Assessing the social context of wildfire-affected areas. The case of mainland Portugal

Oliveira, Sandra; Zêzere, José Luı́s; Queirós, Margarida; Pereira, José M. C.

doi:10.1016/j.apgeog.2017.09.004

Cited by 68 publications

(50 citation statements)

References 90 publications

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“…Without estimating and understanding the source of exposure to wildfires, as we did in this study, it is difficult to implement "all lands" risk management policies (USDA Forest Service, 2014), and the planning areas around communities will likely be on a spatial scale smaller than their exposure . Similarly, Oliveira et al (2017) did not identified places on the landscape with large fire potential due to limited historical fire activity. Further, neither study provided detailed estimates of community level exposure.…”

Section: Discussionmentioning

confidence: 97%

“…Another important aspect and consideration was the scale (Paveglio et al, 2018) of social data used (e.g. community vs. census block group), since it can produce different results when coupled with varying spatial scales of biophysical wildfire related characteristics (Oliveira et al, 2017;Wigtil et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…The extensive literature on social science related to wildfire issues (McCaffrey, 2015) has studied risk perception, mitigation decisions and perceived consequences (Champ & Brenkert-Smith, 2016;Champ, Donovan, & Barth, 2013;Dickinson, Brenkert-Smith, Champ, & Flores, 2015;Gordon, Luloff, & Stedman, 2012); community pre-fire mitigation (Cohn, Williams, & Carroll, 2008) and adaptive capacity ; residents' actions and adaptation (Brenkert- Smith, 2006); and community social diversity and vulnerability (Paveglio, Nielsen-Pincus, Abrams, & Moseley, 2017;Paveglio, Prato, Edgeley, & Nalle, 2016). However, work to assess wildfire risk by integrating social and natural systems is relatively new (Cutter, Boruff, & Shirley, 2003;Davies et al, 2018;Gaither, Goodrick, Murphy, & Poudyal, 2015;Gaither et al, 2011;Oliveira, Zêzere, Queirós, & Pereira, 2017;Parisien et al, 2016;Wigtil et al, 2016) or applied at limited geographic scales (Fischer, Kline, Ager, Charnley, & Olsen, 2014;Nielsen-Pincus et al, In review;Olsen, Kline, Ager, Olsen, & Short, 2017;Paveglio, Edgeley, & Stasiewicz, 2018;Paveglio et al, 2016), leaving a gap in our knowledge about large-scale transboundary risk in relation to behavioral response to fire. Our first goal is to understand where transboundary large fire events originate and how they spread through a mosaic of land tenures, management jurisdictions and fuel models, and quantify their impacts on the communities of three socially and biophysically distinct fire-prone regions of the western US.…”

Section: Introductionmentioning

confidence: 99%

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Social vulnerability to large wildfires in the western USA

Palaiologou

Ager

Nielsen-Pincus

et al. 2019

Landscape and Urban Planning

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Federal land managers in the US can be informed with quantitative assessments of the social conditions of the populations affected by wildfires originating on their administered lands in order to incorporate and adapt their management strategy to achieve a more targeted prioritization of community wildfire protection investments. In addition, these assessments are valuable to socially vulnerable communities for quantifying their exposure to wildfires originating on adjacent land tenures. We assessed fire transmission patterns using fire behavior simulations to understand spatial variations across three diverse study areas (North-central Washington; Central California; and Northern New Mexico) to understand how different land tenures affect highly socially vulnerable populated places. Transboundary wildfire structure exposure was related to populations with limited adaptive capacity to absorb, recover and modify exposure to wildfires, estimated with the Social Vulnerability Index using US Census unit data (block groups). We found geographic heterogeneity in terms of land tenure composition and estimated fire exposure. Although high social vulnerability block groups covered small areas, they had high population and structure density and were disproportionately exposed per area burned by fire. Structure exposure originated primarily from three key land tenures (wildland-urban interface, private lands and national forests). Federal lands proportionately exposed, on an area basis, populated places with high social vulnerability, with fires ignited on Forest Service administered lands mostly affecting north-central Washington and northern New Mexico communities.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Social vulnerability to large wildfires in the western USA

Palaiologou

Ager

Nielsen-Pincus

et al. 2019

Landscape and Urban Planning

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“…In southern Europe ignitions are mainly due to human activities (Lovreglio et al 2010, Meddour-Sahar et al 2013, Curt et al 2016, while fire spread is mainly governed by weather and climate (Pausas & Fernández-Muñoz 2012, Cardil et al 2014, Ruffault et al 2017, Russo et al 2017, even if fuels and land uses can represent key driving factors (Ruiz-Mirazo et al 2012, Curt et al 2013, Fernandes et al 2014, Salis et al 2016. Moreover, other aspects (i.e., the socioeconomic context, topography, firefighting resources, and fuel management) also influence the spatial pattern of fire occurrence and spread (Levin et al 2016, Oliveira et al 2017, Salis et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of fire danger indexes in a Mediterranean area

Sîrca¹,

Salis²,

Arca³

et al. 2018

iForest

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The fire danger (FD) defines the conditions less or more favourable for a fire ignition success and its propagation. FD indexes, that integrates environmental variables related to FD in more or less complex equations and systems, are widely used in wildfire prone countries for both scientific and operational purposes. Assessing the performance of FD indexes is challenging and this issue is quite debated within the fire community, which has been trying to apply several methodologies to evaluate FD indexes. The main aim of this work is to give a contribution to this effort. The analysis was conducted using data from a fire-prone Mediterranean area (Sardinia island, Italy), where 8 FD indexes were evaluated and compared using different statistical approaches. We calculated the daily FD values for the period 2000-2007 over the study area. A set of statistical tools (namely Spearman rank correlation, Index Value Distribution and Percentile Analysis, and Logistic Regression) were applied to evaluate the performance of each FD index by comparing FD values with fire occurrence indicators. The statistical tests revealed a large variability in FD indexes performance, depending also on fire activity conditions. Our results showed that two of the tested FD indexes reached a good overall performance. Findings from this study can help both the scientific community and local fire managers, supporting the evaluation of early warning systems and fire prevention strategies in the Mediterranean Basin.

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“…However, in a second step, treeClust calculates dissimilarities between any two observations based on the proportion of trees in which they land in different leaves, using Gower's distance (20) . While treeClust dissimilarities appear to perform well in practical applications (21,22) , some of their basic properties remain unclear, especially in the context of gene expression analysis. For example, do treeClust dissimilarities capture linear or non-linear associations?…”

mentioning

confidence: 99%

treeClust improves protein co-regulation analysis due to robust selectivity for close linear relationships

Kustatscher

Grabowski

Rappsilber

2019

Preprint

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Gene co-expression analysis is a widespread method to identify the potential biological function of uncharacterised genes. Recent evidence suggests that proteome profiling may provide more accurate results than transcriptome profiling. However, it is unclear which statistical measure is best suited to detect proteins that are co-regulated. We have previously shown that expression similarities calculated using treeClust, an unsupervised machine-learning algorithm, outperformed correlation-based analysis of a large proteomics dataset. The reason for this improvement is unknown. Here we systematically explore the characteristics of treeClust similarities. Leveraging synthetic data, we find that tree-based similarities are exceptionally robust against outliers and detect only close-fitting, linear protein -protein associations. We then use proteomics data to demonstrate that both of these features contribute to the improved performance of treeClust relative to Pearson, Spearman and robust correlation. Our results suggest that, for large proteomics datasets, unsupervised machine-learning algorithms such as treeClust may significantly improve the detection of biologically relevant protein -protein associations relative to correlation metrics. every dataset, as the optimal choice of measure depends on various characteristics of a given dataset, such as the frequency of outliers and missing values.A recent, fundamental change to the expression profiling setup was made possible by improvements in the field of quantitative proteomics: the use of protein abundances rather than mRNAs as readout for gene activity. This increases the accuracy of gene function prediction, because protein abundances are better indicators of gene function than mRNA levels, at least in human (9-11) and mouse (12) . We have recently reported ProteomeHD, a dataset that quantifies the response of 10,323 human proteins to 294 biological perturbations using isotope-labelling mass spectrometry (13) ( https://www.biorxiv.org/content/10.1101/582247v1 , in revision). ProteomeHD is a heterogeneous dataset, incorporating a wide range of perturbation experiments from different laboratories, such as inhibitor treatments, differentiation time courses and cancer cell line comparisons. We compared different coexpression measures for their ability to detect proteins that are co-regulated in response to these perturbations. Surprisingly, we found that the unsupervised machine-learning algorithm treeClust (14, 15) provided a striking improvement over established correlation-based metrics. However, the reason for this improvement remained unclear, because treeClust is a novel algorithm that works in a fundamentally different way to previously used coexpression metrics.The treeClust algorithm uses recursive partitioning (16-18) to create decision trees. Such trees are normally used for supervised classification or regression tasks. In contrast, treeClust uses decision trees to calculate a dissimilarity measure in an unsupervised manner. To do so, treeClust first creat...

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Assessing the social context of wildfire-affected areas. The case of mainland Portugal

Cited by 68 publications

References 90 publications

Social vulnerability to large wildfires in the western USA

Social vulnerability to large wildfires in the western USA

Assessing the performance of fire danger indexes in a Mediterranean area

treeClust improves protein co-regulation analysis due to robust selectivity for close linear relationships

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