver monitoring with an overall riparian 

monitoring effort. 

For other mammals, it appears Grand Canyon National Park is monitoring many of these 

populations. Because of their reliance on flying arthropods, often in association with 

watercourses, bats may be an important group to monitor by the park or GCRMC. 

Additional Recommendations on Animal Sampling 

We were impressed with the effort involved in the overall sampling program for riparian 

zone animals, and we also found the past attempts to link animal and plant patterns was a 
23

useful exercise in terms of exploring ecological mechanisms. However, it appears that the 

past level of animal sampling may not be sustainable because many projects remain 

incomplete, and even four years after sampling ended, journal articles have not been 

published. We encourage the scientists involved with the past 2001-2003 sampling effort 

to complete their analyses because those data sets could serve as foundations for future 

efforts (GCMRC might consider funding wrap up efforts, with the contingency that 

journal articles are produced by any continued funding). 

Past efforts tried to be all-encompassing, attempting to inventory virtually every riparian 

organism along the Colorado River. This type of effort is consistent with an “All Taxa 

Biotic Inventory (ATBI)” but beyond the scope of monitoring biotic change associated 

with operation of Glen Canyon Dam. We recommend that GCMRC efforts now be 

focused on monitoring, not inventories (although any data collected on riparian animals 

can still be shared with the personnel charged with the ATBI proposed by Grand Canyon 

National Park). Moreover, to sustain a decades-long effort to monitor riparian animal 

communities, we believe that some choices will need to be made because it is not feasible 

to monitor the presence, distribution, and abundance of all animal species; rather, 

indicator, keystone, or umbrella species, despite their shortcomings as representatives 

(Lindenmeyer et al. 2002), will need to be selected from the various taxonomic groups. 

To the extent practicable, we further recommend that the vegetation monitoring program 

remain as a framework for animal monitoring to facilitate understanding their ecological 

connections and joint responses to dam operation. 
24

NEED FOR A RESEARCH PROGRAM TO DIRECT MONITORING 

ACTIVITIES 

Statistical Analyses 

One of the key research questions that needs to be addressed in a monitoring effort such 

as this is detectability. How large must changes be before they are statistically 

detectable? Given the background variability in the Grand Canyon, is the design 

sufficiently powerful to have the desired level of detection? Finally, should there be a 

distinction between statistical and ecological change? 

We believe that monitoring plans should address whether the desired measurements are 

of indices or true values of presence, distribution, or abundance. Presently, the metrics 

derived from sampling of animal species in the Grand Canyon should be regarded as 

indices because none of the sampling accounts for detectability of individuals in different 

habitat types, with different sampling schemes, or under different environmental 

conditions. In most cases, the detectability of species by sampling methods is less than 

one; i.e., not all animals (or species) present are observed. This leads to the 

underestimation of animal distribution and abundance, and failure to detect a species 

when it is present. Moreover, variability in detection in different habitats and in different 

weather conditions can confound attempts to establish linkages between species 

abundance or presence and environmental variables. More important is that failure to 

account for detectability introduces a source of error of unknown magnitude. This error 

leads to greater variation in the metrics being monitored, and can increase the time to 

detect statistically significant trends that may require a management response 

(MacKenzie 2005). The key problem, however, is that estimating detectability requires 

repeat sampling and much greater effort, at least initially. If biases in detectability are low 

and constant, indices offer a much more cost-effective method of monitoring populations. 

Characterizing the biases and variation in detectability should be a priority because it will 

dictate whether monitoring of particular groups can rely on indices. For those taxa that 

are inconsistently detected or counted, the GCMRC will have to decide whether to 

conduct more intensive sampling or to discontinue monitoring. A rapidly developing 

literature (summarized in MacKenzie et al. 2006, and specific to particular groups e.g., 

amphibians, Bailey et al. 2004; birds, Alldredge et al. 2007) offers extensive guidance on 

this issue. 

The point of many monitoring schemes is to detect temporal variation in terms of 

population trends, overall fluctuation, or synchrony. A vital concern is the period of 

monitoring that may be necessary to detect a change in population size, which is 

contingent on the precision of population estimates, the temporal fluctuation in 

abundance, the magnitude and direction of change to be detected, and the level of 

confidence one hopes to have in the prediction (Peterman 1990, Thompson et al. 1998). 

For example, in a recent assessment on detecting trends in bird populations in North 

America, Bart et al. (2004) recommended a monitoring scheme that yielded 80% power 

to detect a 50% decline within 20 years based on annual monitoring, noting that detecting 

smaller declines at shorter intervals was impractical. Specification of each of these values 
25

is largely absent from the monitoring plan we have examined. Analyzing the data already 

obtained (similar to the suggestions by Spence), in light of issues involving detectability, 

could result in estimates for these parameters for many of the surveyed taxa (much as was 

done for plants [p. 202] although detectability may also be an issue there when multiple 

observers are used), and help guide decisions about which taxa can be monitored with the 

effort and funding that is available. 

Sampling Frequency 

Currently, recommendations are for sampling to occur every 5 years, with the most recent 

samples being collected in 2003. In general, a 2 year duration of sampling per cycle 

(rather than the 3 used in the past) seems reasonable. Within that 2 year period, sampling 

of individual metrics must account for life cycle patterns, with sampling coinciding with 

peak seasons of growth (plants) or activity (animals). Sampling frequency should be 

determined both by expected rates of change and expected drivers of change (e.g., natural 

droughts, management high-flow events). For example, baseline sampling should occur 

pre- and post-managed-flood event. To capture peak phenology in vegetation, that may 

mean sampling the growing season before and the growing season after, whereas, for 

animals there may be opportunities to get multiple seasons within one or more years 

before and after. Whenever animal data are collected, corresponding habitat data must be 

collected simultaneously even though the full suite of vegetation and habitat data would 

not be collected. 

An example of sampling frequency based on a March year 1 release (e.g., proposed 

March 2008 event) could be: (1) pre-release terrestrial data provided by Kearsley et al. 

2006; (2) post-release 2008 spring, mid-summer and fall animal (e.g., selected 

arthropods, small mammals and birds) sampling and summer vegetation sampling; and 

(3) full suite sampled in 2009 (1 year post-event). If significant changes are found post 

event (either 2008 or 2009), then a year 3 (2010 in this case) sample should be considered 

to capture any rapid rates of change. Depending on rates of change, sampling could be 

repeated year 5 then at intervals (e.g., 5 years) defined both by observed rates of change 

and/or needs to sample based on the occurrence of other drivers of change (e.g., drought). 

Additionally, subsets of samples could be collected opportunistically related to events or 

other management actions (e.g., fire, non-native species removal, restoration, etc.). 

Those data would contribute to long-term monitoring and to adaptive management 

applications. 

The Need for A Reference Standard 

A final concern, and one that applies to nearly all of the monitoring within the Colorado 

River below Glen Canyon Dam, is that the effects of dam operations cannot be fully 

appreciated unless sampling is conducted in portions of the river system that are not 

affected by the dam. All downriver study sites are influenced by dam operations, and 

analyses of data from there are prejudiced by this bias and perhaps by an overly narrow 

geographic perspective. In preparing for this protocol evaluation panel, we reviewed the 
26

recent research and documentation on humpback chub below Glen Canyon Dam as well 

as information on the now-extinct big-river fish once found there. Our conclusion was 

that the current research program would greatly benefit from consideration of how 

populations of these fish are faring elsewhere in the Colorado River basin, which might 

lead to redirecting some of the current research. Similarly, the understanding of changes 

in the OHWZ and NHWZ vegetation is incomplete without consideration of riparian 

vegetation unaltered by flow regulation, especially if one must also account for largescale ecological drivers such as climate change and nonnative species invasions. Thus, 

upriver sites for the study of aquatic food webs, large wood distribution, vegetation 

patterns, bird relations to vegetation structure, and other ecological patterns and processes 

are the necessary controls that underlie reliable inferences about the consequences of the 

dam’s presence and its current and projected operations. To some degree, studies 

conducted adjacent to tributaries may also serve as controls, but for patterns associated 

with the main stem, locations above Glen Canyon Dam will be essential. 

Other topics 

Determining the appropriate and accepted analyses is an important area where research 

can inform the monitoring. Keeping up with the latest and best techniques for data 

analysis is a time-consuming, yet essential piece of a monitoring program. Continuous 

input from someone with this kind of expertise is needed. 

Below, we provide some more specific possibilities for future workers to consider. 

1) For plants, pre-dam aerial photos of the canyon may indicate baseline conditions for 

plant cover, and planned remote sensing efforts might detect on-going temporal change. 

For animals, it could be assumed that they will track changes in vegetation. An important 

study to establish baseline condition could be developed from the analysis of the pre-dam 

air photos. This baseline, along with reference river analyses, would provide an 

important framework for understanding effects of Glen Canyon Dam. This has been 

done to large extent for geomorphic processes, and should be done for riparian 

vegetation. 

2) The Colorado River through Cataract Canyon likely provides the most suitable 

reference site for riparian vegetation and geomorphic processes to the Grand Canyon. 

This river section has similar fan-eddy dominated reaches, with abundant talus, and only 

small patches of channel-margin deposits and eddy bars, to the Grand Canyon (J. 

Schmidt, personal communication). While the Colorado River through this reach is partly 

regulated by Flaming Gorge and numerous dams on the Colorado River, and its 

tributaries, it is the only river reach of similar size and with similar landforms as the 

Grand Canyon, and is as “pre-dam” as can be found in the Colorado River basin. 

3) Streams tributary to the Grand Canyon could also serve as reference streams. The 

advantages of these streams include logistic proximity and climatic conditions for the 
27

Colorado River and its tributaries will be similar. The major drawback is that the main 

stem and its tributaries (or springs) are not ecologically similar. 

4) Other minimally regulated or unregulated rivers in the Southwest already being 

monitored might also be used for reference. These smaller rivers must occur in arid 

region canyons dominated by fan eddy complexes, and support intact vegetation and 

animal communities. An example is the Yampa River canyon within Dinosaur National 

Monument. This option has the advantage of using data that are already being collected, 

and the fact that the habitats are largely similar. However, as with tributaries, these rivers 

will be ecologically different from the Colorado because of their smaller size. 

SUMMARY RECOMMENDATIONS 

Specific recommendations are provided throughout this document. Below we itemize 

some of the most important suggestions. 

1. The Management Objectives (MOs) and Core Monitoring Information Needs 

(CMINs) developed to direct GCMRC actions need re-evaluation as some are 

unrelated to dam operations, others seem unrealistic, and others need clarification. 

2. We recommend that CMINs 6.6.6 (seeps and springs) and 6.6.7 (SW willow 

flycatcher) be discontinued by the GCMRC, and that the GCMRC arrange to 

receive data being collected by other agencies on these topics. 

3. Vegetation maps need to be finalized. 

4. A schedule and strategy for repeat mapping and change detection analyses should 

be developed. 

5. Sample plots should be located randomly within the randomly selected sample 

sites, and plot design should be modified as discussed in the vegetation summary. 

6. The suite of biotic variables chosen for monitoring can be truncated to include 

only organisms conducive to rigorous sampling and methods conducive to 

rigorous analyses. Data on native and non-native plant species should be collected 

simultaneously, but separate analyses should be conducted for each. 

7. Monitoring large wood abundance and location should be considered. 

8. The absence of monitoring sites outside of Glen Canyon reduces the strength of 

inferences about the effects of dam operations. Reference sites not subject to the 

flow regime imposed by Glen Canyon Dam are necessary, but their location may 

vary depending on the suite of monitoring variables under consideration. 

9. Publication of results in peer reviewed journals should be a priority, as there is a 

large audience interested in how flow regulation affects Grand Canyon habitats, 

and the journal review process is a valuable mechanism for quality control. 

10. Sampling frequency should be determined both by expected rates of change and 

expected drivers of change (e.g., natural droughts, management high-flow events). 
28

LITERATURE CITED 

Alldredge, M.W., K.H. Pollock, and T.R. Simons. 2007. Estimating detection 

probabilities from multiple-observer point counts. The Auk 123:1172-1182. 

Anderson, M.J. 2001. A new method for nonparametric multivariate analysis of variance. 

Austral Ecology 26: 32-46. 

Bailey, L.L., T.R. Simons, and K.H. Pollock. 2004. Estimating detectability parameters 

for plethodon salamanders using the robust capture-recapture design. Journal of 

Wildlife Management 68:1-13. 

Bailey, R.C., R.H. Norris, and T.B. Reynoldson. 2001. Taxonomic resolution of benthic 

macroinvertebrate communities in bioassessments. Journal of the North American 

Benthological Society 20:280-286. 

Barnett, D.T., T.J. Stohlgren, C.S. Jarnevich, G.W. Chong, J.A. Ericson, T.R. Davern, 

and S.E. Simonson. 2007. The art and science of weed mapping. Environmental 

Monitoring and Assessment 132:235-252. 

Bart, J., K.P. Burnham, E.H. Dunn, C.M. Francis, and C.J. Ralph. 2004. Goals and 

strategies for estimating trends in landbird abundance. Journal of Wildlife 

Management 68:611-626. 

Birken, A. and D. Cooper. 2006. Processes of Tamarix invasion and floodplain 

development during the 20th century along the lower Green River. Ecological 

Applications 16:1103-1120. 

Breiman, L. 2001. Random forests. Machine Learning 45:5-32. 

Breiman, L., A. Cutler, A. Liaw, and M. Wiener. 2006. Breiman and Cutler's Random 

Forests for Classification and Regression. http://CRAN.R-project.org/ [R package 

version 4.5–16]. 

Chong, G.W., R.M. Reich, M.A. Kalkhan, and T.J. Stohlgren. 2001. New approaches for 

sampling and modeling native and exotic plant species richness. Western North 

American Naturalist 61:328-335. 

Cooper, D.J., D.C. Andersen, and R.A. Chimner. 2003. Multiple pathways for woody 

plant establishment on floodplains at local to regional scales. Journal of Ecology 

91:182-196. 
29

Evans, J.S. and A.T. Hudak. 2007. A multiscale curvature algorithm for classifying 

discrete return lidar in forested environments. IEEE Transactions on Geoscience and 

Remote Sensing 45:1029-1038. 

Grand Canyon Monitoring and Research Center (GCMRC). 2006. Monitoring and 

research plan to support the Glen Canyon Dam adaptive management program, fiscal 

years 2007-2011. 

Gregory, S., K. Boyer, and A. Gurnell (editors). 2003. The Ecology and Management of 

Wood in World Rivers. American Fisheries Society, Symposium 37, Bethesda, 

Maryland. 

Hudak, A.T. and C.A. Wessman. 1998. Textural analysis of historical aerial photography 

to characterize woody plant encroachment in South African savanna. Remote Sensing 

of Environment 66:317-330. 

Kearsley, M., N.S. Cobb, H.K. Yard, D.C. Lightfoot, S.L. Brantley, G.C. Carpenter, and 

J.K. Frey. 2006. Inventory and Monitoring of Terrestrial Riparian Resources in the 

Colorado River Corridor of Grand Canyon: An Integrative Approach. Final Report 

Cooperative Agreement / Assistance Awards 01 WRAG 0044 (NAU) and 01 WRAG 

0034 (HYC). 

Kennedy, R.E., W.B. Cohen, and G. Takao. 1997. Empirical methods to compensate for a 

view-angle-dependent brightness gradient in AVIRIS imagery. Remote Sensing of 

Environment 62:277-291. 

Lenat, D.R. and V.H. Resh. 2001. Taxonomy and stream ecology – the benefits of genusand species-level identifications. Journal of the North American Benthological 

Society 20:287-298. 

Lindenmeyer, D.B., A.D. Manning, P.L. Smith, H.P. Possingham, J. Fischer, I. Oliver, 

and M.A. McCarthy. 2002. The focal-species approach and landscape restoration: a 

critique. Conservation Biology 16:338-345. 

MacKenzie, D.I. 2005. What are the issues with ‘presence/absence’ data for wildlife 

managers? Journal of Wildlife Management. 69:849-860. 

MacKenzie, D.I., J.D. Nichols, G.B. Lachman, S. Droege, J.A. Royle, and C.A. 

Langtimm. 2002. Estimating site occupancy rates when detection probabilities are 

less than one. Ecology 83:2248-2255. 
30

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, J.E. Hines, and L.L. Bailey. 

2006. Occupancy estimation and modeling: inferring patterns and dynamics of 

species occurrence. Elsevier, San Diego, USA. 

Manly, B.F., L. McDonald, D.L. Thomas, T.L. McDonald, and W.P. Erickson. 2002. 

Resource Selection by Animals: Statistical Design and Analysis for Field Studies. 2nd

Edition. Kluwer Academic Publications, Boston, Massachusetts, USA. 

McCune, B. and J.B. Grace. 2002. Analysis of Ecological Communities. MjM Software 

Design, Gleneden Beach, Oregon. 

Minchin, P.R. 1987. An evaluation of the relative robustness of techniques for ecological 

ordination. Vegetatio 69:89-107. 

Moeur, M. and A.R. Stage. 1995. Most similar neighbor: an improved sampling inference 

procedure for natural resource planning. Forest Science 41:337-359. 

Moisen, G.G. and T.S. Trescino. 2002. Comparing five modelling techniques for 

predicting forest characteristics. Ecological Modelling 157:209-225. 

Ohmann, J.L. and M.J. Gregory. 2002. Predictive mapping of forest composition and 

structure with direct gradient analysis and nearest neighbor imputation in coastal 

Oregon, U.S.A. Canadian Journal of Forest Research 32:725–741. 

Peterman, R.M. 1990. Statistical power analysis can improve fisheries research and 

management. Canadian Journal of Fisheries and Aquatic Sciences 47:2-15. 

Thompson, W.L., G.C. White, and C. Gowan. 1998. Monitoring vertebrate populations. 

Academic Press, San Diego, California. 

Stevens, D. and A. Olsen 2004. Spatially-balanced sampling of natural resources. 

Journal of American Statistical Association 99: 262-278. 

Stevens, L. E., J. C. Schmidt, T. Ayers, B. Brown. 1995. Flow regulation, 

geomorphology, and Colorado River marsh development in Grand Canyon, Arizona. 

Ecological Applications 5: 1025-1039. 

Stohlgren, T.J., G.W. Chong, M.A. Kalkhan, and L.D. Schell. 1997. Multiscale sampling 

of plant diversity: effects of minimum mapping unit size. Ecological Applications 

7(3):1064-1074. 

Urquhart, N.S., G.T. Auble, J.G. Blake, D.T. Bolger, T. Gerrodette, S.G. Leibowitz, D.C. 

Lightfoot, and A.H. Taylor. 2000. Report of a peer review panel on terrestrial aspects 
31

of the Biological Resources Program of the Grand Canyon Monitoring and Research 

Center. June 23, 2000. U.S. Department of the Interior, Grand Canyon Monitoring 

and Research Center. Flagstaff, AZ. 49 p. 

Urquhart, N. S. and M. Kearsley. Unpublished manuscript. Executing a spatially 

balanced sampling design along the Colorado River in Grand Canyon National Park, 

AZ.