Interaction Diversity Maintains Resiliency in a Frequently Disturbed Ecosystem

Frequently disturbed ecosystems are characterized by resilience to ecological disturbances. Longleaf pine ecosystems are not only resilient to frequent fire disturbance, but this feature sustains biodiversity. We examined how fire frequency maintains beta diversity of multi-trophic interactions in longleaf pine ecosystems, as this community property provides a measure of functional redundancy of an ecosystem. We found that beta interaction diversity at small local scales is highest in the most frequently burned stands, conferring immediate resiliency to disturbance by fire. Interactions become more specialized and less resilient as fire frequency decreases. Local scale patterns of interaction diversity contribute to broader scale patterns and confer long-term ecosystem resiliency. Such natural disturbances are likely to be important for maintaining regional diversity of interactions for a broad range of ecosystems

A novel approach to fuel biomass sampling for 3D fuel characterization

Surface fuels are the critical link between structure and function in frequently burned pine ecosystems, which are found globally (Williamson and Black, 1981; Rebertus et al., 1989; Glitzenstein et al., 1995) [1–3]. We bring fuels to the forefront of fire ecology through the concept of the Ecology of Fuels (Hiers et al. 2009) [4]. This concept describes a cyclic process between fuels, fire behavior, and fire effects, which ultimately affect future fuel distribution (Mitchell et al. 2009) [5]. Low-intensity surface fires are driven by the variability in fine-scale (sub-m level) fuels (Loudermilk et al. 2012) [6]. Traditional fuel measurement approaches do not capture this variability because they are over-generalized, and do not consider the fine-scale architecture of interwoven fuel types. Here, we introduce a new approach, the “3D fuels sampling protocol” that measures fuel biomass at the scale and dimensions useful for characterizing heterogeneous fuels found in low-intensity surface fire regimes. • Traditional fuel measurements are oversimplified, prone to sampling bias, and unrealistic for relating to fire behavior (Van Wagner, 1968; Hardy et al., 2008) [7,8]. • We developed a novel field sampling approach to measuring 3D fuels using an adjustable rectangular prism sampling frame. This voxel sampling protocol records fuel biomass, occupied volume, and fuel types at multiple scales. • This method is scalable and versatile across ecosystems, and reduces sampling bias by eliminating the need for ocular estimations. Method name: 3D fuels sampling protocol, Keywords: Surface fire, Low-Intensity fire, Forest fire, Longleaf pine, Pinus palustris, Fuel load, Bulk density, Volume, Fine scale, Fuel heterogeneity, Fuel samplin

Effects of Restoration Techniques on Soil Carbon and Nitrogen Dynamics in Florida Longleaf Pine (Pinus palustris) Sandhill Forests

Historic fire suppression and intensive forest management in longleaf pine (Pinus palustris) sandhill forests has resulted in hardwood encroachment and degradation of this fire-dependent ecosystem. Active management is now required to restore native community structure and composition, but little is known about the long-term impacts of typical restoration techniques on ecosystem properties. In 1994, the Longleaf Pine Restoration Project (LPRP) was established in fire-excluded longleaf pine sandhills of Eglin Air Force Base, Florida, to explore the effects of restoration treatments on plant and animal community composition and soil processes. Experimental treatments applied included three hardwood reduction techniques and delayed burn. Reference sites were concurrently monitored. Fifteen years later, we revisited the LPRP plots to determine whether soil processes showed lasting treatment effects. This study showed that there were no differences in soil C and N between the reference and the fire-suppressed plots prior to the treatments, suggesting that soil C and N were relatively resistant to degradation. This study also showed that the restoration treatments had a significant effect by reducing soil C, but this effect was only short-lived (<3 years). In addition, a MRPP (multi-response permutation procedure) analysis showed that only the herbicide treatment was still different from the reference plots 15 years after the initial treatments. Thus, this study suggests that repeated fires (or lack of) or hardwood removal treatments have little detectable effect on soil nutrients in these nutrient-poor ecosystems

Non-Destructive Fuel Volume Measurements Can Estimate Fine-Scale Biomass across Surface Fuel Types in a Frequently Burned Ecosystem

Measuring wildland fuels is at the core of fire science, but many established field methods are not useful for ecosystems characterized by complex surface vegetation. A recently developed sub-meter 3D method applied to southeastern U.S. longleaf pine (Pinus palustris) communities captures critical heterogeneity, but similar to any destructive sampling measurement, it relies on separate plots for calculating loading and consumption. In this study, we investigated how bulk density differed by 10-cm height increments among three dominant fuel types, tested predictions of consumption based on fuel type, height, and volume, and compared this with other field measurements. The bulk density changed with height for the herbaceous and woody litter fuels (p < 0.001), but live woody litter was consistent across heights (p > 0.05). Our models predicted mass well based on volume and height for herbaceous (RSE = 0.00911) and woody litter (RSE = 0.0123), while only volume was used for live woody (R2 = 0.44). These were used to estimate consumption based on our volume-mass predictions, linked pre- and post-fire plots by fuel type, and showed similar results for herbaceous and woody litter when compared to paired plots. This study illustrates an important non-destructive alternative to calculating mass and estimating fuel consumption across vertical volume distributions at fine scales

Terrestrial Laser Scan Metrics Predict Surface Vegetation Biomass and Consumption in a Frequently Burned Southeastern U.S. Ecosystem

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models

Appendix N. Indicator species and change in frequency and abundance for reference conditions and treatment sites.

Indicator species and change in frequency and abundance for reference conditions and treatment sites

Appendix M. Results of the randomized complete block design ANCOVA for number of ruderal species in the ground cover at the treatment scale by year.

Results of the randomized complete block design ANCOVA for number of ruderal species in the ground cover at the treatment scale by year

Appendix J. Results of the randomized complete block design ANCOVA for ground-cover average log abundance at the treatment scale by year.

Results of the randomized complete block design ANCOVA for ground-cover average log abundance at the treatment scale by year