November 1993
USER'S MANUAL:
AVIAN RICHNESS EVALUATION METHOD (AREM)
FOR LOWLAND WETLANDS OF THE COLORADO PLATEAU
by:
Paul R. Adamus
ManTech Environmental Technology, Inc.
US EPA Environmental Research Laboratory
200 SW 35th St.
Corvallis, OR 97333
EPA Project Officer:
Mary E. Kentula
USEPA Environmental Research Laboratory
200 SW 35th St.
Corvallis, OR 97333
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EPA/600/R-?5/24<0
November 1993
USER'S MANUAL:
AVIAN RICHNESS EVALUATION METHOD (AREM)
FOR LOWLAND WETLANDS OF THE COLORADO PLATEAU
by:
Paul R. Adamus
ManTech Environmental Technology, Inc.
USEPA Environmental Research Laboratory
200 SW 35th St.
Corvallis, OR 97333
EPA Project Officer:
Mary E. Kentula
USEPA Environmental Research Laboratory
200 SW 35th St.
Corvallis, OR 97333
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DISCLAIMER
This project has been funded by the U.S. Environmental Protection Agency (EPA) and
conducted through contract #68-C8-0006 to ManTech Environmental Technology, Inc.
This document has been subjected to the Agency’s peer and administrative review and
approved for publication. The opinions expressed herein are those of the author and do
not necessarily reflect those of EPA. The official endorsement of the Agency should not
be inferred. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
This document should be cited as:
Adamus, P.R. 1993. User’s Manual: Avian Richness Evaluation Method
(AREM) for Lowland Wetlands of the Colorado Plateau. EPAJ600fR-9 “O
+ diskette. U.S. Environmental Research Laboratory, Corvallis, Oregon.
Additional copies of the manual are available at cost from:
National Technical Information Service (NTIS)
5285 Port Royal Rd.
Springfield, VA 22161
(phone 1-800-553-NTIS)
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ACKNOWLEDGMENTS
The project of which this manual and software are a part was conceived by Gene Reetz and
David Ruiter of EPA Region 8. Computer programming of the AR.EM models was
performed by Rosemary Owen of the Computer Sciences Corporation. Mary E. Kentula of.
the Wetlands Research Program, USEPA Environmental Research Laboratory-Corvallis,
provided many useful suggestions and administrative support. Brooke Abbruzzese was the
project manager for ManTech Environmental Technology, Inc. The manual and software
benefitted greatly from the review comments of Kristi DuBois, Ted Ernst, Cindy Holland,
Ronald Lambeth, Gerald Niemi, Raymond J. O’Connor, Jeff Price, Antisa Webb, and
Christopher Welsh.
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CONTENTS
1.0 “AREM” AND WHAT IT CAN DO .
2.0 INSTRUCTIONS 8
2.1 Field Phase 8
2.2 Program Installation and Analysis Phase 9
2.3 Summary of the AREM Program Steps 10
2.4 Interpretation Phase 26
2.5 Editing Options 31
2.6 Possible Applications of the Editing Program 41
2.7 Adapting AREM for a New Region or Ecosystem Type 42
3.0 LITERATURE DOCUMENTATION FOR AREM 45
3.1 Documentation of Indicator Thresholds 45
3.1.1 Introduction 45
3.1.2 Area Thresholds 45
3.1.3 Width Thresholds 48
3.1.4 Distance Thresholds 49
3.1.5 Other Thresholds 51
3.2 Documentation of Weighting Factors 53
3.2.1 Species Dependency on Wetland/Riparian Habitat 54
3.2.2 Relative Abundance 54
3.2.3 Taxonomic Uniqueness 55
3.2.4 Neotropical Migrant Status 56
3.2.5 Official Conservation Designations 56
3.2.6 Hunted Status 57
4.0 LITERATURE CITED 58
Appendix A. Field Form: Site Documentation
Appendix B. Field Form (Long)
Appendix C. Field Form (Short)
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LIST OF TABLES
Table 1. Advantages of using AREM 3
Table 2. Limitations and assumptions of AREM 4
Table 3. The context for properly using AREM 5
Table 4. Example showing how AREM scores are calculated 25
Table 5. Use of AREM to select wetland combinations that optimize protection of regional
avian diversity 29
Table 6. Instructions for AREM’s editing program (EDITAREM) 34
Table 7. Meaning of codes used in the species habitat relationships database 36
LIST OF FIGURES
Figure 1. Subregions of the Colorado Plateau addressed by this report 2
Figure 2. AREM’s four introductory screens 12
Figure 3. Part of AREM’s indicator list 14
Figure 4. AREM’s menu for selecting subregion and seasonal period 15
Figure 5. Example of output in AREM’s score table 16
Figure 6. Menu for specifying the seasonal period upon which species are to be weighted
by abundance 17
Figure 7. Menu for finishing the AREM analysis or changing the weights 18
Figure 8. Menu for changing weights of a species characteristic 19
Figure 9. AREM’s concluding instructions 20
Figure 10. Example of documentation output file from an AREM evaluation 21
Figure 11. Menu for combining bird lists and scores from multiple wetlands 30
Figure 12. Menu for selecting AREM databases to edit 33
Figure 13. Part of AREM’s wildlife habitat relationships database (WHRBASE) 35
Figure 14. Part of AREM’s WEIGHTS database 37
Figure 15. Part of AREM’s REGIONS database 38
Figure 16. Part of AREM’s TAXINF database 39
Figure 17. Menu for adding indicator conditions 40
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1.0 “AREM” AND WHAT IT CAN DO
This manual describes how to use and interpret the “Avian Richness Evaluation Method”
(AREM), a standardized procedure for estimating the bird species composition and richness
of lowland wetlands of the Colorado Plateau (Figure 1). Instructions for using AREM are
found in Section 2.0, and no additional training courses should be needed. To apply
AREM correctly, you should first read the information in Tables 1-3. If you desire an in-
depth understanding of the concepts and database programming logic of AREM, you may
refer to Section 5.0 of Adamus (1993a). However, it is not essential to review that
information before using AREM. Section 3.0 of the present manual provides
documentation for some of AREM’s individual indicators.
When used correctly, you can expect AREM to do the following:
o Assign a score to each evaluated wetland, which represents the number of bird
species that could occur in the wetland, multiplied by an estimate of the suitability
of the wetland for each species.
o List the species likely to occur in the evaluated wetland. Such a list can be
combined with lists predicted for other wetlands, to identify minimum combinations
of wetlands that will provide habitat for all bird species in an area.
o Tally the number of species likely to occur in the evaluated wetland and which have
particular characteristics, e.g., neotropical migrants, uncommon or game species. If
desired, users can assign scores to these characteristics and use them as “weights”. in
deriving the wetland score.
Some examples of situations where AREM might be used to assist and document resource
decisions are as follows:
Situation 1. Mitigation Calculations. Resource agencies currently spend time “cover-
typing” lands that will be altered in connection with salinity control projects, water
diversions, and other developments where compensatory mitigation has been deemed
necessary. This process involves measuring various categories of habitat before a project is
begun and then estimating any shifts in acreage that will occur among categories as a result
of the project. Acreages in each cover type category that are believed to exist both before
and after the project are multiplied by coefficients, determined through use of HEP’, that
indicate suitability of each category for selected species during both time periods. In this
manner, net change in habitat suitability is predicted, at least for a few selected species.
Where wetland and riparian cover types are the habitats that are expected to change, AR.EM
might be used in lieu of (or in addition to) HEP. to calculate the habitat suitability
coefficients. If non-wetland cover types are also present, AREM could be expanded and
‘The Habitat Evaluation Procedures (HEP) of the US. Fish and Wildlife SeTvice (1980).
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Figure 1. Subregions of the Colorado Plateau addressed by this report.
UNCOLN
FREMONT
SUBLE1TE
I
IWYOMINGS1J -
I Big Sandy S.CA. I
SWEE1WATER
CARBON
Legend
= State border
= County border
S.CA Salinity Control
f ñI flDAflt)
MOFFAT
I 1±.
RIO BLANCO
MONTROSE
SAN MIGUEL
DOLORES
GARFIELD
a S.CIA.
SAN JUAN
2
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Table I. Advantages of using AREM.
1. Using AREM is relatively simple and rapid. Field data collection requires less than 15 !nnutes per
wetland. Data entry and analyses require less than 30 minutes per wetland. The models AREM uses
to predict habitat suitability for individual species are mathematically simpler than those used by the
Habitat Evaluation Procedure (HEP, U.S. Fish & Wildlife Service 1980) and thus may be easier to
understand and explain.
2. AREM is one of only a few rapid evaluation methods that actually have been validated to some
degree, i.e., accuracy during the breeding season was measured through comparison of observed with
predicted conditions (see Adamus I 993b).
3. The scores that result from an AREM evaluation have a high level of accountability. Users can call
up the daábase for any species to closely examine the habitat model supporting that species. Users can
also call up any indicator condition to identi& all species predicted by that condition. This feature is
of potential use in predicting a species’ response to wetland change, e.g., for impact analysis or planning
of wetland enhancements.
4. AREM users, even those with limited computer knowledge, can interactively edit the database and
revise models for any species. This allows users to adapt AREM for other regions and wetlandlriparian
types, provided habitat requirements of all bird species in these areas are known, or can be determined
with sufficient accuracy.
5. AREM is perhaps the only rapid habitat evaluation method whose major organizing theme and
endpoint is biodiversity. Many government agencies are mandated to account for the impacts of their
activities on biodiversity, and public concern over the global and regional loss of biodiversity appears
to be growing.
6. In contrast to HEP, AREM does not require the user to base a wetland’s score on a few presumed
“indicator species.” Users do not need to assume that habitats which are found to be optimum for a few
species will also be suitable for many species, i.e., be biodiverse.
7. Species lists predicted by AREM for various wetlands can be combined in any local area or subregion
to determine which particular combination of wetlands cumulatively supports the greatest number of
species (see p. 28). This “optimization process” can be further focused by applying constraints related
to species characteristics, land ownership, manag ment costs, or other factors. As such, use of AREM
can provide a complementary, local refinement of the “gap analysis” approach currently being applied
at state and regional levels by the U.S. Fish and Wildlife Service (Scott et al. 1993).
8. One of AREM’s outputs the “unweighted richness” score — is the actual number of species predicted
to occur in a wetland. As such, this is an ecological parameter that can be validated empirically.
9. AREM does not require the user to conduct bird surveys or, for that matter, be an expert on birds or
other wildlife.
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Table 2. Limitations and assumptions of AREM.
1. AREM is a comproI nise between con ’enience and technical certainty. The technical certainty
of many of the species habitat models that comprise AREM might be increased by formulating them
in a more mathematically complex manner and explaining their details and assumptions at greater
length, but in some cases this would sacrifice speed of application, replicability, and clarity to the
typical user. AREM is intended to be intermediate in complexity between the simple, few-indicator
wildlife habitat relationship (WI-ER) models used in landscape classification and the multi-indicator,
few-species FIEP models used for site evaluations. AREM shares some of the limitations of WHR’s
as described by Morrison Ct al. (1992) and limitations of HEP described by Van Home and Wiens
(1991), but avoids others.
2. Indicator conditions used in AREM’s species models in some cases are related to a species’
presence in a loosely deterministic manner, but in other instances are related only empirically, i.e.,
they correlate with a species’ presence but have not necessarily been shown to control use of
habitats through explicit effects on food, cover, or reproduction.
3. Wetlands are dynamic systems, and scores assigned by any evaluation method can change as a
result of natural vegetative succession, flood or drought, management actions, or other factors.
4. AREM pertains only to avian biodiversity. It is not possible to predict the situations in which
wetlands that contain a relatively great variety of bird species also have a relatively great variety
of plants, insects, amphibians, or whatever.
5. It cannot be assumed that wetlands that are species-diverse will always be diverse at genetic,
community, or functional levels, although this is often the case.
6. It cannot be assumed that wetlands that are species-diverse will contain viable populations of
most species, or greater ecological “integrity,” “health,” or “sustainability,” although this is usually
the case.
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Table 3. The context for properly using AREM.
1. AREM is intended for application only to lowland wetlands and riparian areas larger
than 0.1 acre, located within the Colorado Plateau region of western Colorado, eastern
Utah, and southwestern Wyoming (Figure 1, p. 2).
2. Users should be capable of recognizing all indicator conditions specified in the field
forms (Appendices B and C). When evaluating a wetland, users should note situations
in which they feel information requested by the field forms required considerable
judgement, and report this with the results.
3. As is true of other rapid methods for habitat assessment, AREM’s habitat relationship
models for individual species cannot be used to definitively measure the relative or
absolute abundance or density of these species’ populations. Many factors not included
in the species models, e.g., weather, determine population size and even presence/absence
in a given wetland. In some cases the influence of such factors on species distributions
will exceed the influence of habitat quality, but AREM assumes that for most species,
their influence on species presence/absence will be less.
4. AREM should not be used to compare wetland/riparian habitats with other habitats.
Within the Colorado Plateau lowlands, species habitat scores from AREM estimate the
suitability of a wetland or riparian habitat relative only to the suitability of other wetland
or riparian habitats. In some circumstances, some species included in AREM might find
nonwetland habitats more suitable.
5. Scores from AREM should not be used in lieu of species occurrence data from actual
surveys of a wetland, provided such data have been collected with sufficient intensity and
using appropriate methods.
6. Scores from AREM should be considered as only one of several possible inputs used
in the decision-making process. Under most circumstances it is inappropriate to use
AREM as the only means for deciding whether mitigation should be required. A habitat
index, defined as the product of an AREM score and wetland acreage, can be computed
if desired. The values from such an index potentially can be used as one input in
mitigation deliberations, monitoring of restoration/enhancement projects, and description
of the future biodiversity consequences of specified impacts to the indicator conditions.
However, the commonly associated practice of using values from such indices to
rationalize a decision to offset the loss of a collectively large acreage of low-quality
wetlands with the creation of a small acreage of high-quality wetlands must be viewed
cautiously. As is true of other methods, caution is needed because use of simple
multiplication presumes that species richness is related to habitat acreage (wetland size)
in a direct, linear manner. This is not necessarily valid because (a) the effect of wetland
size on richness can vary by species composition, .season, surrounding landscape
characteristics, wetland shape, and other factors, (b) wetland size is “double-counted,”
first as it is included in individual species models, and second as it is applied as a
multiplier, and (c) “enhanced” habitat quality does not necessarily compensate for lost
hahitat cp
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modified by a knowledgeable ornithologist to include these (see Section 2.7 for guidance in
adapting AREM to other habitats or regions).
Situation 2. Diagnosing Impaired Wetland Quality. Where wetlands are officially
considered by agencies to be “waters of a state” or where they exist within certain public
ti-ust lands (e.g., National Wildlife Refuges), a legal need sometimes arises to determine the
degree to which wetland quality has been impaired. AREM alone cannot determine this.
However, AREM can assist in diagnosing the presence of contamination problems by
defining which species “should” be present in a wetland having a particular habitat
structure. If properly designed surveys then fail to find the predicted species, it might be
because non-physical (e.g., chemical) factors unmeasured by AREM are discouraging
wetland use by birds. Some caution is necessary because species absence could be due to
weather conditions, to demographic factors (e.g., suitable habitats being “undersaturated”
with individuals because of impacts to populations that have migrated from neotropical
wintering areas 2 ), or weaknesses in particular species models that comprise AREM.
Nonetheless, AR.EM could be useful as an initial screening tool to help decide whether
more effort should be committed to verify that a problem exists.
Situation 3. Selecting Appropriate Indicator Species. By defining which species to
expect in particular types of wetlands, AREM can narrow the list of species to be
considered for potential use as “indicators” in programs to monitor water quality or physical
habitat suitability. Selecting appropriate indicator species is crucial to proper use of HEP,
as well as to the development of wetland biocriteria and the accurate monitoring of wetland
contamination.
Situation 4. Targeting Habitat Enhancements. Active management of wetlands will
usually be most effective when it focuses on improving conditions for species with low
species habitat scores, while maintaining conditions suitable for species with high species
habitat scores. In combination with other considerations, AREM can be used in this
manner to suggest habitat features whose enhancement will support the largest variety of
species overall, or of species having a particular attribute.
Situation 5. Wildlife-based Classification of Wetland Habitats. Wetland “types” are
commonly defined by their vegetative communities. Wildlife communities or individual
species also can be a useful primary or secondary feature in classifying wetlands for
scientific or administrative purposes. AREM can assist such classifications by predicting
bird species and richness that are associated net only with vegetation, but also with other
2 Local absence of a species whose regional populations appear to be Incresaing is particularly suggestive of contamination problems in
the local habitat when the habitat has been determined to be otherwise suitable (peTs. comm., R. i. O’Connor, Dept of Wildlife, University
of Maine, Orono, Maine).
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environmental factors. Statistically-defined, wildlife-based classes of wetlands could be
identified by applying AREM to a probabilistic sample of wetlands in a region.
Situation 6. Optimizing Biodiversity Protection. Resource agencies and conservation
groups sometimes have opportunities to purchase or trade properties to enhance regional
biodiversity. When biological survey data from the subject properties are lacking, AREM
caii be applied (at any season) to predict avian richness of individual properties. The
AREM computer program (p. 28) can then be used to pool the predicted species lists from
multiple wetlands, to determine which combination of wetlands is likely to support the
greatest diversity. This estimate can be focused further by applying constraints related to
land ownership, species characteristics, management costs, or other factors. As such,
AREM can provide a complementary, local refinement of the “gap analysis” approach
currently used for ecosystem management and biodiversity planning at state and regional
levels by the U.S. Fish and Wildlife Service (Scott et al. 1993).
Intended users of AREM are consultants and employees of government agencies, who have
at least a Bachelor’s degree in one of the environmental disciplines. Users must be able to
recognize a few of the major vegetation types of the Colorado Plateau (e.g., salt cedar,
willow).
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2.0 iNSTRUCTIONS
The steps associated with a wetland assessment using AREM can be. organized into three
phases: Field Phase (Section 2.1), Analysis Phase (Section 2.2), and Interpretation Phase
(Section 2.4).
2.1 Field Phase
Step 1 . Locate the wetland.
AREM can be applied to all or part of any lowland wetland or riparian area in the
Colorado Plateau region (Figure 1). The only requirement is that the part of the
wetlandlriparian area to be evaluated must be at least 0.1 acre in size (about 66 ft to a side,
if squarish). AREM can be used to evaluate riparian and wetland areas, defmed broadly as
ecosystems characterized by the presence of soils that are saturated at least periodically and
whose current vegetation is predominantly of species adapted to high soil moisture
conditions. To perform an AREM assessment, you do not need to first delineate wetland
boundaries using a formal procedure.
In some instances it is difficult to distinguish, for purposes of evaluation, where one
wetland should be considered to end and another begin. For example, in river corridors
you may be faced with the need to assign a single rating to a contiguous area that extends
many miles upstream and downstream. If you have time to accurately assess the entire
corridor as one unit, the resulting AREM rating will probably be the truest representatioi:1 of
the corridor’s actual habitat value. However, it is seldom practical to perform such an
exhaustive assessment with acceptable accuracy. Even if it were, owners of a specific
parcel within the corridor often wish to know, “What is the habitat value of my particular
property?” Or, perhaps you wish to compare the relative values of two habitat patches
within a wetland, each of a different cover type. AREM allows you to do so.
The area you evaluate should be the largest area that you can view accurately while
meeting your particular assessment objectives. What is most crucial is that you
document the boundaries of whatever area you evaluate, e.g., by including on your field
form a sketch of the wetland showing landmarks. Doing so makes it possible for another
person to repeat your evaluation with greater precision, and thus maintains the credibility of
the results.
Stet, 2 . Answer the AREM Questions.
Make a copy of the field forms (Appendices A, B, and C) and then visit the wetland.
Complete the documentation form (Appendix A) and then answer the questions in the long
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form (Appendix B) in sequence, placing marks in the appropriate spaces. The first few
times you use AREM, read through each question and footnote carefully, making sure you
understand it. Recognize that although a few questions are “multiple choice” (one answer
only), for most it is appropriate to check several items. Also, even though AREM is
designed for use by one person, answering questions as a group can improve the credibility
of the results. After you have used AREM several times, you will fmd it faster to use the
short form (Appendix C), which requests the same information in the same order as the
long form (Appendix B). However, you may still wish to use the long form periodically to
refresh your memory of some of the definitions and special conditions that apply.
It is crucial that you answer the questions in a way that reflects habitat conditions at the
season for which you want information. For example, if you want to base a wetland
decision on the relative value of a wetland to migratory shorebirds, you should not use an
AREM assessment of mud habitat (question 5) made during November because in many
irrigated wetlands the extent of mud habitat in November is much greater than during May,
July, and August when most shorebirds are migrating through the region. If an answer is
needed before a wetland can be visited during all desired seasons, the best approach is to
contact local sources with an historical knowledge of the habitat indicators that are most
likely to change seasonally. Also, you might review any appropriate aerial photographs
taken during these periods. In fact, wherever they are available, detailed aerial photographs
from several time periods are an excellent ancillary s urce of information that you can use
to cross-check assumptions you might have made in the field.
2.2 Program Installation and Analysis Phase
Step 3 . Load AREM on a Computer and Begin Program.
A disk containing the AREM computer program accompanies this report. The program on
this disk is necessary to convert the information you marked on your field forms into
biodiversity scares and species lists. The program runs on IBM or IBM-clone personal
computers with MS-DOS version 3.1 or later, and requires a hard drive with 580K RAM
and about 2 megabytes of storage available, as well as a floppy disk drive slot capable of
accepting a high-density 3.5-inch disk. No other specialized software is required. The
program runs most rapidly if the files on the disk are, first transferred to a computer’s hard
drive 3 . To do this, proceed as follows, pressing the “Enter” key after each step:
After uncompressing, you should find the following 16 files: arem.bat, aremc2.exc, aremsetexe, chklistms, comb2.exe. combarem.bat,
editarem.bat, editann.exe, habtablc.dbf. regcom.n , regions.dbf, repct.exe, taxinf.dbf, weights.dbf, wghtcom.ntx, whrbasc.dbf. You may delete
the aremsetexe file at this point, provided you have maintained a copy on a disk or in another directory.
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Computer Says: You Tvte: What This Does
C:\> md arem makes a new directory named “AREM”
C:\>. cd\arem puts you in the new AREM directory
Now, insert the AREM source disk into your computer’s disk drive.
C:\>arem copy a:*.* copies the compressed file into the hard drive’s
AREM directory 4
C:\>arem aremset uncompresses the compressed files
C:\>arem arem starts running the program
Be sure to start the program as directed above, by typing “arem,” rather than by typing the
name of the execution file (aremc2.exe). Also, be sure to retain the original disk containing
the program, because when you later edit any of the files (after transferring them to your
hard drive), a backup copy of the original is not automatically saved.
From this point on, the AREM analysis program is menu-driven. That is, instructions will
appear on your computer screen telling you what to do at each step. Basically, you will be
matching information from your completed field forms (Appendices B and C) with similar
information on the screen, and then prompting the computer to analyze it.
2.3 Summary of the AREM Program Steps
The following pages present the computer screens in the order they appear when you use
the AREM programs. Each screen is accompanied by text that clarifies what the user is
expected to do. The analysis sequence is as follows:
AREM Program
Introductory screens, assign a file name (Figure 2)
Mark the indicators from a list (Figure 3)
Mark the subregion and season (Figure 4)
View the wetland scores (Figure 5)
Quit, list scores/species, or change weighting factors (Fig.7)
Change weighting factors (Figure 8)
Instructions for printing (Figure 9)
View the output file (Figure 10)
If this doesn’t work, type b:
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COMBAREM Program (optional)
Combine species lists from different wetlands (Figure 11)
EDITAREM Program (optional)
Select a database to edit (Figure 12)
Computer keys used for editing (Table 6)
Edit the WHRBASE database (Figure 13)
Codes for WHRBASE (Table 7)
Edit the WEIGHTS database (Figure 14)
Edit the REGIONS database (Figure 15)
Edit the TAXLNF database (Figure 16)
Add an indicator (Figure 17)
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Figure 2. AREM’s four introductory screens.
AVIAN RICHNESS EVALUATION METhOD (AREM)
Version 1.8
Computer program for predicting bird diversity
in lowland wetlands of the Colorado Plateau
Concept, database, and species models by:
Paul H. Adamus, ManTech Environmental Technology Inc. *
Email: adanuspPk Ira .csos .orst.edu
Computer programming by:
Rosemary Owen, Computer Sciences orporat ion*
EPA Project Officer: Mary E. Rentula*
MUSEPA Environmental Research Laboratory
288 SU 35th St.
Corvallis, OR 97333
(5113) 754—4666
PRESS (ENTER> TO ADVANCE TO NEXT PAGE
I
INTRODUCTION
The auian richness evaluation method (AREM) which this software supports
is fully described in the publications:
Adanus. P.R. 1993. Irrigated Uetlands of the Colorado Plateau:
Information Synthesis and Habitat Evaluation Method. EPA/68 /R—93/8?1.
U . S. Enu i ronmenta 1 Protect ion Agency, Env ironmenta 1 Research
Laboratory. Corvallis, Oregon. NTIStIPR93 I86Z6II
Adanus, P.R. 1993. User’ s tlanua I : Au ian Richness Eva luat ion Method
(AREN) For Lowland Uetlands of the Colorado Plateau. U.S .Enuironnental
Protection Agency, Environmental Research Laboratory, Corvallis, Oregon.
These are available from the National Technical Information Service,
(NTIS), phone 1—8811—553—6847. Before using AREM, be sure you understand
its limitations and the proper context for its use.
PRESS TO ADVANCE TO NEXT PAGE
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Figure 2 (continued).
AREM was developed during 1992-93 with the support of US EPA Region 8.
Uork uas conducted at the U.S.. Environmental Protection Agency (EPA)
Research Lab through contract fl68—CU—O B6 to lianTech Environmental
Technology Inc. This computer program has been subjected to the
Agency’s peer and administrative review and approved for general use.
Any opinions represented herein are those of the author and do not
necessarily reflect those of EPA. The official endorsement of the Agency
should not be inferred.
This program is furnished by the U.S. Government and is accepted and used
by the recipient with the express understanding that the U.S. Government
g I yes no warranties , expressed or implied, concerning the accuracy,
reliability, usability, or suitability for any particular purpose of the
information contained in this program. The United States shall be under
no liability to any person by reason of any use made thereof. This
program belongs to the U.S. Governnent therefore, the recipient further
agrees not to assert any proprietar9 rights therein, or to represent
this program to anyone as other than a U.S. Government program.
!RE TO ADUANCE TO NEXT SCREEN
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Figure 3. Part of AREM’s indicator list.
1. BICUATER
2. ANVUATER
3. OPEIIOTHER 4
4. STILLIJATER
4. FISH
4. APIPHIBS
4. ENRICHED
4. DRAUDOUM
S. BARE
6. IIUDBIC
7. TREEDI 4
7. TREENEAR
B. FORESTDENS
B. FORESTOPEN
8. UOODDENS
This list contains the habitat indicators needed to predict the presence of 165 bird species in the Colorado
Plateau. Each abbreviated term corresponds to a question in the field form (Appendices B and C), which is
numbered and named identically. Some numbers are repeating because they refer to different conditions of
the same indicator. You will notice that when you mark particular indicators, AREM automatically marks
certain others; the program also prohibits you from marking some others. These programmed actions are part
of an error-checking routine that helps ensure that the field form has been interpreted consistently and
completely. Be sure you do not attempt to assess a wetland by marking directly the abbreviated
indicator conditions shown on the screen. Use the field form first. because the meaning of some of the
indicator terms is quite specific and not clearly conveyed by the abbreviated terms.
After you have marked all the appropriate indicators and pressed the ESC key, the AREM program extracts
all Colorado Plateau species that use such habitats. The directions you give the computer in the next screen
(Figure 4) will reduce the list of species to just those occurring at a particular season or seasons, in a
subregion you specify. If the program fails to run or takes longer than 10 minutes to process data after you
have pressed “ESC” to initiate data processing, reboot your computer (e.g., by turning it off, then on) and
type this at the C:\> prompt: set clipper=/IE:O
If you later wish to print a list of the indicators you had checked above, at the completion of the AREM run
you should retrieve the file “xxxx.hab,” where “xxxx” represents whatever file name you assigned earlier
(Figure 2, fourth screen). This file is in ASCII format, and to print it you can either type “copy xxxx.hab
pm” at the C:\> prompt, or call up your word processing program and have that program convert it to a
compatible format before you try printing it.
Press SPACE—BAR to tag or untag items
marked on the field form
Press H to move cursor up and down
Press ESC to continue or to exit
14
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Figure 4. AREM’s menu for selecting subregion and seasonal period.
Select the periods(s) for which you
want a species list and/or scores.
Select only the period(s) that
would be represented by the field
data you entered.
BREEDING
MIGRATION -
:‘.....:
Se report for definitions
The subregions listed in the top box are major areas of the Colorado Plateau where salinity control projects
are being implemented (see Figure 1, p. 2). If you wish to evaluate wetlands in a part of the Colorado
Plateau that is not listed, select the subregion closest to that area. Using AREM for areas outside the
Colorado Plateau. or for montane wetlands (>7000 ft elevation) will give erroneous results.
As noted in the lower box, be sure the conditions you marked for the habitat indicators in an earlier screen
(Figure 3) are appropriate for jj the season(s) you mark in the lower box of this screen. Where habitat
conditions change greatly between seasonal periods (as is normally the case), AR.EM should be run separately
for each period, if the objective is to estimate year-round avian diversity (as is usually the case). Indicators
that are most likely to change between seasonal periods include the extent of surface water and open water
(#2, 3), bare soil (#5), and intensive grazing (#19). You should use the following calendar dates to define
seasonal periods: Breeding: June 1 - August 15; Migrating: March 1 - May 31, and August 16 - November
30: Wintering: December 1 - February 28. These dates are appropriate for the majority of Colorado Plateau
wetland species. It is recognized that effects of grazing, burning, and mowing can extend over several time
periods. For example, burning increases the predominance of salt cedar ( Tamarix rarnosissima)(Busch and
Smith 1993), and the effects of this change persist for years in a wetland. This would be accounted for by
shrub type (#12) or density of herbaceous vegetation (#14).
After you have marked items in the upper and lower boxes and pressed ESC, the program consults a
supporting database and eliminates from further consideration all species not occurring in that region during
that season. Thus, the results are the species that are present during all of the periods marked. The next
screen (Figure 5) then appears, showing scores based on this selection of species.
Select the subregion
Grand Valley, CO
Lower Gunn I son /Uncor pahgre, CO
Cortez/llcEIno Creek, CO
Uinta Basin/Price/San Rafael UT
Big Sandy, WY
Highlight the region the wetland is in.
Press Enter to select that region.
15
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Figure 5. Example of output in AREM’s score table.
WETLAND SCORES
Cutoff Level for Species Habitat Scores:
>0 >.25 >.50 >.75
all Possible -. nost
spp. conser vat i ye
tinueighted Habitat Score 8.18 8.18 5.81 0.86
I(Jnueighted Richness Score 14.00 14.00 9.00 1.00
habitat Score Weighted By Species:
Relative Dependency on Wetlands 18.50 18.50 13.76 1.71
Relative Abundance 48.01 40.01 38.52 0.00
Taxonomic Uniqueness 42.57 42.57 31.80 7.71
Neotropical Migrant Status 38.88 38.08 31.34 8.57
Official Conservation Designations 23.86 23.86 21.49 0.86
Hunted Status 8.18 8.18 5.81 8.86
PRESS TO CONTINUE
This is the most important of AREM’s screens. It contains the various scores that have been calculated from
the input information you provided. Higher scores indicate evaluated areas that are likely to provide habitat
suitable to a greater number of bird species. Specifically, the “unweighted richness score” is the number of
species predicted to occur; the “unweighted habitat score” is the species habitat score (on a scale
representing habitat suitability of 0 to I, for each species) summed across all species; the “weighted habitat
score” (“Habitat Score Weighted by Species”) is the product of each species’ species habitat score and a
weighting coefficient, summed across all species. Cutoff levels are thresholds for species habitat scores; their
derivation and meaning is best demonstrated by the example in Table 4.
To interpret these scores for use in resource decisions, see Section 2.4 of this manual. To trace the
calculations that led to the scores in a particular instance, you can select the option, “List wetland scores (to
report file for printing)” on a later screen (Figure 7).
16
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Figure 6. Menu for specifying the seasonal period upon which species are to be weighted
by abundance.
This screen might seem redundant because you already specified seasonal information at an earlier menu
prompt (Figure 4). However, at the earlier prompt you could select multiple periods whereas here only one
seasonal period can be selected. The reason is that the information in the earlier screen was used to eliminate
species that are absent during one or more specified periods, whereas this screen is used to weight the
occurring species by their abundance. Because abundance of most species changes by seasonal period, the
weighting calculations are feasible only if based on a single period.
Minter ing
Weighted Habitat Score b!jRelatiue Abundance
For this characteristic, jou can specif!J onig one period
for which gou want species weighted bg their abundance.
Press Enter to select the period gnu highlighted
17
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Figure 7. Menu for finishing the AREM analysis or changing the weights.
At this point in most applications you will select the option QUIT. However, if you select the first option
(“List wetland scores...”) before quitting, the program will save not only the wetland scores (Figure 5), but
also lists of predicted species and their weights, and the list of habitat indicators you selected earlier (Figure
3). These can be viewed by moving the cursor to QUIT and pressing the Enter key again. Figure 9 will
appear; follow its instructions to print the scores, predicted species, and habitat indicators.
If you select the second option (“Change weighting factors”), the upper screen lets you change the weights
preassigned to different species characteristics. For example, currently the AR.EM program assigns greater
weight to highly dependent species (weight=lO) than to dependent ones (weight=2). If you wish instead to
assign greater importance (weight) to dependent species than to highly dependent species, move the cursor to
the second line and press the Enter key. The screen shown on the next page will pop up.
List Wetland Scores and Species lists
Change weighting factors
QUIT
Press Enter to select the highlighted option
18
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Figure 8. Menu for changing wéiühts of a species characieristic
Species’ Relative Dependency on Wetland
Category Current ueight lieu uezght(Optional)
Highly Dependent 19
• Intermediate 6
• Dependent 2 ••
If you wish to leave weights as they are, move to last line and press Enter
If you wish to change weights, type in new weights, move to last line,
and press Enter . I
I
This menu appears if you previously opted (Figure 7) to change the weights of a species characteristic. If
you do not wish to alter the weights for the first characteristic that appears, move the cursor to the last line
and press Enter. The program will then advance to the next characteristic and ask if you want to change its
weights. This repeats until you’ve been given a chance to change weights of each of the six characteristics.
The program then returns you to Figure 7, where you will probably want to apply the new weights by
selecting the third option (“Create table with new weighting factors”). Then, to recalculate the weighted
habitat scores (Figure 5), move the cursor to the third line (“Create table with new weighting factors”) and
press Enter. The program automatically replaces the earlier file (that was based on the previously-assigned
weights) and gives the new “xxxx.out” file the same name. The new weights you assigned will continue to
be used in all future calculations by AREM. Thus, caution is advised because turning the computer off and
on again will not restore the original weights. To restore the original weights, you must reinstall AREM
from the original disk.
19
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Figure 9. AREM’s concluding instructions.
This screen tells how to print out the list of species kxpected to occur in the evaluated wetland, along with
their individual habitat scores. Another method for printing this information is to begin running your
computer’s word processing software (e.g., WordPerfect). Retrieve, view, and print the file “xxxx.out,” where
“xxx.x” represents whatever file name you assigned earlier (Figure 2, fourth screen). Because the file is in
ASCII format, you will need to first have your word processing program convert it to a compatible format.
Many word processing programs do this automatically.
20
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Figure 10. Example of documentation output file from an AREM evaluation.
The following pages contain the contents of a file that was created by running AREM. In this example, the
user input a wintertime description of a wetland located in the Lower Gunnison Valley of Colorado.
Lines 1-16 provide a printed copy of the score table you saw earlier on the screen (Figure 5). (In the actual
printout, lines are not numbered). See Section 2.4 for guidance in deciding which scores to use for a
particular application.
Line 19 specifies the cutoff level for the species habitat score. In this case, the cutoff of >0.00 means that the
lists on the next two pages contain only species whose species habitat score in the evaluated wetland
exceeded 0.00. AREM provides similar lists for the other cutoff levels (>0.25, >0.50, >0.75) but only
information for the >0.00 level is shown in this figure. On line 20, the unweighted habitat score is the sum
of the species habitat scores for the listed species (as demonstrated in Table 4). On line 21, the unweigbted
richness score is simply the number of species on the list. Again, see Section 2.4 for guidance in deciding
which scores to use for a particular application.
•Lines 25-30 alphabetically list the 6 species that would occur at that season in that subregion and for which
habitat in the particular wetland was predicted to be at least minimally suitable, i.e., the species habitat score
for all listed species is greater than 0. The series of lists that follows provides information and tallies for
each of the weighting characteristics. The weighting characteristics are described in Section 3.2, and include
ordinal values for each species pertaining to the following:
Dependency on WetlandlRiparian Habitat
Relative Abundance
Taxonomic Uniqueness
Neotropical Migrant Status
Offical Conservation Designations
Hunted Status
For example, lines 38-40 report that none (0, 0%) of the species predicted to be present is highly dependent
on water as a substrate in lowland wetlands of the Colorado Plateau at this season; 2 (33.33%) of the 6
species are of intermediate dependency; and 9 (66.67%) are categorized as dependent. The AR.EM program
does not assign these dependency categories in a context-specific manner. That is, they are not meant to
reflect the dependency of individual birds on a specific individual wetland, but rather the dependency of the
species generally on water as a substrate within the region.
Following this, lines 28-38 contain the same list of species and species habitat scores as in lines 25-30, but
organize the species primarily by dependency category. Weights that were used for each category are
reported in the column farthest to the right ( “V ’eight”). These weights were multiplied by the species habitat
scores (“Species Habitat”) to generate the Weighted Score for each species, which when summed for all
species gives dependency score reported on line 34.
Although not essential, additional documentation of the analysis can be viewed. If you have the commercial
database program “dBASE,” or another program that allows you to view “.dbf’ files, you can load and
examine the file labeled “tmpsort.dbf.” For every species predicted to occur in the last wetland you analyzed,
this lengthy file lists each associated indicator condition that led to its predicted occurrence in this wetland.
This file is written over (erased) each time you run AREM.
21
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1 WETLAND SCORE SUMMARY
2
3 Cutoff Level for Species Habitat Scores:
4 >0 >.2S .50 >.75
5 alt possible most
6 spp. conservative
7
8 Unweighted Habitat Score 3.41 3.41 1.99 0.80
9 Unweighted Richness Score 6.00 6.00 3.00 1.00
10 Habitat Score Weighted By Species:
11 ReLative Dependency on WetLands 12.25 12.25 9.41 4.80
12 ReLative Abundance 26.40 26.40 18.65 8.00
13 Taxonomic Uniqueness 20.33 20.33 10.76 7.20
14 NeotropicaL Migrant Status 18.48 18.48 11.95 4.80
15 OfficiaL Conservation Designations 15.11 15.11 9.19 8.00
16 Hunted Status 3.41 3.41 1.99 0.80
17
18
19 Cutoff Level for Species Habitat Score: >0.00
20 Unweighted Habitat Score: 3.41
21 Unweighted Richness Score: 6.00
22
23 Listing of Species
24 Species Name Species Habitat
25 AMERICAN GOLDFINCH 0.64
26 AMERICAN PIPIT 0.56
27 AMERICAN ROBIN 0.47
28 BALD EAGLE 0.50
29 MARSH WREN 0.80
30 SONG SPARROW 0.45
31
32 Habitat Score Weighted By Species
33
36 Weighted Habitat Score by: Relative Oependency on Wetland = 12.25
35
36 Category Current weight Nuiber Percent
37 Species Species
38 HighLy Dependent 10 0 0.00
39 Intermediate 6 2 33.33
40 Dependent 2 4 66.67
41
42 Listing of species
43 Species name Category Weighted Species Weight
44 Scores Habitat
45 AMERICAN PIP1T Intermediate 3.33 0.56 6.00
46 MARSH WREN Intermediate 6.80 0.80 6.00
47 AMERICAN GOLDFINCH Dependent 1.27 0.64 2.00
48 AMERICAN ROBIN Dependent 0.93 0.47 2.00
49 BALD EAGLE Dependent 1.00 0.50 2.00
50 SONG SPARROW Dependent 0.91 0.45 2.00
51
52 Weighted Habitat Score by: Relative Abundance = 26.60
53 Category Current weight Kuther Percent
54 Species Species
55 Uncomon 10 3 50.00
56 Fairly Coimiort 8 1 16.67
57 Cannon 4 1 16.67
58 Abundant 2 1 16.67
59
60 LIsting of species
61 SpecIes name Category Weighted Species Weight
62 Scores Habitat
63 AMERICAN PIPIT Unconinon 5.56 0.56 10.00
64 BALD EAGLE Unconmon 5.00 0.50 10.00
65 MARSH WREN Uncoenion 8.00 0.80 10.00
66 AMERICAN GOLDFINCH Fairly Camnon 5.09 0.64 8.00
67 SONG SPARROW Cannon 1.82 0.45 4.00
68 AMERICAN ROSIN Abundant 0.93 0.47 2.00
22
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70
71
72 Weighted Habitat Score by: Taxonomic Uniqueness = 20.33
73 Category Current weight Nuther Percent
74 Species Species
75 Order 9 1 16.67
76 Suborder B 0 0.00
77 Parvorder 7 0 0.00
78 Suprafaniity 6 2 33.33
79 FamiLy 5 3 50.00
80 SubfamiLy 4 0 0.00
81 Tribe 3 0 0.00
82 Genus 2 0 0.00
83 Species 1 0 0.00
84
85 Listing of species
86 Species name Category Weighted Species Weight
87 Scores Habitat
88 BALD EAGLE Order 4.50 0.50 9.00
89 AMERICAN ROBIN SuprafairiLy 2.80 0.47 6.00
90 MARSH WREN Suprafairity 4.80 0.80 6.00
91 AMERICAN GOLDFINCH FamiLy 3.18 0.64 5.00
92 AMERICAN PIPIT FamiLy 2.78 0.56 5.00
93 SONG SPARROW FamiLy 2.27 0.45 5.00
94
95
96 Weighted Habitat Score by: Neotropical Migrant 18.48
97 Category Current weight Nuiter Percent
98 Species Species
99 TypeA 10 0 0.00
100 TypeB 6 5 83.33
101 Non-neotropiceL species 2 1 16.67
102
103 Listing of species
104 Species name Category Weighted Species Weight
105 Scores Habf tat
106 AMERICAN GOLDFINCH Type B 3.82 0.64 6.00
107 AMERICAN PIPIT Type B 3.33 0.56 6.00
108 AMERICAN ROBIN Type B 2.80 0.47 6.00
109 MARSH WREN Type B 4.80 0.80 6.00
110 SONG SPARROW Type B 2.73 0.45 6.00
111 BALD EAGLE Non-rieotropical. species 1.00 0.50 2.00
112
113
114 Weighted Habitat Score by: OfficiaL Conservation Designations = 15.11
115 Category Current weight NLrter Percent
116 Species Species
117 Threatened 10 0 0.00
118 Endangered 10 0 0.00
119 Candidate Endng/Threat. 10 1 16.67
120 G3 species 10 0 0.00
121 G4 species 10 0 0.00
122 51 species 10 0 0.00
123 52 species (CO onLy) 10 1 16.67
124 S3 species (CO onLy) 10 0 0.00
125 Watch List species (CO) 10 1 16.67
126
127 Listing of species
128 Species name. Category Weighted Species Weight
129 Score Habitat
130 BALD EAGLE Threatened 5.00 0.50 10
131 BALD EAGLE G3 species 5.00 0.50 10
132 MARSH WREN S3 species (CO onLy) 8.00 0.80 10
133
23
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134
135
136 Weighted Habitat Score by: Hunted Status 3.41
137 Category Current weight NuTber Percent
138 Species Species
139 Hunted 10 0 0.00
140 Not Hunted 1 6 100.00
141
142 Listing of species
143 Species name Category Weighted Species Weight
144 Scores Habitat
145 AMERICAN GOLDFINCH Not Hunted 0.64 0.64 1.00
146 AMERICAN PIPIT Not Hunted 0.56 0.56 1.00
147 AMERICAN ROBIN Not Hunted 0.47 0.47 1.00
148 BALD EAGLE Not Hunted 0.50 0.50 1.00
149 MARSH WREN Not Hunted 0.80 0.80 1.00
150 SONG SPARROW Not Hunted 0.45 0.45 1.00
151
152
24
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Table 4. Example showing how AREM scores are calculated.
WETLAND #1:
Species Habitat Conservation Weighted
Species Score (calculated) 5 Priority Weight 6 Habitat Score
Downy Woodpecker 0.77 2 1.54
American Crow 0.63 2 126
Black-billed Magpie 0.27 2 0.54
Lewis’ Woodpecker 0.18 2 0.36
Marsh Wren 0.60 6 120
Wilson’s Phalarope 0.31 6 0.62
Pied-billed Grebe 0.84 10 8.40
Bonaparte’s Gull 0.22 10 220
Common Goldeneye 0.43 10 4.30
Unweighted Habitat Score: 4. ( =sum of the species habitat scores)
Unweighted Richness Scores:
@ species habitat score cutoff of >0.75 =
(2 species: downy woodpecker (0.77), pied-billed grebe (0.84))
@ species habitat score cutoff of >0.50 =
(above 2 species, plus American crow (0.63), marsh wren (0.60) = 4 species)
@ species habitat score cutoff of >0.25 = 7
(above 4 species, plus magpie, phalarope, goldeneye = 7 species)
@ species habitat score cutoff of >0 = 2
(all species above, = 9 species)
Weigi ted Habitat Scores (weighting factor= “water dependence”):
@ species habitat score cutoff of >0.75 = 2.2
(add downy woodpecker (1.54) to pied-billed grebe (8.40))
@ species habitat score cutoff of >0.50 = 12.40
(above, + American crow (1.26) + marsh wren (1.20))
@ species habitat score cutoff of >0.25 = 17.86
(above, + magpie (0.54) + phalarope (0.62) + goldeneye (4.30)
@ species habitat score cutoff of>0 = 20.42
(sum of all weighted habitat scores)
Species having a score of 0 are not Included in this exanple. Also, expect that species lists
from most wetlands will be Longer than this exMple.
6 In this exaffpte, weights in the database that define each species’ relative dependence on water
are used. Users have the option to select other conservation characteristics for which the database
contains a weight for each species (e.g., relative regional ab .aidance , status as a neotropical
migrant).
25
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2.4 Interpretation Phase
The AREM program automatically outputs 32 scores (4 cutoffs x 8 score types). AREM
includes such a large number of scores to give potential users maximum flexibility in
tailoring their use of AREM to a particular evaluation objective. But given this large
variety of output, a common question is “Which number should I use?”
There is no universally appropriate answer to this question. Each user should make the
choice based on a thorough understanding of exactly what each type of score represents,
and a knowledge of the objectives for a particular AREM application. Regardless of which
choice is made, if comparisons are going to be made among wetlands, the same type of
score should be calculated and used for all wetlands. The following paragraphs provide
information to facilitate choices concerning which score may be most appropriate.
Unweighted vs. Weighted Score . If you prefer to consider all species (not habitats) to be of
equal importance, use the unweighted habitat or richness score. However, there are
sometimes situations where habitats (especially in landscapes undergoing transition because
of recent disturbance) superficially seem important because they are rich in species, but
upon further examination, it is found that the particular species comprising this richness are
(a) “generalists” that could thrive almost anywhere, (b) species that have populations which
are non-sustainable in this habitat type, or (c) are less desirable for other reasons. Thus, in
some instances you may wish to consider some species as more important than others. In
such instances, use the weighted habitat score pertaining to the characteristic you consider
to be most important.
Cutoff Level . The species habitat scores cover the numeric range, 0-1.0, and cutoffs have
been established arbitrarily at the >0, >0.25, >0.50, >0.75 points within this range. These
cutoffs represent habitat suitability, not a finite statistical probability of a species occurring
in a. wetland. Likewise, the species habitat score of individual species, which is used to
calculate the unweighted AREM score, describes habitat quality only in relative terms, not
in absolute terms. For example, a species habitat score of 0.78 does not guarantee survival
of minimum viable populations of a species, or mean that a 78% chance exists of fmding
the species during any visit to a particular wetland. The score does suggest that the species
is relatively more likely to use the particular wetland than a species whose species habitat
score for the wetland is, say, 0.53. Testing of AREM during the breeding season (Adamus
1993b) suggested that the 0.75 cutoff predicts actual richness and species composition better
than the other cutoffs. The “best” (most predictive) level for a cutoff will be influenced by
season, regional population levels of the species, and competitive relationships among
species. Ultimately, the choice of a cutoff level will depend on personal preference of the
user. Users wishing to take a very conservative approach to estimating habitat suitability
will use a cutoff of >.75, whereas those taking a broader approach will use a cutoff of >0.
26
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Using the broader .approach will allow the user to make finer distinctions among wetlands
because more species will be included, but whether or not these distinctions are
commensurate with the true sensitivity of each species’ response to habitat is unknown.
Habitat Score vs. Richness Score . The unweighted habitat score accounts for both the
number of species present and the relative suitability of habitat for each, whereas the
unweighted richness score only quantifies the number of species for which the habitat is at
least minimally suitable. The unweighted habitat score is generally more sensitive than the
unweighted richness score in making fme distinctions among wetlands because it has a
wider range of possible values (e.g., it covers the range between about 0 and 120 with
decimal values as well as whole numbers (integers), whereas the unweighted richness score
covers this range only with integers). The unweighted habitat score includes decimal values
because it is the product of an integer variable (species richness) and an ordinal scaler
(habitat suitability, rated on a 0 to 1.0 scale for each species). However, as stated before, it
is uncertain whether such sensitivity is commensurate with the true ability of species to
discriminate among habitats. In contrast, the unweighted richness score is based on a
quantifiable parameter (species richness) that can be independently measured.
Which Weighting Factor ? The choice of which weighting factor to use is purely one of
values -- personal values, agency values, or values of whomever the wetland evaluation is
being conducted for. The six characteristics that AREM uses to weight individual species
represent a set of values that are generally supported by conservation biologists and/or
natural resource agencies. Users may choose to weight wetlands by using just one of these
weighting factors, or by using several (e.g., by adding or multiplying the scores of each).
Section 3.2 describes the categories defined by each weighting factor.
Multiplying by Habitat Area . Any of the non-zero scores from AREM can be multiplied by
area (acreage) to yield a “habitat index” value. Doing so will further increase the ability of
AREM to show fine differences among wetlands, but again, the point at which these
differences actually reflect meaningful differences in habitat importance to biodiversity is
unknown. Moreover, multiplying an AREM score by area is somewhat redundant, because
AREM has already taken into account the effects of area on species richness 7 . Other
AREM has done so in the following two ways:
First, a major reason why richness increases with habitat area is that as area increases, so typically does habitat complexity (e.g., the number
of cover types within a wetland). Greater habitat complexity generally supports more species. AREM accounts for this fact by allowing the
user to note the presence of elements in a wetland that together create habitat complexity (as contrasted with some classification systems that
force the user to assign a single type to a wetland which actually is a mosaic of types).
Second, because species richness in limited circumstances can increase with increasing area independently of changes in habitat complexity,
AREM has directly incorporated habitat patch size into the models for most species. However, this independent effect of area was not
considered to be a continuous linear function but, rather, a discrete stair.step function that recognizes just two gross patch size thresholds
(see p. 46).
27
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objections described in Table 3 (#6, page 5) further underline the need for caution when
interpreting “habitat index” values.
The Most ImDortant Wetlands Aren’t Always the Highest-Scoring Ones . In most instances,
the greatest importance and the most protective administrative action is accorded to
wetlands for which AREM assigns the highest scores. However, as shown in Table 5, there
are sometimes instances where a wetland has relatively low diversity yet contributes
importantly to regional diversity because the few species it has are absent from most other
wetlands. Thus, if time and resources allow, it is desirable to not only examine AREM
scores from a series of wetlands, but also the species composition predicted for each
wetland. Wetlands predicted to contain species that are present in few if any of the other
wetlands would be accorded high priority, in addition to those that have high AREM
scores.
To screen several wetlands to help identify ones most likely to contribute strongly to
regional avian diversity, review the weighted richness score in which Relative Abundance
was used as the weighting characteristic. Large values for this score often (but not always)
highlight wetlands that contribute exceptionally to avian diversity at a regional level. Using
this cue, you can combine the species lists of two wetlands by typing “combarem” at the
C:\>arem prompt. The screen shown in Figure 11 will then appear. Do not try to start the
program by typing the name of the execution file (comb2.exe). By following the menu
instructions shown in Figure 11, you will be able to generate a cumulative species list for
the two wetlands, as well as producing all the usual types of synthesis scores. By doing
this sequentially among all pairs of wetlands (including “new” wetlands that you have
created by combining existing wetlands using the computer), you can identify the set of
wetlands that collectively will contain the greatest avian diversity. Note that when AR.EM
combines wetlands, it calculates the synthesis scores using the species habitat scores only
from the wetland that had the higher score for a particular species.
Doing a Sensitivity Analysis . Sensitivity analysis is an optional process, and consists of
determining what happens to the output score values if the input values andlor the models
are adjusted. Sensitivity analysis will not tell you which type of score to use, but when
applied to a particular wetland, it will estimate the range of values (certainty interval) that
would result if you had answered questions differently or made different model
assumptions. To conduct a sensitivity analysis using your input data, run the AREM
program several times for the same wetland, each time changing your response to questions
that you were unsure how to answer and that describe an indicator condition. For example,
if you are unsure whether the willow vegetation you see in a wetland should be considered
“dense” or “open” (Appendix B, question #12), try each (and both) to see what happens to
the scores. Then, in your evaluation report you can report the scores for that wetland as a
range of values, rather than a single number. If someone subsequently wishes to use the
28
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Table 5. Use of AREM to select wetland combinations that optimize protection of
regional avian diversity.
This simplified example demonstrates the importance of using AREM (or other.methods)
to consider wetland functions and values at a cumulative, regional scale as well as
individually . If the only basis for a wetland decision was the scores for. individual
wetlands, then wetlands A and B below would be selected because they individually have
the most species (i.e., highest unweighted richness score) of any of the wetlands.
However, if the objective is to maintain biodiversity at a regional level rather than
exclusively at an individual site level, then wetlands C and D, which individually are the
poorest in species, would be the best choice because together they have a larger species
list (8 species) than the two richer wetlands combined (6 species), or the combination of
either poorer wetland (C or D) with either of the richer wetlands (A or B). Wetlands C
and D also would usually be chosen if the unweighted habitat score were used instead
of the unweighted richness score. These selection principles can be applied to more than
two wetlands at a time; for large sets of wetlands a computer greatly facilitates the
calculations. In instances where one or both members of an “optimal pair” (as
determined by this process) cannot be protected because of cost or other reasons, a next-
best pairing of sites can be determined; some authors have even proposed that cost-per-
species-protected be used to optimize conservation strategies, i.e., by calculating and
comparing for all possible wetland combinations both the number of species protected
and the associated land stewardship costs. Of course, sustaining the populations of all
species requires some redundancy of species composition among wetlands.
Occurs in Wetland:
Species A B C D
Mallard x x x
Mourning Dove x x x
Black-billed Magpie x x x
European Starling x x x
Song Sparrow x x x
Killdeer x x
Sora x
Yellow-headed Blackbird x
Total Species
(Unweiglned Richness) 6 5 4 4
Collective # of Species, by Combination of Wetlands:
Wetlands A + B = 6 (aU species but Killdeer are redundant);
Wetlands A + C = 7; Wetlands A + D = 7; Wetlands B + C = 6;
Wetlands B + D = 7; Wetlands C + D = 8 (no species are redundant)
Despite a continuing and necessary focus of resource agencies on the individuaL
site LeveL when setting wetLand priorities, the cuiutative assessment principLes .çon
which this exalipLe is based are aLso reLevant and have been noted for years by
conservation bioLogists (e.g., Samson and Knopf 1982, Usher 1986, Vani-Wright et aL.
IQQ1
29
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Figure 11. Menu for combining bird lists and scores from multiple wetlands.
Press SPflCE-BAR to tag or untag Files
Press TI to oue cursor up and down
Press ESC to continue or to exit
30
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scores to differentiate this wetland from others, only the wetlands whose score ranges do
not overlap would be considered different.
To conduct a sensitivity analysis on the models (as opposed to the input data), use the
editing program described in Section 2.4 to vary the habitat models for any or all species.
Then use the modified database to recalculate the scores. Repeat the procedure if you wish,
editing the database and recalculating the scores each time. As described above, in your
evaluation report you can then report each score for that wetland as a range of values,
rather than a single number, so that comparisons among wetlands will be interpreted
meaningfully.
Rei,orting . Results of some AREM evaluations might ultimately be used in administrative
or legal proceedings, or might be subject to wider public scrutiny. Anticipating this
possibility, it is advisable to document all evaluations with a level of detail, appropriate for
the particular application. Some of the key material to retain in a file describing an AREM
evaluation would include the following:
1. AREM Field Form: Documenting Information (Appendix A). This should be
completely filled out and be accompanied by a sketch map showing boundaries of
the area included in the evaluation, as well as its general location.
2. AREM Field Form (Appendix B or C), filled out appropriately.
3. Verification that the original AR.EM database and program were used to generate
the scores. This can be documented partly by printing out a list of the files in your
AREM directory that shows the dates of those files. If certain files in the listing --
WHRBASE.DBF, WEIGHTS.DBF, REGIONS.DBF, and TAXINF.DBF -- are
dated later than I l/93 , then it can be assumed they are the original files, and
program outputs are suspect. The outputs may still be used if each of the specific
changes that were made to the database are explicitly identified and a rationale is
given for each change.
2.5 Editing Options
A unique feature of AREM is that it allows users (regardless of their computer skills) to
edit the main database upon which the species models and ultimately the scores are based.
Keep in mind one crucial point: if you wish to edit any databases, before doing so be
sure you have retained (on ‘at least one separate disk) a backup copy of all files you
wish to save. This is imperative because the AREM program automatically replaces older
Assuming your computer, like most, automatically dates all changes to files.
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files with newer ones every time the editing program is used. Once the backups are made,
to begin editing, do the following:
ComDuter Says: You Tyre: What This Does
C:\>arem editarem starts running the editing program
If the program fails to run when you type “editarem,” type this at the prompt:
set clipper=/IE:O
At this point the screen (Figure 12) asks you to choose a database to edit:
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Figure 12. Menu for selecting AREM databases to edit.
Select “Edit Wi-IRBASE” if you want to change any of the species models, delete or substitute an
indicator, or add or delete a species.
Select “Edit WEIGHTS” if you want to change any of the numeric weights currently assigned to a
particular species, or substitute a weighting characteristic (Contrast this to the option given in the
main AREM program, which allows you to change a particular weight for all species that have that
weight).
Select “Edit REGIONS” if you want to add or delete a species from a subregion, or change the code
describing its relative abundance in a subregion.
Select “Edit TAXrNF” if you want to edit the taxonomic placement of a species.
Select “Add fields to WI-IRBASE” if you want to add a new habitat indicator.
The first field (column) in all the databases contains the species names; all databases contain the same 165
species in the same alphabetical order. Before-attempting to edit these databases, become familiar with keys
needed to move the cursor. These are shown in on the next page (Table 6).
Select I ron the neria
Edit IJHRRASE
Edit WEIGHTS
Edit REGIONS
Add fields toWHRBASE
QUIT
This menu allows you to select one of the databases that supports AREM and edit it:
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Table 6. Instructions for AREM’s editing program (EDITAREM).
Desired Operation Instructions :
Move around in the database use arrow keys and
page up/down keys
Change a code type over the
existing code, then
press Enter
Delete a code Space bar, Enter
Save changes and exit Esc
Delete a species (and all its information) move cursor to
species’ name, press
Del
Undelete a species you deleted this session Del (again)
Move -to end of row (last field) Ctrl, End
Move to beginning of row (species name) Ctrl, Home
Move to last species Ctrl, PgDn
Move to first species Ctrl, PgUp
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Figure 13. Part of AREM’s wildlife habitat relationships database (WHRBASE).
OPIPtONNAPI POTPIAX IIIJPIX BICUATER ANVUATER OPENRIG
If you selected this database to edit, you will be able to change any of the species models, delete or substitute
an indicator, or add or delete a species. Be sure, if you add or delete a species, to add or delete it (with its
associated information) from ALL FOUR databases -- WHRBASE, WEIGHTS, REGIONS, TAXINF;
Each of the abbreviated column headings in the above matrix corresponds to a question in the field form
(Appendices B and C) which is named identically. The codes within this database (x, f, 1, +2, etc.) are explained
on the next page (Table 7), and if additional clarification is desired, Adamus (1993a, Table 12, p. 46) provides
an example. Note that when you edit information on a species in the WI-ER.BASE database, the computer program
automatically recalculates, if appropriate, the value of PotMax (the potential maximum score for the species,
which is divided into the actual score so as to normalize scores across species) and NumX (the number of
required “X” conditions, as specified for that species). These fields are “locked” and cannot be edited.
35
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Table 7. Meaning of codes used in the species habitat relationships
database.
X Means the indicator condition describes habitat that is minimally
acceptable to the species. In the computation of a species’
habitat score, a base score of 5 points is assigned if the
evaluated wetland contains the indicator condition marked in the
database with an “X”. If the database contains two indicator
conditions marked with an “X,” both must be present.
f Means that condition others that are labeled in the database
with an are minimally acceptable to the species. If any
condition marked with an “f’ is present in the evaluated
wetland, 5 points are assigned, unless 5 points were assigned
previously due to occurrence of a condition denoted by an “X.”
I (could also be 2, 3, or 4):
Means the condition supports the species in a compensatory
manner. That is, this particular indicator condition could
support the species, but so could other conditions denoted on a
relative scale (4= more important, 1= less important) in the
database, and not preceded by a + or - sign. In calculating the
species habitat score, AREM selects the maximum of the
numbers associated with conditions of the evaluated wetland,
and adds this value to the base score of 5.
+1 (could also be +2 or +3, or could be preceded by a minus sign; a +2
is considered more influential than a + I, and a -2 more influential than
a-I).
Means the condition supports the species in a cumulative
manner. That is, if more than one of such conditions are
present, their individual effects behave approximately in an
additive manner. In calculating the species habitat score,
AREM adds the values of all such conditions that are present in
the evaluated wetland to the previously calculated sum.
(blank)
Means the indicator condition is not sufficiently relevant to
predicting the suitability for the species (i.e., other indicator
conditions are more predictive)
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Figure 14. Part of AREM’s WEIGHTS database.
Record 1 /165
COPHIONNAN WETDEPEND NEOTROPIC HUNTED ThREATENED ENDANGERED CANDIDATE
6 2 1 1 1 1
AMERICAN BITTERN 6 2 1 1 1 1
AMERICAN COOT 18 2 18 1 1 1
AMERICANCROW 2 2 1 1 1 1
AMERICAN GOLDFINC h 2 6 1 1 1 1
AMERICAN I(ESTREL 2 6 1 1 1 1
AMERICANPIPIT 6 6 1 1 1 1
AMERICAN ROBIN 2 6 1 1 1 1
AMERICANTREESPARH 2 2 1 1 1 1
AMERICAN WIGEON 18 2 18 1 1 1
ASH-THROATED FLYCAT ‘ 2 18 1 1 1 1
BALDEAGLE 2 2 1 18 1
BAND—TAILED PIGEON 2 18 1 1 1
BANK SUALLOLI 2 18 1 1 1 1
BARNOWL 2 2 1 1 1 1
BARN SUALLOW - 2 18 1 1 1 1
BARROW’S GOLDDIEYE 18 2 18 1 1 1
BELTED KINGFISHER 18 6 1 1 1 1
If you selected this database to edit, you will be able to change any of the numeric weights currently assigned
to a particular species, or substitute a weighting characteristic. Column headings are explained in Section
3.2.5.
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Figure 15. Part of AREM’s REGiONS database.
Record i 16S
COMPIONNAM CUB GUM GVU CUR UTB UTM UTU UTR CZB CZM CZU CZR
U U C C C
AMER ICAN BITTERN X U U
AMERICAN COOT Ii C C A U F C
AMERICAN CROLI U F F. C C C
AMERICAN GOLDFINCH U F C C U F C C
AMERICAN KESTREL C C F C C U C C C
AMERICAN PIPIT U U U C
AMERICAN ROBIN C C A C C C C C
AMERICAN TREE SPARROW U U C
AMERICAN WIGEON U F U C C C U
ASH-THROATED FLYCATCHER U X F
BALDEAGLE U U C C C
BAND—TAILED PIGEON X F F
BANK SWALLOW U F U U
BARHOIJL U LI U X X
BARN SWALLOW A C C U C C
BARROW’S GOLDENEYE X X
BELTED KINGFISHER Li U U F F U
If you selected this database to edit, you will be able to add or delete a species from a subregion, or change
the code describing its relative abundance in a subregion. The column headings are abbreviated as follows:
GVB= Grand Valley, breeding; GVM= Grand Valley, migration; GVW= Grand Valley, wintering;
CZB= Cortez, breeding; CZM= Cortez, migration; CZW= Cortez, wintering; UTB=Utah, breeding;
UTM= Utah, migration; UTW= Utah, wintering; WYB= Wyoming, breeding; WYM= Wyoming,
migration; WYW= Wyoming, wintering. Data were from sources listed in Adamus (1993 a). See
Figure 1 (p. 2 ) for amap showing specific portions of these geographic areas that are covered by
AREM.
Codes used in the database are:
A (abundant), C (common), F (fairly common), U (uncommon), X (rare), blank (absent or not
regularly-occurring). See Section 3.2.2 for explanation of how these were efined.
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Figure 16. Part of AREM’s TAXINF database.
Record 1/165
COM1IONNAPI ORDER SUBO PARUO SIJPF FAIl SURF TRB
1 0 0 0 8 8 0
DOUBLE—CRESTED CORMORANT 2 8 8 8 : 6 0
AMERICAN BITTERN 3 8 8 8 1 0 0
GREAT BLUE HERON 3 0 0 0 1 8 6
SNOWY EGRET 3 0 0 0 1 0 0
BLACK—CROWNED NIGHT-HERON 3 0 6 6 1 11
UHITE—FACED IBIS 3 0 0 0 2 0 0
MALLARD - 4 0 0 0 8 0 0
GREEN-WINGED TEAL . , 4 0 0 0 0 0 0
NORTHERN PINTAIL 4 8 0 0 0 0 0
BLUE-WINGED TEAL 4 0 0 0 0 0 0
CINNAMON TEAL 4 0 0 0 0 8 0
NORTHERN SHOVELER 4 8 0 0 0 0 8
GADIJALL 4 8 0 0 0 0 0
AMERICAN WIGEON 4 0 0 0 0 0 0
IJOODD IJCJ( 4 8 8 0 0 0 8
CANUASBACK 4 8 0 0 8 0 0
REDHEAD 4 8 0 0 0 0 0
If you selected this database, you will be able to edit the taxonomic status assigned to species. You should
first read Section 3.2.3. The abbreviations used are: SUBO= suborder, PARV.O= parvorder, SUPF
superfamily, FAM= family, SUBF= subfamily, TRB= tribe. Numbers are used only as markers to link
members of the same taxonomic unit.
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Figure 17. Menu for adding indicator conditions.
If you have chosen to add a new indicator, this menu prompts you to give it a name and number. The
program uses the number to place it in sequence in the list of indicators (Figure 3).
Add Fields to WIIROASE database
Enter new Field nane: 1 ..:.
This must contain no more than 16 characters.
Enter new indicator nunber
40.
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2.6 Possible Applications of the Editing Program
AREM’s editing program can be used to accomplish the following:
1. Adding a species. Suppose you wish to add some mammals, plants, or additional bird
species to AREM. After loading the EDITAREM program, first select WHRBASE. Go to
the last line (Ctrl, PgDn), move the cursor down a line, and type in the new species and its
habitat codes, using a different line for each species added. If you accidentally enter a code
or make a change in the wrong place, just place the cursor on top of the error and press the
space bar to erase the error. Exit the program (Enter, Esc); there will be a delay of perhaps
several minutes as the program automatically saves the information, regardless of whether
you changed the database. Then select each of the other three databases (Figure 12) in turn
and add the new species and their associated information to these databases in a similar
manner.
2. Adding an indicator characteristic. Suppose new evidence suggests that (for example)
soil type is an important indicator of wetland use by a particular species, yet soil type (the
indicator) is not currently one of the indicators used in the species models (WHRBASE) or
in the field form (Appendix B). As shown in Figure 17, the AREM program allows you to
add soil type, and up to 20 other new indicators, to the databases and computer program.
Load the EDITAREM program and select “Add fields to WHRBASE.” You will be
prompted to name the indicator and specify a number that will define its sequence in the
habitat indicators list (Figure 3). The new indicator is automatically added to WI-IRBASE
and you are returned to the editing menu. After selecting “Edit WHRBASE,” for each
species whose presence is indicated by soil type, insert an appropriate code (from Table 7)
that describes the nature of its relationship to soil type Exit the program again. The next
time you use AREM to process some field data, you will be queried for information on the
new indicator. That is, the new indicators will have been automatically added. to the bottom
of the list of indicators shown in Figure 3.
Updating and refining of data for the currently-used weighting characteristics is strongly
encouraged. Consider, for example, the weighting characteristic, “Relative Abundance.”
Currently, in the Utah subregion nearly all species are categorized as “common” because of
lack of more definitive information. With additional information-gathering, the number of
categories could initially be broadened, and with field surveys, the characteristic (Relative
Abundance) could be reformulated as a continuous (rather than categorical) weighting
variable. For example, for many species the proportion of atlas blocks ’ 0 in which a
‘° ATJas blocks in Colorado are areas approximately 3 miles on a side, defined by 1:24,000-scale topographic maps. Breeding birds in
these areas are being surveyed by volunteers statewide. The project is scheduled for completion in 1994, but sufficient amounts of interim
data may be available for some areas. Contact Hugh Kingety ax the Denver Museum of Natural Histovy (phone 303-370-6336). Many states
have similar projects.
41
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species occurs could be considered a reasonable surrogate for a species’ abundance (Lacy
and Bock 1986, Bock 1987). To assign more weight (as AREM does) to species that are
less common or more localized, the AREM user could use the number of adequately-
covered atlas blocks in which a species was found (e.g., Adamus 1987), in lieu of the
current practice of using categorical values that describe Relative Abundance only coarsely.
3. Changing weights. Suppose the database currently considers “tree swallow” to be
“Dependent” on wetlands, but new evidence suggests that it is more appropriate to assign
the swallow to the category “Highly Dependent.” After loading the EDITAREM program,
select WEIGHTS. Move the cursor down to the “tree swallow” row, and go to the column
(field) termed “WETDEPEND”. Change the code from a 1 (dependent) to a 3 (highly
dependent) and press Enter, then save the change by pressing Esc.
2.7 Adapting AREM for a New Region or Ecosystem Type
Suppose you would like to have a procedure similar to AREM for use in rapidly evaluating
the avian diversity of (for example) constructed tidal wetlands in New England or subalpine
forests in Colorado. The following process, which requires assistance from skilled avian
experts, can be used to build the modified AREM.
1. Develop a master list of species regularly occurring in the new region and/or
ecosystem type. “Regularly” means that one or more individuals are present in the
region or ecosystem type at predictable times of the year, at least once annually.
Species lists can be obtained from state Heritage Programs, national wildlife refuges,
national forests, state bird books, and state and local bird clubs.
2. Set up the new REGIONS database. To do so, use the EDITAREM program to
delete all the information in the current REGIONS database, and substitute the new
information from #1. Enter a code describing relative abundance of each species
during each period (season) in each subregion andlor ecosystem type. Choose from
the same codes currently used by AREM. These are: A (abundant), C (common), F
(fairly common), U (uncommon), X (rare), blank (absent or not regularly-occurring).
See Section 3.2.2 for guidance in defming these.
3. Set up the new WEIGHTS database. Again, use the EDITAREM program to
delete all the information in the current WEIGHTS database, and substitute new
weighting information obtained from sources listed in #1 or by contacting other
experts.
4. Construct the species models (first pass). Constructing the species models is
synonymous with setting up the WHRBASE database. Initially, the models should
42
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be constructed by a wildlife biologist who is (a) experienced using rapid evaluation
methods, and (b) familiar, both from field experience and from understanding of
current literature, with the gross habitat requirements of all relevant species. The
model builder (i.e., the wildlife biologist) should choose from the codes currently
used by AREM (Table 7) and should construct each species model as a unique
combination of these codes. The codes should then be entered into the WHRBASE
matrix using the program, EDITA.REM. It is also inevitable that the new set of
species will be associated with different indicators than are appropriate for Colorado
Plateau lowland wetlands. Because of this, new indicators need to be substituted for
certain old ones, as described on p. 41, #2.
5. Test the adapted AREM and modify further as needed. The simplest way to test
the adapted AREM is as follows:
(1) Visit a series of wetlands (or other ecosystems) and apply the adapted
AREM, using it to generate a species list for each area. Ideally, the number
of areas that are visited and the way they are selected should satisfy an
objective of encompassing as many as possible of the conditions of the
indicators used in the adapted AREM.
(2) Simultaneously, contact a knowledgeable local birder for assistance.
Provide this person with a copy of the regional master list, take them to the
same areas as above, and ask them to check off species they know, or would
anticipate, occur in the specific area. If possible, recruit several birders to
conduct this task independently of one another. Also ask the local birders to
review and comment on the relative abundance data you’ve entered in the
REGIONS database.
(3) Compare the lists of one or more of the local birders with the one that
AREM generated for the area. At each wetland where any discrepancies
exist between the AREM list and the birder lists, have the model builder (the
avian expert) intensively query the birder(s) as to why they would or would
not expect to fmd the discrepant species, i.e., which habitat indicators for
which species are poorly defined, missing, irrelevant, or over- or under-
emphasized by AREM.
(4) Using EDITAREM, modify the models (second pass) to reflect the local
input.
A somewhat more involved procedure could be used for testing an adapted AR.EM,
and might come closer to truly validating its accuracy. This procedure would
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progress the same as above, except that in #2, a sufficient number of actual surveys
of birds would be conducted by a skilled birder using appropriate methods, and the
resultant species lists, rather than lists based on a birder’s judgement, would be
compared (in #3) with the lists generated by AREM.
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3.0 LITERATURE DOCUMENTATION FOR AREM
3.1 Documentation of Indicator Thresholds
3.1.1 Introduction
The following narrative describes the basis for the specific thresholds used for particular
indicators contained in the AREM field form (Appendix B). If desired, readers wishing to
gain a broader, more fundamental understanding of the indicators that AREM uses may
consult Adamus (1993a).
Whenever numeric thresholds are included in an evaluation method, there is considerable
risk that some users will assume that these values are soundly supported by extensive
research or are appropriate to all situations. This is unfortunately not true. In reality, few
if any of the specific thresholds included in AREM or other rapid evaluation methods are
conclusively supported by research fmdings. Where they seem to be, there still remains
considerable uncertainty as to whether the supporting research can be validly extrapolated
to all situations where the evaluation method might be used.
Why, then, are specific thresholds used? The reason is that thresholds, by standardizing
important judgements, are essential to improve a method’s replicability (the tendency of
different users to answer questions about the same area in the same manner). When
methods are poorly replicable, their credibility and, in some cases, their legal defensibility
suffers.
Despite the frequent. lack of conclusive documentation for particular thresholds, the
numbers used by AREM are not merely speculative. Their general order of magnitude
reflects the author’s inferences from broadly-accepted ecological principles as defined by
current wetlands literature and expert opinion. Users wishing to substitute other
threshold values may do so, provided they (a) explicitly document where they made
substitutions, and (b) cite appropriate data that supports the alternative values. In
particular, use of alternative thresholds may be justified to reflect differing effects of other
seasons, regions, species, and ecosystem types.
3.1.2 Area Thresholds
AREM uses thresholds for habitat area (i.e., size or acreage) in several places (#‘s below
refer to numbered questions on the field form, Appendix B; boldface has been added below
to emphasize area thresholds that are used):
#1. LOCATION. Is the wetland part of... a majors ...lake? (* ...lake larger
45
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than 40 acres).
#2. SURFACE WATER. Is there at least 0.1 acre of surface water...?
#3. OPEN WATER. How much openS water is present...
> 20 acres...
< 1 acre...
____ Other conditions
#4. SPECIFIC AQUATIC CONDITIONS
> 0.1 acre of the surface water is still
#5. BARE SOIL. Is there at least 0.1 acre of mud...
#6. LARGE MUDFLAT. [ Is] the mud habitat...
_____ At least 1 acre in size
#8. TREE COVER... check the response below that best represents the overall
extent of tree cover:
> 1 acre...
____ 0.1-1 acre...
#11. SHRUBS. Is there at least 0.1 acre of shrubs...
#12. SHRUB SPECIES AND DENSITY... [ by type:]
> 1 acre...
____ 0.1-1 acre...
#13. HERBACEOUS VEGETATION. [ question and threshold similar to #11]
#14. HERBACEOUS SPECIES... [ by type:]
[ question and threshold similar to #12]
Rationale for Area Thresholds . As compiled above, the area thresholds that AREM uses
are 0.1 acre, I aGre, 20 acres, and 40 acres. AR.EM assumes that larger wetlands and larger
patches of habitat within wetlands tend to have more species, but that the exact effects
depend primarily on the local pool of species and the type of habitat. The AR.EM species
models also recognize the role of other factors in influencing the species-area relationship,
such as patch width or shape (Section 3.1.3 below) and proximity to other wetlands or
agricultural lands (for further information see Section 3.1.6, and optionally Section 4.4.1 in
Adamus 1993a).
The larger two thresholds (20, 40 acres) are applied only to open water habitat. Reservoirs
larger than 26 acres are used by the largest numbers of migrant and wintering ducks in arid
western Oklahoma (Copelin 1953), and those larger than 40 acres are preferred by ducks in
north Texas (Hobaugh and Teer 1981) and Quebec (DesOranges and Houde 1989). Geese
most often use wetlands larger than 20 acres (Guthery and Stormer 1984). In the prairie
pothole region. a cumulative wetland area of 200-900 acres might be required to support the
full complement of local species (Brown and Dinsmore 1986), and 10 of 25 species did not
use wetlands smaller than about 2 acres. However, data from north-central Minnesota
(Williams 1985) and Maine (Gibbs 1991) demonstrate the elusiveness of simplified
46
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relationships between avian richness and wetland area.
The two smaller thresholds (0.1 and I acre) are applied to both open water and vegetated
wetland habitats. The 0.1 acre threshold approximates the theoretically smallest home
range of about 67% of the 90 species that regularly breed in the Colorado Plateau
region”, whereas the 1 acre threshold approximates the theoretically smallest home range
size of about 31% of the breeding species. Studies cited in Adamus (1 993 a) and Leibowitz
et al. (1992) document regular bird use of wetlands as small as 1 acre, and a simulation
study by Gibbs (1993) highlights the adverse potential consequences of loss of such
wetlands. However, few species use isolated vegetation patches of less than about I acre,
because such patches often have maximum dimensions no larger than 100 ft, which
severely exposes their inhabitants to the elements and predators. In some arid regions of
North Ameri a, few seasonal wetlands smaller than 0.1 acre and few semipermanent
wetlands smaller than 2 acres persist over time (Stewart and Kantrud 1971), unless
subsidized by irrigation water. Although evidence suggests that one-acre patches of forest
in the eastern U.S. are too small for many breeding birds, many of the species that use
Colorado Plateau lowland wetlands are “edge” species presumably accustomed to using
small patches. In eastern Colorado, one riparian species (tree swallow) nested only in
riparian fragments larger than 37 acres, another (black-capped chickadee) nested in
fragments larger than 17 acres, several nested in fragments larger than about 3 acres
(American kestrel, northern flicker, hairy woodpecker, downy woodpecker), and one (house
wren) nested in fragments as small as 0.3 acre, which was the smallest measured (Gutzwiler
and Anderson 1987).
In a sample of 187 wetlands located within 1000 ft of canals and ditches in the Lower
Gunnison-Uncompahgre subregion, 158 (84%) of the wetlands were smaller than I acre. In
the same general area, a survey of 800 wetlands by Rector et al. (1979) also included
natural bottomland wetlands, and reported the mean wetland size as 27 acres. However,
both of these studies measured the area of an entire wetland, whereas AREM requires
estimation of the area of the individual habitat types within a wetland.
The theoretical home range size estimate was derived using the allomernc equation of Schoener (1968) as demonstrated by Van
Home and Wiens (1991). The equation is:
A 98.6M ’
where A is the predicted home range area (in ha) and M is the body mass (in g). From Dunning (1984, 1993), 1 obtained average body mass
data for 86 of the 102 species that breed regularly in lowlan1 wetland/riparian areas of the region. The 26 breeding species categorized as
“highly dependent” or “intermediate” with regard to use of wetlands were projected to have a mean home range of 5.53 acres (range 0.04-42
acres), whereas the mean for the 60 species categorized simply as “dependent” was projected as 1.03 acres (range 0.01.14 acres). The
accuracy of the allometric formula is difficult to gauge, but an attempt was made to determine its comparability. When I compared (for 40
of the species) the predicted home range areas with areas inferred from Breeding Bird Census (BBC) data (by dividing plot area by number
of pairs, for a small number of plots from wetland habitats nationwide), I found all but 4 of 40 the aiiometric-based ranges to be smaller than
BBC-based ranges. Areas predicted by the ailometric equation were an order of magnitude smaller than BBC.based areas for 17 (43%) of
the 40 species. This could mean that a threshold somewhat larger than one acre might be appropriate to represent home range sizes of
Colorado Plateau breeding bird species.
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3.1.3 Width Thresholds
AREM uses thresholds for width in the following places:
#1. LOCATION. Is the wetland part of...a major river...? (* river wider than
100 ft...)
#3. OPEN WATER. How much open water is present...
____ > 20 acres it is mostly wider than 500 ft
____ < I acre, , >1 acre but mostly narrower than 3 ft
#6. LARGE MUDFLAT. Does the mud habitat have...
o Maximum dimension greater than 100 ft?
#18. PREDATION POTENTIAL. Wetland...is linear (i.e., no more than 10% of the
wetland is farther than 25 ft from a road, canal, or other artificially linear feature.
Rationale for Width Thresholds . As compiled above, the width thresholds that AREM uses
are 3 ft, 25 ft, 100 ft, and 500 ft. AREM assumes that wider wetlands and wider patches of
habitat within wetlands usually support more species, but that exact effects depend
primarily on the species and the type of habitat. The AREM species models also recognize
the role of other factors that influence bird use of narrow or wide patches, such as patch
area (Section 3.1.2 above), and proximity to other wetlands or agricultural lands (Section
3.1.4 below, and Section 4.4.1 in Adamus 1993a).
The AREM specifications that width of open water patches be greater than 500 ft and
certainly not less than 3 ft (question #3) are based on the assumption that narrower patches
would restrict the ability of some larger waterbirds to take flight. Data from other regions
(Ambrose et al. 1983) suggests that, for taking flight, a few waterbird species require open
water or otherwise flat areas wider than about 500 ft. Shorebirds along inland migratory
routes appear to favor mudflats wider than about 100 ft. and especially wider than 1500 ft.
even though large numbers can occur on lakeshore flats as narrow as 3-6 ft (Taylor and
Trost 1992).
Birds nesting in vegetation patches narrower than 100 ft, and certainly those in areas
narrower than 25 feet, are likely to be frequently disturbed by predators (which follow the
edges of vegetation patches) and human visitors (because such narrow patches offer little
visual buffer). Because specific supporting data were lacking from the Colorado Plateau,
these numerical values were based mostly on the author’s judgement. Studies in other
regions report that zones of emergent vegetation are most likely to develop along ditches
wider than about 30 ft (Linde 1969). Along areas of open water, nesting waterfowl prefer
strips of emergent vegetation that are wider than 25 ft; strips wider than 70 ft are
considerably better (Atlantic Waterfowl Council 1972).
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3.1.4 Distance Thresholds
AREM uses thresholds for distance in the following places:
#1. LOCATION. Is the wetland part of, or is it within 0.5 mile of, a major river or
lake?
#7. TREES. Are there at least 3 trees:
____ within 1000 ft of the wetland (including the wetland itself)?
____ in the wetland or within 300 ft?
#8. TREE COVER. Add the tree acreage within 300 ft of the wetland, to the tree
acreage actually within the wetland...
#9. BIG TREES. Are there at least three trees... within the wetland or within 300
ft of its perimeter?
#10. SNAGS. [ question and threshold similar to #9]
#11. SI-IRUBS. Is there at least 0.1 acre of shrubs*:
____ within 1000 ft of the wetland...?
____ in the wetland or within 300 ft?
#12. SHRUB SPECIES AND DENSITY. For each shrub type...add the acreage of
the same shrub within 300 ft of the wetland to...
#13. 1-IERBACEOUS VEGETATION. [ question and threshold similar to #11]
#14. HERBACEOUS SPECIES. [ question and threshold similar to #12]
#15. SURROUNDING LAND COVER. Within 0.5 mi of the wetland, >60% of the
land cover is...
#16. LOCAL LAND COVER. Within 3 mi of the wetland, >60% of the land cover
Is...
#17. VISUAL SECLUSION
(b) there are no paved roads within 600 ft...
#18. PREDATION POTENTIAL
____ Wetland.. .is closer than 1000 ft to a normally-occupied building
#20. NESTING LOCATIONS
____ Semi-open structures.. .suitable for nesting swallows are present within
300 ft
Rationale for Distance Thresholds . As compiled above, the distance thresholds that AREM
uses are 300 ft, 600 ft, 1000 ft. 0.5 mi, and 3 mi. AREM assumes that wetlands with
beneficial habitat features located nearby (especially within these distances) will usually
support more species, and that wetlands with detrimental features located nearby will
support fewer species, other factors being equal.
The 300-ft threshold is intended to approximate a maximum dimension of the theoretical
home range sizes of 16 (19%) of the 86 species that breed in Colorado Plateau, wetlands,
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calculated as described in footnote #10 and assuming circular shapes for home ranges. In
other words, vegetation or other habitat outside the wetland but within 300 ft is likely to be
used by several species that mainly use the wetland, and several species outside the wetland
but within 300 ft will regularly use the wetland. The 300-ft threshold is further
documented by the following: (a) from some limited field data, Croonquist and BroOks
(1993) considered widths of 300 ft to be adequate for maintaining avian richness in wooded
stream corridors in Pennsylvania, (b) in Maryland, a study of riparian birds indicated that
neotropical migrant species breed mostly in corridors wider than about 330 ft (Keller et al.
1993), and (c) in Colorado, the density and type of vegetation is often altered within 295 ft
(90 m) downslope from lined canals, and is sometimes altered up to 590 ft (180 m)
downslope.
The 0.5- and 3-mi thresholds relate to three factors: (a) migratory corridors for shorebirds
and songbirds, (b) foraging distances of larger waterbirds, and (c) dispersal distances of
wetland-dependent songbirds (i.e., the distance that young birds move between wetlands
where they were hatched and, the following year, wetlands where they breed).
Many migratory birds (which comprise about 81% of the region’s wetland avifauna) appear
to concentrate in wetlands in the vicinity of major rivers and lakes, and use these areas as
corridors or “stepping stones” as they migrate long distances. Accordingly, AREM assumes
that wetlands located within 0.5 ml of large water bodies would serve this purpose, but this
value and the values that defmes “large” were based exclusively on the author’s judgement
because no supporting regional data were available. In the central parts of the United
States, small wetlands seem to support the most nesting waterfowl when located within
about 3 ml of a large water body or a permanently or semipermanently flooded wetland to
which birds, can freely travel when foraging (Low 1945, Warren and Bandel 1968, Williams
1985, Dobie 1986, Baldassare and Bolen 1987). Duck broods that are forced to move long
djstances due to a low density of wetlands on the landscape have relatively lower survival
rates (Rotella and Ratti 1992). Average dispersal distances of song sparrows (a wetland
species) are 0.6 to 0.9 mi (Nice 1937, Johnston 1956). In arid irrigated areas of Texas,
pheasants use areas within about 0.7 ml of wetlands (Guthery and Stormer 1984), and
pheasants in Michigan, Wisconsin, and North Dakota have an average travel radius of about
0.25 mi (Sather-Blair and Linder 1980).
The 600-ft threshold for visual seclusion also is based on the author’s judgement, but
reflects generally the literature on sensitive species in other regions. Wintering bald eagles
in some areas take flight when approached from as far away as 800-1600 ft (Sta]master and
Newman 1978, Knight and Knight 1984, McGarigal et al. 1991, Buehler et al. 1991).
Many waterbirds take flight when humans on foot are seen approaching within 75 to 175 ft.
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3.1.5 Other Thresholds
AREM uses several other numeric thresholds, as follows:
#3. OPEN WATER. How much open* water is present...
(* Water deeper than 2 inches..)
#4. SPECIFIC AQUATIC CONDITIONS
(a) ...the surface water is still, i.e., usually flows at less than 1 ft/s
(b) Water transparency in the deepest part of the wetland is usually sufficient
to see an object 10 inches below the surface...
(c) Most.. .of the wetland goes dry at least one year in five...
#7. TREES. Are there at least 3 trees...
#8. TREE COVER...
_____ >1 acre, dense*
____ >1 acre, open...
* Dense= the tree canopy.. .appears at least 50% closed
#9. BIG TREES. Are there at least three trees of >12 inch diameter...
#10. SNAGS. Are there...trees with dead limbs of diameter >5 inches...
#12. SHRUB SPECIES AND DENSITY... [ question and threshold are similar to #8).
#14. 1-IERBACEOUS SPECIES...
Tall= taller than 1 ft
#15. SURROUNDING LAND COVER. Within 0.5 mi of the wetland, >60% of the
land cover is...
#16. LOCAL LAND COVER. [ question and threshold are similar to #15]
#17. VISUAL SECLUSION
(a) wetland is seldom visited by people on foot or boat (less than once
weekly...
#18. PREDATION POTENTIAL
Wetland adjoins a heavily-traveled road (usual maximum of >1 car/minute),
and/or is in a high-density housing area (>1 house/5 acres)...
#20. NESTING LOCATIONS
Vertical, mostly bare dirt banks at least 5 ft high.. .of potential use to nesting
kingfishers, barn owls, and swallows.
Rationales for the Other Thresholds . The first threshold listed above (#3, water >2 inches
deep) describes water that is deep enough for most of the region’s waterfowl species to use,
based on the anatomy and behavior of these species. It is recognized that most waterfowl
prefer greater depths (to at least 12-18 inches depth); AREM accounts for that fact
indirectly by including water body area as an indicator in the waterfowl species models.
The 1-ft/s threshold used in question #4(a) describes a velocity beyond which most ducks
51
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and other birds that use water as a substrate are much less likely to occur. The threshold is
based on the author’s judgement and reflects an assumption that at greater velocities,
waterbirds would spend so much energy maintaining their position that their feeding
success would be lowered.
The 10-inch threshold used in question #4(b) describes a degree of water clarity assumed to
be sufficient to (a) allow at least minimal growth of completely submersed aquatic plants
and associated aquatic insects that are important to many waterfowl species, and (b) permit
waterbirds that feed on subsurface foods to see these foods. This threshold is based on
literature from other regions (as compiled in Kantrud 1990) which suggests that one of the
submersed plants that is most-favored by Colorado Plateau waterfowl -- sago pondweed,
Potamogeton pectinatus -- grows poorly when water column visibility is less than about 10
inches.
The threshold used in question #4(c) -- dry-out occurring at least one year in five --
describes a frequency of water level drawdown that is assumed sufficient to increase the
productivity of open-water wetlands, partiy by oxidizing organic matter and associated
nutrients located in wetland sediments, and facilitating seed germination. Abundant
evidence from other regions (K.nighton 1985, Fredrickson and Taylor 1982) supports the
assumption that wetlands which otherwise are permanently flooded support greater
secondary production if they periodically dry out or exchange waters with major rivers or
lakes. The specific threshold (one year in five) reflects literature on marsh management
that recommends drawdown frequencies of once every 3-5 years (range, 1-10 years)(Harris
and Marshall 1963, Linde 1969, Payne 1992).
The threshold of “three” trees used in question #7 describes a condition assumed to be
minimally acceptable to most riparian species. Birds (particularly perching raptors)
occasionally visit wetlands with only one or two trees, but a threshold of three was set to
allow for minimal resource turnover (i.e., long-term replacement of trees lost from
blowdowns). The threshold is based entirely on the author’s judgement.
The threshold of 50% canopy closure to differentiate dense from open stands of woody
vegetation (#8, 12) also is based entirely on the author’s judgement, because regional data
relating avian richness to canopy closure in lowland riparian areas were not available.
AREM uses a threshold of >12 inches diameter to define large trees (#9) and a threshold of
>5 inches diameter (#10) to define dead trees and limbs useful to cavity-nesting wildlife,
which contribute importantly to avian richness. In northeastern Colorado, cottonwoods
having cavities larger than 1 inch are used (or excavated) by downy woodpecker, black-
capped chickadee, and house wren; those larger than 3-4 inches are used by wood duck,
American kestrel, western screech owl, and northern flicker. Most cavities are found in
5•
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limbs of greater than 6-12 inches diameter (Sedgwick and Knopf 1986).
The threshold of 1 ft for vegetation height, used to differentiate short from tall stands of
herbaceous vegetation (#14), is based on the assumption that many birds which dwell
mostly at ground level (e.g., shorebirds, waterfowl) cannot routinely see above much taller
vegetation. These species often prefer wetlands with vegetation <1 ft so that approaching
predators can be detected and avoided in time. Some other species (e.g., bitterns) seldom
use stands of vegetation shorter than about 20 inches, and especially not shorter than 1 ft.
Studies in Arizona (Ohmart et al. 1985) demonstrated the importance of herbaceous
vegetation height for predicting the birds that use irrigated wetlands.
A threshold of 60% for cover types (#15, 16) within an area of specified size is used to
differentiate landscapes that are functionally (from a bird perspective) dominated by the
specified types of land cover from ones that are not. Recognizing that different species
respond to land cover at different scales, AREM requires that users consider whether this
proportion is attained at distances of 0.5 mi and/or 3 mi from the evaluated wetland. The
proportion (60%) was derived from “percolation theory” by O’Neill et al. (1988) and
reflects a theoretical probability that an unspecified organism will be able to move freely
across a landscape among patches of its favored habitat. The applicability of this
assumption to birds in the Colorado Plateau region is untested.
The thresholds for visual seclusion (#17) and predation potential (#18) are based entirely on
the author’s judgement, because regional data relating avian richness to these types of
disturbances were not available. They are perhaps the most subjective of the thresholds
included in AREM.
The 5-ft threshold for height of banks suitable for bank-nesting birds is based primarily on
the author’s observations and assumes that birds nesting in banks much lower than this
would be more severely harassed by mammalian predators.
3.2 Documentation of Weighting Factors
AREM currently provides users with the option of weighting species according to any of
six characteristics. These are described below. Weights are currently assigned on a 1-10
ordinal scale, but there are no empirical data to support particular numerical values, so
users can choose other values and scales.
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3.2.1 Species Dependency on Wetland/Riparian Habitat
The WEIGHTS database of AREM defines the following categories:
Category Preassigned Weight
Highly Dependent 10
Intermediate 6
Dependent 2
“Highly Dependent” means that surface water is the species’ primary substrate: e.g.,
common goldeneye. “Intermediate” means the species occurs only where water/wetland is
present: e.g., spotted sandpiper. “Dependent” means the species also uses uplands, but uses
wetlands frequently: e.g., warbling vireo. AREM users can see which category each
species has been assigned to, and change the designation if they prefer, by loading the
EDITAREM program (see p. 42), selecting the WEIGHTS database, and editing values in
the WETDEPEND column. Current assignments of species to particular categories were
based entirely on the author’s interpretation of literature and experience with the species.
3.2.2 Relative Abundance
The WEIGHTS database of AREM defines the following categories:
Category Code Preassigned Weight
Uncommon U 10
Fairly Common F 6
Common C 4
Abundant A 2
Rare X (not included)
“Uncommon” species are ones that can be found in suitable habitat at the appropriate
season at a rate of about 1-10 per day, whereas “fairly common” species are encountered at
a rate of 10-25 per day, “common” species at a rate of 25-100 per day, and abundant
species at a rate of >100 per day. These definitions are from Dexter and Lavad (1992), and
separate abundance codes have often been assigned for different seasonal periods. Species
that are not likely to be encountered daily, even at an appropriate season and in suitable
habitat, are not included in A.REM. However, if there are known instances of a regularly-
present species having such a low detection rate solely because of its characteristic
secretiveness or obscurity, then it should nonetheless be factored into calculations of avian
richness. AREM users can see which category each species has been assigned to, and
change the designation if justified, by loading the EDITAREM program, selecting the
WEIGHTS database, and editing values in the column corresponding to the desired
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subregion and season. Current assignments of species to particular categories were based
mainly on literature (cited on p. 70 of Adamus 1993a), but abundance categories of species
breeding in the Grand Valley subregion were modified after data from the author’s field
studies had been analyzed, to reflect insights gained from the data. There is considerable
potential for improving this information, and thus the assignment of appropriate weights,
through collection and analysis of additional field data (see p. 42 for discussion of this).
Information on relative abundance in the WEIGHTS database is identical to information in
the REGIONS database.
3.2.3 Taxonomic Uniqueness
AREM assigns more weight to wetlands having species that are, relatively speaking,
taxonomically unrelated. For example, a wetland whose (say) five species are each from a
different taxonomic order will be calculated to have greater taxonomic diversity than a
wetland whose five species are from taxonomically different families within a single order.
That is because within the hierarchy of taxonomic classification, orders are considered to be
generally more distinct than families. This is important because animal communities that
are taxonomically diverse are often genetically and functionally diverse, i.e., gene pools
contain considerable variability, species sometimes fill a wide variety of trophic roles, and
food webs can be complex, presumably leading to greater adaptability and sustainability of
populations (Vane-Wright et al. 1991).
AREM calculates the taxonomic uniqueness of a wetland in the following manner. After
predicting which species should occur in a wetland, AREM calls up the TAXINF database
and sorts all species present in the evaluated wetland according to the values in the
TAX [ NF database, proceeding from left (beginning with the “Order” column) to right
(ending with the “Species” column). This sequence reflects decreasing taxonomic
uniqueness. Then, the program assigns the maximum value to each species as follows:
SDecies Is The Sole Representative
In This Wetland of Its: Points
Order 9
Suborder 8
Parvorder 7
Superfamily 6
Family 5
Subfamily 4
Tribe 3
Genus 2
Species I
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After assigning weights to all species, AREM sums them to give the final “taxonomic
uniqueness” score for the “weighted habitat.” Information in the TAXINF database was
derived from DeSante and Pyle (1986) and Ehrlich et al. (1988).
3.2.4 Neotropical Migrant Status
The WEIGHTS database of AR.EM defines the following categories:
Category Preassigned Weight
TypeAmigrants 10
Type B migrants 6
Others 2
“Type A” species breed only in the United States and/or Canada and migrate to the
Neotropics. “Type B” species also breed in the Neotropics. Other regional species
generally do not migrate to Neotropics. AR.EM users can see which category each species
has been assigned to, and change the designation if they prefer, by loading the EDITAREM
program, selecting the WEIGHTS database, and editing values in the NEOTROPIC column.
Current assignments of species to particular categories were based entirely on information
compiled by Carter and Barker (1993).
3.2.5 Official Conservation Designations
The WEIGHTS database of AREM assigns a weight of “10” to any species that has j of
the following official conservation designations:
Endangered (federally listed)
Threatened (federally listed)
Candidate for Threatened or Endangered list
G3 (“rare/uncommon globally but not imperiled”)
G4 (“not rare; apparently secure but cause for longterm concern”)
Si (sensitive due to possibly declining populations and/or rarity statewide)
S2 (sensitive due to possibly declining populations and limited in-state distribution)
S3 (sensitive but fairly common statewide)
“Watch List” (apparently sensitive but information lacking)
Species not officially designated as belonging to any of the above categories are assigned a
weight of “1.” AREM users can see which category each species has been assigned to, and
change the designation if warranted, by loading the EDITAREM program, selecting the
WEIGHTS database, and editing values in the corresponding columns (ENDANGERED,
THREATENED, CANDIDATE, G3, G4, Si, S2, S3, WATCHLIST). Species whose
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official designations apply only to breeding populations, and which do not breed in the
study region, were not included unless their nonbreedmg populations also had been
officially assigned special status. Information on official designations of species came from
the state natural heritage programs in Colorado, Utah, and Wyoming.
3.2.6 Hunted Status
The WEIGHTS database of AREM defines just two categories:
Category Preassigned Weight
Hunted 10
Not Hunted I
The “Hunted” species category includes all wetland species, whether resident or migratory,
that can be harvested legally in the study region. AREM users can see which category each
species has been assigned to, and change the designation if warranted, by loading the
EDITAREM program, selecting the WEIGHTS database, and editing values in the
corresponding column (HUNTED).
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4.0 LITERATURE CITED
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931071: U.s. Environmental Protection Agency Environmental Research Laboratory, Corvallis, Oregon.
Adaznus, P.R. 1993b. Validation of a habitat evaluation method. U.S. Environmental Protection Agency Environmental Research
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Ambrose, R.E., CR. Hinide, and C I I Wenzel. 1983. Practices for Protecting and Enhancing Fish and Wildlife on Coal Surface-mined
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Atlantic Waterfowl Council. 1972. Techniques Handbook of Waterfowl Habitat Development and Management Atlantic Waterfowl
Council, Bethany Beach, Delaware.
Baldassane, GA. and E.G. Bolen. 1987. Management of Waste Corn for Waterfowl Wintering on the Texas High Plains. Note 13.
Dept. Range & Wildi. Manage., Texas Tech Univ., Lubbock, Texas.
Bock, C.E. 1987. Distribution-abundance relationships of some Arizona landbirds: A matter of scale? Ecology 68:124-129.
Brown, M. and ii. Dinsmore. 1986. Implications of marsh size and isolation for marsh bird management. 3. WildI. Manage. 50(3):392-
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Buehler, D.A., Ti. Mersmann, J.D. Fraser, and J.K.D. Seegar. 1991. Effects of human activity on bald eagle distribution on the northern
Chesapeake Bay. 3. Wildl. Manage. 55:282-290.
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Forest Serv., Fort Collins, Colorado.
Copelin, F.F. 1953. Waterfowl inventory on small flood prevention reservoirs in western Oklahoma. Proc. OkIa. Acad. Sci. 42:260-263.
Croonquist, Mi. and R.P. Brooks. 1993. Effects of habitat disturbance on bird communities in riparian corridors. 3. Soil Water
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Grand Valley Audubon Society, Grand Junction, Colorado.
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Fredrickson, L.H. and T.S. Taylor. 1982. Management of Seasonally Flooded Impoundments for Wildlife. Resour. Pub. 148. U.S. Fish
& Wildi. Serv., Washington, D.C.
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Guthery, F.S. and F.A. Stormer. 1984. Playa management p. 177B-182B In: F.R. Henderson (ed). Guidelines for Increasing Wildlife
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Keller, C.M.E., CS. Robbins, and J.S. Hatfield. 1993. Avian communities in riparian forests of different widths in Maryland and
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Knight, RL., and 5K. Knight. 1984. Responses of wintering bald eagles to boating activity. J. WildI. Manage. 48:999-1004.
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New York 4:1-247.
O’Neill, RV., B.T. Milne, MG. Turner, and RH. Gardner. 1988. Resource scales and landscape pattern. Landscape Ecol.
2:63-69.
Ohmart, RD., B.W. Anderson, and W.C. Hunter. 1985. Influence of agriculture on waterbird, wader, and shorebird us c along the lower
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Ecosystems and Their Management: Reconciling Conflicting Uses. Gen. Tech. Rep. RM-120, USDA Forest Serv., Fort Collins,
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Rector, C.D., E.W. Mustard, and 3.1. Windell. 1979. Lower G ñnison River Basin Wetland Inventory and Evaluation. USDA Soil
Conservation Service, Denver, Colorado.
Rotella, JJ. and 3.1. Ratti. 1992. Mallard brood movements and wetland selection in southwestern Manitoba. 3. WildI. Manage. 56:508-
515.
Samson, F.B. and FL. Knopf. 1982. In search of a diversity àthic for wildlife management Trans. N. Amer. Wildi Nat Resour. Conf.
47:421-431.
Sather-Blair, S. and R i. Under. 1980. Pheasant use of South Dakota wetlands during the winter. Proc. South Dakota Acad. Sd.
59:147-155.
Schoener, lW. 1968. Sizes of feeding territories among birds. Ecology 49:123-131.
Scott, J.M.,F Davis, a Csuti, R. Noss, B. Butterfield, C. Groves, H. Anderson,.S. Caicco, F. D’Ercltia, T.C. Edwards, Jr., 1. Ulliman,.
and RG. Wright 1993. Gap analysis: a geographic approach to protection of biological diversity. Wildi. Monogr 123:1 41.
Sedgwick, 3A. and F.L. Knopf. 1986. Cavity-nesting birds and the cavity-ttee resource in plains cottonwood .boftomlañds. 3. WildI.
Manage. 50:247-252.
Stalmaster, MV. and JR. Newman. 1978. Behavioral responsâs of wintering bald eagles to human activity: 3. WildI. Manage.
42:506-513.
Stewart, RE. and HA. Kanttud. 1971. Classification of Natur l Ponds and Lakes in the Glaciated Prairie Region. Res. PubI. 92. U.S.
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Taylor, D.M. and C.H. Trost 1992. Use of lakes and reservoirs by migrating shorebirds in Idaho. Great Basin Nat 52:179-184.
U.S. Fish and Wildlife, Service (USFWS). 1980. Habitat Evahiation Procedures (HEP) Manual (IO2ESM). U.S; Fish & Wildi. Serv.,
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Usher, M.B. (Cd.). 1986. Wildlife Conservation Evaluation. Chapman and Hall, London, UK.
Van Home, B. and J.AWiens. 1991. Forest Bird Habitat Suiiability Models and the Development of General Habitat Models. Fish and
Wildlife Research Rep. 8. U.S. Fish & Wildl. Scrv., Washington, D.C.
Vane-Wrght, RI., CJ. Hurnphries, and P.H. Williams. 1991. What to protect? - systematics and the agony of choice. Biol. COnserv.
55:235-254.
Warren, 3. andD. Bandel. 1968. Pothole blasting in Maryland wetlands. Proc. Annu. Conf. Southeast. Game Fish Comxñ. 22:58-68.
Williams, G.L 1985. Classifying wâtlands according to relative wildlife value: Application to water impoundments p. 110-119 In:
M.D. Knighton (compiler). Water Impoundments for Wildlife:IA Habitat Management Workshop. Gen. Tech. 1 ep. NC-l00 ’ USDA
Forest Serv., St Paul Minnesota.
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Appendix A. AREM Field Form: Documenting Information.
(not used in the data analysis)
Name of the Evaluated Area: ___________________________ Date Evaluated:
Name of Associated Computer File (assign one, having 8 characters): __________
Evaluator (s):
Type of Wetland (check one):
On-farm ___ Off-farm
Wetland Water Source (check one or more):
— Subsurface seepage - Mostly Natural
— Subsurface seepage - Mostly Irrigation-related
— Overland runoff - Mostly Natural
— Overland runoff - Mostly Irrigation-related
— Channel or lake overflow - Mostly Natural
— Channel or lake overflow - Mostly Irrigation-related
WETLAND SCORES (insert here after completing the data analysis):
Cutoff Level for Species Habitat Scores:
>O% >25% >50% >75%
___________________________________ ( all possible spp. ) _________ ( most conservative )
Unweighted Habitat Score
Unweighted Richness Score
Habitat Score Weighted 2 By Species’:
Relative Dependency on Wetlands
Relative Abundance
Taxonomic Uniqueness
Neotropical Migrant Status
Official Conservation Designations
Hunted Status
For “Dependency on Wetlands,” largest weights are assigned to species using waler as a subsuate; smallest weights to species
that regularly use upland habitat For “Relative Abundance,” largest weights are assigned to uncommon species, smallest to abundant species.
For “Taxonomic Uniqueness,” largest weights are assigned to species that arc the only ones of their genus in the region; smallest to species
having many congeners. For Neouopical Migrant Status, largest weights are assigned to species breeding only in the U.S. or Canada and
migrating to the Neovopics; smallest weights to nonmigratory species. For “Official Conservation Designations,” largest weights are assigned
species with stale, federal, or Heritage Program designations; smallest weights to others. For “Hunted Status,” largest weights assigned
species that are legally hunted; smallest weights to others.
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Appendix B. AREM Long Form
For each numbered item, check only one response unless noted otherwise. Then proceed to
the next question unless noted otherwise. Parenthetical names are the names of fields in the
supporting software database (W}iRBASE). If a field name is lacking, the information is
nol used directly.
1. LOCATION. Is the area part of, or is it within 0.5 mile of, a majorS river or lake?
* river channel wider than 100 ft. or lake larger than 40 acres
____ Yes (field BigWater) ____ No
2. SURFACE WATER. During this season, does the area contain at least 0.1 acre of
surface water, either obscured by vegetation or not?
* See Figure B-I for guidance in estimating acreage categories.
____ Yes (field AnyWater). Go to next question.
____ No. Skip to question #5.
3. OPEN WATER. During this season, how much open 5 water is present in the area?
* water deeper than 2 inches and mostly lacking vegetation (eccept submerged plants).
____ > 20 acres j4 it is mostly wider than 500 ft (field OpenBig)
< I acre, , >1 acre but mostly narrower than 3 ft (field OpenSmall)
Other conditions (field OpenOther)
4. SPECIFIC AQUATIC CONDITIONS
Check all that apply during this season:
_____ > 0.1 acre of the surface water is still, i.e., usually flows at less than 1 ft/s (field
Still Water)
• The evaluated area can be assumed to contain fish (field Fish)
____ The evaluated area can be assumed to contain frogs, salamanders, and/or crayfish
(field Amphibs)
____ Water transparency in the deepest part of the area is (or would be, if depth is
shallow) sufficient to see an object 10 inches below the surface, the area is not
known to have problems with metal contamination (field Clear)
____ The evaluated area is highly enriched by direct fertilizer applications, water from
nearby feedlots, or other sources (field Enriched)
____ Most of the normally-flooded part of the area goes dry at least one year in five, or,
is subject to flooding from a river at least as often (field Drawdown)
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Figure B-I. Examples of dimensions for various wetland shapes and acreages.
SHAPE
(Not to Scale)
ioftl I
435 ft.
1 acre:
(43,500 tt5
10 acres:
(435,000 ft )
20 acres:
(870,000 ft 2 )
40 acres:
(1,740,0000 ft 2 )
1208 ft.
I k °
1319 ft
lOft.
8Oft
160 ft.
320 ft.
I
I
4350 ft.
(—O.8m1)
I
I
5438 ft.
(—Imi)
5438 ft.
.
(—imi)
0.1 acre:
(4350 ft 2 )
Sauare
LJ66It .
Linear
5438 ft. (—Imi)
63
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5. BARE SOIL. Is there at least 0.1 acre of muds, alkali flat, gravel/sand bar, recently
tilled soil, and/or heavily grazed open (grassy, non-shrubby) areas during this season?
* includes soil that is continually saturated up to the swfaàe, or which was previously covered by water but
has become exposed to the air during this period
____ Yes (field Bare). - Go to next question.
____ No. Skip to question #7.
6. LARGE MUDFLAT. Does the area at this season contain mud that has all these
features?:
o At least 1 acre in size
o Maximum dimension is greater than 100 ft
o Salt crust or salt stains are not apparent
o Not recessed within a wash or canal whose depth (relative to surrounding landscape)
is greater than half its width.
____ Yes (field MudBig) ____ No
7. TREES. Are there at least 3 trees*:
* woody plants taller than 20ft.
____ in the evaluation area? (field Treeln).
____ within 1000 ft of the evaluation area? (field TreeNear). Go to #8.
neither of the above. Skip to #11.
8. TREE COVER. Check one or more responses below that describe the maximum
cumulative acreage of various conditions of tree cover in the evaluation area. Also include
areas within 300 ft:
____ >1 acre, dense*, and wide** (field ForestDens)
____ >1 acre and open; or, dense but narrow (field ForestOpen)
____ 0.1-1 acre, dense (field WoodDens)
____ 0.1-1 acre, open (field WoodOpen)
____ <0.1 acre
* Dense the tree canopy, viewed from the ground during midsummer, appears at least 50% closea as
averaged across an area that is at least as large as the acreage spec /Ied
Wide= the wooded area is wider than 300 ft (average).
9. BIG TREES. Are there at least three trees whose trunk diameter 20 ft above the ground
is >12 inches?
Yes (field TreesBig) ____ No
10. SNAGS. Are there at least three snags, or trees with dead limbs with diameter >5
inches?
Yes (field Snags) No
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11. SHRUBS. Is there at least 0.1 acre of shrubs t :
* woody plants 2-20 ft in height.
____ in the evaluation area? (field Shrubln).
____ within 1000 ft of the wetland (including the wetland itself)? (field ShrubNear). Go
to #12.
____ Neither of the above. Skip to #13.
12. SHRUB SPECIES AND DENSITY. Check one or more responses below that describe
the maximum cumulative extent of various types and conditions of shrub cover in the
evaluation area. Also include areas within 300 ft.
Willow:
____ >1 acre, dense, and widett (field WwMuchDens)
____ >1 acre and open; or, dense but narrow (field WwMuchOpen)
_____ 0.1-1 acre, denset (field WwSomeDens)
____ 0.1-1 acre, open (field WwSomeOpen)
____ <0.1 acre; or larger area but height mostly <4 ft and openly spaced
Greasewood or other tall desert shrubs:
____ >1 acre, dense t , and widet* (field GrMuchDens)
____ >1 acre and open; or, dense but narrow (field GrMuchOpen)
____ 0.1-1 acre, denset (field GrSomeDens)
____ 0.1-1 acre, open (field GrSomeOpen)
____ <0.1 acre
Russian olive, sumac, buffaloberry, wild rose, or others with fleshy fruit:
____ >1 acre, dense t , and widet* (field FrMuchDens)
____ >1 acre, open; or, dense but narrow (field FrMuchOpen)
____ 0.1-1 acre, dense (field FrSomeDens)
____ 0.1-I acre, open (field FrSomeOpen)
____ <0.1 acre; or larger area but height mostly <4 ft
Tamarisk (salt cedar):
____ >1 acre, dense t , and widet* (field TmMuchDens)
____ >1 acre, open; or, dense but narrow (field TmMuchOpen)
____ 0.1-1 acre, dense (field TmSomeDens)
____ 0.1-1 acre, open (field TmSomeOpen)
____ <0.1 acre; or larger area but height mostly <4 ft
* Dense= the shrub canopy, as viewed from a height of 100 ft during midsummer, appears to be >50%
closed, as averaged across an area that is at least as large as the acreage spec fled
Wide= the shrub area is wider than 300 ft (average).
65
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13. HERBACEOUS VEGETATION. Is there at least 0.1 acre of herbaceous vegetation*:
* Nonwoody plants such as cattail, bulrush, sedges, grasses, and forbs.
____ in the evaluation area? (field Herbin).
____ within 1000 ft? (field HerbNear). Go to #14.
____ Neither of the above. Skip to #15.
14. HERBACEOUS SPECIES. Check one or more responses below that describe the
maximum cumulative extent of various types and conditions of shrub cover in the
evaluation area. Also include areas within 300 ft.
Robust emergents (e.g., cattail, phragmites)
____ >1 acre, dense , and wide** (field RbMuchDens)
____ >1 acre, open; or dense but narrow (field RbMuchOpen)
____ 0.1-1 acre, dense (field RbSomeDens)
____ 0.1-1 acre, open (field RbSomeOpen)
Other wet** emergents (e.g., bulrush, sedge)
____ >1 acre, dense*, wide**, and tall*** (field WEMuchDens)
____ >1 acre, tall, open; or dense but narrow (field WEMuchOpen)
____ >1 acre, dense or open, and short (field WEMuchShrt)
____ 0.1-1 acre, tall, dense (field WESomeDens)
____ 0.1-1 acre, tall, open; or dense but narrow (field WESomeOpen)
____ 0.1-1 acre, dense or open, and short (field WESomeShrt)
Drier emergents (e.g., saltgrass, other grasses)
____ >1 acre, dense*, wide**, and tall (field DEMuchDens)
____ >1 acre, tall, open; or dense but narrow (field DEMuchOpen)
____ >1 acre, dense or open, and short (field DEMuchShrt)
____ 0.1-1 acre, tall, dense (field DESomeDens)
____ 0.1-1 acre, tall, open; or dense but narrow (field DESomeOpen)
____ 0.1-1 acre, dense or open, and short (field DESomeShrt)
Dense plants are o close together that the duff layer or soil beneath the plants is mostly obscured by
foliage, when looking down from just above the plan: tops.
Wet= water is visible at or above the soil surface during most of the growing season.
Wide= the shrub area is wider than 300 ft (averag ).
* * Tall= taller than 1 ft.
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14. HERBACEOUS SPECIES (continued):
Broad-leaved Forbs (e.g., milkweed, thistle, alfalfa)
____ >1 acre (field ForbMuch)
0.1-1 acre (field ForbSome)
Aquatic plants (e.g., watercress, sago pondweed, duckweed)
____ >10 acres (field AqMuch)
____ 0.1-10 acres (field AqSome)
15. SURROUNDING LAND COVER. Check one:
Within 0.5 mi of the wetland, > 60% of the land cover is:
____ Pasture, alfalfa, grain crops, row crops, other wetlands, grass lawns, and/or weed
fields (field SurAgwet)
____ Desert shrubs (e.g., sagebrush, shadscale, rabbitbrush)(field SurDesrt)
____ Pinyon-juniper (field SurPJ)
____ Oak scrub (e.g., Gambel oak, serviceberry, skunkbrush)(field SurOak)
Other, or none of the above comprise >60%
16. LOCAL LAND COVER. Check one:
Within 3 mi of the wetland, > 60% of the land cover is:
____ Pasture, alfalfa, grain crops, row crops, other wetlands, grass lawns, and/or weed
fields (field LocAgWet)
____ Desert shrubs (e.g., sagebrush, shadscale, rabbitbrush)(field LocDesrt)
____ Pinyon-juniper (field LocPJ)
____ Oak scrub (e.g., Gambel oak, serviceberry, skunkbrush)(field LocOak)
____ Other, or none of the above comprise >60%
17. VISUAL SECLUSION
Check only one:
____ Both of the following:
(a) wetland is seldom visited by people on foot or boat (less. than once weekly), (b)
there are no paved roads within 600 ft. or if there are, wetland is not visible from
the roads (field SeclusionH).
____ Either (a) or (b) above (field SeclusionM).
Other condition.
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18. PREDATION POTENTIAL
Check only one. The evaluation area:
____ is lmear*, adjoins a heavily-traveled road (usual maximum of.>1 car/minute), andlor
is in a high-density housing area (>1 house/5 acres)
(field PredHPot)
____ adjoins a less-traveled road, andlor is in an area with sparser housing density but is
closer than 1000 ft to a normally-occupied building (field PredMPot)
____ Other condition.
* at least 90% of the area being evaluated is within 25 ft of a canal, road, railroad tracks, or
other artificially linear feature.
19. GRAZED, BURNED, MOWED. Is the area mowed, burned, or grazed intensively (i.e.,
with clearly visible effects on vegetation) during this season?
____ Yes (field GrazBurnMo)
No
20. NESTING LOCATIONS
Check all that apply:
____ Semi-open structures (bridges, barns) suitable for nesting swallows are present
within 300 ft (field SwailNest)
____ Platforms suitable for nesting geese are present in the wetland or along its perimeter
(field GooseNest)
____ Vertical, mostly bare dirt banks at least 5 ft high are present within 0.5 mi., of
potential use to nesting kingfishers, barn owls, and swallows (field Banks)
This concludes the initial evaluation. If you intend to infer the value of this wetland at
seasons or years other than the present one, you should go back over all your responses
and, on a new form, change the responses that would be different at that season/year.
Then, proceed to the analysis described by the User’s Manual.
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Appendix C. AREM short form
(A= acre, ft= feet, in= inches, mi= miles)
1. Location: river/lake <0,5 mi?
_Big Water
2. Surface Water: >0.1 A?
AnyWater
[ skip to #5 if no]
3. Open Water > 2 inches deep:
_OpenBig: >20 A and width mostly >500 ft
_OpenSmall: < 1 A, or >1 A but width <3 ft
_OpenOther: all other
4. Specific Aquatic Conditions:
_StillWater: >0.1 A that flows at <1 ft/s
Fish
_Amphibs: crayfish, frogs, salamanders
_Clear: 10 inch visibility and no metals problem
_Enriched: feedlots etc.
_Drawdown: most of wetland dries out or floods from river 1 year in 5
5. Bare Soil: >0.1 A of exposed mud, alkali flat, recently tilled, heavily grazed, etc. during
this season?
Mud
[ skip to 7 if no]
6. Large Mudflat: >1 A + width>l00 ft + no salt + not recessed
_MudBig
69
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7. Trees: >2 trees. .
_Treeln: in wetland _TreeNear: within 1000 ft
[ skip to 11 ifno]
8. Tree Cover: tree acres within 300 ft + acres in wetland
_ForestDens: >1 A, closed canopy, and >300 ft wide
_ForestOpen: >1 A, open, and >300 ft wide
_WoodDens: 0.1-1.0 A closed, or larger but narrower than 300 ft
_WoodOpen: 0.1-1.0 A open, or larger but narrower than 300 ft
9. Big Trees: >2 trees, >12 inch diameter within 300 ft or in?
_TreesBig
10.Snags: >2 snags (>5 inch diameter), within 300 ft or in wetland?
_Snags
11 .Shrubs: >0.1 acre of shrubs (2-20 ft height)...
_Shrubln: in wetland _ShrubNear: within 1000 ft
[ skip to 13 if neither]
12.Shrub Species and Density: shrub acres within 300 ft + in wetland:
_WwMuchDens: willow >1 A, dense and wide
_WwMuchOpen: willow >1 A, open, or dense but narrow
_WwSomeDens: willow 0.1 - 1.0 A, dense
_WwSomeOpen: willow 0.1 - 1.0 A, open
_GrMuchDens: greasewood etc. >1 A, dense and wide
_GrMuchOpen: greasewood etc. >1 A, open, or dense and narrow
_GrSomeDens: greasewood etc. 0.1 - 1.0 A, dense
_GrSomeOpen: greasewood etc. >1 A, open
_FrMuchDens: Russian olive etc. >1 A, closed
_FrMuchOpen: Russian olive etc. >1 A, open or clumped
_FrSomeDens: Russian olive etc. 0.1 - 1.0 A, closed
_FrS9meOpen: Russian olive etc. 0.1 - 1.6 A, open or clumped
_TmMuchDens: tamarisk >1 A, closed
_TmMuchOpen: tamarisk >1 A, open or clumped
_TmSomeDens: tamarisk 0.1 - 1.0 A, closed
_TmSomeOpen: tamarisk 0.1 - 1.0 A, open or clumped
70
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13. Herbaceous Vegetation: >0.1 A of herbaceous
_Herbln: in wetland _HerbNear: within 1000 ft
14. Herbaceous Species and Density: herbaceous acres within 300 ft + in wetland:
RbMuchDens: robust cattail etc. >1 A, dense and wide
_RbMuchOpen: robust cattail etc. >1 A, open, or dense but narrow
_RbSomeDens: robust cattail etc. 0.1 - 1.0 A, dense
_RbSomeOpen: robust cattail etc. 0.1 - 1.0 A, open
_WEMuchDens: wet emergents (sedge, bulrush) >1 A, dense, wide, and >1 ft tall
_WEMuchOpen: wet emergents (sedge, bulrush) >1 A, open tall or dense but narrow
_WEMuchShrt: wet emergents (sedge, bulrush) >1 A, short (<1 ft), dense or open
_WESomeDens: wet emergents (sedge, bulrush) 0.1 - 1.0 A, dense, >1 ft tall
_WESomeOpen: wet emergénts (sedge, bulrush) 0.1 - 1.0 A, open tall
_WESomeShrt: wet emergents (sedge, bulrush) 0.1 - 1.0 A, short <4 in, dense or open
_DEMuchDens: dry emergents (grasses etc.) >1 A, dense, wide, and tall (>1 ft)
_DEMuchOpen: dry emergents (grasses etc.) >1 A, open, tall, or dense but narrow
_DEMuchShrt: dry emergents (grasses etc.) >1 A, short ( 1 ft)
_DESomeOpen: dry emergents (grasses etc.) 0.1 - 1.0 A, open, tall, or dense but narrow
_DESomeShrt: dry emergents (grasses etc.) >1 A, short, open or dense
_ForbMuch: alfalfa, milkweed, etc. > I A V
_ForbSome: alfalfa, milkweed, etc. 0.1 - 1.0 A
_AqMuch: watercress, sago, duckweed etc. > 1 A
_AqSome: watercress, sago, duckweed etc. 0.1 - 1.0 A
15. Surrounding Land Cover: within 0.5 mi...
_SurAgWet: >60% pasture, alfalfa, grain, row crops, other wetlands, weeds, grass
_SurDesrt: >60% sagebrush, shadscale, rabbitbrush
_SurPJ: >60% pinyon-juniper
_SurOak: >60% oak, serviceberry, skunkbrush
_Other, or none cf above add to >60% V
16. Local Land Cover: within 3.0 mi of wetland...
LocAgWet: >60% pasture, alfalfa, grain, row crops, other wetlands, weeds, grass
_LocDesrt: >60% sagebrush, shadscale, rabbitbrush
_LocPJ: >60% pinyon-juniper
LocOak: >60% oak, serviceberry, skunkbrush
_Other, or none of above add to >60%
71
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17. Visual Seclusion: check ONLY ONE:
_SeclusionH: no road within 600 ft or not visible if road present, <1 visit/week on
foot
_SeclusionM: EITHER of above
other
18. Predation Potential: check ONLY ONE:
_PredHPot: major road, urban, or linear
_PredMPot: other road or building within 1000 ft
other
19. Burned, mowed, or intensively grazed during this season?
GrazBurnMo
20. Nesting Locations:
_SwallNest: swallow sites -- barns, bridges within 300 ft
_GooseNest: goose platforms, in wetland or on perimeter
_Banks: within 0.5 mi, height >5 ft. vertical
72
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