Calvin A. Farris

Multi‐scale controls of historical forest‐fire regimes: new insights from fire‐scar networks

Anticipating future forest-fire regimes under changing climate requires that scientists and natural resource managers understand the factors that control fire across space and time. Fire scars – proxy records of fires, formed in the growth rings of long-lived trees – provide an annually accurate window into past low-severity fire regimes. In western North America, networks of the fire-scar records spanning centuries to millennia now include hundreds to thousands of trees sampled across hundreds to many thousands of hectares. Development of these local and regional fire-scar networks has created a new data type for ecologists interested in landscape and climate regulation of ecosystem processes – which, for example, may help to explain why forest fires are widespread during certain years but not others. These data also offer crucial reference information on fire as a dynamic landscape process for use in ecosystem management, especially when managing for forest structure and resilience to climate change.

Spatial and temporal corroboration of a fire-scar-based fire history in a frequently burned ponderosa pine forest.

Fire scars are used widely to reconstruct historical fire regime parameters in forests around the world. Because fire scars provide incomplete records of past fire occurrence at discrete points in space, inferences must be made to reconstruct fire frequency and extent across landscapes using spatial networks of fire-scar samples. Assessing the relative accuracy of fire-scar fire history reconstructions has been hampered due to a lack of empirical comparisons with independent fire history data sources. We carried out such a comparison in a 2780-ha ponderosa pine forest on Mica Mountain in southern Arizona (USA) for the time period 1937-2000. Using documentary records of fire perimeter maps and ignition locations, we compared reconstructions of key spatial and temporal fire regime parameters developed from documentary fire maps and independently collected fire-scar data (n = 60 plots). We found that fire-scar data provided spatially representative and complete inventories of all major fire years (> 100 ha) in the study area but failed to detect most small fires. There was a strong linear relationship between the percentage of samples recording fire scars in a given year (i.e., fire-scar synchrony) and total area burned for that year (y = 0.0003x + 0.0087, r2 = 0.96). There was also strong spatial coherence between cumulative fire frequency maps interpolated from fire-scar data and ground-mapped fire perimeters. Widely reported fire frequency summary statistics varied little between fire history data sets: fire-scar natural fire rotations (NFR) differed by < 3 yr from documentary records (29.6 yr); mean fire return intervals (MFI) for large-fire years (i.e., > or = 25% of study area burned) were identical between data sets (25.5 yr); fire-scar MFIs for all fire years differed by 1.2 yr from documentary records. The known seasonal timing of past fires based on documentary records was furthermore reconstructed accurately by observing intra-annual ring position of fire scars and using knowledge of tree-ring growth phenology in the Southwest. Our results demonstrate clearly that representative landscape-scale fire histories can be reconstructed accurately from spatially distributed fire-scar samples.

Incorporating spatial non-stationarity of regression coefficients into predictive vegetation models

The results of predictive vegetation models are often presented spatially as GIS-derived surfaces of vegetation attributes across a landscape or region, but spatial information is rarely included in the model itself. Geographically weighted regression (GWR), which extends the traditional regression framework by allowing regression coefficients to vary for individual locations (‘spatial non-stationarity’), is one method of utilizing spatial information to improve the predictive power of such models. In this paper, we compare the ability of GWR, a local model, with that of ordinary least-squares (OLS) regression, a global model, to predict patterns of montane ponderosa pine (Pinus ponderosa) basal area in Saguaro National Park, AZ, USA on the basis of variables related to topography (elevation, slope steepness, aspect) and fire history (fire frequency, time since fire).The localized regression coefficients exhibited significant non-stationarity for four of the five environmental variables, and the GWR model consequently described the vegetation-environment data significantly better, even after accounting for differences in model complexity. GWR also reduced observed spatial autocorrelation of the model residuals. When applied to independent data locations not used in model development, basal areas predicted by GWR had a closer fit to observed values with lower residuals than those from the optimal OLS regression model. GWR also provided insights into fine-scale controls of ponderosa pine pattern that were missed by the global model. For example, the relationship between ponderosa pine basal area and aspect, which was obscured in the OLS regression model due to non-stationarity, was clearly demonstrated by the GWR model. We thus see GWR as a valuable complement to the many other global methods currently in use for predictive vegetation modeling.

A comparison of targeted and systematic fire-scar sampling for estimating historical fire frequency in south-western ponderosa pine forests

Fire history researchers employ various forms of search-based sampling to target specimens that contain visible evidence of well preserved fire scars. Targeted sampling is considered to be the most efficient way to increase the completeness and length of the fire-scar record, but the accuracy of this method for estimating landscape-scale fire frequency parameters compared with probabilistic (i.e. systematic and random) sampling is poorly understood. In this study we compared metrics of temporal and spatial fire occurrence reconstructed independently from targeted and probabilistic fire-scar sampling to identify potential differences in parameter estimation in south-western ponderosa pine forests. Data were analysed for three case studies spanning a broad geographic range of ponderosa pine ecosystems across the US Southwest at multiple spatial scales: Centennial Forest in northern Arizona (100ha); Monument Canyon Research NaturalArea(RNA)incentralNewMexico(256ha);andMicaMountaininsouthernArizona(2780ha).Wefoundthatthe percentage of available samples that recorded individual fire years (i.e. fire-scar synchrony) was correlated strongly between targeted and probabilistic datasets at all three study areas (r ¼0.85, 0.96 and 0.91 respectively). These strong positive correlations resulted predictably insimilar estimates of commonlyused statistical measures of fire frequency and cumulative area burned, including Mean Fire Return Interval (MFI) and Natural Fire Rotation (NFR). Consistent with theoretical expectations, targeted fire-scar sampling resulted in greater overall sampling efficiency and lower rates of sample attrition. Our findings demonstrate that targeted sampling in these systems can produce accurate estimates of landscape-scale fire frequency parameters relative to intensive probabilistic sampling. Received 16 February 2013, accepted 3 April 2013, published online 6 September 2013

Average Stand Age from Forest Inventory Plots Does Not Describe Historical Fire Regimes in Ponderosa Pine and Mixed-Conifer Forests of Western North America

Quantifying historical fire regimes provides important information for managing contemporary forests. Historical fire frequency and severity can be estimated using several methods; each method has strengths and weaknesses and presents challenges for interpretation and verification. Recent efforts to quantify the timing of historical high-severity fire events in forests of western North America have assumed that the “stand age” variable from the US Forest Service Forest Inventory and Analysis (FIA) program reflects the timing of historical high-severity (i.e. stand-replacing) fire in ponderosa pine and mixed-conifer forests. To test this assumption, we re-analyze the dataset used in a previous analysis, and compare information from fire history records with information from co-located FIA plots. We demonstrate that 1) the FIA stand age variable does not reflect the large range of individual tree ages in the FIA plots: older trees comprised more than 10% of pre-stand age basal area in 58% of plots analyzed and more than 30% of pre-stand age basal area in 32% of plots, and 2) recruitment events are not necessarily related to high-severity fire occurrence. Because the FIA stand age variable is estimated from a sample of tree ages within the tree size class containing a plurality of canopy trees in the plot, it does not necessarily include the oldest trees, especially in uneven-aged stands. Thus, the FIA stand age variable does not indicate whether the trees in the predominant size class established in response to severe fire, or established during the absence of fire. FIA stand age was not designed to measure the time since a stand-replacing disturbance. Quantification of historical “mixed-severity” fire regimes must be explicit about the spatial scale of high-severity fire effects, which is not possible using FIA stand age data.

Reconstructing Landscape Pattern of Historical Fires and Fire Regimes

Analysis of historical fire patterns of severity provides a view of fire regimes before they were altered by contemporary forest management practices such as logging, road-building, grazing, and fire suppression. Historical fire data can place contemporary observed fire data in a longer temporal context, and establish prior likelihoods to test outputs from predictive fire behavior and forest vegetation simulation models. When integrated with biophysical and remote-sensing data, fire-history data have been modeled to create both coarse scale (1 km2, Schmidt et al. 2002) and fine scale (30 m2, Rollins and Frame 2006) maps of fire regimes for the contiguous United States (LANDFIRE 2007).

Historical and current fire management practices in two wilderness areas in the southwestern United States: The Saguaro Wilderness Area and the Gila-Aldo Leopold Wilderness Complex

Fire suppression has been the dominant fire management strategy in the West over the last century. However, managers of the Gila and Aldo Leopold Wilderness Complex in New Mexico and the Saguaro Wilderness Area in Arizona have allowed fire to play a more natural role for decades. This report summarizes the effects of these fire management practices on key resources, and documents common challenges in implementing these practices and lessons for how to address them. By updating historical fire atlases, we show how fire patterns have changed with adoption of new policy and practices.