Defining the fire trap: Extension of the persistence equilibrium model in mesic savannas

Freeman, Michelle E.; Vesk, Peter A.; Murphy, Brett P.; Cook, Garry D.; Richards, Anna E.; Williams, Richard J.

doi:10.1111/aec.12516

Cited by 22 publications

(21 citation statements)

References 47 publications

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“…in lower height of all trees/shrubs. These results can be explained by the fact that low FF allows small trees (equal or lower than 2 m high) to escape the fire trap (i.e., a cycle of biomass loss followed by resprouting, maintained by frequent fires, Werner & Prior, 2013) and reach the canopy level (Trollope, 1999). In addition, in the NS landscape low FF sites were associated to lower grass stocks and consequent reduced competition for light and nutrient thus, allowing woody species to grow out of the fire trap.…”

Section: Influence Of Fire Frequency and Landscape On Tree/shrub Comentioning

confidence: 99%

The influence of fire frequency on the structure and botanical composition of savanna ecosystems

Ribeiro

Ruecker²,

Govender

et al. 2019

Ecology and Evolution

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Savannas cover 60% of the land surface in Southern Africa, with fires and herbivory playing a key role in their ecology. The Limpopo National Park (LNP) is a 10,000 km 2 conservation area in southern Mozambique and key to protecting savannas in the region. Fire is an important factor in LNP's landscapes, but little is known about its role in the park's ecology. In this study, we explored the interaction between fire frequency (FF), landscape type, and vegetation. To assess the FF, we analyzed ten years of the Moderate resolution Imaging Spectroradiometer (MODIS) burned area product (2003–2013). A stratified random sampling approach was used to assess biodiversity across three dominant landscapes (Nwambia Sandveld‐NS, Lebombo North‐LN, and Shrubveld Mopane on Calcrete‐C) and two FF levels ( low —twice or less; and high —3 times or more, during 10 years). Six ha were sampled in each stratum, except for the LN versus high FF in which low accessibility allowed only 3 ha sampling. FF was higher in NS and LN landscapes, where 25% and 34% of the area, respectively, burned more than three times in 10 years. The landscape type was the main determinant of grass composition and biomass. However, in the sandy NS biomass was higher under high FF. The three landscapes supported three different tree/shrub communities, but FF resulted in compositional variations in NS and LN. Fire frequency had no marked influence on woody structural parameters (height, density, and phytomass). We concluded that the savannas in LNP are mainly driven by landscape type (geology), but FF may impose specific modifications. We recommend a fire laissez‐faire management system for most of the park and a long‐term monitoring system of vegetation to address vegetation changes related to fire. Fire management should be coordinated with the neighboring Kruger National Park, given its long history of fire management. Synthesis : This study revealed that grass and tree/shrub density, biomass, and composition in LNP are determined by the landscape type, but FF determines some important modifications. We conclude that at the current levels FF is not dramatically affecting the savanna ecosystem in the LNP (Figure 1). However, an increase in FF may drive key ecosystem changes in grass biomass and tree/shrub species composition, height, phytomass, and density.

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Section: Influence Of Fire Frequency and Landscape On Tree/shrub Comentioning

confidence: 99%

The influence of fire frequency on the structure and botanical composition of savanna ecosystems

Ribeiro

Ruecker²,

Govender

et al. 2019

Ecology and Evolution

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“…, Werner and Prior , Freeman et al. ). Further, most mature trees have hollow boles (Werner and Prior ) and thus no early annual growth rings.…”

Section: Study Systemmentioning

confidence: 99%

Savanna canopy trees under fire: long‐term persistence and transient dynamics from a stage‐based matrix population model

Werner

Peacock

2019

Ecosphere

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Fire is a major disturbance driving the dynamics of the world's savannas. Almost all fires are set by humans who are increasingly altering fire timing and frequency on every continent. The world's largest protected areas of savannas are found in monsoonal northern Australia. These include relatively intact, tall, open forests where traditional indigenous fire regimes have been largely replaced in the past half century by contemporary patterns with trees experiencing fire as often as three out of five years. Eucalypt canopy trees form the basic structure of these savannas and changes to the canopy due to fire regimes cascade to affect other plants and animals. In this study, we used data from nearly three decades of field studies on the effects of fire on individual trees to define eight life‐history stages and to calculate transition rates among stages. We developed a stage‐based matrix population model that explicitly considers how fire season and understory influence growth, survival, and recruitment for each life‐history stage. Long‐term population growth rates and transient population dynamics were calculated under five different fire regimes, each in two understory types, using both deterministic and stochastic simulations of seasonal timing of fires. We found that fire was necessary for long‐term persistence of eucalypt canopy tree populations but, under annual fires, most populations did not survive. Population persistence was highly dependent on fire regime (fire season and frequency) and understory type. A stochastic model tended to yield higher population growth rates than the deterministic model with regular, periodic fires, even under the same long‐term frequency of fires. Transient population dynamics over 100 yr also depended on fire regime and understory, with implications for savanna physiognomy and management. Model predictions were tested in an independent data set from a 21‐yr longitudinal field study in Kakadu National Park. This study is a novel and integrative contribution to our understanding of fire in savanna biomes regarding the potential for long‐term persistence and transient dynamics of savanna canopy tree populations. The model is relatively simple, generalizable, and adaptable for further investigations of the population dynamics of savanna trees under fire.

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“…Fire-induced demographic bottlenecks are common in savannas across the globe (Bell 1984, Gambiza et al 2000, Prior et al 2010, Wakeling et al 2011, Bond et al 2012, Werner 2012, Freeman et al 2017, so our results have wide-reaching implications that are well highlighted by the study species. The abundance of Q. laevis and other hardwoods within longleaf pine savannas is recognized to be highly sensitive to fire frequency (Loudermilk et al 2011).…”

Section: Implications For Managementmentioning

confidence: 66%

“…Predicting changes in tree density is a universal challenge to management and modeling of savannas across the globe. To this end, the fire-trap paradigm has been widely used for explaining how fire controls vegetation structure of savanna ecosystems (Bell 1984, Gambiza et al 2000, Prior et al 2010, Wakeling et al 2011, Bond et al 2012, Werner 2012, Freeman et al 2017, Nguyen et al 2019. According to the fire-trap model, frequent fire maintains low tree cover by causing repeated loss of aboveground biomass, thereby keeping saplings in a suppressed state.…”

Section: Introductionmentioning

confidence: 99%

Better lucky than good: How savanna trees escape the fire trap in a variable world

Hoffmann

Sanders

Just

et al. 2019

Ecology

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Fire controls tree cover in many savannas by suppressing saplings through repeated topkill and resprouting, causing a demographic bottleneck. Tree cover can increase dramatically if even a small fraction of saplings escape this fire trap, so modeling and management of savanna vegetation should account for occasional individuals that escape the fire trap because they are "better" (i.e., they grow faster than average) or because they are "lucky" (they experience an occasional longer-than-average interval without fire or a below-average fire severity). We quantified variation in growth rates and topkill probability in Quercus laevis (turkey oak) in longleaf pine savanna to estimate the percentage of stems expected to escape the fire trap due to variability in (1) growth rate, (2) fire severity, and (3) fire interval. For trees growing at the mean rate and exposed to the mean fire severity and the mean fire interval, no saplings are expected to become adults under typical fire frequencies. Introducing variability in any of these factors, however, allows some individuals to escape the fire trap. A variable fire interval had the greatest influence, allowing 8% of stems to become adults within a century. In contrast, introducing variation in fire severity and growth rate should allow 2.8% and 0.3% of stems to become adults, respectively. Thus, most trees that escape the fire trap do so because of luck. By chance, they experience long fire-free intervals and/or a low-severity fire when they are not yet large enough to resist an average fire. Fewer stems escape the fire trap by being unusually fast-growing individuals. It is important to quantify these sources of variation and their consequences to improve understanding, prediction, and management of vegetation dynamics of fire-maintained savannas. Here we also present a new approach to quantifying variation in fire severity utilizing a latent-variable model of logistic regression. . Bradstock. 2013. Defining pyromes and global syndromes of FIG. 4. The relationship between the shape of the stem survival function and variance in fire severity. Panels A and B show two survival functions, and C and D show the corresponding probability density functions of fire severity. Thus, A and C correspond to a scenario with little variation in fire severity, while B and D correspond to a scenario with greater variation in fire severity.

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Defining the fire trap: Extension of the persistence equilibrium model in mesic savannas

Cited by 22 publications

References 47 publications

The influence of fire frequency on the structure and botanical composition of savanna ecosystems

The influence of fire frequency on the structure and botanical composition of savanna ecosystems

Savanna canopy trees under fire: long‐term persistence and transient dynamics from a stage‐based matrix population model

Better lucky than good: How savanna trees escape the fire trap in a variable world

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