Modelling ignition probability for human- and lightning-caused wildfires in Victoria, Australia

Wildfires pose a significant risk to people and property, which is expected to grow with urban expansion into fire-prone landscapes and climate change causing increases in fire extent, severity and frequency. Identifying spatial patterns associated with wildfire activity is important for assessing the potential impacts of wildfires on human life, property and other values. Here, we model the probability of fire ignitions in vegetation across Victoria, Australia, to determine the key drivers of human- and lightning-caused wildfire ignitions. In particular, we extend previous research to consider the role that fuel moisture has in predicting ignition probability while accounting for environmental and local conditions previously identified as important. We used Random Forests to test the effect of variables measuring infrastructure, topography, climate, fuel and soil moisture, fire history, and local weather conditions to investigate what factors drove ignition probability for human- and lightning-caused ignitions. Human-caused ignitions were predominantly influenced by measures of infrastructure and local weather. Lightning-sourced ignitions were driven by fuel moisture, average annual rainfall and local weather. Both human- and lightning-caused ignitions were influenced by dead fuel moisture with ignitions more likely to occur when dead fuel moisture dropped below 20 %. In future, these models of ignition probability may be used to produce spatial likelihood maps, which will improve our models of future wildfire risk and enable land managers to better allocate resources to areas of increased fire risk during the fire season

A global database on holdover time of lightning-ignited wildfires

Holdover fires are usually associated with lightning-ignited wildfires (LIWs), which can experience a smoldering phase or go undetected for several hours, days or even weeks before being reported. Since the existence and duration of the smoldering combustion in LIWs is usually unknown, holdover time is conventionally defined as the time between the lightning event that ignited the fire and the time the fire is detected. Therefore, all LIWs have an associated holdover time, which may range from a few minutes to several days. However, we lack a comprehensive understanding of holdover times. Here, we introduce a global database on holdover times of LIWs. We have collected holdover time data from 29 different studies across the world through a literature review and datasets assembled by authors of the original studies. The database is composed of three data files (censored data, non-censored data, ancillary data) and three metadata files (description of database variables, list of references, reproducible examples). Censored data are the core of the database and consist of different frequency distributions reporting the number or relative frequency of LIWs per interval of holdover time. In addition, ancillary data provide further information to understand the methods and contexts in which the data were generated in the original studies. The first version of the database contains 42 frequency distributions of holdover time built with data on more than 152 375 LIWs from 13 countries in five continents covering a time span from 1921 to 2020. This database is the first freely available, harmonized and ready-to-use global source of holdover time data, which may be used in different ways to investigate LIWs and model the holdover phenomenon. The complete database can be downloaded at https://doi.org/10.5281/zenodo.7352172 (Moris et al., 2022)

Measures of eastern quoll vocalisation extracted using PRAAT

Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (Dasyurus viverrinus), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected from Secret Creek Sanctuary in Lithgow in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Vocalisation measures were extracted using narrowband spectrograms (FFT method, window length 0.05 sec, dynamic range = 70 dB, time-steps = 1,000, frequency steps = 250, Gaussian window shape) produced in the program PRAAT (5.3.84 DSP Package). Source-related parameters using an autocorrelation method were used to detect the fundamental frequency (F0) contour from which measures of Duration, Median F0, Mean F0, Minimum F0, Maximum F0, Range of F0, Standard deviation of F0, Noise-to-Harmonics ratio, Jitter and Shimmer were extracted. Additionally intensity contours were extracted for each call to measure the Minimum amplitude, Maximum amplitude and Amplitude variation. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present within vocalisations not discernible to the human ear. The implication of these different vocalisations and their given context is discussed in relation to the ecology of the species and the potential application of passive acoustic monitoring techniques

Hierarchical cluster analysis used to detect the presence of relatively homogeneous groups of calls.

<p>Parameters used: log-transformed Duration, Median F0, Standard Deviation F0, Ampvar, NHR, Jitter and Shimmer. Coloured bar below dendrogram indicates distribution of putatively described calls. Red boxes indicate the five cluster solution (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179337#pone.0179337.s006" target="_blank">S1 Fig</a>) determined by highest average silhouette score (0.69). Dotted blue line indicates eight cluster solution (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179337#pone.0179337.s007" target="_blank">S2 Fig</a>) determined by Hubert and Arabie Adjusted Rand Index best matching the putative repertoire.</p

Description of behaviours observed when focal animals were recorded vocalising.

<p>Description of behaviours observed when focal animals were recorded vocalising.</p

The acoustic repertoire and behavioural context of the vocalisations of a nocturnal dasyurid, the eastern quoll (<i>Dasyurus viverrinus</i>)

<div><p>Defining an acoustic repertoire is essential to understanding vocal signalling and communicative interactions within a species. Currently, quantitative and statistical definition is lacking for the vocalisations of many dasyurids, an important group of small to medium-sized marsupials from Australasia that includes the eastern quoll (<i>Dasyurus viverrinus</i>), a species of conservation concern. Beyond generating a better understanding of this species' social interactions, determining an acoustic repertoire will further improve detection rates and inference of vocalisations gathered by automated bioacoustic recorders. Hence, this study investigated eastern quoll vocalisations using objective signal processing techniques to quantitatively analyse spectrograms recorded from 15 different individuals. Recordings were collected in conjunction with observations of the behaviours associated with each vocalisation to develop an acoustic-based behavioural repertoire for the species. Analysis of recordings produced a putative classification of five vocalisation types: Bark, Growl, Hiss, Cp-cp, and Chuck. These were most frequently observed during agonistic encounters between conspecifics, most likely as a graded sequence from Hisses occurring in a warning context through to Growls and finally Barks being given prior to, or during, physical confrontations between individuals. Quantitative and statistical methods were used to objectively establish the accuracy of these five putative call types. A multinomial logistic regression indicated a 97.27% correlation with the perceptual classification, demonstrating support for the five different vocalisation types. This putative classification was further supported by hierarchical cluster analysis and silhouette information that determined the optimal number of clusters to be five. Minor disparity between the objective and perceptual classifications was potentially the result of gradation between vocalisations, or subtle differences present within vocalisations not discernible to the human ear. The implication of these different vocalisations and their given context is discussed in relation to the ecology of the species and the potential application of passive acoustic monitoring techniques.</p></div

Boxplots of acoustic features utilised during statistical analysis.

<p>Where B: Bark, Ch: Chuck, Cp: Cp-cp, G: Growl, H: Hiss. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179337#pone.0179337.s003" target="_blank">S3 Table</a> for mean and standard deviation.</p

Proportion of behavioural contexts associated with putative vocalisation types.

<p>Proportion of behavioural contexts associated with putative vocalisation types.</p

Linking fuel, habitat and ground-dwelling mammals in flammable landscapes

Prescribed fire is often applied with the goal of reducing fuel loads and lessening the impact of future wildfires on humans. As fuel represents habitat for some animal species, fuel reduction treatments are likely to affect species' access to resources. Understanding the interrelationships between fuel, habitat and animal occurrence will help managers of flammable landscapes meet the dual objective of reducing fuel loads and conserving biodiversity. In addition, should fuel hazard assessments reflect habitat structure, fuel hazard scores could be used to predict the response of animals to prescribed fire. This would be useful in many regions where fuel hazard assessments are routinely conducted, but data about habitat change or the direct response of animals are lacking. In this study, we tested the capacity of fuel hazard scores to predict both habitat structure and grounddwelling mammal occurrence at 187 sites in the Otway Ranges, south-eastern Australia. First, we explored relationships between habitat structure and fuel hazard. Second, we investigated how animals responded to both habitat and fuel. Habitat complexity was positively related to overall fuel hazard, although this varied with net primary productivity. Habitat attributes were best at predicting the occurrence of eight out of nine grounddwelling mammal species, although seven species were also correlated with components of fuel hazard. Some species were not strongly associated with either habitat or fuel. These species-specific relationships between habitat, fuel and fauna highlight the continuing importance of measuring habitat or animals directly when investigating faunal responses to disturbance. However, in the absence of these data, fire managers can use a common fuel assessment method to predict the effect of fuel reduction on habitat structure and the occurrence of some ground-dwelling mammals