Model/Tool/Technology

FFI: a software tool for ecological monitoring*

Posted on May 29, 2007 by in Books/Articles

FFI: a software tool for ecological monitoring*

Duncan C. LutesA,F, Nathan C. BensonB, MaryBeth KeiferC, John F. CarattiD and S. Austin StreetmanE

ARocky Mountain Research Station, Fire Sciences Laboratory, 5775 US Highway 10 West,
Missoula, MT 59808, USA.
BNational Park Service, National Interagency Fire Center, 3833 South Development Avenue,
Boise, ID 83705, USA.
CNational Park Service, Pacific West Regional Office, 1111 Jackson Street, Oakland,
CA 94607, USA.
DSystems for Environmental Management, PO Box 8868, Missoula, MT 59807, USA. ESpatial Dynamics, 910 N Main St, Suite 342, Boise, ID 83702, USA.
FCorresponding author. Email: dlutes@fs.fed.us

Abstract. A new monitoring tool called FFI (FEAT/FIREMON Integrated) has been developed to assist managers with collection, storage and analysis of ecological information. The tool was developed through the complementary integration of two fire effects monitoring systems commonly used in the United States: FIREMON and the Fire Ecology Assessment Tool. FFI provides software components for: data entry, data storage, Geographic Information System, summary reports, analysis tools and Personal Digital Assistant use. In addition to a large set of standard FFI protocols, the Protocol Manager lets users define their own sampling protocol when custom data entry forms are needed. The standard FFI protocols and Protocol Manager allow FFI to be used for monitoring in a broad range of ecosystems. FFI is designed to help managers fulfil monitoring mandates set forth in land management policy. It supports scalable (project- to landscape-scale) monitoring at the field and research level, and encourages cooperative, interagency data management and information sharing. Though developed for application in the USA, FFI can potentially be used to meet monitoring needs internationally.

Additional keywords: data management, fire effects, monitoring system, Protocol Manager.

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Introduction

FFI (FEAT/FIREMON Integrated) is a software tool developed in the United States and designed to assist managers with collection, storage and analysis of ecological monitoring information. This tool was developed through a complementary integration of two fire effects monitoring systems commonly used in the US: FIREMON (Lutes et al. 2006) and the Fire Ecology Assessment Tool (FEAT ) (Sexton 2003). The National Interagency Fuels Coordination Group sponsored development of FFI and the National Park Service (NPS) was the managing partner.

FEAT was developed from the NPS Fire Monitoring Handbook (FMH) (USDI 1992, 2003) and associated software (Sydoriak 2001). This handbook was initially developed by the Pacific West Region of the NPS to guide fire-related ecological monitoring in California, Oregon and Washington. The handbook provides detailed descriptions for establishing a sampling strategy based on levels of monitoring activity relative to fire and resource management objectives. FMH had a DOS-based software package for entering data into a Microsoft FoxPro database. Beginning in 1995, the NPS conducted a series of regional work shops to examine user needs for fire and ecological monitoring throughout the entire NPS; then in 1996, FMH was adopted by all NPS regions across the US. The handbook was updated first in 2001 and again in 2003 to reflect the national scope of the system. The FMH software was replaced in 2005 with a Windows-based system that became known as the Fire Ecology Assessment Tool. FEAT uses a Microsoft SQL Server database that is much more flexible than the original DOS-based program, allowing data from a greater variety of field-sampling procedures to be stored in the database, greater ability to query data and export data, provided Geographic Information System (GIS) tools, and supported Personal DigitalAssistant (PDA) use.

The FIREMON fire effects monitoring system was developed by the USDA Forest Service (USFS) Missoula Fire Sciences Laboratory through a grant from the Joint Fire Science Program in 2000. Many of the protocols in FIREMON were taken from the ECODATA ecological monitoring program used in Region One of the USFS (Keane et al. 1990). ECODATA used an IINFOS data management system and FORTRAN-77-based data analysis package called ECOPAK. FIREMON uses Java-based data entry software and a Microsoft Access database. The FIREMON software package includes report and analysis software, and a

∗The content of the present paper was written and prepared by US Government employees on official time, and therefore it is in the public domain and not subject to copyright in the US. The use of trade or firm names in the present paper is for reader information and does not imply endorsement by the US Department of Agriculture of any product or service.

handbook with sampling strategy and detailed field-sampling procedures.

FEAT and FIREMON both facilitate fire-ecology monitoring and have similar procedural characteristics and database architecture. Their integration results in an enhanced ecological monitoring tool. FFI includes an extensive list of sampling protocols and users are able to define their own protocols in Protocol Manager, if necessary. Although the core fire ecology components are still part of FFI, the new flexibility means FFI can be used more broadly for monitoring a wide variety of ecosystem attributes. FFI is now better suited to assist managers in meeting the monitoring mandates set forth in land management policy (for example, the US National Environmental Policy Act). It eases data collection; supports cooperative, interagency data management and information sharing, and supports scalable (site-specific to landscape-level) monitoring for both field application and research needs.

FFI provides data entry and storage for a set of ‘standard’ protocols delivered with the software, summary reports, analysis tools, GIS and PDA support. Protocol Manager – described in more detail below – is an FFI component that allows design of new sampling protocols, thus making the FFI database capable of storing data in not just the standard protocols delivered with the FFI software but also any protocol designed by the user.

FFI is designed for Microsoft Windows XP operating systems. Data are stored in a Microsoft SQL Express 2005 database and accessed with SQL and Microsoft Visual Basic.NET programs. ESRI Arc products are used for GIS functionality. The system is designed for the varying information technology requirements of the USFS, NPS, Bureau of Land Management (BLM), Bureau of Indian Affairs (BIA) and the US Fish and Wildlife Service (FWS).

The relationship of the three FFI software components is shown in Fig. 1. The FFI Database Administration component interfaces with SQL Server Express 2005 and is used for general database management functions like creating and deleting databases. This component is also used to add users and user roles to each database. The SQL databases in FFI have either a ‘Protocol Manager’ or ‘Data Capture’ schema. Protocol Manager databases contain the design criteria for each protocol and provide the list of data fields viewed in the FFI Data Entry software. Data capture databases store field data the user enters in the FFI Data Entry software.

Development and testing

Like FEAT and FIREMON before it, FFI incorporates the evolutionary improvements of the systems it was borne from. In addition, FFI has benefited from its own testing and improvement process. Many hours were spent considering use cases and system architecture, testing the user interface and checking coded procedures. The present work was done in cooperation with employees from numerous US land management agencies. An FFI Testing  Workshop was held inAugust 2007 to intensively test the FFI software, again with agency cooperation. After the August workshop, nine additional versions of FFI were built and tested before it was finally released in November 2007. We continue to compile a list of suggested improvements to the system, such as new protocols, and additional summary reports and analysis that will be incorporated in future versions. Where applicable, FFI has either been approved or is in the process of being approved by the US land management agencies.

Fig. 1. Relationship of the three FFI software components.

Species lists

FFI incorporates the US Department of Agriculture Natural Resource Conservation Service PLANTS database (USDA and Natural Resources Conservation Service 2008). Users query the PLANTS database to populate a ‘local’ species list using the FFI species management utility. Species in the local list appear in species dropdown menus on the data entry screens. Species or items not available in the PLANTS database can be included in the FFI local list by adding a ‘user species’. For example, if a user is interested in sampling pine-cone density, then ‘pinecones’ can be added as a user species and it will be included on the species list dropdown menus on the data entry screens. The FFI local species list will also accommodate an unlimited number of ‘unknown’ species. This option is useful when field crews do not have the expertise to identify all the species encountered. In that case, they can record the species as an unknown on the data collection form (for example, UNK01) and collect a sample. When the sample is identified by a botanist, the FFI species management utility can be used to replace the unknown species with its appropriate species name in the FFI local species list and FFI database. The FFI species management utility can also be used to replace a species name if the species was misidentified in the field. The FFI local species list can be exported from one FFI database and imported to another.

The master species list included with FFI is the component most likely to limit the use of FFI; however, with a minimum amount of development, any master species list can be incorporated in FFI, allowing it to be used outside the US. Interested parties can build their own ‘user species’ list and test FFI before making the commitment of incorporating a new master species list. Further, when used in conjunction with the Protocol Manager, a new master species list and sampling protocols will allow FFI to be used for sampling other life forms such as terrestrial wildlife.

Data entry and storage

FFI provides programmed data entry screens for entering data into the Microsoft SQL database. Entry screens are provided for

Table 1. Protocols delivered with FFI

Protocols not available in FIREMON or FEAT are listed as ‘New’ and, when applicable, the source of the protocol is provided plot location, surface fuels, tree data, point intercept, density, line intercept, rare species, cover/frequency, species composition, fire behavior, disturbance history, Fuel Characterization Classification System (FCCS), post-burn severity and compos- ite burn index (CBI). FFI also has a ‘Biomass – Fuels’ protocol for storing ocular or photographic estimates of biomass, for example those found in the USFS Pacific Wildland Fire Sciences Laboratory Natural Fuels photo series. Data entry screens have built-in flexibility to accommodate data from a wide variety of plot-based sampling schemes. The data entry fields represent a combination of those in the FEAT and FIREMON, so data can be collected using the methods described in the FMH (USDI 2003) or the FIREMON manual (Lutes et al. 2006) field manuals and stored in an FFI database. In many cases, the FFI database will also accommodate data collected with field-sampling protocols from other publications.

 

Protocol Source
Biomass – Fuels New
Biomass – Plants FEAT
Composite burn index FEAT and FIREMON
Cover – Line intercept FEAT and FIREMON
Cover – Species
composition (ocular macroplot)		 FEAT and FIREMON
Cover – Individual points FEAT
Cover – Points by
transect		 FIREMON
Cover/Frequency
(Daubenmire)		 FEAT and FIREMON
Density – Belts FEAT and FIREMON
Density – Quadrats FEAT and FIREMON
Fuel Characteristic
Classification System		 NewA
Fire behavior FIREMON
Plot description (biotic,
abiotic variables, fire behavior, photo links)		 FIREMON
Post-burn severity FEAT
Rare plant species FIREMON
Surface fuels (downed
woody material, duff, litter)		 FEAT and FIREMON
Surface fuels – Alaska
duff and litter		 NewB
Surface fuels – Piles NewC
Surface fuels –
Vegetation		 FIREMON
Tree data FEAT and FIREMON

AOttmar et al. 2007.
BAlaska Interagency Fire Effects Task Group 2007.
CHardy 1996.

Sampling protocols

The ‘standard set’ of sampling protocols delivered with the FFI software is listed in Table 1 as well as the source of the protocol, where applicable. The protocols were developed from the existing, recognized methods previously available in FEAT and FIREMON and supplemented with new protocols suggested during FFI development. Protocols that require unit data are available in metric and imperial unit versions. Although FFI was developed from fire effects systems, the wide array of protocols makes the system applicable for monitoring rangeland, forest and other ecosystems regardless of the presence or absence of fire as a disturbance.

Protocol Manager

Protocol Manager is a unique extension to FFI that lets users design new protocols that can then be imported for use in FFI. A protocol is defined as a set of methods implemented separately to perform a certain task. The user defines methods and combines them in Protocol Manager to build a protocol that will facilitate a comprehensive assessment of ecosystem attributes important to the user. User-defined methods can be highly varied, ranging from new methods to monitor vegetation to methods to monitor mammals, birds, amphibians, reptiles, insects or aquatic species. Protocol Manager also records metadata for each protocol (e.g. plot size, plot shape, quadrat size). The data recorded with user-defined protocols are stored in the same database as data collected with the standard FFI protocols.

Queries, reports and analysis

FFI includes the query features found in FEAT with added functionality to allow data to be queried from userdefined protocols designed in the Protocol Manager. The Query screen lets the user retrieve method data in a flexible, ad hoc manner in which values are filtered and parameters are defined through the user interface. The data summary reports and analysis tools are an expanded set of those provided in FIREMON. The FFI summary reports provide plot-by-plot summaries or grouped summaries of measured attributes such as trees per acre, downed woody material biomass, frequency, cover and density. The FFI analysis tools program can perform grouped or ungrouped summary calculations of a measured attribute, or statistical comparisons of grouped or ungrouped plot data taken at different sampling periods. For statistical comparisons, the analysis tools assume data were collected in a randomized block design with each time-point structured as a block. Parametric analyses are made using analysis of variance. If a significant difference in means is noted, Dunnett’s multiple comparison procedure is used to compare treatment groups with a designated control group to identify which means are different. Friedman’s test is provided for non-parametric analyses. A minimum of four plots per group is required for statistical analysis. Reports and graphs can be saved to a file, printed, or cut-and-pasted into other documents. Statistical testing procedures were developed with guidance of station statisticians at the USFS Rocky Mountain Research Station. As an additional feature, tree and fuels data can be exported to build files necessary to run the Forest Vegetation Simulator (FVS) (Dixon 2002).

GIS

The GIS module is an optional component users can add to FFI. It is similar to the GIS module in FEAT and is accessible inArcMap as a tool bar. Users who desire GIS capability need to have an understanding of GIS, and must have ArcGIS 9.2 and Spatial Analyst installed on their computers. The GIS module does not deliver any data layers or attempt to manage GIS data. Users may need the help of a GIS specialist to identify the appropriate GIS data for their needs if they utilize the FFI GIS module.

The GIS module provides support for developing geographic project areas. A custom tool allows users to overlay different types of GIS layers that identify the geographical area of their sample population. The GIS module also allows users to randomly or selectively choose sample points within polygons (e.g. burn severity classes or vegetation classes) that can then be passed to the FFI database. The module supports basic display of FFI macro plot sites and the interactive spatial queries of the collected data using the ArcMap tools. Tools that identify severity thresholds in Differenced Normalized Burn Ratio layers for CBI (Key and Benson 2006) sampling are also included.

Electronic field data collection

Electronic field data collection is facilitated using a PDA or data recorder equipped with the Microsoft Windows Mobile 5 operating system and requires Microsoft ActiveSync to manage the connection between the PDA and the FFI host computer. The PDA application first moves empty electronic field data collection forms to the PDA for user-specified macro plots, protocols, and sampling events. When data collection is complete, the application then moves data from the PDA back into the FFI database, appending the data already stored. Data entered on the PDA are editable on the PDA until they are uploaded to the host FFI database; then they may be edited in the host database if the user has the appropriate permission level.

Computer configuration

Computers used for implementation of FFI fall into three categories: isolated computers, desktop as server and a limited access server (Fig. 2).The configuration chosen by users depends on individual needs and available computer resources. When GIS functionality is desired, ArcGIS 9.2 and Spatial Analyst must be installed and run from computers that have the FFI software installed on them.

Isolated computers as servers
Limited access server
Desktop as server

Fig. 2. The three main computer configurations used with FFI.

Isolated computer as server

The stand-alone computer has no other computers attached to it that share its internal databases. This configuration has both the FFI software and SQL Server installed.

Desktop as server

One computer with FFI and SQL Server installed is connected via a network to other computers that have FFI and SQL Server components installed on them. Data entry can be accomplished on any of the computers. Database storage and management occurs on the desktop server.

Limited access server

A database server is a dedicated computer running a database engine that can be either accessed directly from a server or client computer with password protection or via intranet access. This configuration has SQL Server only installed on the database server and FFI and SQL Server components installed on the connected computers.

System security

FFI supports four levels of internal data access or user permission levels. The goal of the permission levels is to balance system accessibility with data security. For example, some users will only need to query data for summarization and analysis whereas other users will need access to edit data for quality analysis and quality control (QA/QC). Each user role has different permissions for the FFI program and its databases:

  • The FFI Administrator can modify the database schema, create new database instances, import external data, and manage database users. Record locking will require FFI Administrator privileges. Administrators can also do any activities assigned to Managers, Users and Readers.
  • FFI Managers can create protocols and methods. Managers can also do any activities assigned to Users and Readers.
  • FFI Users can read and write FFI data, queries, and reports, and export FFI data. FFI Users cannot change the database schema.
  • FFI Readers will have read-only access to FFI. FFI Readers can export FFI summary reports, analysis reports and query results.

Hardware requirements

The FFI software requires MicrosoftWindows XP Service Pack 2 or XP 2003 operating systems. Data must be stored in Microsoft SQL Server Express 2005 or SQL Server 2005 full edition database. The FFI software and SQL Server Express require 500 MB combined free disk space for installation. The FFI SQL databases range from 100 MB to 4 GB in size (4 GB is the maximum size for SQL Server Express 2005 databases. Larger databases can be stored in SQL Server full edition). Recommended minimum processor speed and random access memory are 1 GHz and 512 MB, respectively. Increasing memory to 1 MB enhances system performance.

Technology transfer

FFI is supported by annual training workshops and on-line presentations. User assistance is provided through the FFI Website, help-desk and Web forum. Training schedules, software installation packages, documentation and technical support contacts are provided on the FFI Website (http://frames.nbii.gov/ffi, accessed 28 April 2009).

Acknowledgements

Funding for FFI was provided by the National Interagency Fuels Coordination Group. Additional support was provided by the NPS, USFS, Systems for Environmental Management and Spatial Dynamics. We specifically thank
Melissa Forder and Dan Swanson (NPS), Clint Isbell (USFS), Charley Martin, Chamise Kramer and Jena Dejuilio (BLM), Bil Graul (San CarlosApache Tribe), Ben Butler (Student Conservation Association), Kristin Swoboda
(Bureau of Reclamation) and Jacque Schei (US Geological Survey) for β testing FFI in August 2007. Jennifer Allen (NPS), Karen Murphy (US FWS), and Randi Jandt (BLM) helped us develop the Alaska Surface Fuels protocol; Roger Ottmar (USFS, Pacific Northwest Research Station) and Susan Pritchard (University of Washington) assisted with development of the FCCS protocol; and Colin Hardy (USFS, Rocky Mountain Research Station) helped us incorporate the Surface Fuels – Piles protocol. Additionally, numerous helpful comments were provided by employees at each of the agencies and organizations already recognized and also the BIA, US Department of the Army and The Nature Conservancy. Chad Keyser (USDA Forest Service, Forest Management Service Center) helped us update the FVS file-building utility in FFI. We thank Rudy King and David Turner of the USFS, Rocky Mountain Research Station, for their assistance in developing the statistical analysis tools available in FFI. Finally, we acknowledge the helpful comments of the anonymous reviewers.

References

Alaska Interagency Fire Effects Task Group (2007) Fire effects monitoring protocol (version 1.0). (Eds J Allen, K Murphy, R Jandt) Available at http://depts.washington.edu/nwfire/publication/AK_Fire_Effects_ Monitoring_Protocol_2007.pdf [Verified 28 April 2009]

Dixon GE (2002) Essential FVS: a user’s guide to the Forest Vegetation Simulator. USDA Forest Service, Forest Management Service Center, Internal Report. (Fort Collins, CO) Available at http://www.fs.fed.us/fmsc/fvs/documents/gtrs_essentialfvs.php [Verified 28 April 2009]

Hardy CC (1996) Guidelines for estimating volume, biomass, and smoke production for piled slash. USDA Forest Service, Pacific Northwest Research Station, General Technical Report PNW-GTR-364. (Seattle, WA)

Keane RE, HannWJ, Jenson ME (1990) ECODATA and ECOPAC: analytical tools for integrated resource management. The Compiler 8, 24-37.

Key CH, Benson NC (2006) Landscape assessment. In ‘FIREMON : Fire Effects Monitoring and Inventory System’. (Eds DC Lutes, RE Keane, JF Caratti, CH Key, NC Benson, S Sutherland, LJ Gangi) USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD. (Fort Collins, CO)

Lutes DC, Keane RE, Caratti JF, Key CH, Benson NC, Sutherland S, Gangi LJ (2006) FIREMON : Fire Effects Monitoring and Inventory System. USDA Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-GTR-164-CD. (Fort Collins, CO)

Ottmar RD, Sandberg DV, Riccardi CL, Prichard SJ (2007) An overview of the Fuel Characteristic Classification System – quantifying, classifying, and creating fuelbeds for resource planning. Canadian Journal of Forest Research 37(12), 2383-2393. doi:10.1139/X07-077

Sexton TO (2003) Fire Ecology Assessment Tool – monitoring wildland fire and prescribed fire for adaptive management. In ‘2nd International Wildland Fire Ecology and Fire Management Congress’, 19 November
2003, Orlando, FL. (American Meteorological Society: Boston, MA) Sydoriak WM (2001) FMH.EXE. Version 3.1x. (National Park Service: Boise, ID)

USDA and Natural Resources Conservation Service (2008) ‘The PLANTS Database.’ (National Plant Data Center: Baton Rouge, LA) Available at http://plants.usda.gov [Verified 28 April 2009]

USDI (1992) ‘Western Region Fire Monitoring Handbook.’Western Region Prescribed and Natural Fire Monitoring Task Force. (National Park Service: San Francisco, CA)

USDI (2003) ‘Fire Monitoring Handbook.’ Fire Management Program Center, National Interagency Fire Center. (National Park Service: Boise, ID) Available at http://www.nps.gov/fire/download/fir_eco_FEMHandbook2003.pdf [Verified 28 April 2009]

Manuscript received 29 May 2007, accepted 16 May 2008

 

Measuring & Monitoring Plant Populations

Posted on July 25, 1998 by in Field Guide/Manual

MEASURING & MONITORING Plant Populations

 

COVER PHOTOS

The cover landscape photo and the picture of the two people  sampling were taken by Daniel Salzer. Both photos were  taken at The Nature Conservancy’s Katharine Ordway Sycan  Marsh Preserve. The individuals shown sampling in the small  photo are Rob Lindsay and Linda Poole Rexroat, both are  TNC employees. The inset flower photo was taken by Linda M. Hardie, and shows grass-widows (Sisyrinchium douglasii),  at the Nature Conservany’s Tom McCall Preserve at Rowena Crest.

MEASURING & MONITORING Plant Populations

 AUTHORS:

Caryl L. Elzinga Ph.D.
Alderspring Ecological Consulting P.O. Box 64
Tendoy, ID 83468

Daniel W. Salzer
Coordinator of Research and Monitoring
The Nature Conservancy of Oregon
821 S.E. 14th Avenue
Portland, OR 97214

John W. Willoughby
State Botanist
Bureau of Land Management
California State Office
2135 Butano Drive
Sacramento, CA 95825

This technical reference represents a team effort by the three authors. The order of authors is alphabetical and does not represent the level of contribution.

Though this document was produced through an interagency effort, the following BLM numbers have been assigned for tracking and administrative purposes:

BLM Technical Reference 1730-1

BLM/RS/ST-98/005+1730

ACKNOWLEDGEMENTS

The production of this document would not have been possible without the help of many individuals. Phil Dittberner of the Bureau of Land Management’s National Applied Resource Sciences Center (NARSC) coordinated the effort for BLM. Ken Berg, former BLM National Botanist, provided support and funding for the project.

The content of many chapters in this Technical Reference has benefited from the review of lecture outlines included in “Vegetation Monitoring in a Management Context,” a week-long  monitoring workshop offered jointly by The Nature Conservancy and the U.S. Forest Service.

The authors would also like to acknowledge those persons who reviewed the document and provided valuable comments, including Jim Alegria of the BLM Oregon State Office; Paul  Sawyer of the BLM Arizona State Office; Rita Beard, Andrew Kratz, Will Moir, and David  Wheeler of the Forest Service; Peggy Olwell of the National Park Service; and Gary White of Colorado State University.

We’d like to thank Sherry Smith of Indexing Services for the many hours she donated to this project in developing the index to this TR.

We extend a special thank you to Janine Koselak (Visual Information Specialist) of NARSC for doing a masterful job in layout, design, and production of the final document.

PREFACE

This technical reference applies to monitoring situations involving a single plant species, such as  an indicator species, key species, or weed. It was originally developed for monitoring special status plants, which have some recognized status at the Federal, State, or agency level because of  their rarity or vulnerability. Most examples and discussions in this technical reference focus on  these special status species, but the methods described are also applicable to any single-species  monitoring and even some community monitoring situations. We thus hope wildlife biologists,  range conservationists, botanists, and ecologists will all find this technical reference helpful.

Monitoring is not a new activity for land management agencies, but there is a renewed interest  and a new national emphasis on improving the quality of monitoring. Monitoring designed and  executed effectively is a powerful tool for better management of resources. Good monitoring,  while initially expensive to implement, is eventually costffective because management problems can be detected at an early stage, when solutions may yet be relatively inexpensive. Good monitoring can demonstrate that management is effective and successful, can silence critics, and can encourage the widespread adoption of an effective management technique.

Often, however, the results from monitoring are inconclusive and fail to provide the information needed to evaluate the success of management. Inconclusive or ambiguous monitoring results are  expensive, both in terms of the resources wasted on the monitoring project and the potential  costs of incorrect action. These costs are often difficult to measure because they are exacted  from the environment in the form of environmental damage, or from industry in the form of  unnecessary controls. Reduced public confidence and litigation expenses are additional hidden costs of poor monitoring.

Many monitoring projects suffer one of five unfortunate fates: (1) they are never completely implemented; (2) the data are collected but not analyzed; (3) the data are analyzed but results are inconclusive; (4) the data are analyzed and are interesting, but are not presented to decision makers; (5) the data are analyzed and presented, but are not used for decision-making because of internal or external factors (see Appendix 1 for some typical scenarios). The problem is rarely the collection of data. Agency personnel are often avid collectors of field data because it is one of the most enjoyable parts of their jobs. Data collection, however, is a small part of successful monitoring.

Because of the difficulty and importance of effective monitoring, agencies developed standard monitoring approaches in the 1960s through 1980s. While these techniques effectively met the challenges of that time, they are inadequate now for several reasons:

  • The resources and management effects of interest today are more variable and complex. It is difficult for standard designs to keep pace with the rapid changes in issues. Monitoring data from standard techniques are sometimes inconclusive because the studies are not specifically designed for the issue in question.
  • Many standard techniques do not address issues of statistical precision and power during design; thus, standard monitoring techniques that involve sampling may provide estimates that are too imprecise for confident management decisions.
  • Commodity and environmental groups have become more sophisticated in resource measurement and are increasingly skeptical of data from standard agency techniques.
  • Funding reductions are restricting resources available for monitoring projects. Concurrently, agencies are being required to more clearly demonstrate through monitoring that funds are being used to effectively manage public lands. This situation requires the design of efficient monitoring projects that provide data specific to the current issues.

The challenges of successful monitoring involve efficient and specific design, and a commitment to implementation of the monitoring project, from data collection to reporting and using results.  We have designed this technical reference with these challenges in mind. Our approach differs radically from the development of standard techniques for field offices to apply. We instead provide technical guidance that assists field personnel in thinking through the many decisions that they must make to specifically design monitoring projects for the site, resources, and issues. We base this approach on the belief that local resource managers and specialists understand their issues and their resources best and, therefore, are best able to design monitoring to meet their specific needs. With this technical reference, local personnel can design much of the monitoring done at the local level, and recognize when they need additional specialized skills for a successful project.

We encourage you to treat this technical reference not as a step-by-step guide on how to implement a monitoring study, but as a collection of pieces that you need to choose among and put together for your particular situation and species. We have organized this technical reference to follow a logical progression of planning and objective setting, designing the methodology, taking the measurements in the field, analyzing and presenting the data, and making the necessary management responses. Many of these steps, however, occur simultaneously, or provide feedback to others. Decisions made at each step of the monitoring process can affect the whole project, and those made at later stages sometimes require the reassessment of previous decisions. A listing and short content description of each chapter should make it clear that those chapters we have placed in the latter part of the reference are also important in the conceptual stage if the monitoring is to be efficient and effective:

Chapter 1.     Introduction—Describes the role of monitoring in adaptive management. Contrasts monitoring with other data-collection activities, such as inventory and long-term ecological studies.

Chapter 2.     Monitoring Overview—Provides a step-by-step overview of the entire monitoring process, and references chapters where information on each step can be found in more detail. Flow charts are included to illustrate feedback loops and interrelationships among the steps.

Chapter 3.     Setting Priorities and Selecting Scale—Presents criteria and techniques for setting priorities among species or populations and choosing the most appropriate scale and intensity for monitoring.

Chapter 4.     Management Objectives—Illustrates the foundational nature of management objectives and describes their components, types, and development.

Chapter 5.     Basic Principles of Sampling—Describes basic terms and concepts relevant to sampling using simple examples. This chapter provides background information critical to understanding material presented in Chapters 6, 7, and 11.

Chapter 6.     Sampling Objectives—Describes objectives that complement management objectives whenever the monitoring includes sampling procedures. A sampling objective sets a specific goal for the level of precision or acceptable error rates associated with the sampling process.

Chapter 7.     Sampling Design—Describes how to make the six basic decisions that must be made in designing a sample-based monitoring study: (1) What is the population of  interest? (2) What is an appropriate sampling unit? (3) What is an appropriate sampling unit size and shape? (4) How should sampling units be positioned? (5) Should sampling units be permanent or temporary? (6) How many sampling units should be sampled?

Chapter 8.     Field Techniques for Measuring Vegetation—Discusses selecting an appropriate vegetation attribute to measure when monitoring (e.g., cover, density, frequency, biomass, etc.) in terms of the biology and morphology of the species, and the practical limitations involved in each type of measurement. Field techniques for measuring each vegetation attribute and advice on field techniques and tools are provided.

Chapter 9.     Data Management—Covers different ways of recording monitoring data in the field and describes means for entering and managing field monitoring data sets with computers.

Chapter 10. Communication and Monitoring Plans—Encourages the use of monitoring plans to solicit involvement in the development of a monitoring project, and to document the accepted monitoring protocol. Describes parties whose support may be critical for a successful monitoring project.

Chapter 11. Statistical Analysis—Describes the methods used to analyze monitoring data collected using sampling procedures, the use of graphs to examine data prior to analysis and to display the results of analysis, and the interpretation of monitoring data following analysis.

Chapter 12. Demography—Describes techniques for demographic analysis of populations and provides cautions and suggestions for their use.

Chapter 13. Completing Monitoring and Reporting Results—Summarizes the final stages of a monitoring project and describes methods for reporting results.

Effective monitoring is not easy; it requires a commitment of time and a willingness to think through alternatives during planning and design. We believe you will find that increasing time spent in design reduces total monitoring costs by making monitoring more efficient and effective. Above all, we hope to help you avoid wasting time on a monitoring project that fails to yield results useful for management decisions.

Because this is a somewhat novel approach, and because we intend to eventually update this handbook, we are especially interested in receiving your comments and opinions. You can send comments to:

Dr. Phil Dittberner

National Applied Resource Sciences Center, RS-140
Denver Federal Center, Building 50
P.O. Box 25047
Denver, CO 80225-0047

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