DOI: 10.5061/dryad.573n5tbdh
title: Data from: Mediterranean bioclimate zones of the world for the understanding of fire regimes

description: Aim: To quantify the role of soil fertility in the spatial variability of
 fire activity and to identify the mechanisms that drive this variability.
 Location: The five Mediterranean-type climate regions of the world. Time
 period: 2002 – present. Major taxa studied: Terrestrial plants. Methods:
 We compiled remotely sensed data on fire activity, climate, net primary
 productivity, and chemical soil properties for bioclimatically homogeneous
 zones within the five Mediterranean-type climate regions of the world.
 Putative direct and indirect effects of the environmental variables on
 fire activity were evaluated through structural equation modelling.
 Results: Fire activity increased with net primary productivity, as
 expected for ecosystems with fuel-limited fire regimes. Soil acidity and
 the concentration of exchangeable aluminium also increased fire activity,
 supporting the idea that low fertility promotes plant characteristics that
 favour fire initiation and spread. Main conclusions: Our research supports
 a positive relationship between wildfires and low soil fertility in
 Mediterranean-type climate regions across the globe. Therefore, soil
 fertility should be incorporated into models predicting future fires in a
 warming world.
description: # Data from: Mediterranean bioclimate zones of the world for the
 understanding of fire regimes # Description: (1) Text file including the
 aggreated geographical and enviornmental data for the bioclimatic zones
 within the Mediterranean biome, including: \- ID: unique identifier. \-
 realm: biogeographic realm cf. Olson et al. (2001 Bioscience, 5, 933-938).
 \- köppen: code for the Köppen-Geiger climate classification cf. Beck et
 al. (2018 Scientific data, 5, 1-12). \- climate: name for the
 Köppen-Geiger climate classification cf. Beck et al. (2018 Scientific
 data, 5, 1-12). \- area: surface area (km2) \- fire: fire activity cf.
 Pausas & Ribeiro (2013) Global Ecology and Biogeography, 22, 728-736.
 \- aridity: aridity index obtained from an adaptation of the De Martonne
 aridity index (cf. De Martonne 1926 Comptes Rendus de L’Acad Sci, Paris,
 182, 395-1398). \- NPP: netprimary produtivity cf. Running & Zhao
 (2019 NASA EOSDIS Land Processes DAAC). \- pH: soil pH cf. Shangguan et
 al. (2014 Journal of Advances in Modelling Earth Systems, 6, 249-263). \-
 Corg: organic carbon (C) in the soil cf. Shangguan et al. (2014 Journal of
 Advances in Modelling Earth Systems, 6, 249-263). \- Ntot: total nitrogen
 (N) in the soil cf. Shangguan et al. (2014 Journal of Advances in
 Modelling Earth Systems, 6, 249-263). \- C:N: C:N ratio in the soil
 calculated from Corg and Ntot. \- CEC: soil cation exchange capacity cf.
 Shangguan et al. (2014 Journal of Advances in Modelling Earth Systems, 6,
 249-263). \- Al: exchangeable aluminium in the soil cf. Shangguan et al.
 (2014 Journal of Advances in Modelling Earth Systems, 6, 249-263). \-
 Papa: edaphic phosphorous in apatite cf. Yang et al. (2014
 . \- PC1:
 loadings for the first principal component of the principal component
 analysis conducted with aforementioned soil variables. \- PC2: loadings
 for the second principal component of the principal component analysis
 conducted with aforementioned soil variables. \- HFP: human footprint cf.
 Venter et al. (2016 Scientific data, 3 1-10). \- Lightning: mean annual
 flash rate cf. Cecil et al. (2014 Atmospheric Research, 135, 404-414). (2)
 Text file that includes the R codes for structural equation modeling. (3)
 Maps of the environmental variables, including: 1.koppen: name for the
 Köppen-Geiger climate classification cf. Beck et al. (2018 Scientific
 data, 5, 1-12). 2.fire: fire activity cf. Pausas & Ribeiro (2013)
 Global Ecology and Biogeography, 22, 728-736. 3.aridity: aridity index
 obtained from an adaptation of the De Martonne aridity index (cf. De
 Martonne 1926 Comptes Rendus de L’Acad Sci, Paris, 182, 395-1398). 4.npp:
 netprimary produtivity cf. Running & Zhao (2019 NASA EOSDIS Land
 Processes DAAC). 5.pH: soil pH cf. Shangguan et al. (2014 Journal of
 Advances in Modelling Earth Systems, 6, 249-263). 6.corg: organic carbon
 (C) in the soil cf. Shangguan et al. (2014 Journal of Advances in
 Modelling Earth Systems, 6, 249-263). 7.ntot: total nitrogen (N) in the
 soil cf. Shangguan et al. (2014 Journal of Advances in Modelling Earth
 Systems, 6, 249-263). 8.cn_ratio: C:N ratio in the soil calculated from
 Corg and Ntot. 9.cec: soil cation exchange capacity cf. Shangguan et al.
 (2014 Journal of Advances in Modelling Earth Systems, 6, 249-263). 10.al:
 exchangeable aluminium in the soil cf. Shangguan et al. (2014 Journal of
 Advances in Modelling Earth Systems, 6, 249-263). 11.papa: edaphic
 phosphorous in apatite cf. Yang et al. (2014). 12.pc1: loadings for the
 first principal component of the principal component analysis conducted
 with aforementioned soil variables. 13.pc2: loadings for the second
 principal component of the principal component analysis conducted with
 aforementioned soil variables.