Rationality of the study and hypothesis
The open Caldén forest of central Argentina is part of the neotropical
dry forest biome in which fire is known to govern ecosystem structure
and function (Pennington et al. 2000). Anthropogenic shifts from fires
of low to high severity have been hypothesized to result in a shift
from open Caldén forest to a shrub-dominated ticket state (Dussart et
al. 2011, González-Roglich et al. 2015, Peinetti et al. 2019). We
hypothesized the existence of a fire severity threshold at which open
forest resilience is lost leading to an abrupt transition to a
persistent thicket state (Peinetti et al. 2019). Grazing and
controlled fires usually practiced in open Caldén forest may act to
lock in a trajectory to shrubland thicket states following high
severity fires. In this paper, we quantified the relationship between
fire severity and the resilience of open forest states (Twidwell et
al. 2013, Johnstone et al. 2016). To do this, we used a major wildfire
event in a Caldén forest that produced a highly variable fire pattern.
We evaluated the response of forest patches to the increase in fire
severity in the absence of a management adjustment. The identification
of a fire severity threshold is a first step toward reducing the
likelihood of undesirable state transitions.
Study site
The study site covers 100 ha of a Caldén forest within a fenced
pasture of 800 ha in a private ranch, located 38 km north of Santa
Rosa, La Pampa, Argentina (36°26’46’’ S and 64°40’40’’W). The
landscape occurs on an inclined plain with a slope < 1.5%. The
climate is temperate with mean annual precipitation of 740 mm and a
temperature amplitude of 13.6 °C (values correspond to means of the
last 40 years of daily records from a weather station 30 km away).
Soil is sandy loam and a root restrictive, calcium carbonate hardpan
occurs at depths > 150 cm.
Fire severity characterization
The 2006 wildfire severity (Lentile et al. 2006, Keeley 2009) was
described using a delta normalized burn ratio index
(dNBR)
(Snyder et al. 2005,
Miller and Thode 2007, Escuin et al. 2008). dNBR
indices were calculated as a difference between pre- and post-fire
normalized burn ratio (NBR) indices (Key and Benson 2006):
dNBR =(pre-fire NBR - post-fire NBR) * 10^3 (1)
where NBRpre and NBRpost
were calculated based on Landsat images of 30 m pixel size, taken on
the 13th and
22nd of October 2005 and 2006,
respectively, and corrected by radiometry (Key and Benson 2006,
Chander et al. 2009) and geographic position using GPS ground points.
Vegetation surveys
The structure and composition of woody and herbaceous vegetation was
measured during the 2017-18 growing season (September to March) in
areas affected by different severities of the 2006 wildfire.
Measurement sites were limited to areas of the forest that had an
estimated canopy cover >50% the year prior to the fire event (which
included 76 ha of the 108 ha we inspected). Pre-fire woody canopy
cover was characterized from a high resolution (< 1m) panchromatic
Digital Globe image from September 2005. Woody canopy cover was
extracted using Feature Analyst TM (Overwatch
Textron Systems, Providence, RI, USA) in ArcGIS
10 (ESRI, Redlands, CA, USA). We followed an iterative
classification procedure using available tools to correct
misclassified or non-digitized features and shapes. The spatial
resolution of the digitized woody canopy cover layer was later
downgraded to 30 m pixels to correspond to the geographic resolution
of the dNBR map. Within this area, three fire severity classes were
characterized that included low, moderate, and high fire severity
(LFS, MFS and HFS, respectively), defined by the following ranges of
dNBR values: LFS: 270-440; MFS: 440-580; and HFS: 580-760. These
ranges of dNBR were based on an existing scale of dNBR values (Key and
Benson 2006) and corresponded with different effects of fires on woody
vegetation observed in the field (Table 1). The area of the LFS class
was much smaller than that of the other classes (9.2, 32.8, and 33.9
ha, for LFS, MFS, and HFS, respectively), but the LFS areas were
arranged in several clusters that facilitated sampling and analysis.
Four sampling sites per severity class were chosen randomly from
random points generated in areas assigned to each class, with a buffer
zone of 100 m. At each survey site, a 30 x 20 m plot was established
by placing a 10 m belt on both sides of a 30 m transect line. The
direction of a transect line was set perpendicular to a slope or
randomly in case of horizontal terrain.
We estimated canopy cover and density of woody plants for both
post-fire (2017/18) and pre-fire (2005) conditions. Post-fire canopy
cover was measured from the horizontal projection of canopies in five
30 m transect lines (5 m spacing) within each plot. We differentiated
tree (≥ 5 m) and understory woody canopies (≤ 3 m). Plants with a
height from 3 to 5 m were considered as trees if there was a spatial
separation between woody and herbaceous canopy volume; if there was no
separation, plants were considered understory canopy. Pre-fire canopy
cover in each surveyed plot was estimated from the digitized woody
cover area based on the 2005 satellite image. To estimate the
post-fire woody density, we counted all woody plants inside each plot
by species and life form, differentiating trees from shrubs. In
addition, we measured the height of P. caldenia
plants and the diameter of the main stem of trees or the largest live
or dead basal stem in the case of shrubs. Pre-fire woody density was
limited to dominant trees. It was calculated by summing all living
individuals in each plot with a diameter of basal trunks ≥ 30 cm and
all dead or resprouted individuals with a diameter ≥ 25 cm, assuming a
radial growth rate of 0.5 cm per year; following Dussart et al.
(2015). We also recorded the foliar and basal cover of the herbaceous
layer and litter and bare ground cover along one 30 m (middle)
transect line using the line-point intercept method (Herrick et al.
2005)
Statistical analysis
The degree to which fire severity (dNBR) and woody canopy cover were
spatially patchy was evaluated using Moran’s I spatial auto- and
cross-correlations in GeoDa (Anselin 2003), using a queen contiguity
matrix of order one. Next, we tested for break points in the
relationship between dNBR and a) the change from pre- to post-fire in
the density of trees > 25 cm diameter and b) the post-fire density
of woody plants < 3 m in height using piece-wise regression
following (Ryan and Porth 2007). Woody and herbaceous variables were
compared between fire severity classes using one-way ANOVAs on
transformed variables. A Tukey test was used to compare means when the
ANOVA was significant at p ≤ 0.05. A post-hoc Wilcoxon test was used
when overall differences were significant (p ≤ 0.05). Statistical
analyses were conducted using dplyr package in R version 4.0.2 (R Core
Team 2020).
Literature cited
Anselin, L. 2003. GeoDa 0.9 user’s guide. Spatial Analysis Laboratory
(SAL). Department of Agricultural and Consumer Economics, University
of Illinois, Urbana-Champaign, II.
Chander, G., B. L. Markham, and D. L. Helder. 2009. Summary of current
radiometric calibration coefficients for Landsat MSS, TM, ETM+, and
EO-1 ALI sensors. Remote Sensing of Environment 113:893–903.
Dussart, E. G., C. C. Chirino, E. A. Morici, and H. R. Peinetti. 2011.
Reconstrucción del paisaje del caldenal pampeano en los últimos 250
años. Quebracho - Revista de Ciencias Forestales 19:54–65.
Dussart, E., A. Medina, and S. Bogino. 2015. Dendroecología en la
pampa Argentina: investigaciones actuales, pasadas y futuros desafíos.
Ecosistemas: 24:51–59.
Escuin, S., R. Navarro, and P. Fernández. 2008. Fire severity
assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized
Difference Vegetation Index) derived from LANDSAT TM/ETM images.
International Journal of Remote Sensing 29:1053–1073.
González-Roglich, M., J. J. Swenson, D. Villarreal, E. G. Jobbágy, and
R. B. Jackson. 2015. Woody plant-cover dynamics in Argentine savannas
from the 1880s to 2000s: The interplay of encroachment and agriculture
conversion at varying scales. Ecosystems 18:481–492.
Herrick, J. E., J. W. Van Zee, K. M. Havstad, L. M. Burkett, and W. G.
Whitford. 2005. Monitoring manual for grassland, shrubland and savanna
ecosystems. USDA-ARS Jornada Experimental Range. The University of
Arizona Press.
Johnstone, J. F., C. D. Allen, J. F. Franklin, L. E. Frelich, B. J.
Harvey, P. E. Higuera, M. C. Mack, R. K. Meentemeyer, M. R. Metz, G.
L. W. Perry, T. Schoennagel, and M. G. Turner. 2016. Changing
disturbance regimes, ecological memory, and forest resilience.
Frontiers in Ecology and the Environment 14:369–378.
Keeley, J. E. 2009. Fire intensity, fire severity and burn severity: A
brief review and suggested usage. International Journal of Wildland
Fire 18:116–126.
Key, C. H., and N. C. Benson. 2006. Landscape assessment: Ground
measure of severity, the Composite Burn Index; and Remote sensing of
severity, the Normalized Burn Ratio. Pages 1–15 in D. C. Lutes, R. E.
Keane, J. F. Caratti, C. H. Key, N. C. Benson, S. Sutherland, and L.
J. Gangi, editors. FIREMON: Fire Effects Monitoring and Inventory
System. USDA Forest Service, Rocky Mountain Research Station, Ogden,
UT.
Lentile, L. B., Z. A. Holden, A. M. S. Smith, M. J. Falkowski, A. T.
Hudak, P. Morgan, S. A. Lewis, P. E. Gessler, and N. C. Benson. 2006.
Remote sensing techniques to assess active fire characteristics and
post-fire effects. International Journal of Wildland Fire:319–345.
Miller, J. D., and A. E. Thode. 2007. Quantifying burn severity in a
heterogeneous landscape with a relative version of the delta
Normalized Burn Ratio ( dNBR ) 109:66–80.
Peinetti, H. R., B. T. Bestelmeyer, C. C. Chirino, A. G. Kin, and M.
E. Frank. 2019. Generalized and Specific State-and-Transition Models
to Guide Management and Restoration of Caldenal Forests. Rangeland
Ecology & Management 72:230–236.
Pennington, R. T., D. E. Prado, and C. A. Pendry. 2000. Neotropical
seasonally dry forests and Quaternary vegetation changes. Journal of
Biogeography 27:261–273.
R Core Team. 2020. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
https://www.R-project.org/.
Ryan, S. E., and L. S. Porth. 2007. A Tutorial on the Piecewise
Regression Approach Applied to Bedload Transport Data. Gen. Tech. Rep.
RMRS‐GTR‐189: U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station.
Snyder, A., P. Fulé, and J. Crouse. 2005. Comparison of burn severity
assessment using Differenced Normalized Burn Ratio and ground data.
International Journal of Wildland Fire 14:189–198.
Twidwell, D., S. D. Fuhlendorf, C. A. Taylor, and W. E. Rogers. 2013.
Refining thresholds in coupled fire-vegetation models to improve
management of encroaching woody plants in grasslands. Journal of
Applied Ecology 50:603–613.
