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

 

Managing forests with prescribed fire: Implications for a cavity-dwelling bat species

Justin G. Boylesa, Doug P. Aubreyb,*

a Department of Ecology and Organismal Biology, Indiana State University, Terre Haute, 47809, USA
b USDA Forest Service, Savannah River, P.O. Box 700, New Ellenton, SC 29809, USA
Received 1 June 2005; received in revised form 27 September 2005; accepted 30 September 2005


Abstract

Prescribed burning is used as a restoration and management technique in many deciduous forests of eastern North America. The effects of fire have been studied on habitat selection of many vertebrate species, but no studies have reported the effect of fire on bat roosting habitat. Fire initially leads to an influx of dead and dying trees, an increase of light availability, and a decrease of canopy and sub-canopy tree density. These characteristics are beneficial to many forest-dwelling vertebrates including cavity-roosting bats. We evaluated evening bat (Nycticeius humeralis) roost-site selection at the stand-scale in order to determine roosting preferences as they relate to prescribed burning. Standard radiotelemetry techniques were used to locate evening bat roost trees. Canopy light penetration and overstory tree density were measured in both burned and unburned forests. Sixty-three trees used as roosts by both male and female evening bats were located during both the summer and winter and all 63 roosts were located in the burned portion of the study area. Canopy light penetration was higher and canopy tree density was lower in the burned forest than unburned forest. An increase in light availability may release bats from one of the constraints suggested for many forest-dwelling bat species in roost tree selection—sun-exposure. This should increase the abundance of trees with characteristics suitable for roosting and may allow bats to roost throughout the interior of the forest as opposed to only on forest edges, thereby allowing bats to roost closer to foraging grounds and possibly lessening predation rates. Lower tree density may allow for ease of flight within the forest as well as more efficient locating of roost trees. In addition, there were a significantly higher proportion of dead trees, which evening bats commonly use as roost trees, in burned forests compared to unburned forests. Prescribed burning appears to initially lead to creation or restoration of favorable cavity-dwelling bat habitat and its continual implementation perpetuates an open sub-canopy. Therefore, we suggest that prescribed burning may be a suitable tool for management of roosting habitat for cavity-roosting bats.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Fire ecology; Forest restoration; Nycticeius humeralis; Oak-hickory forest; Prescribed burning; Roost selection

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1. Introduction

Fire is critical in regulating and maintaining many forest ecosystems (Huddle and Pallardy, 1996; van Lear, 2002). In particular, much of the western portion of North America’s eastern deciduous forest is thought to have been shaped and maintained through fires set by Native Americans prior to European settlement (Cottam, 1949; Ladd, 1991; Pyne, 1982). Prescribed burning has been shown to alter many characteristics of forest habitat potentially affecting forest-dwelling bats including tree mortality, which increases available trees commonly used by wildlife (Huddle and Pallardy, 1996; Arthur et al., 1998; Hartman and Heumann, 2003; Aubrey, 2004); pathogen susceptibility, which expedites cavity formation (Burns, 1955; Paulsell, 1957; Smith and Sutherland, 2001); and canopy light penetration (Anderson and Brown, 1986; McCarty, 1998; Aubrey, 2004), which is known to affect roost suitability (Kurta et al., 1993; Brigham et al., 1997).


* Corresponding author. Tel.: +1 803 725 1758; fax: +1 803 725 0311.
E-mail address: daubrey@fs.fed.us (D.P. Aubrey).

0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2005.09.024


Many forests are managed for timber harvest by the use of mechanical thinning or clear-cutting. Prescribed fire differs from these management techniques because fire is generally used to maintain or restore natural forest ecosystems and reduce understory competition, while other silvicultural methods are predominately used to maximize harvest yield and quality. When first implemented, prescribed burning decreases overstory tree density and basal area (Anderson and Brown, 1986; Peterson and Reich, 2001). Following this initial fire with frequent  burns generally reduces fuel accumulation and subsequent burns are therefore relatively cool and somewhat nonuniform (Ladd, 1991). However many fire-sensitive seedlings and saplings are generally eliminated or prevented from regenerating (Lorimer, 1985; Ladd, 1991; Moser et al., 1996; Brose and van Lear, 1997; Barnes and van Lear, 1998). Fire-tolerant trees remain as overstory dominants and canopy recruitment coincides with periods of decreased burn frequency or intensity (Crow et al.,1994). For example, oaks possess thick bark which insulates them from heat associated with burning and are capable of rapid compartmentalization when damaged by fire which inhibits fungal infection and makes mature individuals relatively tolerant of fire (Lorimer, 1985; Abrams, 1985; Crow, 1988; Stearns, 1991; Smith and Sutherland, 1999; Peterson and Reich, 2001). Much of the western portion of the eastern deciduous forest is believed to have resembled the structure and composition of a woodland (i.e. moderate to low canopy coverage) more than a forest (i.e. high canopy coverage) prior to European settlement, which is largely attributed to fire (Cottam, 1949; Curtis, 1959; Nuzzo, 1986; Ladd, 1991). Therefore, managing forests with prescribed fire reintroduces a historic disturbance process that other silvicultural techniques lack, and may provide a more heterogeneous habitat for wildlife that is similar to forested areas prior to wide-spread fire suppression by European settlers. Furthermore, prescribed burning has become increasingly common over the past few decades, as land managers have seen the effects of fire suppression. Specifically, fire has been most commonly reintroduced to systems where conservation of biodiversity and restoration of historic ecosystem processes are paramount.

Numerous studies have focused on vertebrates in forests maintained by prescribed burning (e.g. reptiles, McLeod and Gates, 1998; small mammals, Simon et al., 2002; amphibians, Schurbon and Fauth, 2003; birds, Blake, 2005). However, to our knowledge, there are no studies reporting habitat selection of tree-dwelling bats in burned forests, although the need for such studies has been suggested (Menzel et al., 2001b; Carter et al., 2002). Furthermore, the effects of forest management on roosting habitat of bats are not clear and it has been suggested that wildfires and prescribed burning may have detrimental (Chambers et al., 2002) or beneficial (Carter et al., 2002) impacts on bats. For some bat species, such as the eastern red bat (Lasiurus borealis), fire may pose a direct threat to survival as early spring burns remove litter where occasional winter roosting occurs and can potentially scorch hibernating individuals (Moorman et al., 1999; Rodrigue et al., 2001). Bats roosting in snags (standing dead trees) are also susceptible to direct disturbance from fire if roost trees ignite (Rodrigue et al., 2001), but this is probably not a major cause of mortality unless the roost burns quickly and the bat is in deep torpor (Carter et al., 2002). Availability of suitable roosts likely influences habitat selection (Kunz, 1982) and roosting sites are thought to be of critical importance to conservation of many bat species (Fenton, 1997). Fire generally removes understory saplings and may lead to the creation of suitable roost trees for cavity-roosting bats. Fire may also remove standing snags if fuel loading is high. However, fuel should not accumulate directly under a snag when burning is frequent so snags should persist under these circumstances. As fire increases the heterogeneity of forest structure and composition (Fule et al., 2004), there should be an increase in the diversity of available roosting habitat.

Previous studies have focused on activity (Humes et al., 1999; Patriquin and Barclay, 2003; Tibbels and Kurta, 2003; Mazurek and Zielinski, 2004), demographic parameters (Miller, 2003), and roosting preference (Campbell et al., 1996; Menzel et al., 2002; Elmore et al., 2004) of bats in heavily managed forests, but have not examined fire-based management. With the exception of a few anecdotal reports of L. borealis being driven from their leaf litter  ibernacula during winter burns (Moorman et al., 1999; Rodrigue et al., 2001), there is little empirical data about how fire affects forest dwelling bats.

The objective of this study was to determine evening bat (Nycticeius humeralis) roost-site selection at the stand-scale in a forest heavily managed by fire and to understand those characteristics of the forest that may influence roost tree selection. The evening bat is a locally abundant cavity-dwelling bat species found throughout much of the southeastern United States, but it may be declining in parts of its range (Whitaker et al., 2002; Whitaker and Gummer, 2003). Evening bats are known to roost in large numbers in man-made structures (Watkins, 1969; Watkins and Shump, 1981; Bain and Humphrey, 1986; Wilkinson, 1992) and tree cavities (Wilk-inson, 1992; Bowles et al., 1996; Menzel et al., 1999, 2001a; Boyles et al., 2003; Boyles and Robbins, in press). Evening bats roost mainly in cavities in trees of various stages of decay (Bowles et al., 1996; Boyles and Robbins, in press), but relatively little is known about the formation or microclimate of the roosts. During the summer, evening bats roost mainly in large dead trees, but during the winter, live trees are commonly used (Boyles and Robbins, in press). They have a short wingspan and high wing loading so it has been predicted that they are not highly maneuverable relative to other bat species (Norberg and Rayner, 1987). Due to relatively inefficient flight, this species may avoid long foraging trips (Norberg and Rayner, 1987) and it has been suggested that evening bats forage close to their roosting areas  Duchamp et al., 2004). Evening bats feed heavily on coleopterans, homopterans, and hemipterans (Whitaker, 2004). As with other tree roosting bat species, they spend over half of its time each day in roosts (Brigham et al., 1997), so conserving roosts is important in managing this species. Evening bats are commonly referred to as migratory in middle latitudes (Jones et al., 1967; Humphrey and Cope, 1968; Watkins, 1969; Wilkinson, 1992; Sparks et al., 1999; Geluso et al., 2004), but it appears that the population discussed herein is largely non-migratory (Boyles et al., 2003; Boyles and Robbins, in press) so roost trees refer to trees used throughout the year.

It was predicted that evening bats would prefer roosting in burned forests because of an increase in the density of dead and dying trees, an increase in light penetration and an overall decrease in overstory and understory tree  ensity. These characteristics should benefit forest-dwelling bats and promote use of burned forests as roosting habitat. This study compliments previous work (Boyles and Robbins, in press), which reports the characteristics of roost trees and the surrounding habitat used by this population of evening bats during both the summer and winter. The results presented herein focus on roost selection at the forest stand-level.


2. Methods

2.1. Study area

We conducted this research on the Drury Conservation Area (DCA) in Taney County, Missouri (UTM 40.47.000N, 4.93.000E). DCA is a 1200 ha area located in extreme southwestern Missouri in the Ozark Mountains Region and is bordered on two sides by Bull Shoals Lake. It is actively managed by the Missouri Department of Conservation, which has implemented prescribed burning on approximately 55% of the potential area available for roosting habitat in an attempt to restore historic glades and oak-hickory woodlands and reduce red cedar (Juniperus virginiana) concentrations. Burning was initiated in 1999 after nearly 50 years of fire suppression and the area was then burned on a biennial schedule. All burning was conducted in March or April. The initial 1999 burn was probably more intense than subsequent burns because of accumulated fuels; therefore more overstory tree mortality may have occurred during or as a result of the initial burn (Aubrey, 2004).

Approximately 60% of DCA is dominated by oak-hickory forest with the remainder of the area being glades, wildlife food plots, ponds, and riparian areas (Missouri Department of Conservation, 1991). Elevation ranges from 185 to 335 m on DCA. Several gravel roads facilitate access to the interior of much of the forest and one large gravel road serves as the firebreak between burned and unburned portions of the forest. The canopy of the area consists almost entirely of deciduous trees from the white and red oak groups (Quercus spp.), hickories (Carya spp.), elms (Ulmus spp.), and ashes (Fraxinus spp.).

2.2. Location of roosting sites

Bats were captured from March 2003 to March 2004 using mist nets (Avinet, Dryden, NY, USA) of various lengths (6, 9, 12, or 18 m) placed across ponds or forest roads. The majority of netting sites were on the gravel road that acts as a firebreak between the burned and unburned forest, but one pond and two roads in the burned forest and one pond and one creek bed in the unburned forest were also netted. Approximately 55% of the available roosting area for evening bats was located in an area treated with prescribed fire. Thus, if evening bats were roosting at random, we would expect 55% of roost trees to be located in burned forest and 45% of roost trees to be located in unburned forest. Twenty-three evening bats were fitted with radio- transmitters (0.52 g; Model LB-2N, Holohil, Carp, Canada) by clipping fur to the skin in the interscapular region and affixing the transmitter with surgical adhesive (Skin Bond, Smith and Nephew Inc., Largo, FL, USA). The range of weights of bats fitted with transmitters was 7.5-13.5 g; therefore the transmitter represented 3.9-6.9% of the individual’s body mass.

Each bat’s roost tree was located every day following attachment of the transmitter and tracking was discontinued when the transmitter expired, was shed by the bat, or the bat remained outside of the study area for more than 5 days. This study was conducted year-round so roost trees include those used during both summer and winter by males and females in all reproductive classes (pregnant, lactating, post-lactating, and non-reproductive). All animal handling methods follow guidelines of the American Society of Mammalogists (Animal Care and Use Committee, 1998).

2.3. Canopy light penetration sampling

Leaf area index (LAI) was used as a measure of canopy light penetration to determine if three biennial prescribed burns had resulted in a more open canopy. LAI is an estimate of the ratio of overstory leaf area relative to ground area. A LAI of 0 indicates complete canopy light penetration whereas a LAI of 12 indicates no canopy light penetration (Hyer and Goetz, 2004). Here we define canopy light penetration (CLP) as: 12 LAI.

Two blocks each containing three transects in burned and unburned forests were monitored throughout the 2003 growing season. Unburned and burned forests were adjacent to one another with a gravel road acting as a fire buffer between them. LAI was obtained indirectly at each transect using an AccuPAR PAR-80 light interception device (Decagon Devices Inc., Pullman, WA, USA). Individual light measurements were collected 1.2 m above ground-level at five randomly spaced points along each transect. These five measurements were then averaged to calculate one LAI value, and therefore, one CLP value for each transect per sample period. Measurements were collected monthly throughout the 2003 growing season (April through September). The majority of canopy trees were deciduous species; thus, CLP was high in both burned and unburned forests during winter and was not measured from October through March.

2.4. Tree density

Canopy tree density was estimated in 0.05 ha circular plots centered on 25 randomly selected trees in each of the burn treatments. A random number generator was used to select UTM coordinates for the trees. Coordinates were located using a GPS unit (eTrex, Garmin International Inc., Olathe, Kansas) and selected the tree nearest that point to serve as the center of the plot. All trees in the plot greater than 10 cm diameter at breast height (dbh) were considered overstory trees and were counted and classified as either live or dead.

2.5. Statistical analysis

A binomial probability distribution was used to determine if bats roosted at random or at a proportion different from what would be expected given the amount of burned and unburned forest available. The effect of prescribed fire on CLP was assessed using a multi-factorial analysis of variance (ANOVA). The experiment was a nested block design with repeated measures. Month (April-September) and habitat (burned forest and unburned forest) were treated as fixed factors. Block (n = 2) and transect (n = 12) were treated as random factors, with transect nested under treatment. Mean separations were performed using a post hoc Tukey test. Tree density was analyzed using a t-test. In addition to absolute densities, the arcsine of the proportion of dead trees compared to total trees was calculated and analyzed using a t-test (Zar, 1984). All statistical analyses were conducted in Minitab 14. Alpha is 0.05 for all analyses.


3. Results

3.1. Roost tree location

Fig. 1. Seasonal trends (mean S.E.) in canopy light penetration (CLP) calculated as 12 leaf area index in burned and unburned forest on Drury Sixty-three roost trees were used by 23 (11 females and 12 males) evening bats from 9 March 2003 to 31 March 2004. All 63 roost trees were located in the portion of the area that was subjected to prescribed burning, although many of the bats were captured on the road that serves as the break between burned and unburned forest or within 200 m of that road. Bats roosted in the burned forest exclusively and significantly more often than expected if they selected burned or unburned forest randomly (P < 0.001). In addition, nearly all the trees used as roosts were located more than 50 m from any forest edge (62 of 63). The one exception was a large white oak Quercus alba (L.) used by a male evening bat in October that was located at the forest edge less than 50 m from where that individual was captured. This tree was not used the first day after capture; it was used on the second and fourth days, so it appears that this tree was actively selected and was not used in response to stress caused by handling.

3.2. Canopy light penetration

Both the main effects of treatment (P < 0.001) and month (P < 0.001) as well as their interaction (P < 0.001) signifi-cantly affected CLP. Maximum leaf expansion occurred in May for the burned forest and July in the unburned forest. Averaged over the 6-month sample period, canopy light penetration was significantly greater in burned forest as compared to unburned forest (P < 0.001). CLP was high in both the burned and unburned forests in April (Fig. 1). Pairwise comparisons suggest that there was no difference in canopy light penetration between burned and unburned forests in April or May but differences were significant throughout the rest of the growing season (June-September, P < 0.05 in all comparisons). As expected, measurements collected prior to leaf expansion (April) suggest that there was no difference in CLP between the burned and unburned forests; therefore, there was likely no difference in light availability between the treatments during the winter.

Fig. 1.  Seasonal  trends  (mean    S.E.)  in  canopy  light  penetration  (CLP) calculated as 12    leaf area index in burned and unburned forest on Drury Conservation Area, Taney County, Missouri during the 2003 growing season. CLP, as defined herein, is measured on a scale of 0-12, with 12 indicating complete light penetration and 0 indicating no light penetration. All measurements were collected mid-month. Asterisks indicate significant differences (at alpha = 0.05) between burned and unburned forests in each month

3.3. Tree density

Mean overstory tree density per hectare was significantly higher in unburned forest (612.0 25.0) relative to burned Conservation Area, Taney County, Missouri during the 2003 growing season. CLP, as defined herein, is measured on a scale of 0-12, with 12 indicating complete light penetration and 0 indicating no light penetration. All measurements were collected mid-month. Asterisks indicate significant differences (at alpha = 0.05) between burned and unburned forests in each month.

 


forest (513.6 24.2; t = 2.83, P = 0.007) and live tree density was significantly higher in unburned forest (576.0 24.6) than burned forest (468.0 24.1; t = 3.13, P = 0.003). The density of dead trees was not significantly different between the burned (45.6 7.3) and unburned forests (36.0 4.3; t = 1.14, P = 0.263). However, the proportion of dead trees compared to total trees was significantly higher in burned forest (0.092 0.014) relative to unburned forest (0.060 0.007; t = 2.08, P = 0.045).

 


4. Discussion

In our study area, evening bats showed a strong preference for forests managed with prescribed fires. Although there are no data on roost tree selection by evening bats before prescribed burning was initiated, it is likely that the forest characteristics were similar between what are now burned and unburned forests (Aubrey, 2004). All roost trees used by both males and females throughout the year were in the burned portion of the study area. For at least a few years after the initial burn, prescribed fire enhances roosting habitat for evening bats in several ways. First, burning increases the abundance of dead trees in some forests (Burns, 1955; Paulsell, 1957; Huddle and Pallardy, 1996; Peterson and Reich, 2001; Fule et al., 2004) and therefore increases the number of tree cavities formed by decay. We did not directly test the effect of fire on overstory tree mortality; however, dead trees were found in higher densities and at significantly higher proportions in the burned forest. Initial burns with high fuel accumulation can result in the formation of dead trees (Paulsell, 1957; Scowcroft, 1966; Anderson and Brown, 1986; White, 1986; Peterson and Reich, 2001) and leads to a forest with a large number of trees in various stages of decay. Ambient temperatures vary widely during the winter in southwestern Missouri so a large number of trees in varying stages of decay may provide sufficient options for evening bats to meet thermoregulatory needs simply by roost-switching. It has also been suggested that fire-scars allow entry for fungal pathogens, which can lead to heartrot and possibly mortality, which should promote cavity formation (Smith and Sutherland, 2001; Parsons et al., 2003). This population of evening bats is known to roost in trees in all decay stages (Boyles and Robbins, in press) and frequent burning should increase options for roost tree selection.

Second, CLP is significantly higher in burned forest than the unburned forest during the growing season. Although evening bats are not known to preferentially roost on forest edges, many tree-dwelling bats are thought to select roost trees that receive high levels of sun-exposure during the summer months (Kurta et al., 1993); therefore, trees selected as roosts are commonly taller than the canopy (Vonhof and Barclay, 1996; Britzke et al., 2003), near forest edges (Grindal, 1999) or areas with an open canopy (Menzel et al., 2001a). High light exposure is known to improve roost suitability (Kurta et al., 1993; Brigham et al., 1997) so high light penetration into the interior forest will allow bats to roost in trees that would not receive adequate sunexposure to facilitate thermoregulation in unburned forests. The ability to roost in interior forest trees that would not receive adequate sun-exposure in unburned forest may yield many benefits for evening bats. For example, the opportunity to use interior forest trees as roosts increases the availability of suitable roost trees in the habitat relative to populations that are forced to roost near forest edges or openings. This increase in suitable roost trees may allow bats to roost closer to their preferred foraging area and therefore reduce commuting distance and energy expenditure. Predation by meso-carnivores (Sparks et al., 2003) should also be lessened because the roosting sites are not concentrated in small forest patches or along forest edges, which may be easily found by and accessible to predators. An abundance of suitable roost trees may also facilitate frequent roost switching, which in turn may lessen ecto-parasite loads (Lewis, 1995).

Finally, prescribed burning may improve foraging habitat, thereby encouraging bats to roost in the vicinity. High fire frequency of low to moderate intensity prevents the regeneration of a sapling layer if preceded by a high intensity fire that removes this layer (Peterson and Reich, 2001). Exclusion of this stratum will reduce obstructions and make navigation easier in burned forest compared to unburned forest (Boyles and Robbins, in press). Evening bats forage in open areas and wooded habitats (Duchamp et al., 2004) and it has been noted that activity is higher for some bat communities in thinned forest compared to unthinned forest (Humes et al., 1999). It has been predicted that evening bats are not highly maneuverable and have relatively inefficient flight (Norberg and Rayner, 1987). This may prohibit evening bats from roosting in unburned areas because of dense understory that would make straight flight difficult. Other studies have also noted a preference for roosting in areas with a more open understory (Castleberry et al., 2005). Previous work has suggested that evening bats forage close to their roosting areas (Duchamp et al., 2004); therefore roost selection in burned forest may simply be an artifact of selection of burned habitat for foraging. In addition, the major food source of evening bats, coleopterans (Whitaker, 2004), have been found at both higher abundances and greater species richness in burned forests than unburned forests (Saint-Germain et al., 2004). Although it is difficult to distinguish if roosting habitat or foraging habitat is more important in observed habitat selection, anecdotally there does appear to be a close association between the two in this population of evening bats.

It is unlikely that any one of these reasons alone can adequately explain the preferential habitat selection seen in evening bats. Combinations of any or all of these broad forest characteristics may contribute to suitable habitat for this species and each may be of fluctuating importance throughout the year. For example, the difference in light penetration between burned and unburned forest is probably only important during the summer months. Because of the deciduous nature of the majority of the trees in southwestern Missouri, the difference in light penetration between burned and unburned forest will be lessened during the winter. Therefore, it is likely that evening bats roosted exclusively on the burned portion of the area during the winter because of prey availability, lessened clutter, or simple roost fidelity. However, it should be noted that many tree-dwelling bat species in the eastern United States only roost in trees during the summer months, so this may be inconsequential in reference to those species.


5. Conclusions

Prescribed burning has the potential to provide habitat for evening bats and possibly other species and should be taken into consideration when constructing management plans for these species. Fire may initially increase both the quantity and quality of roosting habitat for evening bats by creating an influx of dead and dying trees as well as facilitating disease and decay in live trees. This study deals only with the first 6 years following the initial burn, and long-term effects of burning may not be as beneficial to evening bats. For example, frequent burning may lead to a decline in roost trees by felling suitable snags and creating a canopy of fire-tolerant species that are not generally killed by fire (Curtis, 1959; Pyne, 1982; Crow, 1988; Ladd, 1991). However, anecdotal evidence suggests that snags persist through these low intensity burns, possibly because leaf litter fails to accumulate underneath a snag (personal observation). The same type of anecdotal evidence exists in southeastern long-leaf pine systems where biennial fire leaves snags intact because of reduced fuel accumulation near the base of the tree (S. Castleberry, University of Georgia, personal communication). To our knowledge, no studies have addressed the fate or residence time for snags suitable for roosting in a frequently burned system. Fire frequency should not remain a static part of management strategy. If biennial burns are continued, eventually the only trees left in the canopy will be fire-tolerant. It is  necessary to withhold fire from areas to allow a sufficient fuel source to accumulate, which will allow for highintensity fires that can cause canopy tree mortality and create snags. However, benefits of managing with prescribed fire may surpass those of mechanical thinning techniques because of the random and delayed mortality imposed on canopy trees (Loomis, 1974). Furthermore, prescribed burning is a more cost-effective management tool than mechanical methods (Wade and Lunsford, 1988; Dubois et al., 1999).

This study demonstrates the preference of evening bats to periodically burned forest, but fire can also be beneficial to other bat species that utilize trees as roosts. For example, the Indiana bat (Myotis sodalis), listed as federally endangered in the United States, is known to use a large number of dead and dying trees as roosts because these trees often offer the exfoliating bark that this species uses as a roost-site (Kurta et al., 1993). Through mortality and damage to bark, fire will increase the abundance of trees with exfoliating bark (Burns, 1955; Paulsell, 1957; Smith and Sutherland, 1999) and thereby increase available roosting habitat for species such as the Indiana bat. Other bat species could benefit from fire as a management tool and it should be investigated in other regions with other bat species.

Fire does have the potential to negatively affect forest dwelling bat habitat. For species that preferentially roost in trees in late stages of decay, frequent fire may destroy roost trees before they become suitable for roosting. Frequent fire may also cause the loss of winter habitat for litter-roosting species such as L. borealis (Moorman et al., 1999; Boyles et al., 2003) by continually removing accumulated leaf litter from the forest floor. Long-term studies are necessary to understand the patterns and processes of snag residence time in a periodically burned forest. Resource managers should consider the different habitat needs and life history traits of both tree-dwelling and litter roosting bats when creating management plans. Furthermore, forests respond differently according to the season when burning occurs. For example, summer burns have been shown to cause increased overstory tree mortality relative to burns conducted in other seasons (van Lear and Waldrop, 1991; Brose and van Lear, 1997). Fires in this season would therefore benefit tree-dwelling species as well as allow leaf litter to accumulate in fall benefiting litter-roosting species. More research examining the effect of burn frequency and season of burning on a variety of taxa is necessary to properly manage forests for biodiversity.

Acknowledgements

Funding for this project was provided in part by the Missouri Department of Conservation, the City of Springfield, MO, Dickerson Park Zoo, and Southwest Missouri State University. We would like to thank L. Robbins and D.A. Wait for assisting in obtaining funding and providing equipment for this project. The Missouri Department of Conservation and the Bull Shoals Field Station provided vehicles, equipment, and access to their property. J. Timpone, M. Milam, B. Mormann, P. Brown, and many others assisted in capturing and tracking bats and conducting vegetation analysis. M. Milam, M. McKnight, M. Coleman, J. Orrock, and anonymous reviewers provided many helpful comments on earlier versions of this manuscript.

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Summary

In mature forests of the Ozark Highlands, MO, USA, we evaluated fire effects on the survival and growth of tree seedlings and saplings (i.e., advance regeneration), and used this information to develop species-specific models that predict the probability of survival based on initial tree size and number of times burned. A 1000 ha forest area was divided into five units that were randomly chosen to receive one, three or four dormant season surface fires during the period 1998-2001. A total of 2741 stems of advance regeneration, ranging in size up to 15 cm in basal diameter and 15 m in height, were permanently marked and measured in all the units. One and four years after initiating the burn treatments, height of survivors was measured. Although most stems experienced shoot dieback following the first fire, survival was high (>90%) for all species as most trees produced new shoots from the living rootstock. The probability of surviving one fire was significantly related to initial stem size (basal diameter and height). With additional burning, the probability of survival increased with increasing initial tree size, and decreased as the number of burns increased. For a given initial diameter, black oak and post oak had the highest probability of survival after three or more burns (e.g., 88%for 5 cm stems), followed closely by white oak (80%), and scarlet oak (60%). For similar sized stems, flowering dogwood had low probabilities of survival (e.g., 25%), and blackgum was devastated by frequent burning (2%). Sassafras showed the greatest tolerance to burning, and more than 90% of stems survived three or more fires over a 4-year period. The probability of survival significantly decreased with increasing number of bums for most species. However, frequency of burning had less influence on the probability of survival for larger (e.g., 27.6 cm) diameter advance regeneration than it did for smaller stems. One fire significantly altered the height distribution of advance regeneration, concentrating most of the stems in the srnallest height class (< 1 m tall). Recovery of height was slow even 4 years after a burn due to the suppression of regeneration by the overstory canopy that averaged 18 m2/ha in basal area (69% stocking). Overall, repeated burning in the dormant season reduces understory structure and favors oak advance regeneration. Survival models can be used to plan for woodland and savanna restoration. ic 2005 Elsevier B.V. All rights reserved.

Daniel C. Dey “,*, George Hartman “Research Forester, U.S. Forest Service, North Central Research Station, 202 Natural Resources Bldg., Columbia, MO 65211, USA Fire Ecologist, Missouri Department of Conservation, 11 10 S. College, Columbia, MO 65201, USA Received 21 September 2004; received in revised form 29 April 2005; accepted 2 May 2005

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Wildland Fire in Ecosystems Effects of Fire on Air

General Technical Report RMRS-GTR-42-volume 5
December 2002


Abstract

Sandberg, David V.; Ottmar, Roger D.; Peterson, Janice L.; Core, John. 2002. Wildland fire on ecosystems: effects of fire on air. Gen. Tech. Rep. RMRS-GTR-42-vol. 5. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 79 p.

This state-of-knowledge review about the effects of fire on air quality can assist land, fire, and air resource managers with fire and smoke planning, and their efforts to explain to others the science behind fire-related program policies and practices to improve air quality. Chapter topics include air quality regulations and fire; characterization of emissions from fire; the transport, dispersion, and modeling of fire emissions; atmospheric and plume chemistry; air quality impacts of fire; social consequences of air quality impacts; and recommendations for future research.

Keywords: smoke, air quality, fire effects, smoke management, prescribed fire, wildland fire, wildfire,
biomass emissions, smoke dispersion


Authors

David V. Sandberg, Research Physical Scientist, Corvallis Forestry Sciences Laboratory, Pacific Northwest Research Station, U.S. Department of Agriculture, Corvallis, OR 97331

Roger D. Ottmar, Research Forester, Seattle Forestry Sciences Laboratory, Pacific Northwest Research Station, U.S. Department of Agriculture, Seattle, WA 98103

Janice L. Peterson, Air Resource Specialist, Mt. Baker-Snoqualmie National Forest, U.S. Department of Agriculture, Mountlake Terrace, WA 98053

John Core, Consultant, Core Environmental Consulting, Portland, OR 97229


Cover photo—Photo by Roger Ottmar. Smoke blots out the sun during the 1994 Anne Wildfire in western Montana.


Preface

In 1978, a national workshop on fire effects in Denver, Colorado, provided the impetus for the “Effects of Wildland Fire on Ecosystems” series. Recognizing that knowledge of fire was needed for land management planning, state-of-the-knowledge reviews were produced that became known as the “Rainbow Series.” The series consisted of six publications, each with a different colored cover, describing the effects of fire on soil, water, air, flora, fauna, and fuels.

The Rainbow Series proved popular in providing fire effects information for professionals, students, and others. Printed supplies eventually ran out, but knowledge of fire effects continued to grow. To meet the continuing demand for summaries of fire effects knowledge, the interagency National Wildfire Coordinating Group asked Forest Service research leaders to update and revise the series. To fulfill this request, a meeting for organizing the revision was held January 4-6, 1993, in Scottsdale, Arizona. The series name was then changed to “The Rainbow Series.” The five volume series covers air, soil and water, fauna, flora and fuels, and cultural resources.

The Rainbow Series emphasizes principles and processes rather than serving as a summary of all that is known. The five volumes, taken together, provide a wealth of information and examples to advance understanding of basic concepts regarding fire effects in the United States and Canada. As conceptual background, they provide technical support to fire and resource managers for carrying out interdisciplinary planning, which is essential to managing
wildlands in an ecosystem context. Planners and managers will find the series helpful in many aspects of ecosystem-based management, but they will also need to seek out and synthesize more detailed information to resolve specific management questions.

— The Authors
December 2002


Acknowledgments

The Rainbow Series was compiled under the sponsorship of the Joint Fire Science Program,a cooperative fire science effort of the U.S. Department of Agriculture, Forest Service, and the U.S. Department of the Interior, Bureau of Indian Affairs, Bureau of Land Management, Fish and Wildlife Service, National Park Service, and U.S. Geological Survey. Several scientists provided significant input without requesting authorship in this volume. We acknowledge valuable contributions by Sue A. Ferguson, Timothy E. Reinhardt, Robert Yokelson, Dale Wade, and Gary Achtemeier. We also thank the following individuals for their suggestions, information, and assistance that led to substantial technical and editorial improvements in the manuscripts: Scott Goodrick, Allen R. Riebau, Sue A. Ferguson, and Patti Hirami. Finally, we appreciate Marcia Patton-Mallory and Louise Kingsbury for persistence and
support.

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