Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure

Swan, Matthew; Christie, Fiona J.; Sitters, Holly; York, Alan; Stefano, Julian Di

doi:10.1890/14-1533.1

Cited by 66 publications

(65 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Coarse woody debris cover, for example, was not associated with TSFI in tall mixed forest but was positively associated with TSFI in wet forest (Swan et al. ). In contrast, the BN modeled forest type (which was categorical) as an ordinal variable and found similar relationships between TSFI and habitat structural variables in each vegetation type (Fig.…”

Section: Discussionmentioning

confidence: 99%

Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. Fire is a major driver of community composition and habitat structure and is extensively used as an ecological management tool in flammable landscapes. Interactions between fire and other processes that affect animal distributions, however, cause variation in faunal responses to fire and limit our ability to identify appropriate fire management regimes for biodiversity conservation. Bayesian networks (BNs) have not previously been used to examine terrestrial faunal distributions in relation to fire, but offer an alternative statistical framework for modeling complex environmental relationships as they explicitly capture interactions between predictor variables. We developed a conceptual model of the interactions between drivers of faunal distributions in fire-affected landscapes, and then used a non-parametric BN modeling approach to describe and quantify these relationships for a suite of terrestrial native mammal species. We also tested whether BNs could be used to predict these species' distributions using only remote-sensed or mapped variables. Data were collected at 113 sites across 47,000 ha of continuous eucalypt forest in the Otway Ranges, southeastern Australia; time-since-fire (TSF) ranged from six months to 74 yr. Habitat complexity increased with TSF and forest wetness. Critical-weight-range (35-5500 g) marsupials and rodents were generally more likely to occur at long unburnt sites with high habitat complexity, and in wetter forest types. In contrast, large grazers and browsers preferred less complex habitats and younger or drier forest. Species occurrences were more strongly affected by habitat complexity than TSF, coarse woody debris cover, or invasive predator (Vulpes vulpes or Felis catus) occurrence. Bayesian network models effectively discriminated between the presence and absence of most native mammal species, even when only provided with data on remote-sensed or mapped variables (i.e., without field-assessed data such as habitat complexity). Non-parametric BNs are an effective technique for explicitly modeling the complex and context-dependent influence of fire history on faunal distributions, and may reduce the need to collect extensive field data on habitat structure and other proximate drivers.

show abstract

Section: Discussionmentioning

confidence: 99%

Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…One potential explanation for the large diversity of animal responses to fire is that species response trajectories could be mediated by other variables. For example, the habitat accommodation model (Fox, ) argues that responses to time since fire are mediated by changes in habitat suitability between seral stages (Swan et al., ). Similarly, the effect of fire on biota can be altered by the presence of specific functional groups, such as grazers (Foster et al., ) or carnivores (Doherty et al., ).…”

Section: Introductionmentioning

confidence: 99%

Effects of time since fire on frog occurrence are altered by isolation, vegetation and fire frequency gradients

Westgate

MacGregor

Scheele

et al. 2017

Diversity and Distributions

View full text Add to dashboard Cite

Aim: To quantify how frogs in terrestrial environments respond to recurrent fire, and to what extent this is mediated by isolation from breeding sites or vegetation structure.Location: Jervis Bay, south-eastern Australia. Methods:We used data from 8 years of pitfall trapping, collected via a random stratified design, to quantify frog occurrence at 110 locations. We then used an information theoretic approach to compare 13 logistic generalized linear mixed models, each of which related frog occurrence to a distinct combination of additive and interactive effects of fire, vegetation structure and proximity to known breeding sites.Results: For all four species, the effect of one or more fire variables on frog occurrence depended on both the density of breeding sites in the surrounding area, or on the vegetation structure at the trap locality. A classic "fire averse" response of initial declines followed by post-fire recovery did occur, but only in frequently burned, lowquality terrestrial habitats (i.e., heath vegetation for Uperoleia tyleri, or locations with few available breeding sites for the remaining species), or in some cases, when suitable habitats were infrequently burned. However, a "fire-dependent" result of negative effects of time since fire was also evident for some species and contexts. Main conclusions:The effect of fire on frog occurrence can be mediated by environment. Therefore, a single species could be identified as either "fire dependent" or "fire averse" depending on the combination of isolation, vegetation types and fire histories in the study region. Failure to account for the context specificity of fire response curves could lead to incomplete conclusions regarding the effect of time since fire-or the cumulative impacts of multiple fires-on faunal assemblages. K E Y W O R D Samphibians, disturbance, fire regime, pyrodiversity

show abstract

“…Heathland occurs in low-lying dry areas in the northeast, 0-250 m above sea level with approximately 624 mm annual rainfall. The wet forests are located in areas in the southwest at higher elevations of approximately 650 m above sea level with 1259 mm of annual rainfall [23]. A total of 116 sites were surveyed with post fire ages from 1 to 78 years (for detailed site description see Table 1 and Sitters, et al [24]).…”

Section: Methodsmentioning

confidence: 99%

Visual Assessment of Surface Fuel Loads Does Not Align with Destructively Sampled Surface Fuels

McColl‐Gausden

Penman

2017

Forests

View full text Add to dashboard Cite

Fuel load and structure are fundamental drivers of fire behaviour. Accurate data is required for managers and researchers to better understand our ability to alter fire risk. While there are many ways to quantify fuel, visual assessment methods are generally considered the most efficient. Visual hazard assessments are commonly used by managers, government agencies and consultants to provide a fuel hazard score or rating but not a quantity of fuel. Many systems attempt to convert the hazard score or rating to a fuel load for use in fire behaviour models. Here we investigate whether the conversion table in the widely used Overall Fuel Hazard Guide (OFHG) matches destructively sampled fuel loads from 116 sites across five forest types. We specifically examine whether there are quantifiable differences that can be attributed to forest type. We found there is overlap between the two methods for low, moderate and high hazard categories, however for the very high and extreme hazard categories, visual assessment overestimated fuel load in four of the five forest types. Using a commonly applied fire behaviour model, we found that the overestimation of fuel load in very high and extreme hazard categories leads to an overestimation of fire behavior in these hazard categories.

show abstract

Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure

Cited by 66 publications

References 59 publications

Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions

Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions

Effects of time since fire on frog occurrence are altered by isolation, vegetation and fire frequency gradients

Visual Assessment of Surface Fuel Loads Does Not Align with Destructively Sampled Surface Fuels

Contact Info

Product

Resources

About