Back

A laboratory method for simulating the effects of subsequent fires on pyrogenic organic matter at different exposure depths in a sand matrix

Luo, M.; Yedinak, K.; Bourne, K.; Whitman, T.

2024-07-31 ecology
10.1101/2024.07.30.605814 bioRxiv
Show abstract

BackgroundAcross a variety of anthropogenic and natural contexts, fire can reoccur in a previously burned location. However, effects of subsequent fire on preexisting pyrogenic organic matter (PyOM) stocks are difficult to discern. Laboratory experiments offer a powerful approach to investigating how subsequent fire impacts the preexisting PyOM. AimsWe aimed to design a highly repeatable laboratory method to effectively measure the impacts of subsequent fires on PyOM at different soil depths while addressing key limitations of previous methods. MethodsJack pine (Pinus banksiana Lamb.) log burns were used to parameterize realistic heat flux profiles. Using a cone calorimeter, these profiles were applied to buried jack pine PyOM to simulate variable reburn fire intensities. Key resultsIn general, higher heat flux and shallower depths led to more mass loss of PyOM from combustion and more heat exposure. ConclusionsOur reburn method offers a highly replicable way to simulate specific fire scenarios. Conditions that result in more heat exposure (higher heat fluxes, shallower depths) are likely to lead to more loss of PyOM in subsequent fires. ImplicationsThe customizable method could simulate different fire scenarios to investigate spatial variability within a given fire event, or to study the effects of fire on different types of biomass or organisms, such as microbes. Summary textOur paper illustrates a laboratory method to better quantify loss of preexisting PyOM in soil after a fire. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/605814v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@10e9104org.highwire.dtl.DTLVardef@152cb49org.highwire.dtl.DTLVardef@a04c41org.highwire.dtl.DTLVardef@1ee543d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
PLOS ONE
4510 papers in training set
Top 12%
14.7%
2
Ecosphere
53 papers in training set
Top 0.1%
9.2%
3
Science of The Total Environment
179 papers in training set
Top 1.0%
8.2%
4
Journal of Environmental Management
11 papers in training set
Top 0.1%
6.8%
5
Methods in Ecology and Evolution
160 papers in training set
Top 0.6%
6.4%
6
Journal of Applied Ecology
35 papers in training set
Top 0.2%
3.6%
7
Forest Ecology and Management
25 papers in training set
Top 0.2%
3.6%
50% of probability mass above
8
Ecological Applications
28 papers in training set
Top 0.1%
3.6%
9
GeoHealth
10 papers in training set
Top 0.1%
3.1%
10
Scientific Reports
3102 papers in training set
Top 45%
2.6%
11
Frontiers in Microbiology
375 papers in training set
Top 4%
2.5%
12
Environmental Health Perspectives
17 papers in training set
Top 0.2%
2.1%
13
Ecology and Evolution
232 papers in training set
Top 2%
1.9%
14
Soil Biology and Biochemistry
29 papers in training set
Top 0.2%
1.9%
15
Ecological Indicators
20 papers in training set
Top 0.2%
1.7%
16
Journal of Ecology
47 papers in training set
Top 0.3%
1.7%
17
PeerJ
261 papers in training set
Top 9%
1.3%
18
Frontiers in Plant Science
240 papers in training set
Top 4%
1.3%
19
Environmental Science & Technology
64 papers in training set
Top 2%
1.3%
20
Limnology and Oceanography: Methods
11 papers in training set
Top 0.3%
1.2%
21
Global Change Biology
69 papers in training set
Top 1%
1.1%
22
Ecography
50 papers in training set
Top 1.0%
1.0%
23
Ecology
70 papers in training set
Top 0.7%
0.9%
24
Environmental Research Letters
15 papers in training set
Top 0.5%
0.9%
25
Environmental Pollution
35 papers in training set
Top 2%
0.8%
26
Agriculture, Ecosystems & Environment
15 papers in training set
Top 0.3%
0.7%
27
Communications Earth & Environment
14 papers in training set
Top 1%
0.6%
28
Environment International
42 papers in training set
Top 1%
0.6%